Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the introductory editoral for this issue of the publication.

In this paper, a generalized signal model is presented to accommodate both narrowband and wideband signals in a multi-input multi-output (MIMO) sensor system scenarios. The derived model is then used to define a MIMO ambiguity function based on the Kullback–Leibler divergence. Moreover, the proposed formulation is parametrized using the signal and channel correlation matrices to account for different waveform and sensor placement designs, thereby allowing a flexible modeling approach. A comparison between the proposed definition and the more conventional approach of summing the squared matched filter outputs is presented for different sensors and waveforms configurations.

This paper presents the development of a controller for a fleet of unmanned aerial vehicles based on a distributed path planning strategy under a multiagent systems framework. The issue, treated as an online optimization problem, is solved using a particle swarm optimization (PSO) algorithm. The proposal was validated in experiments, considering different scenarios like fixed and mobile targets, external disturbances, and the loss of an agent. The proposed PSO is implemented independently in each vehicle in order to determine, by minimizing a cost function, the best paths that ensure the fleet formation control, target tracking, and collision avoidance.

Detection and localization in urban environments is a very recent radar problem. In this paper, we investigate the possibility of detecting and locating targets not in direct line of sight (NLOS) areas with a single portable radar by exploiting multipath returns. We propose two algorithms, which handle the information provided by multipath returns in different ways to detect and estimate the NLOS target position. We also present an original method to select the number of paths to take into account in the algorithms in order to maximize detection probabilities. Numerical results show good efficiency of the proposed algorithms for problems of both detection and localization. We show that applying these algorithms improves detection performance compared to a classic matched filter in a typical urban scenario. Experimental results on a real dataset allow us to validate our multipath model in urban environments, and in particular to show that it is possible to retrieve the target location even with rough knowledge of the scene geometry.

Recently, a variety of small spacecraft have been launched and used for interplanetary missions. Conventionally, reaction wheels (RWs) and thrusters are used for these attitude control systems in almost all interplanetary spacecraft. While these actuators are promising for attitude control, the lifetime of the spacecraft mission is limited by the extra fuel needed for the thrusters. Moreover, it is difficult to install thrusters in all small spacecraft due to low reliability and strict limitations on mass and power consumption. To obtain both fuel-free and available attitude control for small spacecraft, this study proposes an interplanetary magnetic attitude control system including attitude stabilization and angular momentum unloading based on an interplanetary magnetic field (IMF) Kalman filter. In the proposed method, an electromagnetic coil interacting with the IMF is used for an attitude control system. To achieve the proposed method, the faint magnetic field must be detected. However, the IMF is too weak to sense using only on-board magnetic sensors. To deal with the technical issue, this study proposes a magnetic attitude control system with an unscented Kalman filter using gyro measurements and generated magnetic moment by the electromagnetic coil to estimate the weak magnetic field. With the estimated magnetic field vector, the spacecraft can achieve fuel-free attitude stabilization and RW unloading under the constraints. This proposed system does not require fuel for attitude control. Furthermore, the simple structure and electronic circuits of the electromagnetic coil allow the spacecraft to achieve a simple and reliable attitude control system. Numerical simulations demonstrate the effectiveness of the proposed attitude stabilization and RW unloading methods.

Automatic target recognition (ATR) is an important part for many computer vision applications. Despite the extensive research which has been carried out in this area for many years, there is no ATR system which performs well on all applications. Recently, different object recognition frameworks have been proposed which yield a high performance in baseline databases. However, our experiments showed that they can fail in real-world scenarios, when dealing with a limited number of data samples. In this paper, we propose a new ATR system, based on deep convolutional neural network (DCNN), to detect the targets in forward looking infrared (FLIR) scenes and recognize their classes. In our proposed ATR framework, a fully convolutional network is trained to map the input FLIR imagery data to a fixed stride correspondingly-sized target score map. The potential targets are identified by applying a threshold on the target score map. Finally, the corresponding regions centered at these target points are fed to a DCNN to classify them into different target types while at the same time rejecting the false alarms. The proposed architecture achieves a significantly better performance in comparison with that of the state-of-the-art methods on two large FLIR image databases.

This paper presents a new filter estimating quaternion using inertial and magnetic sensors. Using a reference coordinate system multiplicative quaternion error representation and a constrained structure filter gain, the proposed filter has a separation property, where the magnetic sensor output does not affect pitch and roll angle estimation. Furthermore, the proposed filter gain can be computed just from five scalar equations. Through simulation, the separation property of the proposed filter is verified.

We consider a comprehensive synchronization strategy for burst-mode transmission of shaped-offset quadrature phase-shift keying (SOQPSK) signals over the additive white Gaussian noise channel. Due to the similarities between SOQPSK and continuous phase modulation (CPM), we make use of recent results for synchronization of burst-mode CPMs. We first derive a training sequence that is optimal in the sense that it jointly minimizes the Cramér–Rao bounds (CRBs) for frequency offset, phase offset, and timing offset estimation. Additionally, we develop a maximum likelihood data-aided algorithm for joint estimation of the synchronization parameters for SOQPSK signals. We show that the proposed algorithm for the optimal training sequence can be adapted to work with the suboptimal training sequence that is being considered for use in burst-mode integrated network enhanced telemetry. This demonstrates an immediate practical application for our approach. We present numerical results on the mean-squared error performance of the proposed algorithm for both training sequences and for different versions of SOQPSK. The numerical results show that our joint estimation algorithm yields performance that is very close to the CRBs for all three synchronization variables. Finally, we compare the overall performance of our proposed training sequence and synchronization algorithm for different sequence lengths by simulating a burst-mode SOQPSK receiver. This allows us to employ the right training sequence length based on the desired complexity and bit error rate performance.

This paper presents an efficient method for estimating the probability of conflict between air traffic within a block of airspace. Autonomous sense-and-avoid is an essential safety feature to enable unmanned air systems to operate alongside other (manned or unmanned) air traffic. The ability to estimate the probability of conflict between traffic is an essential part of sense-and-avoid. Such probabilities are typically very low. Evaluating low probabilities using naive direct Monte Carlo generates a significant computational load. This paper applies a technique called subset simulation. The small failure probabilities are computed as a product of larger conditional failure probabilities, reducing the computational load while improving the accuracy of the probability estimates. The reduction in the number of samples required can be one or more orders of magnitude. The utility of the approach is demonstrated by modeling a series of conflicting and potentially conflicting scenarios based on the standard Rules of the Air.

This paper proposes an adaptive antiwindup fault-tolerant control system for a hypersonic vehicle with parametric uncertainties. Not only the loss-of-effectiveness and stuck-type elevator fault, but also the reverse-type fault causing the uncertainty of control direction is considered. The fault-tolerant controller designed through feedback linearization is combined with an online parameter estimator and a Nussbaum gain function, so that the uncertainties can be estimated adaptively. An antiwindup compensator is introduced to the control system to handle the input constraints.

A recent trend in distributed multisensor fusion is to use random finite-set filters at the sensor nodes and fuse the filtered distributions algorithmically using their exponential mixture densities (EMDs). Fusion algorithms that extend covariance intersection and consensus-based approaches are such examples. In this paper, we analyze the variational principle underlying EMDs and show that the EMDs of finite-set distributions do not necessarily lead to consistent fusion of cardinality distributions. Indeed, we demonstrate that these inconsistencies may occur with overwhelming probability in practice, through examples with Bernoulli, Poisson, and independent identically distributed cluster processes. We prove that pointwise consistency of EMDs does not imply consistency in global cardinality and vice versa. Then, we redefine the variational problems underlying fusion and provide iterative solutions thereby establishing a framework that guarantees cardinality consistent fusion.

We present a technique to jointly estimate the channel taps and the frequency offset due to the Doppler effect of a special class of doubly dispersive aeronautical channels. The algorithm includes the use of pulse repetition techniques at the transmitter and the “power method” subspace iteration at the receiver. We show that transmitting constant-amplitude zero-autocorrelation sequences for channel sounding yields estimators with low computational complexity. Numerical simulations indicate performance comparable to the estimation-theoretic lower bound.

The trajectory estimation problem of a thrusting/ballistic object in three-dimensional (3-D) space has been previously solved with 2-D measurements (azimuth and elevation angles from a fixed passive sensor, either starting from the launch time or with a delayed acquisition) under the assumption of constant mass. However, since the mass decreases as the fuel burns, this should be accounted for. This paper investigates several approaches with different parameter vectors to solve the trajectory estimation and impact point prediction (IPP) with measurements starting after the launch time, i.e., delayed acquisition for both constant mass motion model and mass ejection motion model. For the mass ejection motion model, the mass ejection rate is an extra component of the parameter vector to be estimated. The invertibility of the Fisher information matrix (FIM) of the parameter vectors is also used to confirm the observability (estimability) of the system. The Cramer–Rao lower bound (CRLB) is the inverse of the FIM if it is invertible. The CRLB of the IPP is also derived. We develop the maximum likelihood estimator of the considered motion parameter vectors. Performance comparison between the models considered is given and the statistical efficiency of the best model is confirmed via simulation results.

The electric sail is an innovative concept for spacecraft propulsion, which can generate continuous thrust without propellant by reflecting solar wind ions. In previous studies, the thrust of an electric sail is described by a classical model that neglects the effects of the electric sail attitude on the propulsive thrust modulus and direction. This paper reappraised the performance of the electric sail in the Vesta and Ceres exploration mission with an advanced thrust model that considers the effect of the spacecraft attitude on both the thrust modulus and direction. By using a hybrid optimization method, the trajectory optimization of the electric-sail-based spacecraft from earth to Vesta and Ceres is implemented in an optimization framework. Numerical results show that the minimal flight time with the advanced thrust model is longer than that with the classical model. The difference in performance between the classical and advanced models is attributable to overestimation of the maximum thrust cone angle and the thrust modulus by the classical model.

Current studies on unmanned aerial vehicle (UAV) based cellular deployment consider UAVs as aerial base stations for air-to-ground communication. However, they analyze UAV coverage radius and altitude interplay while omitting or over-simplifying an important aspect of UAV deployment, i.e., effect of a realistic antenna pattern. This paper addresses the UAV deployment problem while using a realistic three-dimensional directional antenna model. New tradeoffs between UAV design space dimensions are revealed and analyzed in different scenarios. The sensitivity of coverage area to both antenna beamwidth and height is compared. The analysis is extended to multiple UAVs and a new packing scheme is proposed for multiple UAVs coverage that offers several advantages compared to prior approaches.

A new random channel access framework, named as zigzag-division multiple access (ZDMA), is proposed for wireless networks with long and heterogeneous propagation delays to achieve system throughput significantly larger than 1. The key idea is to allow simultaneous transmissions at the transmitters and employ zigzag decoding at the receiver. After studying the zigzag decoding, we present three new random access protocols. Extensive numerical studies show that incorporating ZDMA in existing slotted Aloha and tree splitting protocols can significantly increase system throughput. The third protocol of greedy scheduling with ZDMA achieves the highest throughput performance.

An underwater vehicle may utilize underwater transponders (UTs) for navigation in the absence of global navigation satellite system signals. However, the position of each UT must be known by the underwater vehicle. The problem of an autonomous surface vehicle (ASV) optimally planning measurement locations to localize a set of arbitrarily predeployed acoustic UTs is considered. The ASV is assumed to make noisy range measurements to the UTs. A maximum a posteriori estimator is derived to localize the UTs. In addition, a multistep look-ahead (MSLA) ASV optimal measurement location planning (OMLP) strategy is developed. This planning strategy prescribes future multistep measurement locations. A physical interpretation of the proposed planner in the single-step, single transponder case is provided. Simulation results are presented demonstrating the tradeoff between expected localization performance and computational time associated with various look-ahead horizons and travel distances. Experimental results are given illustrating the proposed MSLA OMLP strategy's performance in environments containing one and two UTs. The proposed OMLP strategy is able to localize UTs to within 4 m of their true locations. Additionally, increasing the planning horizon is demonstrated to yield better UT localization at the cost of increased computational burden.

The recently developed polyphase-coded FM implementation for physical radar waveforms is generalized to higher-order representations to facilitate greater design freedom (with MATLAB scripts provided in the appendix). Being FM, waveforms realized with these implementations have the benefit of being readily amenable to a high-power radar transmitter while possessing parameterized structures that are advantageous for optimization. Here various attributes of these implementations are examined. Specifically, it is shown that higher-order representations can, in special cases, be made equivalent, and through these relationships appropriate signal structure attributes can be inferred. Higher-order coding guidelines are also derived based on the need to ensure spectral containment. Example waveforms are optimized for each particular implementation to highlight their individual properties, towards the ultimate goal of establishing new ways to realize waveform-diverse emission structures that are physically realizable.

Maneuver models are dedicated to accurate representation of unknown motion pattern. However, for near space hypersonic jump gliding targets of high speed, diverse movement pattern, and mobility, the conventional maneuver models are difficult to describe the complex movement characteristics, and then leading to high and unstable tracking error. In order to solve this problem, a new model is proposed based on the attenuation of oscillation function. The core of the model is to consider the target acceleration as a zero mean random process with attenuation oscillation. With the model, the equations of the maneuvering target are constructed and the system dynamic error of this model was deduced taking Kalman filter as the filtering algorithm. Moreover, the corresponding relations among parameters are discussed, and an adaptive method is proposed for setting parameters appropriately in the situations when a priori information is unknown. In this way, the parameters can be adjusted online through jumping point identification. Theoretical analysis and simulation experiments are conducted to demonstrate the effectiveness of the proposed model. Comparing to commonly used models, it shows lower filtering errors tracking near space hypersonic jump gliding targets. Finally, the rationality and validity of the parameters adaptive method are explained.

A method for using interior point methods for generating phase-coded waveforms that have low autocorrelation sidelobes is described. The procedure is capable of generating polyphase Barker codes as well as non-Barke sequences that have comparatively low sidelobes. An analysis of the estimated number of possible polyphase Barker codes is presented.

The paper deals with the design and testing of a power generation system for the future and existing aircrafts. A new design algorithm of the power generation components is presented. The proposed design algorithm was tested on a prototype: a full-size starter-generator and transformer–rectifier unit prototype was created with a transformer magnetic core made up of an amorphous magnetic material. Its stand tests were carried out both for each element and for the entire system. The proposed algorithm can be used to create power supply systems for aircraft with different voltage levels, including 270 Vdc and 200 Vac. The developed power generation system turned out to be 25–40% better than serial solutions by mass and efficiency.

A robust attitude quadrotor control based on a novel fractional order PI nonlinear structure is proposed and experimentally validated. The proportional action depends on a nonlinear transformation of the sliding variable, while the integral action of fractional order rejects nondifferentiable disturbances, such as turbulent effects and gust winds, enforcing a sliding motion in finite time. The exponential convergence of a quaternion-based error is guaranteed for well-posed attitude tracking, and the regularity of the control signal can be adjusted with respect to the fractional order. Notably, the proposal includes, as particular cases, first-order and second-order sliding mode control schemes. Experiments illustrate the viability of the proposal, and a comparative study versus PD, PI-like, first-order sliding mode and second-order sliding mode supertwisting controllers is presented and discussed.

We consider a decentralized, multihop wireless network consisting of frequency division duplex (FDD) nodes, which use separate frequency bands for transmission and reception. The use of FDD communication in a multihop wireless network partitions the nodes in two operating modes (or genders), depending on the frequency bands used for the transmission and reception. Since the FDD nodes of the same gender, located in a one-hop neighborhood, cannot communicate with each other, it can limit the availability of communication links between the neighboring nodes and also lead to network partitioning. Therefore, the operating mode of these nodes should be selected such that every node can establish links with its one-hop neighbors. We model the multihop network as a graph and design a novel, distributed bipartite graph coloring scheme, for mode selection of FDD nodes. Unlike the existing graph coloring schemes, which use the entire network topology and yet do not ensure network connectivity, our algorithm requires only the local information of one-hop neighborhood of each node in a distributed manner. The simulation results show that our mode selection algorithm ensures that every FDD node can establish the communication links with approximately half of its one-hop neighbors for omni as well as directional communication, without introducing any disconnected node. This mode selection algorithm also has a lower computational complexity and provides a robust network connectivity, which would help in fault tolerance and establishing stable routes in the network.

This paper addresses the fuzzy-logic-based fixed-time geometric backstepping attitude tracking for the rigid-body spacecraft modeled on the nonlinear differential manifold three-dimensional special orthogonal group [SO(3)]. The fixed-time geometric controller is deduced by the backstepping method. Moreover, an adaptive fuzzy logic system is employed to compensate the unknown disturbances. The almost globally fixed-time stabilization of the closed-loop system is verified via Lyapunov analysis. Simulation results demonstrate the effectiveness of the proposed controller.

In this paper, we present the CubeSat version of a scientific instrument called the multi-Needle Langmuir Probe (m-NLP). The m-NLP instrument measures the electron density in the ionosphere with kHz sampling rate, yielding meter scale resolution on low Earth orbit satellites. The sounding rocket version of m-NLP has flight heritage from nine sounding rockets. However, to get an in-orbit demonstration of the system a CubeSat implementation has been developed. The m-NLP measurement principle is based on several fixed bias probes, where each probe has to be biased above the spacecraft potential. To ensure that this requirement is fulfilled, the CubeSat version of the m-NLP will feature a new miniaturized thermionic electron emitter, which can actively control the potential of the satellite. The emitter is designed to accommodate the low size, weight, and power challenges of the CubeSat platform. Together with the in-flight determination of the spacecraft floating potential, it can autonomously control the potential of the spacecraft by emitting electrons. Preliminary, test results from the plasma chamber at the European Space and Technology Center in Holland are shown, verifying that a miniaturized electron emitter is able to actively control the floating potential of the spacecraft and, hence, improve the accuracy of the electron density measurements.

This paper considers measurement extraction for two closely-spaced objects with unknown equal intensities in an imaging sensor's focal plane array (FPA). Given a screen of FPA data, the first part of the measurement extractor, target location estimator, can extract the location estimates for two targets or one, with the corresponding accuracy given by the Cramér Rao lower bound (CRLB). The second part of the measurement extractor, target detector, selects among the hypotheses of two resolved targets and a single one using information-theoretic criteria and hypothesis tests. Simulation results have been conducted to evaluate the measurement extraction performance including the probability of resolving the two hypotheses, and the efficiency and unbiasedness of the target location estimates for the selected hypothesis using different hypothesis detection schemes. The generalized likelihood ratio test (GLRT) based on linearized observation model using second order Taylor series expansion is most appealing as it provides an explicit expression of the probability of detecting two targets as a function of the target separations, the signal-to-noise ratio at a given false resolution probability. It is shown that the simulation-based resolution performance for the GLRT using the estimated center location of the two targets matches well with the analytic performance assuming known center.

In previous works, methods were explored for position estimation utilizing satellite-borne signals of opportunity, mainly the global positioning system (GPS). The GPS signal was exploited for use in a multistatic passive coherent location (PCL) system. The GPS signal is especially attractive for PCL applications because of the native capability to produce position and velocity estimation. This paper examines the signal specifications for PCL implementation and explores the potential limitations of the proposed solutions. GPS specific methods are developed for multistatic PCL velocity estimation in a three-dimensional plane. The method developed is combined with previously completed work of GPS PCL position estimation for a complete system design in range and Doppler. The PCL system is evaluated against conventional GPS position estimation and velocity estimation and proves to have comparable metrics of performance. Analysis and simulation are performed for verification and validation of the developed methods.

The homodyned-K (HK) distribution is a three-parameter density function used to study mixed-species radar and ultrasound scattering phenomena. We analyze the distribution that results from corrupting the HK with additive Gaussian noise (HKN). This modification enables analytic study of the HK in the presence of system thermal noise in radar and ultrasound systems. The moments of intensity of the HKN are derived and a new moment method parameter estimation algorithm is tested in simulation.

This paper studies a fault-tolerant control problem of singularly perturbed systems with faults and disturbances. The original faulty system is decomposed into a reduced subsystem and a boundary-layer subsystem. Then, a composite fault-tolerant controller is designed with one part dealing with the reduced subsystem and the other dealing with the boundary-layer subsystem. Through regulating the two parts of the controller, the original faulty system is input-to-state stable with respect to disturbances when the perturbation parameter is small enough. An application to the longitudinal control system of a hypersonic vehicle is given at last. Its singularly perturbed model is derived and a composite fault-tolerant controller is designed to illustrate the effectiveness of the proposed method.

Low-cost unmanned aerial vehicles (UAVs) need multiple refuels to accomplish large area coverage. We propose the use of a mobile ground vehicle (GV), constrained to travel on a given road network, as a refueling station for the UAV. Determining optimal routes for a UAV and GV, and selecting rendezvous locations for refueling to minimize coverage time is NP-hard. We develop a two-stage strategy for coupled route planning for UAV and GV to perform a coverage mission. The first-stage computes refueling sites that ensure reachability of all points of interest by the UAV and feasible routes for both the UAV and GV. In the second stage, mixed-integer linear programming (MILP) based exact methods are developed to plan optimal routes for the UAV and GV. As the problem is NP-Hard, we also develop computationally efficient heuristics that can find good feasible solutions within a given time limit. Extensive simulations are conducted to corroborate the effectiveness of the developed approaches. Field experiments are also performed to verify the performance of the UAV-GV solution.

This paper presents the design, implementation, and the experimental verifications of a 2.4-GHz multibeam array receiver based on two-dimensional (2-D) spatially bandpass (SBP) digital plane wave (PW) filters. The digital array receiver consists of 2.4-GHz 16-element uniform linear array, and quadrature sampling is employed to process the broadband PWs at the baseband. Both 2-D finite impulse response (FIR) and infinite impulse response (IIR) digital filters having multiple trapezoidal-shaped passbands are employed as the 2-D SBP digital PW filters. Both 2-D FIR and IIR trapezoidal filters are implemented on a Xilinx Virtex 6 sx475t field programmable gate array chip that comes with the reconfigurable open architecture computing hardware version-2 (ROACH-2) platform. The 16 in-phase and quadrature-phase downconverted signals are sampled using the ROACH-2 platform and are processed using the digital architectures to form multiple beams. Example radio-frequency beam patterns corresponding to each filter architecture are measured and reported. The main lobes of the measured multibeam patterns are well aligned with the simulated multibeam patterns.

This paper proposes a command generation approach based on the smooth interpolation without twist constraints and the input–output relation developed by the generalized model predictive static programming. Commands are efficiently generated to achieve the desired terminal condition at a specified final time in an interpolative manner featuring a noniterative nature. The effectiveness of this approach is demonstrated through three different simulation studies including a double integrator benchmark, a soft lunar landing problem, and a cluster missile guidance problem.

This paper deals with the design, realization, and testing of an earth magnetic field simulator, which allows us to validate hardware-in-the-loop algorithms, as well as to test new actuators. The design is driven by typical small satellites functional requirements. The subsystems that compose the simulator are described in detail. The validation of the simulator is performed by assessing its functioning, the uniformity of the recreated magnetic field, and the functionality of a magnetorquer.

Only a few publications exist at present on heterogeneous track-to-track fusion (T2TF). A common limitation of the current work on heterogeneous T2TF is that the cross covariance due to common process noise cannot be computed. This is due to the fact that two local trackers use different dynamic models, and hence, it is difficult to account for the common process noise. We consider a heterogeneous T2TF problem in three dimension (3-D) using a passive infrared search and track (IRST) sensor and an active air moving target indicator (AMTI) radar with the nearly constant velocity motion of the target. The active AMTI tracker uses the Cartesian state vector with 3-D position and velocity, and the dynamic model is linear. A passive IRST tracker commonly uses modified spherical coordinates (MSCs) for the state vector, where the dynamic model is nonlinear. In this formulation, the common process noise is explicitly modeled in both dynamic models. Therefore, it is possible to take into account the common process noise. We use the cubature Kalman filter (CKF) in both trackers due to its numerical stability and improved state estimation accuracy over existing nonlinear filters. The passive tracker uses a range-parameterized MSC-based CKF, and the active tracker uses a Cartesian CKF. We perform T2TF using the information filter (IF), where each local tracker sends its information matrix and the corresponding information state estimate to the fusion center. The IF handles the common process noise in an approximate way. Results from Monte Carlo simulations show that the accuracy of the proposed IF-based T2TF is close to that of the centralized fusion with varying levels of process noise and communication data rate.

Feature-aided tracking can often yield improved tracking performance over the standard multiple target tracking (MTT) algorithms. However, in many applications, the feature signal of the targets consists of sparse Fourier-domain signals. It changes quickly and nonlinearly in the time domain, and the feature measurements are corrupted by missed detections and misassociations. In this paper, we develop a feature-aided multiple hypothesis tracker for joint MTT and feature extraction in dense target environments. We use the atomic norm constraint to formulate the sparsity of feature signal and use the $\ell _1$ -norm to formulate the sparsity of the corruption induced by misassociations. Based on the sparse representation, the feature signal are estimated by solving a semidefinite program. With the estimated feature signal, refiltering is performed to estimate the kinematic states of the targets, where the association makes use of both kinematic and feature information. Simulation results are presented to illustrate the performance of the proposed algorithm.

By exploiting the sparsity of the scene containing only a few moving targets, a high-resolution and real-time range-Doppler map generation algorithm for passive bistatic radar is proposed. The proposed algorithm divides the long integration time into multiple short batches, from which a few batches are randomly selected on the basis of compressive sensing theory. A one-dimensional cross correlation is performed for each selected batch to obtain the range-compressed profile. Mean-value subtraction is then performed to suppress the direct path interference and stationary target reflections. Finally, an extended orthogonal matching pursuit algorithm is proposed for the effective estimation of target Doppler frequency. Practical application of this novel algorithm is examined by the detection of airplanes and ships via two synchronized general-purpose software-defined radio receivers. The results show that the proposed algorithm can achieve an improved resolution and a reduced sidelobe level compared to the conventional algorithms.

Noncoherent signal models are widely applied to angular superresolution in forward-looking imaging radars. However, the relative phase influences the resolvability of the model in coherent applications. We reveal the reason why phase shifts affect deconvolution and propose a coherent signal model by replacing the antenna power pattern with the radiation pattern. REGU, TSVD, and IAA are utilized in numerical tests and simulations. Experimental results have demonstrated significant resolution improvements for forward-looking imaging in coherent scanning radars.

Spread spectrum (SS) signal acquisition in satellite communication is a very computation intensive technique, which hinders the development of real-time spread spectrum signal acquisition. In this paper, in order to achieve real-time acquisition, we propose a multi-GPU based SS signal acquisition algorithm. First, sliding correlation, the computation kernel, is formulated and efficiently parallelized by CUDA. Second, a CUDA-enabled SS signal acquisition algorithm is implemented by adopting the CUDA-enabled sliding correlation. Third, a multi-GPU based algorithm is implemented by using multi-GPU programming. The performance is evaluated in a real SS telemetry system. Real-time acquisition is achieved in all cases by using a single K40 GPU. Furthermore, an average of 374.7 $\times$ speedup (max 473 $\times$ ) in 6 datasets is achieved when using four K40 GPUs. Good scalability is observed when varying the parameters.

Rather than using baseline-oriented approaches, autonomous spacecraft trajectory planning within asteroid systems may be based on human behavior that is learned by demonstration. In this paper, we conducted a numerical experiment to observe a human agent steering in real-time spacecraft motion within simulated binary asteroid environments. The resulting collection of human-planned trajectories demonstrates effective guidance mechanisms and reveals underlying path-planning processes that may serve for more agile and autonomous asteroid exploration.

Adaptive beamformer is very sensitive to model mismatch, especially when the signal-of-interest is present in the training data. In this paper, we focus on the topic of robust adaptive beamforming (RAB) based on interference-plus-noise covariance matrix (INCM) reconstruction. First, we analyze the effectiveness of several INCM reconstruction schemes, and particularly analyze the impacts of interference power estimation on RAB. Second, according to the analysis results, we develop a simplified algorithm to estimate the interference powers, and a RAB algorithm based on INCM reconstruction is then presented. Compared with some existing methods, the proposed algorithm simplifies the interference power estimation of INCM reconstruction. Aligned with our analysis, simulation results demonstrate that the overestimation of interference powers hardly degrades the performance of adaptive beamforming, and our proposed algorithm achieves nearly optimal performance across a wide range of signal-to-noise ratios.

This paper considers nonlinear state estimation subject to inequality constraints in the form of linear and linear-matrix inequalities. Rewriting the standard maximum likelihood objective function used to derive the Kalman filter allows the Kalman gain to be found by solving a constrained optimization problem with a linear objective function subject to a linear-matrix-inequality constraint. Additional constraints, such as weighted-norm- or linear-inequality constraints, that the state estimate must satisfy are easily augmented to the constrained optimization problem. The proposed constrained estimation methodology is applied in the extended Kalman filter (EKF) and sigma point Kalman filter (SPKF) frameworks. Motivated by estimation problems involving a vehicle that can rotate and translate in space, multiplicative versions of the constrained EKF and SPKF formulations are discussed. Simulation results for a ground-based mobile robot operating in a constrained three-dimensional terrain are presented and are compared to results that use the traditional multiplicative EKF and SPKF, as well as filters that enforce inequality constraints by simply projecting the state estimate into the constrained domain along the shortest Euclidean distance.

Nowadays, the identification of ballistic missile warheads in a cloud of decoys and debris is essential for defense systems in order to optimize the use of ammunition resources, avoiding to run out of all the available interceptors in vain. This paper introduces a novel solution for the classification of ballistic targets based on the computation of the inverse Radon transform of the target signatures, represented by a high-resolution range profile frame acquired within an entire period of the main rotation of the target. Namely, the precession for warheads and the tumbling for decoys are taken into account. The pseudo-Zernike moments of the resulting transformation are evaluated as the final feature vector for the classifier. The extracted features guarantee robustness against target's dimensions and rotation velocity, and the initial phase of the target's motion. The classification results on simulated data are shown for different polarizations of the electromagnetic radar waveform and for various operational conditions, confirming the validity of the algorithm.

For maneuvering targets, their motion during long observing time will deteriorate the integration results and degrade the performance of range and motion parameters estimation. To solve this problem, a computationally efficient method based on the time-reversal (TR) process and maximum likelihood (ML) principle, i.e., TR-MLE is proposed. The proposed method decouples the joint parameter estimation problem into two simpler problems, which not only increases the efficiency but also improves the estimation performance in low signal-noise-ratio. Furthermore, a fast method using Chirp-Z transform and Newton's method is developed for a more efficient implementation. The theoretical analysis of the noise properties after the TR process is carried out. Then, the corresponding Cramér–Rao lower bound that can evaluate the performance loss introduced by the TR process is discussed in detail. Simulated data and real data are used to assess the performance of the proposed method.

In this paper, we address the problem of radar range-Doppler imaging in the presence of clutter. Specifically, we formulate the range-Doppler imaging problem as that of recovery of a sparse vector contaminated by clutter in addition to noise. We propose a sparse Bayesian learning (SBL)-based algorithm to jointly obtain the range-Doppler image, variance of the noise, and covariance matrix of the clutter. Furthermore, we adapt a simple pruning mechanism that reduces the computational cost and improves the convergence speed.

In this paper, an airborne multi-user (MU) multiple-input multiple-output (MIMO) communication system is investigated, consisting of multiple users sources, multiple users destinations, and an aerial platform acting as a decode-and-forward (DF) relay. In this context, a novel three-dimensional (3-D) geometry-based optimization method for the relay location is proposed and expressions for the outage probability are presented. The results highlight the impact of the relay position, power allocation, fading severity, and number of antennas on the system performance.

In this paper, we propose a framework, based on the combined use of single- and multiuser detection, to jointly optimize the achievable rates of two signals sharing the same frequency in the forward link of a multibeam satellite system. We then propose the application of the described framework to two different scenarios of interest. First, we consider a uniform coverage scenario, aimed at maximizing the average throughput per beam in a realistic coverage condition. We compare different solutions based on alternative frequency reuse (FR) schemes and different receiver strategies. We demonstrate that the use of multiuser detection can achieve significant gains over a reference strategy based on single-user detection. Next, we analyze a “hotspot” case, where resources are pulled from empty beams to serve a beam with a high service demand. Also in this case, we compare several strategies and FR schemes. We show that the best performance is achieved by a scheme adopting three colors and single-user detection.

Fuzzy logic controllers are designed for spacecraft hovering flight in a binary asteroid system. The binary asteroid system is modeled as an ellipsoid-sphere system where the spherical harmonics method is adopted to represent the gravitational field of the asteroids. The method to design the fuzzy logic controllers is introduced in detail. Hovering flight about the inner collinear equilibrium point L 1 of binary asteroid Didymos is numerically presented. Detailed steps on roughly estimating the scaling gains of fuzzy logic controllers are also summarized. Numerical simulations are performed to show the effectiveness of the proposed method. Moreover, the aforementioned fuzzy logic controllers can be used for both continuous and impulsive thrusters. The fuzzy controllers with continuous thrust, the fuzzy controllers with fixed thrust, and the other two kinds of sliding-mode controllers are optimized in the same way and compared in the same simulation environment together, to show tremendous advantages of fuzzy logic controllers and continuous thrusters on both control performance and propellant consumption.

An adaptive finite-time control scheme is developed for noncooperative spacecraft fly-around subject to input saturation, full-state constraints, dynamic couplings, parameter uncertainties, and disturbances. Different from traditional fly-around model based on C–W equation, the derived 6-DOF spacecraft fly-around model can be suitable for noncooperative case in close proximity. By using the backstepping control technique, an integrated adaptive finite-time control law is designed, in which the tan-type barrier Lyapunov function (BLF) is incorporated to handle the full-state constraints. Meanwhile, the unknown dynamic couplings, parameter uncertainties, and disturbances are attenuated effectively by using adaptive estimation technique and the adverse effects raised from input saturation are reduced by the designed saturation compensator. Based on the constructed BLF, it is shown that the designed adaptive finite-time controller can guarantee that full-state constraints are not breached, but also can drive relative position and attitude tracking errors into the accurate convergent regions with finite-time convergence. Finally, the performance and advantage of the designed adaptive finite-time control scheme are demonstrated by numerical simulations.

This paper presents a method for spacecraft to achieve the task of fast maneuvering and fast stabilization. To realize this task, a new type of vibration isolation platform whose actuators are based on magnetic suspension techniques and an attitude controller to suit the spacecraft with this new vibration isolation platform are presented. High-frequency vibrations would reduce the stability of the attitude control, and low-frequency vibrations would reduce the maneuvering time of the attitude control. The vibration isolation platform presented in this paper is assembled between the spacecraft bus and the attitude control actuators and acts to reduce the high-frequency vibrations. The vibration isolation platform, which consists of the vibration isolation strut with magnetic suspension, has better performance in the region of high frequency according to the frequency-domain analysis. An appropriate controller for the vibration isolation strut is designed based on the frequency-domain analysis. Then, the attitude controller of the spacecraft bus is designed using the finite-time control theory to reduce the low-frequency vibrations, thus reducing the maneuvering time. Finally, the numerical simulations show that the vibration isolation platform and the attitude controller do work and cooperate well.

The backscattered signals of objects under spinning motion or with rotating parts provide very rich information that can be used for classification tasks, parameter extraction, etc. Obtaining such information from noncooperative objects with an unknown target-aspect is often a complicated task with a monostatic configuration. A multistatic radar on the other hand, can exploit the spatial diversity to extract the information from the time–frequency representations obtained from multiple aspect-angles. In this paper, we propose a tomographic approach for characterizing spinning objects in terms of their shape, size, and rotation parameters using a narrow-band multistatic radar. A two-dimensional image is reconstructed after a full rotation period using tomographic methods that allows not only to estimate the shape of the target but also the rotation parameters and the dimensions of the object. This is done very efficiently by combining the tomographic images from different aspect-angles on the transformed log-polar space, instead of the time–frequency representations. Simulations and measurements were conducted for the proof of concept. The measurement results with a simple target and a continuous wave K-band radar show errors below 3 $^{\circ }$ for the orientation estimation and below 5% for the estimation of the object's diameter.

This paper deals with the impulsive formation control of spacecraft in the presence of constraints on the position vector and time. Determining a set of path constraints can increase the safety and reliability in an impulsive relative motion of spacecraft. Specially, the feasibility problem of the position norm constraints is considered in this paper. Under assumptions, it is proved that if a position vector be reachable, then the reach time and the corresponding time of impulses are unique. The trajectory boundedness of the spacecraft between adjacent impulses are analyzed using the Gerschgorin and the Rayleigh–Ritz theorems as well as a finite form of the Jensen's inequality. Some boundaries are introduced regarding the Jordan–Brouwer separation theorem which are useful in checking the satisfaction of a constraint. Two numerical examples (approximate circular formation keeping and collision-free maneuver) are solved in order to show the applications and visualize the results.

This paper addresses the problem of achieving a desired impact angle against a stationary target with seeker's field-of-view limits. A bearings-only information based guidance law is investigated as a prospective solution. Analyzing the look-angle and the line-of-sight angle relationship, closed-form expressions of the guidance gains are derived for the desired impact angle and maximum look-angle constraints. A detailed analysis is carried out for lateral acceleration boundedness resulting in a design solution expressed in impact angle-maximum look angle space. Validating the guidance law, numerical simulations are performed using a kinematic vehicle model and a realistic model with given thrust and aerodynamic characteristics. Overall, the work offers an easily implementable guidance method with simple structure and closed-form guidance gains.

Blind source separation of frequency-hopped signals is a challenging problem for cases involving multipath exhibiting temporal and angular spread. A novel solution is proposed that exploits polarization-frequency correlation to separate signals, enabling association of hopped signal in these adverse conditions. Performance depends upon signal-to-noise ratios and channel-induced signal feature differences. The method has the advantage of not requiring model order estimation of multipath. The efficacy of the approach is demonstrated in experiments and numerical analyses.

This paper describes a formal language for describing intent of quadrotor aircraft. The aim of this language is to support the prediction of quadrotor trajectories modeling guidance instructions in a compact and univocal manner, enabling to efficiently share trajectory information by several actors. A trajectory computation engine, translating from sentences of this language into a common trajectory is also described. Its use is demonstrated for a complex flight and in real-flight operations.

This paper proposes a new approximation of the theoretical signal to interference plus noise ratio (SINR) loss of the low-rank (LR) adaptive filter built on the eigenvalue decomposition of the sample covariance matrix. This new result is based on an analysis in the large dimensional regime, i.e., when the size and the number of data tend to infinity at the same rate. Compared to previous works, this new derivation allows us to measure the quality of the adaptive filter near the LR contribution. Moreover, we propose a new LR adaptive filter and we also derive its SINR loss approximation in a large dimensional regime. We validate these results on a jamming application and test their robustness in a multiple input multiple output space time adaptive processing application where the data size is large.

We propose a new deceptive jamming method using networked receivers to perform deceptive jamming against multimode synthetic aperture radar. Using multiple receivers to measure the time difference of beam arrival, we solve the instantaneous modulation parameters precisely via linear equations. We then solve the time delays and frequency offsets of the deceptive signal without approximation to avoid accumulating electronic reconnaissance errors with traditional deceptive methods. Additionally, we design the distribution of receivers to minimize the condition number, reducing our method's sensitivity to measurement error. Simulation results prove the effectiveness of the proposed method.

High-precision relative position and attitude measurement is crucial for consensus control and collision avoidance in multi-CubeSat formation flying. However, the traditional relative navigation systems comprise many sensors and are not suitable for CubeSats due to large volume, complexity, and cost. In this paper, we propose a new approach, called Multi-CubeSat relative State determination by Array Signal detection (MUSAS). The approach utilizes the existing communication systems and antenna arrays on CubeSats without the need of extra components. In MUSAS, deputy vehicle (DV) CubeSats in a formation broadcast orthogonal spread spectrum signals simultaneously. Two chief vehicle (CV) CubeSats receive and separate the signals and extract the multiple-input multiple-output channel response of each DV CubeSat. Then, by utilizing the bi-directional spatial spectrum estimation, the angles-of-arrival and angles-of-departure of the propagation paths from each DV CubeSat to the CV CubeSats are estimated. Finally, the attitudes and positions of all DV CubeSats relative to the CV CubeSats are determined using the derived rotation matrices. We have theoretically proved the proposed MUSAS algorithm and performed extensive simulations to compare its performance with existing methods. Furthermore, we also developed the testbed of MUSAS and conducted field experiments. The simulation and experiment results have verified that, by exploiting the spread spectrum gain and antenna array gain, MUSAS can achieve high accuracy in relative state determination, even using small antenna arrays and low transmission power.

Conventional velocity-filtering-based track-before-detect (VF-TBD) methods integrate the energy of a cell in a frame with that of the cell closest to the predicted target position in the last frame of the processing batch, assuming a certain target velocity. However, the target may not exactly be on the quantized cell and its echo envelope may occupy multiple adjacent cells. This often leads to significant energy loss and echo envelope degradation. In this paper, a novel VF-TBD method based on pseudo-spectrum (PS-VF-TBD) is presented to address this problem. For every cell, a pseudo-spectrum is constructed around the predicted position according to the assumed velocity using a truncated point spread function. Samples of the pseudo-spectrum on the cells that are located in the truncated spread area are added onto the last frame of the processing batch to integrate the target energy within multiple cells. Due to the use of the point spread model and the accurate sampling of the predicted spectrum, energy loss can be mitigated and the echo envelope is well maintained. This approach simultaneously maximizes the signal-to-noise ratio (SNR) gain and enables improved parameter estimation utilizing the envelope characteristics. The procedure for pseudo-spectrum construction and multiframe accumulation is derived in detail and the output SNR is analyzed theoretically. It is found that the proposed PS-VF-TBD can achieve an SNR gain greater than that by the conventional VF-TBD method. To deal with a target with unknown velocity, a bank of pseudo-spectrum-based velocity filters is proposed. The signal gain loss resulting from velocity mismatch is investigated and the $\mu$ -width of the envelope in the velocity domain is analyzed. Finally, a method for improved position and velocity estimation is presented. Simulation results demonstrate the superiority of the proposed method in terms of SNR gain, detection probability, and estimation accuracy at the expense of increased computational complexity.