In practice, adaptive beamforming usually faces non-ideal situations where a limited number of snapshots are available, the training data are corrupted by desired target signals, and the array mismatches exist. Traditional methods often degrade significantly under the above situation. In order to solve this problem, a new adaptive beamforming method based on atomic-norm optimization technique is proposed

This paper presents batch and online algorithms of a stereo acoustic echo cancellation (SAEC) method. In SAEC, the non-uniqueness problem causes performance degradation, especially for highly coherent far-end signals. In our method, this problem can be avoided without an additional decorrelation preprocessor or multi-microphones by overestimating far-end echoes and compensating for the overestimated

Remote sensing enables multitemporal information of the Earth’s surface and the dynamic processes that affect the environment. Given the considerable data availability, methods to summarize multitemporal datasets are needed to support the analysis. Our study introduces and compares methods to monitor temporal variations of water bodies based on multitemporal image composition. For this purpose, the

Sinusoidal frequency-modulated (SFM) signal is widely applied in radar, and wireless communication. Therefore, there is significant importance in studying the estimation of SFM signal parameters. However, traditional estimation algorithms have high computational complexity while the corresponding estimation performance degrades when either modulation index or modulation frequency of SFM signals increases

Low-rank matrix/tensor factorizations play a significant role in science and engineering. An important example is the canonical polyadic decomposition (CPD). There is also a growing interest in multi-set extensions of low-rank matrix/tensor factorizations in which the associated factor matrices are partially shared. In this paper we propose a more unified framework for multi-set matrix/tensor factorizations

For extended object tracking, the random matrix approach is a computationally efficient framework that is capable of estimating the kinematic state, and extension of the object jointly, and thus is gaining momentum in recent years. Existing random matrix approaches have an underlying assumption that scatter centers are symmetrically distributed around the centroid. In many real scenarios, however,

Due to the broad range of applications of stochastic multi-armed bandit model, understanding the effects of adversarial attacks and designing bandit algorithms robust to attacks are essential for the safe applications of this model. In this paper, we introduce a new class of attacks named action-manipulation attacks. In this class of attacks, an adversary can change the action signal selected by the

Conducting surveillance of the geocenric orbits is a key task towards achieving space situational awareness (SSA). Our work focuses on the optical detection of man-made objects (e.g., satellites, space debris) in Geostationary orbit (GEO), which is home to major space assets such as telecommunications and Earth observing satellites. GEO object detection is challenging due to the distance of the targets

This paper addresses the adaptive detection of subspace signals in the noise whose covariance matrix is unknown. The partially homogeneous scenario, where the primary data have the same noise covariance matrix with the training data up to an unknown scaling factor is considered. We exploit the persymmetric structure of the noise covariance matrix to enhance the matched detection performance in the

Drawing a spectrally homogeneous region of interest in a remotely sensed image is a common task for an image analyst when performing, for instance, atmospheric correction or end-member selection. Manually selecting a homogeneous sample of pixels can be tedious and error prone due to the limits of human perception and data visualization. I present a region shrinkage method that automates the extraction

This study presents a novel method for time–frequency (TF) signal analysis and chirp rate estimation dedicated for electromagnetic spectrum sensing, electronic warfare, electronic intelligence and/or passive bistatic radar purposes in which frequency modulated signals occur. The approach is based on the double-adaptive chirplet transform, providing optimal analysis window parameters, which is a crucial

The authors deal with the problem of detecting point-like targets in the presence of diffuse multipath under the assumption of a partially homogeneous Gaussian disturbance by introducing an unknown scaling factor which represents the mismatch between the noise contribution of the cell under test and the training samples. Also, they model the target echo as a superposition of direct plus multipath components

For high-resolution scene mapping and object recognition, optical technologies such as cameras and LiDAR are the sensors of choice. However, for future vehicle autonomy and driver assistance in adverse weather conditions, improvements in automotive radar technology and the development of algorithms and machine learning for robust mapping and recognition are essential. In this study, the authors describe

Drifting of anchor nodes due to ocean currents and tides is a challenging issue in efficient localisation for underwater acoustic sensor networks. This study therefore proposes a correction method to compensate the error caused by drifting (and accurately localise the target) by introducing an extra floating anchor node. The floating anchor node is placed in the middle of the localisation area and

The decomposition of multi-component micro-Doppler signals overlapping in the time-frequency (T-F) domain is critical and challenging, especially in the case of irregular instantaneous Doppler frequency. The authors propose a novel time-based decomposition method called the short-time variational mode decomposition (STVMD) to analyse the irregular (FM) micro-Doppler signals, and present an optimal

In this study, the authors take further insight into the sparse geometry which offers a larger array aperture than uniform linear array with the same number of physical sensors. A novel direction of arrivals (DOAs) estimation model with flexible sparse geometry, which possesses the potential to significantly improve the estimation performance especially when the placed space and the weight of carrier

Human identification plays a vital role in daily lives. A majority of biometric technologies require the active cooperation of humans, while gait recognition does not. Compared with other identification technologies, radar-based technology can monitor the human body around the clock without being affected by light/weather, and is not easy to be forged while protecting privacy. Previous researches have

Combined sky-wave and surface-wave monostatic high-frequency (CSSM-HF) radar is a new type of mono-static high frequency (HF) radar that uses multiple combined propagation modes of skywave and surface wave to detect targets. However, the detection range of different propagation modes partially overlaps, which results in multiple mode echoes from one target. To track the target in the presence of multi-mode

The derivation of tight estimation lower bounds is a key tool to design and assess the performance of new estimators. In this contribution, first, the authors derive a new compact Cramér–Rao bound (CRB) for the conditional signal model, where the deterministic parameter's vector includes a real positive amplitude and the signal phase. Then, the resulting CRB is particularised to the delay, Doppler

The authors propose an algorithm to estimate the path followed by refracted signals from a source to a target, through a medium formed by uniform parallel layers with known different refractive indices, a common model used for ice radio-echo sounding. The analytical solution is a polynomial with a degree that exponentially depends on the number of layers, being computationally inefficient. For low

An adaptive extend Kalman filter（EKF） is designed to estimate the roll angle for trajectory correction projectile only using radial magnetometers. By analysing the characteristics of the radial magnetometer output signal and the complex angular motion of the high-spin projectiles, the measurement equation is established. Two important measures, namely noise parameter adaptation and increasing system

Cognitive radars have the capability of adapting the transmitted waveform with the dynamic state of target and environment, to enhance the radar performance. In this study, a cost function is proposed for designing the waveform parameters in cognitive radars, along managing the radar time resources. The proposed cost function has the capability of adapting to any radar measurement vector. The purpose

Underdetermined wideband direction of arrival (DOA) estimation based on the sparse array is studied here and a novel algorithm is developed to improve the estimation performance of off-grid targets in the framework of sparse Bayesian learning. First, the narrowband off-grid model is extended to a wideband case and the sparse Bayesian model containing off-grid biases is deduced. Then, a sequential solution

With the multi-function need for airborne space-time adaptive processing (STAP) radar, range synthesis is usually necessary for the follow-up function of target recognition. Utilising the excellent performance on orthogonality and bandwidth synthesis of the frequency division multiple access (FDMA) waveform and the longer accumulation time of multiple-input multiple-output (MIMO) radar, a robust low-sidelobe

The construction of the third generation of the Chinese BeiDou navigation satellite system (BDS), which provides global positioning, navigation, and timing (PNT) services, will be completed in 2020. As BDS satellites contain the geostationary orbit (GEO), inclined geosynchronous (IGSO), and medium earth orbit (MEO), orbital manoeuvres occur more frequently. Thus, orbit manoeuvre detection is important

Based on the mean arrival time and temporal broadening of pulsed waves propagating through turbulent media, this study investigates the ionospheric effects induced by irregularity on medium-earth-orbit synthetic aperture radar (MEOSAR) range imaging quality. Considering radar signal two-way propagating through ionosphere irregularity, an analysis model for ionospheric effects induced by irregularities

Cognitive radars have the capability of using the prior environmental information to improve the radar performance. In this study, a cost function for fully adaptive waveform parameters design for cognitive tracking radars is proposed. These parameters include – but not limited to – pulse length, pulse repetition frequency, number of transmitted pulses, and target revisit rate. Thus, the proposed cost

This study proposes an intelligent tracking method based on the expectation maximisation approach. To obtain the consistent tracking performance, the observer should maintain contact with the target and continuously update its tracking data. Unfortunately, a significant problem arises from the uncertainty of the target motion. The reasons behind this uncertainty include losses, time-varying noises

High-frequency surface wave radar (HFSWR) is an effective method for detecting and monitoring the targets on the ocean surface and above. To improve the detection performance of HFSWR, a new detector is proposed by applying weighted distance iteration (WDI) processing to the joint domain localised (JDL) matrix constant false alarm rate (CFAR) detector. Based on information geometry theory and Riemannian

Gait-based human identification aims to identify individuals by their walking style. In this study, the authors investigate the use of micro-Doppler (m-D) signatures retrieved from a frequency-modulated continuous-wave radar sensor to identify individuals based on their natural gait characteristics. The gait dataset of 20 persons has been collected in an indoor environment where each subject was allowed

In this article, we study the communication, and (sub)gradient computation costs in distributed optimization. We present two algorithms based on the framework of the accelerated penalty method with increasing penalty parameters. Our first algorithm is for smooth distributed optimization, and it obtains the near optimal $O(\sqrt{\frac{L}{\epsilon (1-\sigma _2(W))}}\log \frac{1}{\epsilon })$ communication

We investigate spatio-temporal event analysis using point processes. Inferring the dynamics of event sequences spatio-temporally has many practical applications including crime prediction, social media analysis, and traffic forecasting. In particular, we focus on spatio-temporal Hawkes processes that are commonly used due to their capability to capture excitations between event occurrences. We introduce

In this study, a novel security-enhanced filter (SEF) is proposed for the system state and malicious attack signal estimation of the stochastic jump-diffusion systems with the external disturbance, measurement noise and malicious attack signal on system and sensor. To efficiently estimate the system state and malicious attack signal by the traditional Luenberger-type filter, a novel smoothed signal

Bayesian filtering deals with computing the posterior distribution of the state of a stochastic dynamic system given noisy observations. In this paper, motivated by applications in counter-adversarial autonomous systems, we consider the following inverse filtering problem: Given a sequence of posterior distributions from a Bayesian filter, what can be inferred about the transition kernel of the state

Covariance matrices play a major role in statistics, signal processing and machine learning applications. This paper focuses on the semiparametric covariance/scatter matrix estimation problem in elliptical distributions. The class of elliptical distributions can be seen as a semiparametric model where the finite-dimensional vector of interest is given by the location vector and by the (vectorized)

Networks and data supported on graphs have become ubiquitous in the sciences and engineering. This paper studies the ‘blind’ community detection problem, where we seek to infer the community structure of a graph model given the observation of independent graph signals on a set of nodes whose connections are unknown. We model each observation as filtered white noise, where the underlying network structure

Exact recovery of $K$ -sparse signals ${\boldsymbol{x}}\in \mathbb{R}^{n}$ from linear measurements ${\boldsymbol{y}}=\boldsymbol{A}{\boldsymbol{x}}$ , where $\boldsymbol{A}\in \mathbb {R}^{m\times n}$ is a sensing matrix, arises from many applications. The orthogonal matching pursuit (OMP) algorithm is widely used for reconstructing ${\boldsymbol{x}}$ based on ${\boldsymbol{y}}$ and $\boldsymbol{A}$

Subspace clustering, the task of clustering high dimensional data when the data points come from a union of subspaces, is one of the fundamental tasks in unsupervised machine learning. Most of the existing algorithms for this task require prior knowledge of the number of clusters along with few additional parameters which need to be set or tuned apriori according to the type of data to be clustered

Effective hyper-parameter tuning is essential to guarantee the performance that neural networks have come to be known for.In this work, a principled approach to choosing the learning rate is proposed for shallow feedforward neural networks. We associate the learning rate with the gradient Lipschitz constant of the objective to be minimized while training. An upper bound on the mentioned constant is

Ramanujan's sum has been recently applied to detect different periodic components in 2D image. By filtering in frequency domain and obtaining the least common multiplier (lcm) of periodicity matrices, the basic 2D periodic pattern can be found. This paper introduces a multiplier-less structure to efficiently implement this process, based on the well-known number theoretic function called Möbius function

Intelligent reflection surface (IRS) has recently been recognized as a promising technique to enhance the performance of wireless systems due to its ability of reconfiguring the signal propagation environment. However, the perfect channel state information (CSI) is challenging to obtain at the base station (BS) due to the lack of radio frequency (RF) chains at the IRS. Since most of the existing channel

The surveillance of large areas to ensure local security requires remote sensors with high temporal and spatial resolution. Captive balloons with infrared and visible sensors, like ALTAVE captive balloon system, can perform a long-term day–night surveillance and provide security of large areas by monitoring people and vehicles, but it is an exhaustive task for a human. In order to provide a more efficient

Evapotranspiration (ET) quantification improves the comprehension of the water, heat, and carbon interactions and the feedback to the climate, which is essential for global change research. We aimed to model ET using artificial neural networks (ANNs) based on Landsat-8 and reanalysis data from the National Centers for Environmental Prediction over the grasslands of the Pampa biome. The output variable

The new method for the discrete Fourier transform computation over a finite field is introduced. This method is a nontrivial generalization of the Duhamel–Hollmann algorithm with replacement of the Toeplitz convolution calculation by the normalized cyclic convolution calculation. Both algorithms have the smallest multiplicative complexity.

This article considers the problem of online trajectory design under time-varying environments. We formulate the general trajectory optimization problem within the framework of time-varying constrained convex optimization and propose a novel version of online gradient ascent algorithm (OGA) for such problems. Respecting the online nature, we carefully select the step size of OGA at each iteration so

This paper investigates the localization of a moving source in position and velocity, by observing the emitted frequency from the source that is subject to the Doppler shift effect at a number of stationary sensors. Previous attempts rely on exhaustive grid search or numerical polynomial optimization to obtain a solution. We shall propose a constrained optimization to formulate the localization problem

In many modern applications, large-scale sensor networks are used to perform statistical inference tasks. In this article, we propose Bayesian methods for multiple change-point detection using a sensor network in which a fusion center (FC) can receive a data stream from each sensor. Due to communication limitations, the FC monitors only a subset of the sensors at each time slot. Since the number of

The potential of deploying large-scale antenna arrays in future wireless systems has stimulated extensive research on hybrid transceiver designs aiming to approximate the optimal fully-digital schemes with much reduced hardware cost, and signal processing complexity. Generally, this hybrid transceiver structure requires a joint design of analog, and digital processing to enable both beamsteering, and

In this paper, we address the problem of noise-robust multiple-input multiple-output (MIMO) adaptive filtering that is optimal in least-squares sense with application to multichannel acoustic echo cancellation. We formulate the problem as minimization of a multichannel least-squares cost function that incorporates near-end speech and noise statistics resulting in a novel noise-robust framework for

Non-orthogonal multiple access (NOMA) based beamspace multiple-input multiple-output (MIMO) is a MIMO-NOMA scheme for millimeter-wave (mmWave) communications to improve the number of connections with increased sum rate. However, most of existing works only aim at maximizing the sum rate, which may cause an unbearable rate loss to weak users. To guarantee the rate performance for all served users, we

This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for multi-target tracking: one to estimate the set of alive trajectories at each time step and another to estimate the set of all trajectories, which includes alive and dead trajectories, at each time step. The filters are based on propagating a Poisson multi-Bernoulli (PMB) density on the corresponding set of trajectories through

The advanced baseline imager (ABI) instrument onboard Geostationary Operational Environmental Satellite (GOES)-16 is the first of National Oceanic and Atmospheric Administration (NOAA’s) new-generation geostationary earth orbiting (GEO) imagers that provides high-quality calibrated and geolocated Earth observations in six reflective solar bands (RSBs). The spectral similarity between the Visible Infrared

The advanced baseline imager (ABI) on the Geostationary Operational Environmental Satellite (GOES)-R Series is a great improvement compared to the legacy GOES imager. For example, there are more spectral bands at improved spatial resolution and more frequent imagery. The vast majority of the images generated by the ABIs are free of visual defects, well calibrated, and produced in a timely fashion.

Ordinary multispectral (MS) image change detection (CD) techniques have not excavated the inherent information of MS images. Deep learning methods are a type of effective method that grabs the changed and unchanged information by deep feature extraction. However, a larger number of labeled samples are difficult to obtain for CD in MS images. An unsupervised MS image CD method is proposed based on a

Water vapor is an important greenhouse gas. The atmospheric water vapor content is monitored by remote methods, which require accurate spectroscopic information about H2O absorption line parameters. The problems of accuracy of accounting the selective water vapor absorption in the simulation of atmospheric transfer of solar radiation are considered in this work. The atmospheric spectra are simulated

The main objective in our study was to derive an accurate wetland inventory of the Dınàgà Wek’èhodì region, Northwest Territories, while also enhancing our previously established wetland mapping workflow. Our methods used multidate optical and radar satellite imagery and fused these data with ArcticDEM topographic variables. Additionally, few studies to date have assessed the ArcticDEM for wetland

In coastal waters, colored dissolved organic matter (CDOM) is often present in moderate to high concentrations due to input from nearby landmasses and rivers. CDOM absorbs light sharply in the UV-blue region, causing waters to appear yellow or brown. This has a significant effect on optical and biogeochemical processes in coastal waters, as it inhibits the penetration of light within the water. It

The dust storm is one of the severe natural disasters that has been recently threatening the Middle East region due to climate changes and human activities. This phenomenon has become a national crisis in some countries in this region in previous years, especially in spring and summer. This research aims to detect and monitor the areas covered by the seasonal and occasional dust storm from (Moderate

The unmanned aerial vehicle (UAV) is an emerging technology applied recently in land cover classification, owing to its ability to acquire very high-resolution spatial data, that has provided an effective means for detailed land cover mapping, especially for a small island area. Selecting suitable UAV-acquired data and exploring the combined use of UAV multitype data are of significance for island

Impervious surfaces (ISs) are significant indicators of the overall environmental health of watersheds. Monitoring the IS spatial patterns of watersheds is helpful for improving water management and regional pollution assessment. A random forest regression (RFR) approach was developed to estimate the subpixel IS percentage (ISP) from Chinese GF-5 hyperspectral imagery in 30 m pixels in the Nansi Lake