A new approach for the synthesis of one-dimensional (1D) and 2D reconfigurable sparse arrays generating sum and difference power patterns is presented in this study. In the case of 1D arrays, the design procedure provides solutions in which only one set of amplitude coefficients is required and the reconfigurability is obtained by modifying only the excitation phases. In the case of 2D arrays, the

This study proposes an advanced direction-of-arrival (DOA) estimation method for automotive multiple-input multiple-output (MIMO) fast-ramp radar systems. To distinguish the transmitted signal at the receiving antenna, the orthogonal code can be applied to each transmitted signal. In this case, to correctly estimate the angles of the targets, the authors also have to match the distinguished signals

Low-cost signal jammers, which are illegal in many countries but continue to be traded in the black market, emit weak global navigation satellite system (GNSS) jamming signals. This study proposes a method to detect, identify, and localise these signals based on a cloud-data processing technique in which the jamming detection, identification, and localisation are performed sequentially after gathering

The instantaneous kinematic velocity obtained by using a stand-alone global navigation satellite system (GNSS) receiver has attracted increasing attention, and has significant for numerous applications. Using the time difference carrier phase (TDCP) measurements, or the displacements they produced, we propose two real-time instantaneous velocity determination methods based on the moving-window polynomial

The signal separation method based on the short time Fourier transform (STFT) and the L-statistics can effectively eliminate the interference of the micro-Doppler effect on inverse synthetic aperture radar (ISAR) imaging. The insufficient frequency resolution of STFT will result in poor interference cancellation when the azimuth range between two adjacent scatterers is small. Then, a novel method for

An active sensing system, such as radar, extracts the useful features of the targets of interest, by transmitting a certain waveform to a particular area and analysing the returned signal. There are two factors that are critical to the system performance, namely the receive filter and the transmitted waveform. Transmitted signal is among the most interesting factor affecting the radar system performance

This study considers the Bayesian programming methodology for recognition and classification of radio emission sources. A mathematical model of Bayesian programming proposes forming a family of probability distributions based on known parameters contained in a training sample (database). The correlations between the object classes have been estimated according discussed methodology. The received assessment

Time delay estimation (TDE) between receivers plays a significant role in various fields such as radar, sonar, global positioning system, and underwater communications. TDE is the first step in the localisation and tracking of the source. Estimation of the time delay of a passive sonar system in a reverberant environment is one of the most challenging tasks. For some decades, the well-known generalised

The increasing amount of space debris poses a growing challenge in the development of space travel. In previous work, a proposed system comprising of a low budget space-borne passive radar based on CubeSAT, flying in Low Earth Orbit (LEO), demonstrated promising results in terms of space debris detection performance. In this work, a novel shadow inverse synthetic aperture radar (SISAR) model for space

The carrier phase (CP) technique based on the global positioning system is a widely applied spatial tool for precise time and frequency transfer. However, the traditional CP technique considers the receiver clock offset to be a white noise stochastic process, which introduces unknown errors into the model and thereby compromises time and frequency transfer performance. To improve the performance of

Phase adjustment with the autofocus process is essential to inverse synthetic aperture radar (ISAR) imagery of manoeuvring targets. Conventional parametric and non-parametric autofocus methods usually degrade in front of error model mismatch and noise interference. Especially, the target manoeuvre also induces higher order phases that have the spatial-variant characteristic. In this study, a range-dependent

The mutual interference between frequency-modulated continuous-wave (FMCW) automotive radars increases the noise floor, even submerges the information of targets, and creates ghost targets. To reduce the impact of the mutual interference between automotive radars and improve the detection performance, this study proposes an iterative modified threshold method based on empirical mode decomposition (IMT-EMD)

Image matching is an important step that is taken in most synthetic aperture radar (SAR) images applications. In this study, a method is proposed for texture image reconstruction to improve SAR image matching. This method consists of three major steps. First, textural features are extracted. Second optimal texture images (OTIs) are created using extracted features. Texture optimisation is conducted

This study considers the 3D scenario where an autonomous underwater vehicle (AUV) tracks an emitter while estimating the emitter's location by measuring the emitter's frequency and elevation–azimuth angle. To enhance the emitter estimation based on frequency–angle measurements, this study presents an AUV's path planner considering acceleration limits, such as the bounded turn rate. As far as the author's

This study considers cooperative localisation and exploration of multiple unmanned underwater vehicles. The author assumes that one vehicle, called the communication/navigation aid (CNA), moves near the sea surface and that the vehicle can use a global positioning system (GPS) to localise itself. To explore a deep sea, the author's strategy is to make each vehicle move at a distinct depth such that

Forecasting of total electron content (TEC)/global positioning system (GPS) signal delays in storm conditions is considered the most challenging task for accurate position estimation, especially in critical applications. Therefore, a storm-time ionospheric model is proposed to forecast TEC based on the artificial neural network (ANN) using singular spectrum analysis (SSA). The study area covers four

In the underwater sound source localisation systems, the sound wave signal is accompanied by time delay and Doppler shift, where the cross-ambiguity function (CAF) is commonly applied as a method of estimating them. This study proposes a novel method for calculating the exact value of the CAF,based on the theory of curved surface interpolation. Firstly, two optimisation problems with equality and inequality

Sparse representation can be extended to high dimensions and can be used in many applications, including three-dimensional (3D) Inverse synthetic aperture radar (ISAR) imaging. In this study, the high-dimensional sparse representation problem and a recovery method called high-dimensional smoothed least zero-norm (HDSL0) are formulated. In this method, the theory and computation of tensors and approximating

Learning discriminative features is difficult for deep learning-based target recognition in synthetic aperture radar (SAR) images with small training samples. To achieve a better feature learning, this study proposes a new deep network, a compact convolutional autoencoder (CCAE) for SAR target recognition. CCAE minimises the reconstruction loss and the distance between intra-class samples simultaneously

Initial relative parameters are of great importance as inputs of the subsequent filter process based on the stereovision system. The relative dynamic model is approximated to estimate the initial relative state. Firstly, the error distribution property of feature point captured using cameras can be approximately regarded to be zero-mean Gaussian distribution when the measurement error of pixel is assumed

The random matrix approach for extended object tracking (EOT) is appealing. This approach assumes that the measurements are linear in the kinematic state and measurement noise. In many practical applications, however, this linear condition cannot always be satisfied. First, this study derives a linearised measurement model. The model first employs a decorrelated unbiased technique to convert the non-linear

This study presents the concept, design, and operation of a low-cost and lightweight multiple-input multiple-output (MIMO) radar system, dedicated to the characterisation of complex volumetric environments such as snow, forest, sand etc. using synthetic aperture radar tomographic focusing. The system consists of six transmitting and six receiving antennas, operated in a multi-static configuration,

Parametric calibration of antenna arrays uses mathematical models of the array manifold with adjustable parameters. In this study, the author shows that the standard model used in that array signal processing literature may not be capable of accurate calibration because of the mismatch between the assumed manifold and the true electromagnetic manifold. To improve calibration quality, the author uses

On account of the different parameters of delay-locked loop, multipath effect and code bandwidth, the Global Navigation Satellite System (GNSS) pseudorange noise of each frequency is different. An unequal-weighted three-carrier ambiguity resolution (TCAR) algorithm for long-baseline static triple-frequency ambiguity resolution is proposed. The algorithm is conducted in cascaded steps. When calculating

Among the existing binary offset carrier (BOC) signal receiving technologies, the dual binary phase-shift keying tracking (DBT), originally designed for alternate BOC (AltBOC) modulated Galileo E5 signal and then extended to standard BOC signals, has its unique characteristics. It incorporates both single-band and dual-band processing schemes into a unified and simple structure to meet both requirements

The authors consider the problem of direction-of-arrival (DOA) estimation for the uniform noise (UN) or non-UN (NUN) in a passive radar. The radar waveform to be estimated is modelled as a deterministic and unknown process. Under this condition, the well-known expectation maximisation (EM) algorithm can be utilised for the estimation. In the conventional EM algorithm, the DOA is updated in every iteration

This study proposes a ground-based threat probability model based on a Markov chain to evaluate multiple ground threats faced by the platform during flight. On this basis, a new threat evaluation and jamming allocation system is constructed by combining the model with jamming resource allocation methods. In the threat evaluation phase, the interaction relationship between the platform and radar is

In some tracking applications, target states are subjected to a trajectory shape constraint, where only the trajectory shape is known a priori. By incorporating the trajectory shape constraint, the tracking performance can be improved. However, the existing state estimation methods with trajectory shape constraints are either of high computational load or of poor robustness due to the nature of the

The statistical characteristic of ocean ambient noise plays an important role in developing underwater signal processors. Considering that the noise in shallow water shows the impulsive nature, non-Gaussian noise models are usually applied to model the ocean ambient noise. In this study, the ocean ambient noise is modelled by using the Middleton Class B model, which can be decomposed into Gaussian

Radar cross-section (RCS) of an object is a complex function of various geometric variables, frequency and angles of incidence. In this work, an artificial intelligence solution is provided to predict the non-deterministic characteristics of RCS using the supervised machine learning algorithm that involves Gaussian process (GP) regression. A parametrised aircraft model is used to generate training

The linear time-varying frequency errors (LTFE) caused by the mismatch of transmitter and receiver oscillators can defocus the imaging result of distributed inverse synthetic aperture radar (ISAR) seriously. The LTFE calibration method based on the entropy minimization principle is sensitive to signal-to-noise ratio (SNR), and its performance is degraded significantly under low SNR conditions. In addition

Most research regarding passive radar exploiting ‘illuminators of opportunity’, such as FM radio, has focused on detecting and tracking targets. This study explores adding automatic target recognition (ATR) capabilities to such systems. The ATR algorithms described here use the radar cross-section (RCS) of potential targets, collected over a short period of time. The received signal model accounts

Using the frequency-division duplex long-term evolution (LTE)-based multistatic passive radar system for unmanned aerial vehicle (UAV) detection is considered. The typical outdoor terrestrial LTE eNodeBs and the broadband direct air-to-ground communications network eNodeBs are used to cover the UAVs at the low altitude and at the medium-high altitude, respectively. The bistatic range measurements are

The ionosphere causes serious phase error in P-band inverse synthetic aperture radar (ISAR) systems, which makes it difficult to obtain a high-quality image. Recently, the convolutional neural network (CNN) has gained much attention in signal processing, and it can automatically extract features to realise an image super-resolution reconstruction. As a popular CNN-based network, U-net can work with

The generalised Pareto distribution is an efficient model to characterise the high-resolution sea clutter. Robust and precise estimation of the model's parameters is a precondition of effective target detection in sea clutter. It is known that the explicit bipercentile (BiP) estimators are efficient in computation and robust to outliers. In this study, it is proved that the explicit BiP estimators

Air-to-air target tracking is a challenging task for practical military and civilian applications. In this study, an unbiased converted measurement Kalman filter in Earth-centred Earth-fixed (ECEF) coordinates (E-UCMKF) is proposed for air-to-air target tracking, which takes account for noisy measurements from the airborne radar and the inertial navigation system/global navigation satellite system

Most existing methods for direction-of-arrival (DOA) estimation are severely influenced by impulsive noise due to their Gaussian noise assumption. As a typical non-linear similarity measure, the maximum correntropy criterion (MCC) has been considered to restrain impulsive noise, because of its ability to exploit the high-order statistics of signal. However, Gaussian kernel-based MCC method is not always

Automatic modulation classification of radar signals, which plays a significant role in both civilian and military applications, is researched in this study through a deep learning network. In this study, a novel network combined a shallow convolution neural network (CNN), long short-term memory (LSTM) network and deep neural network (DNN) is proposed to recognise six types of radar signals with different

Sparse representation displays remarkable characteristics when applied to image processing and classification. The critical point in the success of sparse representation-based classification is to learn an authentic dictionary. The present study proposes a virtual dictionary-based sparse representation for automatic target recognition. Based on the properties of the synthetic aperture radar (SAR) images

In this study, the issue of power allocation in the statistical multiple-input–multiple-output (MIMO) radars is investigated to reduce the probability of interception. In MIMO radars, orthogonal waveforms are commonly used, in which waveforms are orthogonal frequency-diversity or phase-coded (PC) waveforms. Therefore, the low probability of intercept (LPI) optimisation problem is considered for different

Simulation of radar cross-sections of pedestrians at automotive radar frequencies forms a key tool for software verification test beds for advanced driver assistance systems. Two commonly used simulation methods are the computationally simple scattering centre model of dynamic humans and the shooting and bouncing ray technique based on geometric optics. The latter technique is more accurate but computationally

This study investigates the feasibility of pilot-based detection for passive coherent location (PCL) radars exploiting digital video broadcasting-terrestrial (DVB-T) signals. The DVB-T signal is formed by two parts: a data signal and a pilot signal. The parameters of the pilot signal are known thanks to the DVB-T standards that permit the generation (at the receiver) of the pilot signal. The pilot

The prevalent imaging method in inverse synthetic aperture radar (ISAR) system is range-Doppler (RD) method, which is implemented by the fast Fourier transformation (FFT). FFT is computationally efficient, but it comes with a price – the problems of wide main-lobes and high side-lobes, especially under the sparse apertures condition. In addition, the resolution of RD method is determined by the radar

The single-sensor Poisson multi-Bernoulli (MB) mixture (PMBM) filter has been developed for multi-target tracking (MTT). However, there is a lack of research on the multi-sensor (MS) extensions of this filter. Because the conjugate density of PMBM filter is a hybrid form, which makes it difficult to extend directly using existing methods. In this study, a general MS Poisson MB filter based on an MS

BPSK is the basis of current GNSS (Global Navigation Satellite System) signals. BDS (Beidou navigation satellite system) RDSS (Radio Determination Satellite Service) system also adopts BPSK to realize communication and ranging simultaneously. To realize higher system capacity, RDSS signals overlap in time and frequency domain. The signal performance is heavily determined by the MAI (Multiple Access

In this study, by employing the intra-pulse cross-correlation (IPCC) operation, an unambiguous velocity estimation method is proposed for narrow-band long-range radars with high carrier frequency and low pulse repetition frequency. This estimation algorithm is simple and could be easily implemented in existing radar systems without changing the radar hardware or the pulse transmitting scheme. Comparing

In this paper, a novel prediction-discrepancy based on innovative particle filter (PDIPF) is proposed to solve the unmanned aerial vehicle (UAV) positioning problem in the presence of the global positioning system (GPS) spoofing attack, supposing that the GPS spoofing effects are in the form of unknown but bounded errors. To cope with the GPS spoofing attacks as unknown sudden changes of system state

To apply the Ramanujan filter bank based on the compressed sensing theory to the period estimation of multiple sets of radar pulses, alternatives to period data and the Ramanujan Subspace are proposed in this study. First, the rationality of alternatives to the TOA (time of arrival) model is illustrated by comparing the advantages and disadvantages of the time-point model and the TOA model. Next, by

Magnetron-based marine radar technology is mature, affordable, reliable, and very effective for maritime safety applications. Commercial systems may be procured at a modest cost as compared to fully coherent solid-state systems. Magnetron oscillators inherently generate random phase signals. Phase instability on a pulse-to-pulse basis impedes this class of marine radars from success in applications

The frequency diverse array multiple-input multiple-output (FDA-MIMO) radar provides range estimation capability by exploiting a small frequency offset across the transmit sensors, which has been utilised in numerous applications. However, the estimation performance is basically limited by the array geometry and signal bandwidth. In this study, the authors propose a new FDA-MIMO framework, i.e. the

High-frequency surface-wave radar (HFSWR) is widely used in vessel and aircraft detection, sea-state sensing and wind-field mapping. Target detection suffers from the ionospheric clutter, which is reflected by the ionosphere. Adaptive beamforming (ABF) has been used for ionospheric clutter mitigation. However, the performance of ABF is limited by the heterogeneous ionospheric clutter and degrees of

This study handles the three-dimensional (3D) angle-only tracking (AOT) problem, which is calculating the target's location and velocity by measuring the target's elevation and azimuth angle. Nowadays, a non-civilian (unmanned) aircraft can accelerate in an unpredictable manner. This study presents a stochastic filter re-start strategy for tracking a target which can abruptly change its velocity in

The vector tracking structure is receiving growing attention due to its better tracking performance than the traditional scalar tracking structure. For the scalar tracking structure, signal quality monitoring (SQM) methods can effectively detect spoofing attacks based on the influence of correlation peaks overlap on the coherent integration results. However, the methods are invalid when the overlap

An attractive and challenging problem in source localisation is to locate a target in three-dimensional (3D) space using a single station. To satisfy the observability requirements and achieve higher accuracy, the authors draw on the idea of the inverse synthetic aperture radar for single-station localisation, which leverages the mobility of target and a time serial measurements of angle of arrival

This study presents a convolutional neural network-based drone classification method. The primary criterion for a high-fidelity neural network-based classification is a real dataset of large size and diversity for training. The first goal of the study was to create a large database of micro-Doppler spectrogram images of in-flight drones and birds. Two separate datasets with the same images have been

The direction of arrival (DOA) is one of the most important parameters measured by electronic warfare systems. Monopulse direction finding techniques based on amplitude comparison have many advantages because of their simplicity but they generally have limited accuracy. There are many sources of error in such a system. This work focuses on improving the noise error. An algorithm is proposed for error

The multi-jointed autonomous underwater vehicle (AUV) has higher manoeuvrability and a smaller turning radius in comparison with an AUV with a rigid body. It is able to implement spatially denser sample collection in three-dimensional space in the deep ocean for scientific expeditions. As conventional positioning approaches, such as ultra-short-baseline and long-baseline, cannot be employed to obtain

This study investigates the continuous coherent direction-of-arrival (DOA) estimation, and concentrated on developing grid-less sparsity-based methods to Gaussian coloured noise environment. The noise component is greatly suppressed by applying fourth-order cumulants (FOC) due to its blind property to additive Gaussian noise. Two grid-less sparse models are designed separately. The first sparse representation

Collocated multiple-input multiple-output radar can track multiple targets simultaneously by transmitting multiple orthogonal beams and adopting the digital beamforming technology. In this scenario, the authors propose a joint power and time width allocation approach, which combines a cognitive tracking model based on the posterior Cramér-Rao lower bound (PCRLB) and the square-root cubature Kalman

One of the main shortcomings of received signal strength-based indoor localisation techniques is the labour and time cost involved in acquiring labelled `ground-truth' training data. This training data is often obtained through fingerprinting, which involves visiting all prescribed locations to capture sensor observations throughout the environment. In this work, the authors present a helmet for localisation