Recent progress in bistatic radar techniques can be used to improve performances of classical monostatic radar. A prominent limitation of coherent radar is its inability to measure the complete velocity vector (magnitude and direction) of a detected target. A single coherent detection can provide range-rate only. At least two detections, separated in time, are needed to estimate the target's velocity vector. This study discusses how the velocity vector can be determined by two simultaneous detections spaced in distance. The second detection is obtained by an auxiliary distant bistatic coherent receiver; an approach proposed in the 1990s to enhance meteorological radar. Being a very simple case of a distributed radar system allows for a simple demonstration of how to calculate the target's position and velocity vector and how to analyse the estimation accuracy, including geometric dilution of precision plots of the velocity error. Also discussed are two methods to identify correct data association when more than one target is detected.

Three-dimensional (3D) synthetic aperture radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range, and elevation coordinates. A common 3D SAR focusing approach is compressed sensing (CS)-based SAR tomography, but this technique brings image quality problems because of the undesired side-lobes in the focused two-dimensional (2D) images. Moreover, the amount of raw data, which is used for 3D imaging, is still very large. To reduce the amount of raw data and achieve satisfying 3D resolving ability, a novel 3D imaging method based on multidimensional CS (MCS) is proposed for multi-baseline SAR. In this study, the multi-baseline SAR 3D raw signals are presented in space-time domain; and the proposed 3D imaging algorithm based on MCS is used to reduce the amount of raw data. The range, azimuth and elevation profiles can be reconstructed with an extremely low Pulse Recurrence Frequency (PRF) with this algorithm. Comparisons with the existing CS-tomographic focusing method are also presented. Experimental results demonstrate that the proposed algorithm can efficiently solve the 3D imaging task with limited PRF sampling.

For targets with extreme manoeuvres, inverse synthetic aperture radar (ISAR) imaging suffers from translational motion (TM), which is modelled as a one-dimensional (1D) phase error, and non-uniform rotational motion (RM), which is a multidimensional (MD) phase error that causes severe blurring in ISAR images. Full-aperture data collection is often unachievable because of interference with other radar activities, resulting in sparse-aperture (SA) data. In this study, the authors present a new framework for SA-ISAR imaging and cross-range scaling for manoeuvring targets based on compressive sensing. Instead of solving conventional optimisation problems constrained by a sparsity of signals, the proposed method utilises the sensing-matrix estimation technique for ISAR image reconstruction using parametric signal-model reconstruction. To do this, it looks for basis functions that best represent the behaviour of a sensing-dictionary matrix comprising the observed SA data. The sensing-matrix reconstruction is based on a modified orthogonal matching pursuit-type basis function-searching scheme. Finally, they generate a well-focused and scaled ISAR image from the recovered complete ISAR signal using the conventional Fourier transform after the removal of signals corresponding to 1D TM and MD RM phase errors. They utilise both simulated and real measured datasets to confirm the effectiveness of proposed method.

A new analytical algorithm is developed to address the problem of joint sensor registration and multi-target tracking with varying target number and observation uncertainty based on the labelled random finite set (RFS) and expectation maximisation (EM). A new complete data log-likelihood function is derived with the measurement and state RFS variables, and the EM approach is employed to jointly estimate the sensor biases and target states. Moreover, a recursive implementation is provided to deal with the measurement accumulation, and the situation of the time-varying biases is handled. Since the estimates of the bias and state are analytically calculated, the performance of the proposed method is better than that of the traditional methods. The effectiveness and superiority of the proposed algorithm are verified using numerical simulations.

When multiple targets are located in the same antenna beam, angle measurements using monopulse technique are degraded due to mutual interference caused by overlap among the signals from multiple targets, even though the targets are measured as individual targets by the radar. If multiple targets are closely spaced within a few resolution cells, their mutual interference becomes significant, resulting in both a large loss in detection performance and poor accuracy in angle estimation. To solve this, the authors propose a novel scheme to improve the accuracy of the angle estimation of multiple targets by combining the independent component analysis (ICA) and the phase-comparison monopulse technique.

In order to reduce radar interference on communication systems within shared bands, the radar transmit waveform forms frequency notches in the shared bands. However, because of the notches, the auto-correlation of the transmit waveform with frequency notches leads to high-range sidelobes. In order to achieve low-range sidelobes, the authors propose a mismatched filter. The mismatched filter for the transmit waveform with frequency notches has the same range sidelobes as the auto-correlation of the transmit waveform without frequency notches at the expense of a signal-to-noise ratio (SNR) loss. Simulation and analysis results demonstrate that the proposed mismatched filter always has low sidelobes at the cost of a SNR loss. In addition, simulation results show that the SNR loss is acceptable even for the deep notches (i.e. 20 dB), when the ratio of the waveform energy in the shared bands to the total waveform energy is moderate (i.e. <8%). However, the SNR loss increases rapidly with the depth of notches, when the ratio is large.

This study deals with the target detection problem embedded in multiple range jamming which is usually employed to mask the useful signal or deceive the radar systems. Exploiting the observation that the waveform of the range jamming would be not identical to the probing signal of the radar system, the authors propose a coupled sequential estimation algorithm to obtain simultaneously the range profiles of target and jamming. Each iteration of the proposed algorithm designs a set of linear filters for each range bin based on the minimisation of the output average power by utilising the obtained range profiles of target and jamming at the last iteration. The proposed algorithm is capable of eliminating the jamming in the target range profiles and shares very low range sidelobes. The numerical simulations are provided to prove the effectiveness of the proposed algorithm also in comparison with the matched filter.

Agility radar with the carrier frequency random hopping and the pulse repetition frequency (PRF) staggering from pulse to pulse achieves superior performance against the electromagnetic jamming. This novel scheme leads to the discontinuity of phase in a coherent processing interval, thus the fast Fourier transform-based method is no longer a valid way to estimate the velocity of a target. A novel sparse optimisation method based on compressed sensing is proposed for high-resolution range–Doppler reconstruction from random frequency hopping and PRF-jittering pulses. The performance of moving target detection of the proposed method for frequency agile and PRF-jittering radar is analysed by comparing it with parameters-fixed pulse Doppler radar. Both simulation and field experimental results demonstrate the effectiveness of the proposal.

A fundamental problem in the fusion of wireless communications and radar is to design suitable waveforms that can be simultaneously used for information transmission and radar sensing. In this study, the authors extend the orthogonal frequency division multiplexing (OFDM) waveform by embedding communication codes to communication-embedded OFDM chirp waveforms for delay-Doppler radar applications. The waveforms are characterised by jointly utilising the advantages of classic phase modulation in communications and chirp frequency modulation in high-resolution radar. The integrated radar-communication system scheme is formulated, which can operate as wireless communications and delay-Doppler radar simultaneously. In a generic monostatic radar setting, a closed-form expression is derived to evaluate the ambiguity function of the designed waveforms, along with the average and variance analysis of the ambiguity function.

In this study, a method is presented to deal with the problem of polarimetric detection of a point-like target with energy spillover in non-Gaussian clutter. Consecutive range samples are employed to enhance the detection and range estimation performances. Within the framework of the generalised likelihood ratio test, the decision statistic of the proposed polarimetric detector involves the textures and the covariance structure of the clutter, the target return, and the residual delay. Suitable estimates of these parameters are plugged into the decision statistic. Simulations show that, the proposed detector achieves a considerable performance improvement over the polarimetric adaptive detectors that ignore either the non-Gaussian clutter or the spillover, in terms of both the probability of detection and the root mean squared error of range estimation.

To address track association problem for more than two radars under the influence of measurement biases, multi-radar anti-bias track association algorithm based on the reference topology feature (RET) is proposed. Influence of radar measurement biases and random errors on the RET is analysed and application conditions of track association algorithm based on the RET are clarified. The distance among multi-radar multi-track RETs is defined and anti-bias track association algorithm for more than two radars is proposed. Simulation results demonstrate the application conditions, RET algorithm is sufficiently effective in multi-radar circumstances and is strongly adaptable to radar measurement biases.

Multitarget tracking is a sequential estimation problem where conditioned on noisy sensor measurements, state variables of several targets need to be estimated recursively. In this study, the authors propose a novel performance measure for multitarget tracking named as Deficiency Aware Subpattern Assignment (DASA), that can be used to consistently compare algorithms in a broad spectrum of formulations ranging from conventional data association methods to random finite set based multitarget tracking algorithms. The DASA metric combines three components (localisation, type 1 and type 2 errors) in order to represent the behaviour of the tracking filter coherently. Furthermore, a Monte Carlo method is proposed in order to set the cut-off parameter for the case that the measurement model is known. They illustrate in their simulations that DASA improves upon the previously proposed Optimal Subpattern Assignment metric.