This study deals with the imaging problem of one-stationary bistatic forward-looking synthetic aperture radar (BFSAR). This BFSAR has extensive potential applications, such as self-navigation and self-landing in the airport. However, the forward-looking (or squint) angle is usually large in one-stationary BFSAR. It causes large range migration and strong range dependence on the secondary range compression term. Therefore it is difficult to use traditional imaging algorithms to focus one-stationary BFSAR. To solve this problem, an imaging algorithm based on squint minimisation method is proposed in this study. The squint minimisation operation can shear the high forward-looking angle data spectrum and decrease the coupling between range and azimuth. Then, the authors derive a modified range Doppler algorithm (RDA) to focus the sheared data. Numerical simulations show that by combining squint minimisation method and modified RDA, the raw data of one-stationary BFSAR can be well imaged.

The authors propose an extrapolation method to improve cross-range resolution in forward-looking imaging radar with multi-input/dual-output configuration. The extrapolation, which is based on autoregressive (AR) model, has different prediction directions according to the position of transmitting antennas. They tested the proposed method in a simulation with noise and in an actual experiment. In the simulation and experiment, the proposed method, which uses Burg method as an estimator of AR model, shows better image quality than other methods.

A method to generate several phase centres on a single parabolic reflector antenna fed by a digital beamforming array is presented. Among other applications, along-track SAR interferometry at higher frequencies, as for example, in the Ka-band, is considered. Flexibility in size, position and amount of phase centres is similar as for a setup using a direct radiating array.

In this study, a detector/estimator is proposed for compressed sensing radars, which does not need to reconstruct the radar signal, and which works directly from compressive measurements. More precisely, through direct processing of the measurements, and without the need for reconstructing the original radar signal, the system performs target detection, and then estimates range, Doppler frequency shift and radar cross section in the presence of a Gaussian clutter. It can be seen that for large compression ratios, the detection performance and estimation quality is comparable with a common radar system while having a much lower data rate and with less computational load.

Spread-Doppler clutter caused by multi-mode propagation seriously affects the over-the-horizon radar detection performance for slow ships. To solve this problem, the authors propose to suppress multi-mode clutter using spatial information in the multiple-input–multiple-output over-the-horizon radar system. Traditional processing algorithms need prior information of the target direction. However, it is often not readily available. In this study, they propose a novel multi-mode clutter suppression algorithm based on the second-order blind identification (SOBI) technique. The proposed algorithm does not need prior information of the target direction and can effectively suppress the multi-mode clutter. Considering that the SOBI algorithm affected by the similarity in sources spectra, they improve the proposed algorithm using the spatial smoothing technique. Simulation results show that, in the single target scene, the improved algorithm can effectively eliminate the spectrum burrs caused by incomplete separation, making separated signals smoother. Signal-to-clutter-noise ratio is greatly improved. The good multi-mode clutter suppression performance is obtained. In the multi-mode scenario that double targets with the same radial velocity, that is, sources spectra have similarities, the algorithm based on SOBI cannot separate all targets. Although the improved algorithm can make all targets visible and effectively suppresses the multi-mode clutter.

For low-angle targets, the performance of altitude measurement is affected by multipath phenomenon. Especially, for complex terrain case, the main problem is due to the fact that the multipath signal is perturbed by irregular reflector, which leads to the mismatch of the steering vector, and thus degrades the performance of or even fails the existing methods. To deal with this problem, the authors propose a new perturbational multipath signal model, where perturbation caused by complex terrain is considered as the gain and phase errors of the steering vector of the multipath signal. With the spatial sparsity of the incident signals, the sparse Bayesian learning technique is adopted to estimate the perturbation and direction of arrival (DOA) iteratively. The computer simulation results show that their algorithm is able to estimate the effect of perturbation with high precision and to enhance the DOA accuracy compared with existing algorithms. Furthermore, real data analysis validates the efficiency of altitude measurement in practice. Finally, the proposed perturbational multipath signal model is also applicable to other situations where complex multipath phenomenon is not negligible.

Doppler imaging is a technique that can generate fine two-dimensional (2D) imagery in the absence of large bandwidths. Radar imaging has traditionally consisted of combining wide bandwidth waveforms with aperture synthesis. Wide bandwidth waveforms provide high resolution in down-range (line-of-sight), whereas aperture synthesis enables high resolution in the orthogonal direction. However, in today’s increasingly congested spectral environment, using wide bandwidths waveforms is becoming less and less attractive. Consequently, imaging techniques that require only narrow bandwidth waveforms offer very attractive and practical advantages. An approach, examined here, is to use phase in conjunction with aperture synthesis to achieve high resolution in down-range while using narrowband waveforms. This method has the added benefit of using the same collection geometry as traditional stripmap synthetic aperture radar imaging. Therefore Doppler imaging is compatible with use in spectrally congested environments where wide bandwidth waveforms might be prohibited. In this study, the basic Doppler imaging concept is explained, a simple framework for its operation is introduced and resolution limits are examined. Results are presented from both simulations and full-scale experiments, which demonstrate range resolutions as fine as 23 cm using an 8.9 GHz CW waveform and 2 m long synthetic aperture.

On the basis of compressive sensing radar, this study presents a new method of reconstructing the moving target's high-resolution range profile (HRRP) with low computational complexity. With regard to the spatial sparsity of the radar scene, only a few sub-pulses of frequency-stepped chirp signal (FSCS) are used to sample the target's frequency responses. To reduce the computational complexity of traditional compressive sensing (CS) algorithm which uses FSCS to reconstruct moving target's HRRP, a dynamically deduced sensing matrix is established based on target's velocity estimation. Besides, the orthogonality of the deduced sensing matrix is analysed from the perspective of FSCS frequency encoding types to enhance the HRRP construction accuracy using CS algorithms. Numerical simulations demonstrate that the proposed method performs better than traditional algorithm with smaller estimation error and better robustness against noise.

On the basis of the real data acquired with an X-band airborne wide-area surveillance (WAS)-GMTI radar, a practical signal processing algorithm for WAS-GMTI mode is proposed, and it is simple and effective for real-time processing. The key feature of the new algorithm is that the spatial steering vector and fixed channel error are estimated according to the result of the Doppler centroid estimation. This is due to the fact that the angle corresponding to the Doppler centroid is just the antenna look direction. In this study, the detailed description of the algorithm is presented with four steps: pre-processing, clutter suppression, constant false alarm rate detection and parameter estimation. Besides, to observe the effect of the proposed algorithm, the real data processing results are shown after each step. Finally, moving target positioning results of five successive scan periods is presented to demonstrate the effectiveness of the new algorithm.

The histogram probabilistic multi-hypothesis tracking (H-PMHT) algorithm is a multi-target track-before-detect method that has been demonstrated to be scalable and efficient and shows good performance on video imagery. However, its application has been limited to intensity-only imagery. In practice, colour and information, such as texture, are often available and can be very helpful when targets are close together. This study compares two alternative methods that incorporate colour information in the H-PMHT: an existing method referred to as spectral H-PMHT and a new approach that treats the colour information as attribute data. The methods are compared with intensity-only H-PMHT on simulations and benchmark data from the VS-PETS03 archive.

A phased array radar has the ability to rapidly and adaptively position beams and adjust dwell times, thus enabling a single radar to perform multiple functions, such as surveillance, tracking and fire control. A radar resource manager prioritises and schedules tasks from the various functions to best use available resources. Networked phased array radars that are connected by a communication channel are studied. This study considers whether coordinated radar resource management (RRM), which exploits the sharing of tracking and detection data between radars, enhances performance compared with independent RRM. Two types of distributed management techniques for coordinated RRM are proposed, with each type characterised by varying amounts of coordination between the radars. A two-radar network and 30-target scenario are modelled in the simulation tool Adapt_MFR, to analyse the performance of the two coordinated RRM techniques against the baseline case of independent RRM. Results indicate that the coordinated RRM techniques achieve the same track completeness as independent RRM, while decreasing track occupancy and frame time. Therefore, coordinated RRM can improve reaction time against threats, at the expense of sending data across a communication channel. The performance of coordinated RRM for a communication channel with errors is also modelled and analysed.

The high resolution of the digital video broadcasting terrestrial (DVB-T) signals leads to a dramatically large number of observation samples in an integration time of a DVB-T-based passive radar. Hence, because of the high computational complexity (CC), applying extensive cancellation algorithm and generalised likelihood ratio (GLR) detector seems to be impractical in the DVB-T-based passive radars. In this study, the authors derive a reduced complexity GLR detector for the DVB-T-based passive radar in the presence of the clutter, the interfering targets and the noise. The proposed detector employs the split received signal to reduce the CC substantially compared with the methods that process the whole signal at once. Moreover, the CC reduction in the proposed method does not lead to a degradation in Doppler resolution and the detection performance. Simulation results are also corroborated with the theoretical analysis.

A time-domain signal tracking and mitigation algorithm is proposed to estimate the frequency of interference in a global navigation satellite system (GNSS) so as to classify different types of interference and to mitigate interference. The frequency of GNSS interference can be obtained using the properties of the trigonometric functions of received signal samples, but these values contain numerous errors caused by measurement noise and frequency changes associated with the interference. To reduce these errors, an adaptive fading Kalman filter is applied in the proposed algorithm. Furthermore, a low-pass differentiator and a pattern enhancement algorithm are used to estimate the sweep period of chirp-type interference, which is used to reset the filter parameter for estimating the frequency of the interference accurately. By estimating the sweep period, the interference identification logic is designed to select the proper system model of the Kalman filter. Finally, in order to mitigate the interference, the denoised frequency from the filter is used to design a notch filter which eliminates the interference in the received signal. The frequency tracking performance of the proposed algorithm is verified to compare with conventional algorithms and the mitigation performance of the proposed algorithm is evaluated by means of Monte Carlo simulations.

This study shows the experimental validation of the first photonics-based radar system demonstrator in a real maritime environment. The radar system demonstrator exploits photonic technologies for both the generation and the detection of the RF radar signals, and it has been previously proved allowing increased performance and unprecedented potential flexibility. Here, the photonics-based radar is compared with a commercial system for maritime applications provided by ‘GEM elettronica’. The analysis has checked the performance of the photonics-based system in terms of transmitted signal integrity and receiver pre- and post-detection capability, by means of ad-hoc laboratory tests and an on-field direct comparison, run into the operative scenario of the port of San Benedetto del Tronto (Italy), by observing non-cooperative targets. The reported results show that the photonics-based radar system, although at a demonstrator stage, proves comparable performance with the commercial radar used as reference. Beyond the actual implementation of the proposed system, the outcomes of this comparison confirm that the photonics-based approach can lead new advances in radar architecture, by the development of a completely frequency and waveform agility, application-transparent photonic transceiver.

In this study, two algorithms of single-target tracking in clutter using a high pulse repetition frequency radar are extended: the Gaussian mixture measurement likelihood-integrated track splitting (GMM-ITS) algorithm and the enhanced multiple models (MM) to multi-target tracking algorithm, that is, the GMM-joint ITS algorithm and the enhanced MM-joint probabilistic data association algorithm, respectively. Both algorithms are extended on the basis of the optimal Bayes approach that creates track clusters for determining the nearby tracks that share measurements by enumerating and evaluating all the feasible joint measurement allocations. In all cases, the track trajectory probability density function is a Gaussian mixture, and both algorithms enable false track discrimination using the probability of target existence.

In this study, a new distributed fuzzy maximum-censored mean level detector (MX-CMLD) constant false alarm rate (CFAR) detection based on fuzzy space and voting fuzzy fusion rule is presented. In the distributed fuzzy MX-CMLD CFAR detector, each sensor computes the value of the membership function to the false alarm space from the samples of the reference cells and transmits it to the fusion centre. The values are combined according to the voting fuzzy fusion rule and the credibility measure of each sensor to produce a global membership function to the false alarm space in the fusion centre. The simulation results show that the detection performance of the distributed fuzzy MX-CMLD CFAR detector is better than the other fuzzy distributed detectors in homogeneous and non-homogeneous background. Furthermore, the simulation results indicate that the fuzzy algebraic product operator rule gives better performance than the binary AND and the binary OR fusion rules.

In a typical surveillance situation the number and the trajectories of targets are a priori unknown. Each measurement has an unknown source; either clutter, a target being tracked or a new target. The tracks are initialised and updated using measurements, thus both true and false tracks exist at any given time. The authors present a particle filter approach which recursively calculates the probability of target existence, which may be used as a track quality measure for the false track discrimination. Single target, joint multitarget and linear multitarget versions are presented. The algorithms assume manoeuvring (multiple trajectory propagation models) targets and state dependent probability of target detection.

This study concerns the fractal properties of sea clutter in the power spectrum domain. To overcome the deficiencies of Fourier transform analysis, the power spectrum of the sea clutter is obtained by autoregressive (AR) spectrum estimation. The AR model is a linear predictive model, which estimates the power spectrum of sea clutter form its autocorrelation matrix and has a higher frequency resolution than Fourier analysis. This study concentrates on analysing the fractal property of the power spectrum based on AR spectral estimation and its application on weak target detection. First, fractional Brownian motion is taken as an example to prove the fractal property of the power spectrum. Then, real measured X-band data is used to verify the fractal property of the power spectrum of sea clutter. Finally, a novel detection method based on AR Hurst exponent is proposed and the factors influencing the fractal properties of power spectrum are analysed. The results show that the Hurst exponent of AR spectrum is effective for weak target detection in sea clutter background. Compared with the existing fractal method and the traditional constant false alarm rate (CFAR) method, the proposed method has a better detection performance.

In target tracking, most tracking algorithms are model based, and serious errors can arise when the models fail to fit the physical target motions, especially during coordinated turns. To address this problem, a two-step tracking algorithm is proposed. First, considering the diversity of the target manoeuvres, five elemental Singer models with carefully designed parameter α interact with each other in an interacting multiple model (IMM) algorithm to accomplish the tracking and estimate the target's kinematic parameters. Second, if a turn motion occurs, then it discriminates the turn motion into a horizontal turn or a three-dimensional (3D) turn; and the real-time turn rate is calculated to refine the corresponding model for tracking. Since the filtering stage is important in estimating the kinematic parameters, sparse-grid quadrature Kalman filter is employed to improve the filtering capability. The proposed algorithm is exemplified by simulation tests and data tests in 3D, and is compared with that of an IMM algorithm utilising constant velocity, constant acceleration and 3D coordinated turn models. All of the test results demonstrate that the proposed algorithm is effective and has higher accuracy.

In multiple input and multiple output synthetic aperture radar (MIMO-SAR) the unwanted cross-correlation output sexist inevitably in the superposed echoes of multiple transmitted orthogonal waveforms in the same frequency coverage, which may deteriorate the quality of the reconstructed SAR image because of the high synthetic integrated sidelobe level (SISL). In this study, a novel signal decomposition based on oblique projection (SDOP) method is proposed to suppress the cross-correlation outputs. At first, the superposed echo of each receiver is modelled as a linear-combined vector of transmitted multiple waveforms. Then, the echo of each transmitted waveform is obtained by signal decomposition on the combined vector via SDOP, respectively. Besides, two new metrics, that is, synthetics integrated sidelobe level ratio improved factor and integrated sidelobe level ratio loss factor, are also proposed to assess the separation performance. It is found that the cross-correlation can be effectively suppressed among multiple waveforms via low-complexity projection operators and the output signal noise ratio can be remarkably improved, accordingly. Finally, the numerical experiments are also provided to demonstrate the effectiveness of the proposed method.

Conditional random field (CRF) is a useful tool for optical and remote sensing image segmentation for its ability of incorporating the feature and texture information. However, its application is restricted in successive-approximation resistor (SAR) image segmentation, since SAR images often contain complex non-stationary contents. The triplet Markov field (TMF) model improves the non-stationary image segmentation ability by introducing an auxiliary field to characterise different stationary parts in non-stationary image. Combining the advantages of CRF and TMF, the pixel-level conditional TMF (CTMF) had been proposed. To further improve the segmentation efficiency, a superpixel-level CTMF (SL-CTMF) is proposed in this study. The superpixel representation comes from the improved TurboPixels algorithm and the superpixel representation has better performance in edge location. The auxiliary field U in SL-CTMF is reconstructed on superpixels. With the superpixel-level feature and texture information, the unary and pairwise potentials are derived. Finally, SL-CTMF is applied to real SAR image segmentation with the maximum posterior marginal inference. The experimental results demonstrate the accuracy and the efficiency of the proposed method on SAR images.

Multi-frequency high frequency surface wave radar (MHFSWR) shows good capability of anti-interference, sea-state information extraction and target detection. The authors develop a new technique to produce a specified Doppler frequency offset on the echoes spectra for each operating frequency in simultaneous MHFSWR system. The radar transmits frequency modulated interrupted continuous wave signals with different linear phase offsets which are counted up from sweep cycle to sweep cycle. After the echoes being demodulated, the signals of different operating frequencies from same range can be distinguished by the preset frequency offsets in a single Doppler spectrum, though they are indiscernible in the A-Scope display. In the receiver, both the data transmission channel and the signal processing channel of different operating frequency, including digital down-conversion, data rate conversion and range–Doppler transform, are shared significantly reducing the hardware cost and system complexity. The simulation results and the initial observations obtained by the radar verify the feasibility of the proposed technique. This technique is applicable in monostatic radars, bistatic radars and networking radars to apply multi-frequency observations simultaneously.

Motion information of the non-cooperative targets observed by inverse synthetic aperture radar (ISAR) is an important feature for target recognition. Owing to the addition of time dimension, ISAR image time-series are effective carriers about motion features; however, motion feature extraction is not robust enough for the restriction of ISAR imaging and traditional feature point extraction methods. In this study, the authors propose a novel method called as sparse and low-rank approach for motion feature extraction from ISAR image time-series. This method first models the ISAR image time-series as a mixed matrix composed of a low-rank matrix and a sparse matrix, which correspond to the low-rank structure of the static feature points and the sparse structure of the dynamic feature points, respectively. Then these feature points can be separated by sparse and low-rank matrices decomposition to prepare for estimation of motion parameters, such as precession angle and precession period of the targets. The results of experimental validation suggest that the new approach is more robust than traditional methods.

The authors deal with the robust design of multiple-input–multiple-output (MIMO) space–time transmit code (STTC) and space–time receive filter (STRF) for a point-like target embedded in signal-dependent interference. Specifically, they assume that the radar exploits knowledge provided by dynamic environmental database, to roughly predict the actual scattering scenario. Then, they devise an iterative method to optimise the (constrained) STTC and the (constrained) STRF which sequentially improves the worst-case (over interfering scatterers statistics) signal-to-interference-plus-noise ratio (SINR). Each iteration of the algorithm is handled via solving two (hidden) convex optimisation problems. The resulting computational complexity is linear with the number of iterations and polynomial with the sizes of the STTC and the STRF. At the analysis stage, they assess the performance of the proposed algorithm in terms of the achieved SINR. They show that properly exploiting the spatial degrees of freedom offered by the MIMO system, it is possible to obtain considerable SINR gains with respect to the conventional single-input–single-output system. Moreover, their results highlight the capability of the proposed method to robustify the performance of the designed system against possible knowledge inaccuracies.