Maritime security is related to national economic and political interests and is strategically important. One efficient way to accomplish maritime border protection is to use the netted forward scatter radar (FSR). FSR is a special type of bistatic radar that operates in a relatively narrow scattering area along the transmitter–receiver baseline, where the effect of the electromagnetic waves forward scattering on targets is dominant above other scattering mechanisms, and in this case, a forward scatter (FS) cross section may increase by orders of magnitude in comparison with the monostatic radar cross section (RCS). Considered in this study are the major problems of marine forward scattering radar detection and estimation of length of low-profile (small and slow) marine targets using a pre-processing approach. It is based on the assumption that the variation of the phase and amplitude in the Doppler signal signature is stronger inside the FS zone than in an outside region. Two variants of pre-processing algorithms are presented in the study, one for the envelope and the other for the phase. Both variants are based on the use the local variance filtering. The results obtained prove the sufficient improvement in a signal-to-clutter ratio (SCR). Estimation of the marine target length under the low SCR is designed using the assumption of known or previously estimated velocity. Presented results demonstrate high accuracy of length estimation. Considered steps of targets detection and target attributes evaluations are necessary for maritime targets classification. The designed algorithms are verified using a set of experimental records of signals from different marine targets obtained using marine FSR developed by the teams from University of Birmingham, UK and Sofia University, Bulgaria.

This study presents the methods and results of a new signal synchronisation algorithm for passive imaging by means of space–surface bistatic synthetic aperture radar with global navigation satellite system transmitters. The operating principles of the algorithm are described analytically. The results of the experimental testing with algorithms are presented and discussed. The performance of synchronisation results are calibrated by image inspection compared with theoretical counterparts. It is experimentally shown that the algorithm can handle data acquired both from GPS, GLONASS and Galileo with sufficient level of accuracy.

This study aims to reduce the sensitivity of the unscented Kalman filter (UKF) estimates with respect to uncertain model parameters, leading to a more robust UKF. The standard minimum-variance cost index is augmented to include a penalty on the sensitivities of the state estimates about parameter uncertainties in the form of a weighted norm. A new filter gain is thus obtained, and the desensitised UKF (DUKF) provides better estimation of the true states than the standard UKF with an imperfect plant model. Numerical examples are shown to demonstrate the efficacy of the DUKF.

In this study, the exploitation of a block cipher output as phase-code for pulse compression in a radar system is introduced. This method is as an effective electronic protection technique against some electronic intelligence-based electronic attacks. Security enhancement of the mentioned scheme is discussed and some autocorrelation properties of it are investigated. Probabilities of false alarm and detection in case of using a block cipher generated phase-code are also studied. A new method for countering the effects of repeater jammers and digital radio frequency memories is also proposed which is based on block cipher encryption key update. This new method tries to implement a low probability of identification radar. All of the mentioned subjects are investigated by mathematical equations and simulation verifications.

This study examines the problem of direction of arrival (DOA) estimation in FM-based passive bistatic radar using a four-element Adcock antenna array. The authors investigate the influence of the target echo, involved in the reference signal, on the DOA estimation of the corresponding target considered. Analysis shows that, unlike the pure reference signal case, a weak target echo involved in the reference signal may not affect the detection of the corresponding target. It can often have a significant impact on the estimation of a target's DOA; thus typical DOA estimate methods degrade heavily. A robust method is proposed to cope with this problem. Simulation and experimental results demonstrate the effectiveness of the method.

This study addresses distributed state estimation of jump Markovian systems and its application to tracking of a manoeuvring target by means of a network of heterogeneous sensors and communication nodes. Two novel consensus-based multiple-model filters are presented. Simulation experiments in a tracking case study, involving a strongly manoeuvring target and a sensor network characterised by weak connectivity, demonstrate the superiority of the proposed distributed multiple-mode filters with respect to existing solutions.

Inverse synthetic aperture radar (ISAR) can form two-dimensional (2D) electromagnetic images of a target, but it cannot provide the third dimensional information about the target. Conventional 3D turntable ISAR imaging requires data collection over densely azimuth-elevation samples, which needs a large amount of data storage. In this study, an effective 3D ISAR imaging algorithm for turntable model based on compressive sensing is proposed, which exploits the sparsity in the image domain to achieve 3D reconstruction by using a limited number of measurements. Firstly, the 3D data tensor is converted into a 2D matrix by stacking slices of data along one specific dimension; then a 2D optimisation reconstruction approach is applied to solve a sparsity-driven optimisation problem to obtain the 2D distribution of the scatterers. Lastly, 3D ISAR images are generated by rearranging the scatterer distribution in the 2D map into a 3D volume. This imaging scheme only needs a small number of measurements, and reduces the required memory and computational burden significantly. Simulation results are finally shown to validate the proposed algorithm.

A digital wideband receiver signal processing technique using two I/Q channel-based receivers with a sampling timing offset is proposed. With I/Q imbalance compensation, the proposed receiver can maintain high instantaneous dynamic range and cover multiple Nyquist zones thus reducing the receiver's sampling rate requirement. Simulation results are presented to verify validity of the proposed method. The limitations of the proposed method and possible solutions are also discussed.

Meteor trail arises as a result of meteorites burning in the Earth's atmosphere at far altitudes with the characteristics of short lifetime and the ability of scattering high-frequency (HF) signal. It is a classical universal interference for high-frequency surface wave radar, which can provide the capabilities of target monitoring and ocean remote sensing by working on HF band. In this study, the authors propose the superiorities of utilising the space-time adaptive processing-based algorithm for meteor trail suppression. They modify the calculation of the covariance matrix based on the distribution and characteristics of meteor trail aiming to fit for meteor trail better. Finally, they verify the effectiveness of the modified algorithm by measured data. The results show that the modified algorithm can not only detect targets better, but can also make Bragg lines buried in meteor trail visible.

The transient interference can dramatically degrade the performance of over-the-horizon radar. Traditional interference suppression methods need to detect the interferences, excise the corrupted data and reconstruct the data via interpolation. The detection is the precondition of the subsequent steps. In the cases where the interferences are weak, the traditional methods cannot efficiently detect and suppress them. In this study, the authors propose a novel interference suppression method based on matrix recovery. The proposed method formulates the interference suppression as a matrix recovery problem and solves the convex optimisation problem via the augmented Lagrange multiplier method. Unlike the traditional methods, the proposed method does not need the detection, excising and interpolation steps. The proposed algorithm can suppress not only the interferences including the weak and strong ones but also the noise. Experimental results have demonstrated the effectiveness of the proposed method.

An approach based on recurrence quantification analysis (RQA) measures is proposed to detect the low observable target within sea clutter in this study. Based on the criterion named the fixed amount of nearest neighbours, recurrence plots (RPs) are constructed. RQA measures are used to describe the line structures of the RPs. According to the differences between the RQA measures of sea clutter with and without a target, the low observable target within sea clutter can be detected. Four sea clutter datasets under different sea conditions in the IPIX OHGR database are selected to analyse the target detection performance of RQA measures. Compared with the multifractal correlation spectrum approach and the cell average constant false alarm rate detector, the author's approach can achieve a higher detection probability. Furthermore, the authors analyse the receiver operating characteristic curves of RQA measures in all of polarisations under different sea conditions. The experimental results show that the horizontal–horizontal polarisation dataset and one of the horizontal–vertical and vertical–horizontal polarisation datasets should be selected for the target detection, and the significant wave height has a greater influence on the target detection performance than the wind speed does.

The capability to exploit multiple sources of information is of fundamental importance in a battlefield scenario. Information obtained from different sources, and separated in space and time, provides the opportunity to exploit diversities to mitigate uncertainty. In this study, the authors address the problem of automatic target recognition (ATR) from synthetic aperture radar platforms. The author's approach exploits both channel (e.g. polarisation) and spatial diversity to obtain suitable information for such a critical task. In particular they use the pseudo-Zernike moments (pZm) to extract features representing commercial vehicles to perform target identification. The proposed approach exploits diversities and invariant properties of pZm leading to high confidence ATR, with limited computational complexity and data transfer requirements. The effectiveness of the proposed method is demonstrated using real data from the Gotcha dataset, in different operational configurations and data source availability.

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is an essential tool for modern remote sensing applications. Owing to its size and weight constraints, UAV is very sensitive to atmospheric turbulence that causes serious trajectory deviations. In this study, an improved phase gradient autofocus (PGA) motion compensation approach is proposed for UAV-SAR imagery. The approach is implemented in two steps. The first step determines the length of each segment depending on number of good quality scatterers and motion errors obtained from navigation data. In the second step, a novel minimum-entropy phase correction based on the discrete cosine transform (DCT) coefficients is proposed. In this approach, transform phase error estimated by PGA to DCT-coefficients that represent the phase error in the frequency or time–frequency domain. The entropy of a focused image is utilised as the optimisation function of the DCT-coefficients to improve the final image quality. Finally, real-data experiments show that the proposed approach is appropriate for highly precise imaging of UAV-SAR equipped with only low-accuracy inertial navigation systems.