The authors consider an optimisation problem of multistatic radar system (MSRS) deployment, which includes both the antenna placement and the transmitted power allocation. To improve the surveillance performance of MSRS that equipped with different detection methods, they mainly aim at two goals: (i) to improve the coverage ratio of a surveillance region; (ii) to get an even distribution of signal energy in the surveillance region. Through introducing two objective functions for the above two mentioned goals, respectively, they formulate a multi-objective optimisation problem. To overcome drawbacks caused by the significant difference between the values of these two objective functions, they propose a multi-objective particle swarm optimisation (MOPSO) algorithm with non-dominated relative crowding distance (MOPSO-NRCD). Specifically, through the non-dominative relationship comparison and normalisation of crowding distance, a novel method is proposed to select the global best solution. Moreover, a multi-swarm structure is applied to labour division of all particles. Comparing with the MOPSO with the traditional crowding distance, simulation results show that the MOPSO-NRCD can provide better candidate schemes, which are capable to satisfy more stringent performance requirements.

This study assessed the satellite orbit and clock corrections of the L-band experiment (LEX) signal correction messages broadcasted by the Japan Quasi-Zenith Satellite System (QZSS) by comparing them with those of the international global navigation satellite system (GNSS) service real-time (IGS-RT) products. Using datasets collected at a fixed static station and a kinematic vehicle experiment, the authors evaluated the application of multi-GNSS advanced demonstration of orbit and clock analysis (MADOCA) products in precise point positioning (PPP) through static and kinematic data processing modes. The results show that the MADOCA-LEX products bring slightly larger root mean square and standard deviation of three-dimensional positions than the IGS-RT products. For kinematic PPP positioning, the convergence time was longer than IGS-RT products. However, from the global scope, the positioning accuracy of the MADOCA-LEX products, at a level of 20 cm, is comparable with that of the IGS-RT products, which satisfies the need of global PPP.

The ship detection is the key importance for the maritime surveillance. If the sea clutter is contaminated by the ship targets or interferences, that is the outliers, the detection performance will deteriorate. Aiming at this question, the study develops the detection method of the extended ship targets using the online update of the clutter map in circular scanning synthetic aperture radar (SAR) mode. The algorithm first removes the outliers using the prior clutter map, and then further obtains the pure clutter distribution based on the iterative censoring algorithm which rejects the outliers using the fuzzy statistical theory, the spatial property of the ship target and goodness-of-fitness test. After censoring the outliers, the constant false alarm rate detection is used. As the intensity of a ship target is fluctuant, the detected ship may be split into several parts, and thus the target clustering is an imperative procedure. Meanwhile, the false targets are rejected by the prior size of the ship. Finally, the effectiveness of the proposed detection method is verified by the real data from airborne circular scanning SAR. Compared with other conventional methods, the experimental results indicate that the proposed method has a good performance and strong availability.

In the electronic countermeasure, the electronic support measure (ESM) receiver intercepts the interleaved radar pulse streams, which radiates from surrounding radar emitters. At present, deinterleaving algorithm based on time of arrival which is called pulse repetition interval (PRI) deinterleaving occupies an important part of the ESM. The sequential difference histogram (SDIF) is a well-known and effective PRI deinterleaving algorithm, but it has several disadvantages. When the PRI jitter and pulse missing exist in the pulse streams, the SDIF algorithm cannot estimate the real PRI value reliably, and then cannot separate the radar pulse trains corresponding to the real PRI value from the interleaved pulse streams. In order to overcome these disadvantages, an improved SDIF algorithm using clustering algorithm and PRI transform is proposed. Simulation results have proved that this improved algorithm has higher reliability and accuracy in the presence of PRI jitter and pulse missing.

In the Bayesian context, the rationale for using a particular probability density function (pdf) for a synthetic aperture radar (SAR) image improves the performance of unsupervised change detection (CD). So, this work is concentrated on promoting the consequence of selecting an empirical pdf and its parameter estimation technique. Rayleigh pdf is used to model approximation and detail sub-bands of a dual tree complex wavelet transform (DTCWT) of the log ratio difference image. The chosen pdf has a potential impact on impulsive SAR images. The scale parameter of Rayleigh pdf is estimated using first-order moments by exploiting Mellin transform. This iteration-free parameter estimation technique, made the method faster. In each sub-band at each level of DTCWT decomposition, the binary threshold is estimated to represent the change signal at different scales. The performance in the final change map is improved by combining intra-scale along with inter-scale sub-band coefficients. The proposed method is tested on three flooding SAR image data sets acquired from European Space Agency environmental satellite, European remote-sensing satellite (ERS)-2 and ERS-1 SAR instruments. The experimental results show that the proposed method achieves good CD performance in terms of false alarm rate and overall detection accuracy in less computing time.

The authors present initial results from the fourth-generation receiver for the Manastash Ridge radar, which is a distributed passive radar system used for ionospheric physics and engineering studies. This receiver permits simultaneous access to the high-frequency (HF), very HF (VHF), and ultra HF (UHF) spectrum by sampling at speeds up to 5 billion samples/s on multiple antennas. This system has large aggregate bandwidth; it can simultaneously collect the entire VHF FM broadcast band as well as several UHF DTV broadcasts. The receiver adopts direct sampling architecture; therefore, high dynamic range is achieved with decimation for narrowband signals. Most of the analogue signal path is eliminated, yielding excellent linearity, and high-speed digital signal processing in the field programmable gate array (FPGA) yields low-latency real-time operation. The authors also discuss algorithms to make effective use of the FPGA. For example, the sampler runs 16 times faster than the FPGA, so initial FPGA processing requires parallel algorithms. In the authors design, the downconverter passband centre frequencies and spectral widths are selectable at run time, and can be changed in a few milliseconds. The authors FPGA computes in fixed point math, which presents both opportunities and challenges in managing precision during the signal processing for networking and for subsequent signal processing.

In this study, a joint parameter estimation method is proposed to realise the long-time coherent integration and estimate the motion parameters for multiple weak manoeuvring targets with high speed and acceleration, involving the first-order range migration (FRM), second-order RM (SRM) and Doppler frequency migration. In the proposed method, the effect of the target's Doppler spectrum spanning over two neighbouring pulse repetition frequency bounds on first-order keystone transform (FKT) is first analysed and a Doppler shifting function is constructed to solve this problem. Then, FKT and the velocity ambiguity factor searching are performed to eliminate the FRM. After that, SRM is removed through the phase compensation function related to acceleration. Finally, the Lv's distribution is applied to realise the coherent integration and obtain the estimates of velocity and acceleration. The numerical experiments using both simulated and real data demonstrate that the proposed method can achieve close detection and estimation performance with much lower computational complexity, compared with the generalised radon-Fourier transform. Moreover, it can estimate the motion parameters of the targets without knowing any prior knowledge of targets and obtain good coherent integration performance in low signal-to-noise ratio scenarios.

Considering a multi-stage stochastic task, in which a multi-static radar system (MSRS) is applied to assist with missile interception, the authors study an optimisation problem of radar resource management. Specifically, under restriction of a fixed energy budget, the authors devote to minimise the loss, which is caused by the unsuccessfully intercepted missiles, through optimal power allocation (OPA) of MSRS within multiple stages. The design of OPA can be translated into a sequential decision-making problem. The authors formulate the problem through variable definition and modelling the missile interception procedure. As the authors need to consider the randomness of multiple coupled stages and jointly allocation power between multiple radar nodes, to solve the proposed problem is of huge computational load. The authors propose a solution that combines with reinforcement learning and particle swarm optimisation. Comparing with the uniform power allocation scheme, the simulation results demonstrate that the OPA scheme designed by the proposed method is capable to achieve preferable and more stable performance for the whole missile interception. The authors' contributions include a novel optimisation resource management model for a multi-stage stochastic task and an effective solution for the optimal resource management scheme.

The widely used distributions in Bayesian compressed sensing (BCS) inverse synthetic aperture radar (ISAR) imaging do not support stable linear dimensionality reduction, and the compressibility of the recently proposed distributions is hard to be proved theoretically. However, generalised pareto distribution (GPD) can overcome these shortcomings. So a GPD-based ISAR imaging method has been proposed. Interestingly, shape parameter can be set to a small value directly based on mathematical proof in one kind of GPD-Meridian distribution, which is totally different from the existing methods. It not only simplifies the method but also improves the sparsity of the solution. It has been proved that the proposed method now is equal to the log-sum minimisation, which verifies the correctness of the parameter selecting algorithm. Compared with the conventional methods, the new method can recover signals with fewer measurements or in looser sparsity conditions or with smaller recovery errors. When applied into simulated and real ISAR data imaging, the proposed method has obtained better images than the conventional methods. Therefore, the proposed method is a promising imaging method in BCS ISAR imaging.

In this study, a novel method for overcomplete dictionary construction for sparse recovery (SR) space–time adaptive processing (STAP) is proposed, which can effectively mitigate the off-grid effect. The proposed method utilises the clutter ridge, calculated via some prior knowledge such as radar system parameters and modern inertial navigation system information, to discretise the grids in the angle–Doppler plane. The authors also investigate the accuracy of clutter ridge in the presence of several typical non-ideal factors such as array errors, intrinsic clutter motion and aircraft crab. In particular, a parameter is introduced to adaptively adjust the grid interval to avoid the strong column coherence of the dictionary. The simulation results show that the signal-to-clutter-plus-noise ratio for the SR STAP with the proposed dictionary is significantly improved, compared with the one utilising conventional dictionary. Moreover, it is shown that the novel method not only works more robustly for the side-looking array, but also has better performance for other array orientations.

As known to us all, the Cramér–Rao bounds (CRBs) is a fundamental tool of performance assessment which expresses the lowest possible mean-squared error among all unbiased methods. Although existing results display the closed-form expression of CRBs for co-prime array and give the conditions under which the CRBs exist, they are not applicable when the authors consider two or more kinds of parameters estimation in multi-dimensional sparse sensing, especially parameters estimation in the spatio-temporal coarray domain. The authors first derive the CRBs for joint angle-frequency estimation based on spatio-temporal co-prime sampling and present the closed form of the joint CRBs. Then, the sufficient and necessary conditions under which the CRBs exist are explored. Finally, they discuss and analyse the asymptotic behaviour of the joint CRBs as a function of the number of snapshots and signal-to-noise ratio, respectively. The correctness of their results is demonstrated by simulation experiments.

This study focuses on the cognitive waveform optimisation for radar imaging. Classical cognitive waveform optimisation methods could degrade the point spread function and affect the imaging quality. To achieve high imaging quality, a waveform optimisation method maximising the receiving signal-to-clutter ratio under dual constraints including transmitting energy constraint and range profile constraint is designed. The proposed method is based on the response power ratio between a target and clutter, without the requirement of the precise prior knowledge about target and clutter response models. Combined with the shadow ghost mitigation in the through-wall-imaging radar (TWIR) imaging application, implementation of the proposed method is discussed. Simulations predict the performance of the proposed method, and through-wall imaging experiments were conducted, employing an ultra-wideband stepped frequency continuous waveform TWIR, and indicate that the proposed method can efficiently suppress the shadow ghosts in radar images, and simultaneously guarantee the imaging quality.