An enhanced Doppler resolution and sidelobe suppression have long been practical issues for moving target detection using Golay complementary waveforms. In this paper, Golay complementary waveform radar returns are combined with a proposed processor, the pointwise thresholding processor (PTP). Compared to the pointwise minimization processor (PMP) illustrated in a previous work, which could only achieve a Doppler resolution comparable to existing methods, this approach essentially increases the Doppler resolution to a very high level in theory. This study also introduced a further filtering process for the delay-Doppler map of the PTP, and simulations verified that the method results in a delay-Doppler map virtually free of range sidelobes.

In recent years, sparsity-driven regularization and compressed sensing (CS)-based radar imaging methods have attracted significant attention. This paper provides an introduction to the fundamental concepts of this area. In addition, we will describe both sparsity-driven regularization and CS-based radar imaging methods, along with other approaches in a unified mathematical framework. This will provide readers with a systematic overview of radar imaging theories and methods from a clear mathematical viewpoint. The methods presented in this paper include the minimum variance unbiased estimation, least squares (LS) estimation, Bayesian maximum a posteriori (MAP) estimation, matched filtering, regularization, and CS reconstruction. The characteristics of these methods and their connections are also analyzed. Sparsity-driven regularization and CS based radar imaging methods represent an active research area; there are still many unsolved or open problems, such as the sampling scheme, computational complexity, sparse representation, influence of clutter, and model error compensation. We will summarize the challenges as well as recent advances related to these issues.

Advanced waveform design schemes have been widely employed for radar and sonar remote sensing analysis such as target detection and separation, where significant range sidelobe is a main factor that limits the improvement of analysis performance. As an extensional type of Golay complementary waveforms, complementary sets are a waveform design scenario of concern that shows more diversity in the design of transmission order, and results in a different distribution of range sidelobes. This work proposes an oversampled generalized Prouhet–Thue–Morse (OGPTM) method for the transmitted signal design of complementary sets, with comprehensive analysis to the influence on the sidelobe distribution. Based on this idea and our previous work, we further put forward a pointwise multiplication processor (PMuP) to integrate two delay-Doppler maps of oversampled complementary sets, which achieve much better sidelobe suppression performance on high-speed target detection with range migration.

Multilevel thresholding is an important approach for image segmentation which has drawn much attention during the past few years. Traditional methods for multilevel thresholding are computationally expensive because they use the exhaustive searching strategy. To overcome the problem, metaheuristic algorithms are widely applied in this research area for searching the optimal thresholds recently. In this paper, a modified flower pollination algorithm (MFPA), as a novel improved metaheuristic algorithm, is proposed for multi-level thresholding. Two modifications are proposed to improve the original flower pollination algorithm (FPA). Firstly, a fitness Euclidean-distance ratio strategy is employed to modify the local-pollination of the original flower pollination algorithm (FPA). Secondly, the global-pollination in the original FPA is also biologically modified to improve exploration. Experiments are conducted between seven state-of-the-art metaheuristic algorithms and the proposed one. Both real-life images and remote sensing images are used in the experiments to test the performance of the involved algorithms. The experimental results significantly demonstrate the superiority of our method in terms of the objective function value, image quality measures and convergence performance.

This paper deals with the problem of censoring outliers in a class of complex multivariate elliptically contoured (MEC) distributed radar data, which is a vital issue in radar signal processing applications, such as adaptive radar detection and space-time adaptive processing (STAP). The maximum likelihood (ML) estimate of the outlier subset is derived resorting to the generalized likelihood function (GLF) criterion. Since the ML estimate involves the solution of a combinatorial problem, a reduced complexity but approximate ML (AML) procedure is also considered. At the analysis stage, the performance of the AML method is evaluated in the presence of both simulated and real radar data, also in comparison with the conventional generalized inner product (GIP) and the reiterative censored GIP (RCGIP) algorithms. The results highlight that the AML technique achieves a satisfactory performance level and can outperform both GIP and RCGIP in some situations.

In the presence of colored Gaussian disturbance, this paper considers the robust transmit code and receiver filter design against target interpulse fluctuation while guaranteeing the dynamic range and energy of the transmit code. Aiming at enhancing the worst-case Signal-to-Noise-Ratio (SNR) over a spherical set, the design is cast as a non-convex minimax problem. It is shown that the filter can be directly derived based on the strong max-min property of the objective function. Then, the semi-definite relaxation form of the resultant problem is proved to be hidden convex by exploiting the strong duality of the Quadratically-Constrained Quadratic Programming (QCQP) problem. Finally, the robust code-filter pair can be effectively synthesized based on a randomization scheme. At the analysis stage, numerical results are shown to highlight the superiority of the proposed design.