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.

Reconfigurable Intelligent Surface (RIS) brings a transformative potential to radar systems by providing a new dimension to control electromagnetic waves. This paper focuses on enhancing target detectability through the design and analysis of RIS-aided radar. A comprehensive signal model is established, recognizing both Line of Sight (LoS) and Non-Line of Sight (N-LoS) returns, factoring in RIS location and range gate shifts. This modeling enables an examination of the RIS-dependent effect of enlarged clutter region, underscoring the essential need for precise RIS phase optimization. A joint design problem encompassing transmit waveform, receive filter, and RIS phase is then formulated with the aim to optimize Signal-to-Interference-plus-Noise-Ratio (SINR), complying with practical waveform constraints and discrete RIS phase alphabet. For this nonconvex problem, an iterative algorithm is developed to monotonically enhance SINR, ensuring convergence by alternately updating the radar waveform and RIS phases. Through the majorization-minimization framework, radar waveform updates are achieved using the feasible point pursuit technique, while a quasi-closed form solution is employed for the RIS phases. Simulation results demonstrate the efficacy of the proposed design, revealing the crucial role of RIS in the system.

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.

We address radar waveform design problem for extended targets under the Peak-to-Average Ratio (PAR) constraint. Based on the stochastic model of the Target Impulse Response (TIR), the distribution of output Signal-to-Interference-pulse-Noise Ratio (SINR) is derived. After that, a novel waveform design metric, named Probabilistically Robust Detection (PRD), is proposed to improve the stability of detection performance against the stochastic TIR. The geometric and limit interpretations for the PRD metric are given to show its rationality. Leveraging on the Semi-Definite Relaxation schemes and the Dinkelbach's algorithm, we devise algorithms to optimize waveform covariance matrix based on the proposed metric in high and low SINR situations, respectively. It is theoretically proved that the global optimal waveform covariance matrix can be achieved in high SINR situations. On the contrary, only the sub-optimal waveform covariance matrix can be obtained in low SINR situations. The transmit waveform is synthesized from the optimized waveform covariance matrix through randomization schemes. Numerical experiments are carried out to evaluate the PRD metric as well as the proposed algorithms. Results verify the effectiveness of the proposed algorithms. Compared with the current design metric, the proposed PRD metric exhibits strong probabilistic robustness on detecting extended targets.

We address the robust waveform-filter design problem for extended targets with a colocated Multiple-Input Multiple-Output (MIMO) radar . The goal is to maximize the worst-case Signal-to-Interference-pulse-Noise Ratio (SINR) at the receiver against the uncertain Target Impulse Response (TIR) with the Peak-to-Average Ratio (PAR) constraint imposed on the waveform, which results in a non-convex minimax optimization problem. Two kinds of uncertainty sets for the TIR, namely, the spherical set and the annular set, are considered. Combing the duality theory in optimization and the Semi-Definite Relaxation (SDR) technique, we devise the Lagrangian Duality Semi-Definite Relaxation (LDSDR) and the Lagrangian Duality Double SemiDefinite Relaxation (LDDSDR) algorithms to tackle the associated waveform-filter design problems against the spherical and the annular sets, respectively. The convergences of the proposed algorithms are proved theoretically. Numerical results verify the effectiveness of the proposed algorithms. Compared with the current algorithms for the spherical set, the proposed LDSDR algorithm achieves the highest worst-case SINR with a reduced computational complexity

This correspondence considers the problem of robust radar waveform design against target interpulse fluctuation with constraints on the transmit energy, similarity, and signal dynamic range. We aim to make robust the transmit waveform and receive filter with respect to the Target Amplitude Vector (TAV) uncertainties by enhancing the worst-case Signal-to-Noise-Ratio (SNR) over the modeled ellipsoidal set. First of all, the filter is directly solved leveraging on the existence of saddle point condition, which leads to a suitable simplification of the original non-convex problem. Then, to handle the resultant max-min problem, the Sequential Lagrange Dual Approximation (SLDA) procedure is devised. Specifically, we update the transmit waveform via solving a sequence of max-min approximate problems, which are proven to be hidden convex relying on the theory of Lagrange duality. Finally, the filter is synthesized after solving a Quadratical Constraint Quadratic Programming (QCQP) problem based on the optimized waveform. Numerical results highlight the robustness of the proposed design which guarantees an enhanced worst-case SNR with respect to TAV uncertainties.