The cross-ambiguity function (CAF) is a crucial performance evaluation metric for transmit waveform. To improve the interference suppression performance in the wireless sensing field, we address the problem of synthesizing radar CAF by jointly designing the transmit waveform and receive filter in this paper. We first establish the joint design problem as a non-convex fractional problem by maximizing the signal-to-interference plus noise ratio (SINR) under practical constraints. To overcome the challenges posed by the peak-to-average ratio (PAR) constraint, we then propose an novel iterative optimization algorithm based on the Majorization-Minimization (MM) framework. This algorithm synthesizes the transmit waveform and receive filter alternately and efficiently. Finally, we conduct numerical simulations with different parameters to demonstrate the effectiveness of our proposed CAF synthesizing algorithm.

Designing a transmit sequence set with desired multidimensional properties can enhance target sensing performance but increases the complexity of problem. In this paper, we focus on designing transmit sequence set in multiple input multiple output radar to obtain desired properties in multiple dimensions. Specifically, a multi-constrained optimization model is formulated, which takes into account the performance in the range-Doppler, spatial, and frequency dimensions. In order to solve the high-order, fractional and non-convex resulting problem, an algorithm based on successive convex approximation (SCA) is proposed, in which each iteration contains semi-definite programming and eigenvalue decomposition. To expedite the iterative convergence, a SCA-acceleration strategy is introduced, which utilizes the square accelerated iteration method in multi-constrained conditions. Finally, numerical experiments validate the superiority of the proposed algorithm over stateof-art methods. Simultaneously, multi-dimensional joint shaping experiments are conducted to investigate the trade-off between the sidelobe performance of the ambiguity function and the beampattern.

Cognitive radar enhances traditional radar systems by equipping them with the ability to sense, understand, learn, reason, and make decisions, thus enabling these systems to adapt to increasingly complex and dynamic electromagnetic environments. This adaptive capability significantly improves radar performance in specific missions, including target detection, tracking, and recognition. However, despite its promising potential, cognitive radar is still in the early stages of development, transitioning from theoretical concepts to practical implementation, with many foundational theories and technologies remaining underdeveloped. Current works are primarily focused on optimizing waveforms under the assumption of prior knowledge, often overlooking a comprehensive and practical perspective of radar systems. These approaches lead to challenges such as overly idealistic assumptions, difficulties in obtaining prior knowledge, and the complexity of waveform diversity schemes and design algorithms. To advance the practical application of cognitive radar systems, this paper addresses key issues in two critical domains: clutter suppression and jamming countermeasures. We propose solutions based on cognitive radar, focusing on the development of the required hardware systems for these tasks and the design of their closed-loop operation processes. Experimental results, derived from actual measurement data, demonstrate the effectiveness of the proposed architecture in both clutter suppression and jamming countermeasures.

The water-filling (WF) algorithm is a widely used design strategy in the radar waveform design field to maximize the signal-to-interference-plus-noise ratio (SINR). To address the problem of the poor resolution performance of the waveform caused by the inability to effectively control the bandwidth, a novel waveform-related optimization model is established in this paper. Specifically, a corrected SINR expression is first derived to construct the objective function in our optimization model. Then, equivalent bandwidth and energy constraints are imposed on the waveform to formulate the waveform-related non-convex optimization model. Next, the optimal frequency spectrum is obtained using the Karush–Kuhn–Tucker condition of our non-convex model. Finally, the transmit waveform in the time domain is synthesized under the constant modulus constraint. Different experiments based on simulated and real-measured data are constructed to demonstrate the superior performance of the designed waveform on the SINR and equivalent bandwidth compared to the linear frequency modulated signal and waveform designed by the WF algorithm. In addition, to further evaluate the effectiveness of the proposed algorithm in the application of cognitive radar (CR), a closed-loop radar system design strategy is introduced based on our waveform design method. The experiments under real-measured data confirm the advantages of CR compared to the traditional open-loop radar structure.

In order to address the low radar-cross-section target detection challenge under high sea states and remove the dependence of existing methods on idealized prior knowledge, a two-stage closed-loop adaptive pulse-agile radar framework is proposed in this letter. First, the clutter and target parameters are estimated with the expectation-maximization scheme, followed by the processing of principal component analysis-based clutter suppression and generalized likelihood ratio test detection. Then, the multi-pulse polyphase-coded frequency modulate waveform is designed to minimize the complementary integrated sidelobe level in strong clutter regions with the iterative method. Numerical experiments are conducted to demonstrate the advantage of the proposed method in clutter suppression. To further confirm the robustness of our method, we also present the Monte Carlo experiments in challenging sea condition.

The sidelobe performance of a radar's transmit waveform significantly affects its ability to detect weak targets in complex environments. Phase-coded (PC) waveforms are commonly used in low-sidelobe waveform design due to their flexibility. However, the abrupt phase transitions between subpulses in PC waveforms generate high-frequency components that can cause nonlinear distortion in transmitter power amplifiers. To overcome this issue, we propose a method for designing low-sidelobe complementary polyphase-coded frequency modulated (PCFM) waveform sets along with mismatched receive filters. This approach enhances flexibility in both interpulse accumulation and receiver processing. We formulate the waveform design as a multivariate, multiobjective, nonconvex optimization problem, taking into account both the complementary cross-correlation sidelobe levels and signal-to-noise ratio loss. An alternating iterative algorithm based on gradient descent and the majorization–minimization framework is then used to suppress sidelobes while preserving peak energy after mismatched filtering. Simulation results show that, aided by interpulse integration gain, the average sidelobe level of the proposed PCFM waveform and its corresponding mismatched filter is significantly reduced. In the numerical experiments, the proposed method successfully detects both weak point targets and extended tank targets simulated through electromagnetic modeling, reducing the sidelobe masking effect from nearby strong scatterers. Finally, hardware implementation confirms the PCFM waveform's low-distortion performance in real transmitter systems.