This paper investigates the joint channel estimation and localization problem for integrated sensing and communication (ISAC) systems assisted by a reconfigurable intelligent surface (RIS). The key technical challenges involve addressing the computational complexity of high-dimensional sparse recovery while effectively exploiting geometric relationships in parameter estimation. To begin with, we formulate the joint estimation problem as a geometrically-constrained sparse recovery task based on maximum a posteriori estimation principles, incorporating physical feasibility constraints. Then, we propose the geometrically-constrained separable multidimensional orthogonal matching pursuit (GSMOMP) algorithm, which iteratively executes three key procedures: (1) coarse candidate selection via separable projections to avoid high-dimensional tensor construction; (2) geometric feasibility filtering to eliminate physically implausible propagation paths; and (3) refined atom selection with joint parameter updates that concurrently optimize both channel estimates and location parameters. The convergence is ensured by analytically demonstrating the monotonic decrease of the residual error. Finally, numerical results verify the effectiveness of the proposed algorithm, which achieves substantial computational efficiency improvement compared to conventional MOMP while maintaining comparable localization and angular estimation accuracy.

This article considers the dynamic time resource scheduling problem for a phased array radar performing joint search and tracking tasks in an adversarial jamming environment. Uniform resource scheduling in such conditions leads to security-critical failures, including the missed detection of weak threats and loss of tracking. To address this challenge, a multi-step dynamic optimization model with a receding-horizon strategy is formulated for joint search-and-tracking time resource scheduling under adversarial jamming. Then, a framework integrating Pontryagin’s maximum principle and the alternating direction method of multipliers (ADMM) is developed to solve the resulting problem. The maximum principle produces future-aware adjoint signals that quantify the future risk of jamming-induced sensing loss, while ADMM enforces per-stage feasibility and handles the non-convex optimization problem. Numerical simulations are conducted to verify that the proposed algorithm maintains higher tracking accuracy, stronger jamming resilience, and higher total utility than several comparison methods, thereby improving sensing robustness in adversarial conditions.

This paper addresses the design of Mismatch Filter (MMF) for Linear Frequency Modulation (LFM) waveform so as to suppress the high range sidelobe. Firstly, the sidelobe level energy of correlation function between LFM and a spectrum weighted MMF is formulated to minimize. To handle this problem, an iterative gradient optimization utilizing Polyak heavy ball method is developed. Finally, numerical simulation are performed to evaluate its effectiveness, revealing that the proposed methodology shares lower Peak Sidelobe Level Ratio (PSLR) and Integral Sidelobe Level Ratio (ISLR), smaller Signal Noise Ratio (SNR) loss, and weaker mainlobe widening in comparison with the traditional windows.

This paper deals with the integrated signal design to simultaneously achieve a desired radar beampattern and multi-users communication for a dual-function Multiple Input Multiple Output (MIMO) system. Inter-pulse modulation strategy via jointing the information embedding subpulses positions selections and differential phase modulation is proposed for communication function, while beampattern Integrated Sidelobe Level (ISL) is minimized to enhance radar detectability. Meanwhile, constant envelope, communication modulation and mainlobe width constraints are forced. To tackle the resulting nonconvex and NP-hard optimization problem, the Linearized Approximation Alternating Direction Penalty Method (LA-ADPM) is proposed. Finally, numerical results demonstrate the designed integrated signal is capable of realizing high data rate communication and ensuring acceptable radar performance.

This letter proposes a novel window function design to synthesize asymmetric beampatterns while minimizing the gain loss for clutter/interference suppression as required in practical applications, such as airborne phased array radar (APAR). Compared with beampattern synthesis in terms of designing the weight vector directly, the proposed method can achieve asymmetric sidelobes for multiple steering directions resort to designing one window while circumventing repetitive computations. To tackle the resultant non-convex optimization problem, an iterative alternating optimization (IAO) algorithm is introduced where each iteration involves multiple subproblems that can be solved via either convex approximation (CA) approach or closed-form solutions. Finally, numerical simulations are provided to verify the effectiveness of the proposed method comparing with the classical adjustable window functions (CAWFs).

In this article, the watermarking framework for electromagnetic systems is established with nonblind, semiblind, and blind watermarking demodulation processes mitigated to applications like radar detection, synchronization, integrated dual function, etc. Desired for similar advantages and trade-offs of watermarking technology, the dual function radar and communication (DFRC) system is specifically concerned for covert communication and low possibility of interception (LPI) radar sensing. In this respect, we propose a novel DFRC waveform design method where peak sidelobe level (PSL) of autocorrelation function (ACF) is considered as the figure of merit with information embedded via discrete Fourier transform (DFT) watermarking strategy. Meanwhile, the peak-to-average ratio (PAR) and energy constraints are forced to ensure compatibility with current hardware technique. To handle the resulting NP-hard design problems, the proximal method of multipliers (PMM) is employed with the overall computational burden linear with the amount of information per pulse and quadratic with respect to the code length. Finally, numerical and experimental results are provided to evaluate the effectiveness of the proposed DFRC waveform design scheme with application in joint synthetic aperture radar (SAR) imaging and communication.

The multi-function radar (MFR) is capable of transmitting complex signals and achieving beam agility according to different tasks, thus leading to many challenges in the work mode recognition field. This paper, therefore, develops an Encoder-Decoder model based on the gated recurrent units (GRU) network to achieve work mode recognition. It involves the use of the Encoder structure to extract the temporal features and the work mode transition regulations of the intercepted pulse group sequence while leveraging the Decoder structure to decode the features and transition regulations as a part of the input for the next decoding process. Additionally, the label substitution (LS) method is utilized for improving the ability of the Decoder structure to recognize work mode under non-ideal situations. Simulation results show that the proposed method is less sensitive in the presence of non-ideal situations and shares better performance than existing work mode recognition methods.

Accurate sensing of the main-lobe active deception jamming is critical for radar anti-jamming and extended target detection in complex electromagnetic environment. This letter therefore deals with the problem of multiple active deception jamming recognition in extended target settings. A residual convolutional neural network with attention mechanism-based radar active deception jamming recognition algorithm is proposed leveraging a hybrid model to capture much rich features through multi-domain feature fusion. The proposed method can outperform state-of-the-art methods in terms of recognition accuracy, model size, and convergence speed. Experimental results demonstrate its effectiveness and robustness.

Ambiguity clutter for airborne radar system is usually caused by uniform pulse repetition interval (UPRI) waveform, which significantly degrades target detection and location performance. To address this issue, this paper proposes an ambiguity clutter suppression method resorting to pseudorandom pulse repetition interval (PrPRI) waveform. First, airborne radar clutter model accounting for multiple range rings with PrPRI waveform is developed. Next, a non-uniform coherent processing framework is introduced to eliminate the clutter folded in the range and Doppler domain. In particular, the reasons for the enlargement of the area free of clutter corresponding of PrPRI mode are analyzed, as well as the maximum unambiguous Doppler frequency, maximum unambiguous range and design principles for PRI are derived. Finally, numerical examples are designed to verify that the non-uniform coherent processing framework can enlarge the clutter-free area and achieve the unambiguity target detection and location.

Due to the division of the transceiver, a bistatic synthetic aperture radar (SAR) gains many advantages, such as forward-looking imaging and powerful anti-interference capabilities. In the meantime, information sharing (e.g., positions and status) between the transmitter and the receiver is required for SAR imaging. This article addresses the co- use waveform design for SAR-dual-functional radar communication (SAR-DFRC). To this end, we embed information into a time-frequency spectrum of the phase coded signal, which can be a feasible solution to SAR-DFRC, bringing the possibility of realizing a light-weighted, miniaturized, low-costed, spectrum reusable, and more confidential system. A novel time-frequency spectrum shaping (TFSS) SAR-DFRC architecture based on short-time Fourier transform (STFT) is proposed for the first time. The weighted peak sidelobe level (WPSL) is considered a figure of merit for SAR imaging performance. Information is embedded by nulling the time-frequency spectrum of the waveform. Here, we develop the majorization-minimization PSL-TFSS (MMPSL-TFSS) algorithm to solve the resulting nonconvex NP-hard optimization problem. The designed waveform can ensure imaging performance and obtain high communication capacity in the meantime. Experimental and numerical results highlight the effectiveness of the proposed SAR-DFRC framework for imaging performance and the secure transition to communication information.

This paper is focused on the joint design of transmit waveform and receive filter in the presence of deceptive interference. In particular, a design criterion incorporating a weighted sum of integrate sidelobe levels (ISLs) with respect to the receive filter and transmit waveform as well as the receive filter and interference waveform is developed to minimize along with interference nulling, constant modulus and signal-to-noise ratio (SNR) restrictions. To tackle the resulting non-convex optimization problem, a new decoupled alternating direction penalty method (DCADPM) is proposed based on the ADPM framework. In each iteration, it converts the considered problem into multiple tractable subproblems with closed-form solutions via introducing auxiliary variables. Finally, numerical results are provided to demonstrate the effectiveness of the proposed methodology, highlighting that it is capable of rejecting many kinds of deceptive interferences.

This article considers the waveform design problem for airborne multiple-input-multiple-output (MIMO) radar systems with space-time adaptive processing (STAP). We choose the mutual information between the received signal and the target impulse response as the design metric to achieve enhanced detection performance under the influences from neighboring range cells. In order to solve the resulting optimization problem, we first decompose the objective function into two parts and consider their optimization individually. We optimize the first part by relaxing it using an upper bound and optimize the second part using a simple optimization procedure based on the majorization-minimization (MM) algorithm. The alternation direction method of multipliers (ADMM) framework is then adopted to derive the overall solution, where the MM algorithm is employed again to handle the fourth-order term of the waveform vector. Numerical results are provided to show that the proposed algorithm has better detection performance than that of the existing methods.

This paper deals with constant modulus waveform design in spectrally dense environments assuming a discrete phase code alphabet. The goal is to optimize the radar detection performance while rigorously controlling the injected interference energy within each shared band and enforcing a similarity constraint to manage some relevant signal features. To tackle the resulting NP-hard optimization problem, an iterative procedure characterized by a polynomial computational complexity, is introduced leveraging the coordinate descent method. Numerical results are provided to show the effectiveness of the technique in terms of detection performance, spectral shape and autocorrelation features.

This letter focuses on the local ambiguity function shaping through unimodular sequence design. An accelerated iterative sequential optimization (AISO) algorithm is proposed to minimize the average value of the weighted integrated sidelobe level (WISL) over specific Doppler bins and range bins of interest. We evaluate the effectiveness of the AISO algorithm in terms of its achieved WISL and computational complexity in comparison with the gradient method via numerical examples. The capability of using the design to detect high-speed targets is also evaluated.

This letter considers the distribution of product for two correlated real Gaussian random variables with nonzero means and arbitrary variances, which arises widely in radar and communication societies. We determine the exact probability density function (PDF) in terms of an infinite sum of modified Bessel functions of second kind, which includes some existent results, i.e., zero-means and/or independent variables, as special cases. Then, we study the approximation error and convergence rate when finite summations are exploited in practice. Finally, we evaluate the PDF behaviors of the derived expression as well as the Monte Carlo simulations.