A Unified Anti-Jamming Design in Complex Environments Based on Cross-Modal Fusion and Intelligent Decision-Making

With the rapid development of radar jamming systems, especially digital radio frequency memory (DRFM), the electromagnetic environment has become increasingly complex. In recent years, most existing studies have focused solely on either jamming recognition or anti-jamming strategy design. In this paper, we propose a unified framework that integrates interference recognition with intelligent anti-jamming strategy selection. Specifically, time-frequency (TF) features of radar echoes are first extracted using both Short-Time Fourier Transform (STFT) and Smoothed Pseudo Wigner-Ville Distribution (SPWVD). A feature fusion method is then designed to effectively combine these two types of time-frequency representations. The fused TF features are further combined with time-domain features of the radar echoes through a cross-modal fusion module based on an attention mechanism. Finally, the recognition results, together with information obtained from the passive radar, are fed into a Deep Q-Network (DQN)-based intelligent anti-jamming strategy network to select jamming suppression waveforms. The key jamming parameters obtained by the passive radar provide essential information for intelligent decision-making, enabling the generation of more effective strategies tailored to specific jamming types. The proposed method demonstrates improvements in both jamming type recognition accuracy and the stability of anti-jamming strategy selection under complex environments. Experimental results show that our method achieves superior performance compared to Support Vector Machines (SVM), VGG-16, and 2D-CNN methods, with respective improvements of 1.41%, 2.5%, and 14.51% in overall accuracy. Moreover, in comparison with the SARSA algorithm, the designed algorithm achieves faster reward convergence and more stable strategy generation.

Mainlobe Jamming Suppression Using MIMO-STCA Radar

Radar jamming suppression, particularly against mainlobe jamming, has become a critical focus in modern radar systems. This article investigates advanced mainlobe jamming suppression techniques utilizing a novel multiple-input multiple-output space-time coding array (MIMO-STCA) radar. Extending the capabilities of traditional MIMO radar, the MIMO-STCA framework introduces additional degrees of freedom (DoFs) in the range domain through the utilization of transmit time delays, offering enhanced resilience against interference. One of the key challenges in mainlobe jamming scenarios is the difficulty in obtaining interference-plus-noise samples that are free from target signal contamination. To address this, the study introduces a cumulative sampling-based non-homogeneous sample selection (CS-NHSS) algorithm to remove target-contaminated samples, ensuring accurate interference-plus-noise covariance matrix estimation and effective noise subspace separation. Building on this, the subsequent step is to apply the proposed noise subspace-based jamming mitigation (NSJM) algorithm, which leverages the orthogonality between noise and jamming subspace for effective jamming mitigation. However, NSJM performance can degrade due to spatial frequency mismatches caused by DoA or range quantization errors. To overcome this limitation, the study further proposes the robust jamming mitigation via noise subspace (RJNS) algorithm, incorporating adaptive beampattern control to achieve a flat-top mainlobe and broadened nulls, enhancing both anti-jamming effectiveness and robustness under non-ideal conditions. Simulation results verify the effectiveness of the proposed algorithms. Significant improvements in mainlobe jamming suppression are demonstrated through transmit-receive beampattern analysis and enhanced signal-to-interference-plus-noise ratio (SINR) curve.

Monopulse Parameter Estimation based on MIMO-STCA Radar in the Presence of Multiple Mainlobe Jammings

The monopulse technique is characterized by its high accuracy in angle estimation and simplicity in engineering implementation. However, in the complex electromagnetic environment, the presence of the mainlobe jamming (MLJ) greatly degrades the accuracy of angle estimation. Conventional methods of jamming suppression often lead to significant deviations in monopulse ratio while suppressing MLJ. Additionally, the monopulse technique based on traditional radar cannot jointly estimate the target's range. In this paper, the four-channel adaptive beamforming (ABF) algorithm is proposed, which adds a delta-delta channel based on conventional sum-difference-difference three-channel to suppress a single MLJ. Moreover, considering the suppression of multiple MLJs and sidelobe jammings (SLJs), the row-column ABF algorithm is proposed. This algorithm utilizes more spatial degrees of freedom (DOFs) to suppress multiple jammings by the row-column adaptive beamforming at the subarray level. The key ideal of both algorithms is to suppress MLJ with null along one spatial direction while keeping the sum and difference beampatterns undistorted along another spatial direction. Therefore, the monopulse ratio remains undistorted while suppressing the MLJ, ensuring the accuracy of monopulse parameter estimation. Furthermore, by utilizing the additional degrees of freedom (DOFs) in the range domain provided by the multiple-input multiple-output space-time coding array (MIMO-STCA) radar, joint angle-range estimation can be achieved through the monopulse technique. Simulation results highlight the effectiveness of the proposed methods in suppressing multiple MLJs and enhancing the accuracy of monopulse parameter estimation, as verified by the low root mean square error (RMSE) in the parameter estimation results.

A Hybrid Training-time and Run-time Defense Against Adversarial Attacks in Modulation Classification

Motivated by the superior performance of deep learning in many applications including computer vision and natural language processing, several recent studies have focused on applying deep neural network for devising future generations of wireless networks. However, several recent works have pointed out that imperceptible and carefully designed adversarial examples (attacks) can significantly deteriorate the classification accuracy. In this paper, we investigate a defense mechanism based on both training-time and run-time defense techniques for protecting machine learning-based radio signal (modulation) classification against adversarial attacks. The training-time defense consists of adversarial training and label smoothing, while the run-time defense employs a support vector machine-based neural rejection (NR). Considering a white-box scenario and real datasets, we demonstrate that our proposed techniques outperform existing state-of-the-art technologies.

Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning

Choosing the values of hyper-parameters in sparse Bayesian learning (SBL) can significantly impact performance. However, the hyper-parameters are normally tuned manually, which is often a difficult task. Most recently, effective automatic hyper-parameter tuning was achieved by using an empirical auto-tuner. In this work, we address the issue of hyper-parameter auto-tuning using neural network (NN)-based learning. Inspired by the empirical auto-tuner, we design and learn a NN-based auto-tuner, and show that considerable improvement in convergence rate and recovery performance can be achieved.

Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks

In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical applications. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to "model" the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals. We then represent the trained NN with a factor graph, and design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm. The implementation of a turbo receiver with the proposed Bayesian detector is also investigated. Extensive simulation results demonstrate that the proposed technique delivers remarkably better performance than state-of-the-art methods.

Model-aided Deep Neural Network for Source Number Detection

Source number detection is a critical problem in array signal processing. Conventional model-driven methods e.g., Akaikes information criterion (AIC) and minimum description length (MDL), suffer from severe performance degradation when the number of snapshots is small or the signal-to-noise ratio (SNR) is low. In this paper, we exploit the model-aided based deep neural network (DNN) to estimate the source number. Specifically, we first propose the eigenvalue based regression network (ERNet) and classification network (ECNet) to estimate the number of non-coherent sources, where the eigenvalues of the received signal covariance matrix and the source number are used as the input and the supervise label of the networks, respectively. Then, we extend the ERNet and ECNet for estimating the number of coherent sources, where the forward-backward spatial smoothing (FBSS) scheme is adopted to improve the performance of ERNet and ECNet. Numerical results demonstrate the outstanding performance of ERNet and ECNet over the conventional AIC and MDL methods as well as their excellent generalization capability, which also shows their great potentials for practical applications.