Joint Waveform Adaptation and Data-Assisted Jamming Suppression for Multiple Target Tracking Based on Deep Reinforcement Learning

To enhance the adaptive capabilities of radar, this work focuses on the problem of joint waveform adaptation and data-assisted jamming suppression in multiple target tracking scenarios with high-power sidelobe jamming. Utilizing deep reinforcement learning (DRL) methods, we develop agents capable of decision-making based on the environmental perception information, thus enabling beamforming for jamming suppression and enhancing tracking performance. Jamming suppression faces challenges due to the computational complexity of beamforming and the large sample size of jamming. Furthermore, the multidimensionality of waveform parameters and the fluctuating states of targets pose additional challenges. To address these challenges, we employ deep reinforcement learning for jamming number perception, leverage convolutional neural networks (CNNs) to extract features, and incorporate the self-attention mechanism for jamming number decision-making. For waveform adaptation, we segment each parameter into a distinct decision space, with distinct agents making decisions using value-decomposition networks (VDNs). These agents are augmented with gated recurrent units (GRUs) to manage diverse target states and maintain historical data. The focus on tracking robustness and accuracy can be tailored by designing the reward function weights. A separate training approach is developed to obtain effective joint policies. To avoid signal-to-noise ratio (SNR) constraints and enhance applicability to real radar systems, we develop an intelligent radar tracking simulation at the signal level and evaluate the performance of the proposed method. Simulation results demonstrate that the receiver agent can balance the perception of jamming numbers and real-time capabilities to assist in jamming suppression. Furthermore, compared with criterion-based methods and an entropy reward-based method, the transmitter agent can enhance tracking robustness and accuracy by adapting waveform parameters. Moreover, field experiments are conducted to verify that the proposed DRL method can improve the actual multitarget tracking performance through waveform adaptation.