HSD-WO:A Hybrid Stochastic-Deterministic Waveform Optimization Method for Joint Tracking and Anti-Interference

The increasing radio frequency interference (RFI) caused by other in-band radio frequency (RF) users, like communication systems, affects the waveform-agile tracking performance of cognitive tracking radar (CTR). To improve tracking performance while mitigating the RFI, both the spectrum occupancy and waveform parameters of CTR need to be jointly optimized based on real-time perceptual information. Therefore, we formulate the above tracking waveform optimization problem in the congested spectrum environment as waveform optimization for joint tracking and anti-interference (WO-JTAI) and propose a hybrid stochastic-deterministic waveform optimization (HSD-WO) method for WO-JTAI based on the JTAI-oriented multi-step objective function. Firstly, to establish the coupling relationship between spectrum occupancy, waveform parameters and the potential long-term performance of joint tracking and anti-interference, we propose a JTAI-oriented multi-step objective function based on the WO-JTAI-oriented partial observable Markov decision process (POMDP) model. This specific POMDP model can deal with the imperfect state information in the interference-limited tracking scenarios. Based on this, the objective function incorporates the tradeoff between bandwidth utilization and interference avoidance, and the predicted conditional (PC) Bayes risk based on tracking accuracy-oriented criterion. Secondly, to solve the optimal radar policy by minimizing the above objective function, based on a CTR framework with a dual controller structure, we propose the HSD-WO method for WO-JTAI. By decoupling this objective function and solving it sequentially, this two-step decoupling method iteratively performs the stochastic optimization (SO)-based spectrum occupancy optimization and deterministic optimization (DO)-based waveform parameters optimization until converging to the optimal policy. Simulation results demonstrate that effectiveness of the proposed method by comparing with other methods.