Comparative Performance Analysis of Adaptive and Fixed Waveforms in Multistatic Radar Systems

الملخص

This paper presents a comparative performance analysis of adaptive and fixed waveform selection strategies in multistatic radar systems, with a focus on target tracking in cluttered environments. We propose a novel adaptive waveform selection algorithm designed specifically for multistatic configurations, which optimizes waveform parameters to minimize the predicted mean square error of target state estimation. The algorithm is integrated with a Probabilistic Data Association (PDA) filter to create a robust framework for tracking in cluttered multistatic environments. Through extensive simulations, we compare the performance of our adaptive waveform approach against fixed and random waveform strategies across various metrics, including position and velocity estimation accuracy, and track continuity. The impact of varying clutter densities on the relative performance of adaptive and fixed waveform strategies is also investigated. Results demonstrate that the adaptive waveform selection algorithm consistently outperforms fixed and random waveform approaches, achieving lower root mean square errors in both position and velocity estimation, and improved track continuity, particularly in high-clutter scenarios. This paper highlights the significant potential of adaptive waveform selection in enhancing the performance of multistatic radar systems in challenging environments, with important implications for applications such as air traffic control, maritime surveillance, and military target tracking.