Target Tracking Algorithm Based on Generalized Regression Neural Network for Passive Bistatic Radar

In passive bistatic radar, high-performance target tracking is of crucial importance to a variety of applications. However, most existing tracking algorithms cannot achieve satisfactory performance due to poor azimuth estimation and the nonlinear relationship between the target position and measurements. This article proposes a novel tracking algorithm based on improved generalized regression neural networks (GRNNs) and Kalman filtering (KF). First, the dependency between the measurements and the target position is modeled by GRNN regression. A cooperative co-evolutionary genetic algorithm (CCGA) is then introduced to optimize the sample size and smoothing factor of the GRNN model to improve the performance. Finally, this article applies KF to CCGA-GRNN localization results and forms the smoothing trajectories of the targets. Simulation and experimental results show that the performance of the proposed algorithm is better than that of the geometric localization (GL), GRNN, and extreme learning machine (ELM)-based tracking approaches. Furthermore, field experiment data verify the feasibility of the proposed algorithm.