Signal-domain Kalman filtering: An approach for maneuvering target surveillance with wideband radar

摘要

As unmanned aerial vehicles (UAVs), owing to their low cost and wide variety of applications, become increasingly indispensable to modern society, and monitoring their illegal use through multichannel radar tracking has drawn widespread attention. Unfortunately, traditional tracking methods face challenges due to the UAV target with high-maneuverability and low-observability. In addition, the traditional measurements suffer some significant information loss because of their treatments like thresholding, clustering and peak sampling, which decreases tracking performance. In order to track UAV precisely, we propose a novel UAV tracking method based on the joint estimation of range and direction of arrival (DOA) in this paper. A complex-valued reference signal is introduced by coherently integrating in sliding windows to obtain SNR gain and preserve the complete structure and motion information of UAV. Besides motion state, the complex-valued reference signal is also utilized to predict the current return signal based on dynamic equation, and then a Bayes based method is derived to jointly estimate the range and DOA errors by comparing reference signal and return signal. In order to adapt to the maneuverable feature, a precise measurement model for motion variables is constructed to realize tracking combined with Kalman filter. Due to the utilization of the complex-valued reference signal and the precise measurement model, the proposed method has outstanding performance in the scene of low SNR and high maneuverability. In addition, there is no any information loss when the raw data is used to realize estimating and tracking. Finally, simulated and real-measured experiments confirm its remarkable performance.