Micro-Doppler Curves Extraction Based on High-Order Particle Filter Track-Before Detect

Abstract

Micro-Doppler (MD) radar signatures characterize rich motion information of the targets and are of great significance in target recognition. In this letter, we propose a novel high-order particle filter track-before-detect (PF-TBD) approach for the MD curves extraction. In the proposed approach, the sinusoidal Doppler frequency curve is treated as the state, whose dynamic model is described as a high-order Markov chain. First, the state equation is divided into two parts, the translational motion part represented as a first-order dynamic process and the micromotion part represented as a high-order dynamic process including static model parameters. Then, a kernel smoothing approach is introduced for the static model parameters estimation, and the auxiliary particle filter (APF) is utilized for the instantaneous Doppler curves extraction. Finally, the experiments on the electromagnetic analysis data are carried out to validate the performance of the proposed method.