A Novel Scatterer Trajectory Association Method Based on Markov Chain Monte Carlo Algorithm

Abstract

Precise scatterer trajectory association is the basis of three-dimensional (3D) reconstruction utilizing sequential inverse synthetic aperture radar (ISAR) images. To address the occlusion and trajectory crossing of different scatterers, we propose a novel scatterer trajectory association method based on Markov chain Monte Carlo (MCMC) algorithm. Firstly, we derive the ellipse movement characteristics of each scatterer trajectory under stationary rotation motion model of the observed target. Then, we present a Bayesian model and inference algorithm for the scatterer trajectory association problem. MCMC is applied to estimate the scatterer trajectory matrix. Particularly, we design new prior and likelihood evaluation criteria in MCMC by making use of the elliptical movement characteristics of each scatterer trajectory. Simulation results on simulated data validate the effectiveness of the proposed method.