Low Observable Moving Target Tracking Based on Modified PDA-AI

Abstract

During tracking low observable moving target in electro-optical (EO) imaging tracking system, multiple false alarms resulted from low signal-to-noise rate (SNR) would seriously debase the performance of target tracking. Probabilistic data association with amplitude information (PDA-AI) assumes that amplitude of target is not correlative among different sampling instants and larger amplitude is, greater the probability of validated measurement being target of interest would be. In EO imaging tracking system, amplitude and motion of target of interest are consistent and highly correlative. A modified PDA-AI is discussed and developed to resolve the inconsistency between PDA-AI and EO tracking system in this paper, which analyzes target motion by means of modeling amplitude and motion as well as their consistency as stationary random signal. The theory analysis with Cramer-Rao lower bound (CRLB) and experiments results show that the performance of low observable target tracking of the modified PDA-AI would be more accurate and more reliable than that of the original PDA-AI.