Small moving infrared space target tracking algorithm based on probabilistic data association filter

Numerous false alarms for low signal-to-noise ratio (SNR) would seriously debase the performance for infrared low observable (LO) space target tracking. Due to the motion (i.e. azimuth, elevation and their derivative velocity), amplitude and size of infrared target are almost invariable and highly correlative, a multi-feature association approach based on probabilistic data association (PDA) is presented to track target in this paper. Firstly, the motion, amplitude and size of target are modeled as stationary random signal afforded Gaussian distribution. The probability of motion, amplitude and size of measurement originated as the target of interest is then estimated by Gaussian distribution, and that of false alarm is distributed uniformly. Subsequently, the combined probability of motion, amplitude and size is derived by PDA, and their weight coefficients are estimated adaptively according to their fluctuations. Finally, the relevant parameters including combination measurement are predicted and updated. Some experiments are included and the results show that the performance of target tracking by the proposed approach is significantly enhanced.