An adaptive early-detection ML/PDA estimator for LO targets with EO sensors

The batch Maximum Likelihood Estimator, combined with the Probabilistic Data Association Algorithm (ML-PDA), has been shown to be effective in acquiring low observable (LO)-low SNR-non-maneuvering targets in the presence of heavy clutter. The use of signal strength or amplitude information (AI) in the ML-PDA estimator facilitates the acquisition of weak targets. In this paper we present an adaptive algorithm, which uses the ML-PDA estimator with Al in a sliding-window fashion, to detect high-speed targets in heavy clutter using electro-optical (EO) sensors. The initial time and the length of the sliding-window are adjusted adaptively according to the information content of the received measurements. A track validation scheme via hypothesis testing is developed to confirm the estimated track, that is, the presence of a target, in each window. The sliding-window ML-PDA approach, together with track validation, enables early detection by rejecting noninformative scans, target reacquisition in case of temporary target disappearance and the handling of targets with speeds evolving over time. The proposed algorithm is shown to detect the target, which is hidden in as many as 600 false alarms per scan, 10 frames earlier than the Multiple Hypothesis Tracking (MHT) algorithm.