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 signal-to-noise ratio (SNR)-nonmaneuvering 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. We present an adaptive algorithm, which uses the ML-PDA estimator with AI in a sliding-window fashion, to detect possibly maneuvering 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 track detection by rejecting noninformative scans, target reacquisition in case of temporary target disappearance, and the handling of targets with velocities evolving over time. We demonstrate the operation of the adaptive sliding-window ML-PDA estimator on a real scenario for tracking a fast-moving F1 Mirage fighter jet using an imaging sensor. 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 algorithm. This ability to successfully process large amounts of data, with near real-time performance, under time-varying low SNR conditions makes the proposed estimator superior to other existing approaches.