Fusion of Multipath Data with ML-PMHT for Very Low SNR Track Detection in an OTHR

摘要

TheMaximumLikelihoodProbabilisticMulti-HypothesisTracker(ML-PMHT) is formulated for and applied to an Over-The-Horizonradar (OTHR) scenario. In this scenario there are two ionospherelayers acting as reflectors of the electromagnetic (EM) waves andeach scan can contain multiple measurements (up to four) originatingfrom each target; each of these target-originated measurementstakes one of four possible round-trip paths. The ML-PMHT likelihoodratio is modified to model this uncertainty in the measurementpath which then allows the fusion of multipath data in the presenceof false measurements.This tracker is shown to have a high track detection probabilityand track accuracy with a low probability of false track in verylow signal to noise ratio (SNR) OTHR scenarios. It is also shown tobe a statistically efficient estimator. Consequently, the ML-PMHTholds great promise in increasing the sensitivity and robustness ofthe next generation OTHR.Results indicate that one can achieve for a very low observable(VLO) target a true track detection probability above 95% and afalse track rate under one per 24 hours.