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

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1 December 2015
Kevin Romeo, Yaakov Bar-Shalom, Peter Willett

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The Maximum Likelihood Probabilistic Multi-Hypothesis Tracker (ML-PMHT) is formulated for and applied to an Over-The-Horizon radar (OTHR) scenario. In this scenario there are two ionosphere layers acting as reflectors of the electromagnetic (EM) waves and each scan can contain multiple measurements (up to four) originating from each target; each of these target-originated measurements takes one of four possible round-trip paths. The ML-PMHT likelihood ratio is modified to model this uncertainty in the measurement path which then allows the fusion of multipath data in the presence of false measurements.

This tracker is shown to have a high track detection probability and track accuracy with a low probability of false track in very low signal to noise ratio (SNR) OTHR scenarios. It is also shown to be a statistically efficient estimator. Consequently, the ML-PMHT holds great promise in increasing the sensitivity and robustness of the next generation OTHR.

Results indicate that one can achieve for a very low observable (VLO) target a true track detection probability above 95% and a false track rate under one per 24 hours.