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In skywave over-the-horizon radar, the ionosphere often supports multiple propagation paths, and a single target can appear as a family of returns. This paper presents two new tracking approaches for this environment: The first uses measurement fusion, and the second uses group-target track-before-detect algorithm. In contrast to previous studies, we find that the biggest benefit is not improved detection sensitivity but rather associating the whole pattern of multipath returns significantly improves the ability to follow maneuvers. [ABSTRACT FROM AUTHOR]

Unlike other tracking algorithms the probabilistic multi-hypothesis tracker (PMHT) assumes that the true source of each measurement is an independent realisation of a random process. Given knowledge of the prior probability of this assignment variable, data association is performed independently for each measurement. When the assignment prior is unknown, it can be estimated provided that it is either time independent, or fixed over the batch. This paper presents a new extension of the PMHT, which incorporates a randomly evolving Bayesian hyperparameter for the assignment process. This extension is referred to as the PMHT with hysteresis. The state of the hyperparameter reflects each model's contribution to the mixture, and thus can be used to quantify the significance of mixture components. The paper demonstrates how this can be used as a method for automated track maintenance in clutter. The performance benefit gained over the standard PMHT is demonstrated using simulations and real sensor data. [ABSTRACT FROM AUTHOR]