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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]

This paper demonstrates how the data-association technique known as the probabilistic multi-hypothesis tracker (PMHT) can be applied to the feature-based simultaneous localization and map building (SLAM) problem. The main advantage of PMHT over other conventional data-association techniques is that it has low computational complexity, while still providing good performance. Low complexity is a particularly desirable feature for the SLAM problem where the estimators used may already be costly to implement. The paper also proposes an estimation approach based on generalized expectation-maximization iterations of the PMHT SLAM problem, which is able to achieve low computation complexity at the expense of local convergence. The performance of the PMHT SLAM algorithm is compared with other approaches, and its output is demonstrated on a benchmark data set recorded in Victoria Park, Sydney, Australia. [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]

The above article [1] introduced an algorithm for multitarget track-before-detect based on a multi-Bernoulli random finite set model (MB-TBD). This new algorithm was compared with the Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) on simulated data examples containing multiple targets with non-linear dynamics. The authors reported poor performance from H-PMHT and described several deficiencies of the algorithm. This note highlights unnecessary assumptions made in the assessment of H-PMHT and repeats two of the simulation examples after relaxing them. We demonstrate a substantial improvement in performance compared with the originally published results. The simulation example is also shown to be a relatively high signal to noise problem and good performance is obtained from a conventional detect-then-track algorithm. [ABSTRACT FROM PUBLISHER]

Track-Before-Detect (TkBD) is a paradigm that combines the target detection and estimation processes that are usually sequentially applied to sensor data in a conventional system. Existing literature uses only the envelope of complex data; this article presents an approach that also includes the phase information. The inclusion of phase is shown to both improve the discrimination of targets from noise and reduce the computation overhead, with improved performance demonstrated using three representative algorithms. [ABSTRACT FROM AUTHOR]