Ratnasingham Tharmarasa

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—A standard assumption in most tracking algorithms, like the Probabilistic Data Association (PDA) filter, Multiple Hypothesis Tracker (MHT) or the Multiframe Assignment Tracker (MFA), is that a target is detected at most once in a frame of data used for association. This one-to-one assumption is essential for correct measurement-to-track associations. When(More)
—Performance evaluation is one of the most important steps in any target tracking problem. The objective of this paper is to present a brief review of different approaches available for the performance analysis of multiple target tracking algorithms. Metrics are first classified into sensor-dependent and tracker-dependent ones. Then, the tracker-dependent(More)
—In most hypothesis-oriented Multiple Hypothesis Tracking (MHT) implementations, the target-to-measurement data association is typically solved by using the Murty's algorithm. However, the Murty's algorithm has no control over the diversity of target-to-measurement associations-often the top associations vary only slightly. In addition, in practical(More)
—In this paper, based on non-homogeneous Poisson point processes (NHPP), a kernel clutter spatial intensity estimation method is proposed. Here, the clutter spatial intensity estimation problem is decomposed into two parts: (1) estimate the probability distribution of the clutter number per scan; (2) estimate the spatial variation of the clutter intensity(More)