In this article we propose a new Rao-Blackwellized particle filtering based algorithm for tracking an unknown number of targets. The algorithm is based on formulating probabilistic stochastic process models for target states, data associations, and birth and death processes. The tracking of these stochastic processes is implemented using sequential Monte Carlo sampling or particle filtering, an...

We propose a new Rao-Blackwellized sequential Monte Carlo method for tracking multiple targets in presence of clutter and false alarm measurements. The advantage of the new approach is that Rao-Blackwellization allows the estimation algorithm to be partitioned into single target tracking and data association sub-problems, where the single target tracking sub-problem can be solved by Kalman filt...

Tracking multiple targets using a single estimator is a problem that is commonly approached within a trusted framework. There are many weaknesses that an adversary can exploit if it gains control over the sensors. Because the number of targets that the estimator has to track is not known with anticipation, an adversary could cause a loss of information or a degradation in the tracking precision...

The data association problem occurs for multiple target tracking applications. Since non-linear and non-Gaussian estimation problems are solved approximately in an optimal way using recursive Monte Carlo methods or particle filters, the association step will be crucial for the overall performance. We introduce a Bayesian data association method based on the particle filter idea and the joint pr...

Despite the minimal information provided by a binary proximity sensor, a network of these sensors can provide significant target tracking performance. This article deals with the performance examination of such a network for tracking multiple targets. We began with geometric arguments that address the problem of counting the number of distinct targets, given a snapshot of the sensor readings. T...

Abstract A new data association method is presented for multiple target tracking. The proposed method is formulated using reverse prediction weighted neighbor to calculate the probability of candidate measurements from targets. The purpose of the proposed method is to eliminate the need to acquire prior knowledge such as detection probability and clutter density. The probability between targets...

Vast quantities of EO and IR data are collected on airborne platforms (manned and unmanned) and terrestrial platforms (including fixed installations, e.g., at street intersections), and can be exploited to aid in the global war on terrorism. However, intelligent preprocessing is required to enable operator efficiency and to provide commanders with actionable target information. To this end, we ...

Modern sensors are able to rapidly change mode of operation and steer between physically separated objects. While control of such sensors over a rolling planning horizon can be formulated as a dynamic program, the optimal solution is inevitably intractable. In this paper, we consider the control problem under a restricted family of policies and show that the essential sensor control trade-offs ...

A practical probabilistic data association filter is proposed for tracking multiple targets in clutter. The number of joint data association events increases combinatorially with the number of measurements and the number of targets, which may become computationally impractical for even small numbers of closely located targets in real target-tracking applications in heavily cluttered environment...