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—Data association, the problem of reasoning over correspondence between targets and measurements, is a fundamental problem in tracking tracking. This paper presents a graphical model formulation of data association and applies an approximate inference method, belief propagation (BP), to obtain marginal association probabilities. We prove that BP is(More)
—Random finite sets (RFSs) has been a fruitful area of research in recent years, yielding new approximate methods for multiple target tracking such as the probability hypothesis density (PHD), cardinalised PHD (CPHD), and multiple target multi-Bernoulli (MeMBer) filters. These new approaches have largely been based on approximations that side-step the need(More)
The problem of tracking targets in clutter naturally leads to a Gaussian mixture representation of the probability density function of the target state vector. Modern tracking methods maintain the mean, covariance and probability weight corresponding to each hypothesis, yet they rely on simple merging and pruning rules to control the growth of hypotheses.(More)
Resource management in distributed sensor networks is a challenging problem. This can be attributed to the fundamental tradeoff between the value of information contained in a distributed set of measurements versus the energy costs of acquiring measurements, fusing them into the conditional probability density function (pdf) and transmitting the updated(More)
Data association is the problem of determining the correspondence between targets and measurements. In this paper, we present a graphical model approach to data association and apply an approximate inference method, loopy belief propagation, to obtain the marginal association weights (e.g., for JPDA). In general, loopy belief propagation is not guaranteed(More)
Recent derivations have shown that the full Bayes random finite set filter incorporates a linear combination of multi-Bernoulli distributions. The full filter is intractable as the number of terms in the linear combination grows exponentially with the number of targets; this is the problem of data association. A highly desirable approximation would be to(More)
Sensor management may be defined as those stochastic control problems in which control values are selected to influence sensing parameters in order to maximize the utility of the resulting measurements for an underlying detection or estimation problem. While problems of this type can be formulated as a dynamic program, the state space of the program is in(More)
The probability hypothesis density (PHD) and multitarget multi-Bernoulli (MeMBer) filters are two leading algorithms that have emerged from random finite sets (RFS). In this paper we study a method which combines these two approaches. Our work is motivated by a recent paper, which proves that the full Bayes RFS filter naturally incorporates a Poisson(More)
The joint probabilistic data association (JPDA) filter is a popular tracking methodology for problems involving well-spaced targets, but it is rarely applied in problems with closely spaced targets due to its complexity in these cases, and due to the well-known phenomenon of coalescence. This paper addresses these difficulties using random finite sets(More)