Jason L. Williams

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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)
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, 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)
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)
The problem of tracking targets in clutter naturally leads to a Gaussian mixture representation of the probability density function of the target state vector. Stateof-the-art Multiple Hypothesis Tracking (MHT) techniques maintain the mean, covariance and probability weight corresponding to each hypothesis, yet they rely on ad hoc merging and pruning rules(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)
In many estimation problems, the measurement process can be actively controlled to alter the information received. The control choices made in turn determine the performance that is possible in the underlying inference task. In this paper, we discuss performance guarantees for heuristic algorithms for adaptive measurement selection in sequential estimation(More)
Data association, or determining correspondence between targets and measurements, is a very difficult problem that is of great practical importance. In this paper we formulate the classical multi-target data association problem as a graphical model and demonstrate the remarkable performance that approximate inference methods, specifically loopy belief(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)