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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)

—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)

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 probability hypothesis density (PHD) and multi-target 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 sister paper, which proves that the full Bayes RFS filter naturally incorporates a Poisson… (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)

Random finite sets (RFSs) has been a fruitful area of research in recent years, yielding new approximate filters such as the probability hypothesis density (PHD), cardinalised PHD (CPHD), and multiple target multi-Bernoulli (MeMBer). These new methods have largely been based on approximations that side-step the need for measurement-to-track association.… (More)

—Recent derivations have shown that the full Bayes random finite set (RFS) filter is comprised of 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. A highly desirable approximation would be to find the multi-Bernoulli… (More)