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This work addresses the problem of tracking multiple moving targets by recursively estimating the joint multitarget probability density (JMPD). Estimation of the JMPD is done in a Bayesian framework and provides a method for tracking multiple targets which allows nonlinear target motion and measurement to state coupling as well as non-Gaussian target state(More)
This paper considers the problem of simultaneously detecting and tracking multiple targets. The problem can be formulated in a Bayesian framework and solved, in principle, by computation of the joint multitarget probability density (JMPD). In practice, exact computation of the JMPD is impossible, and the predominant challenge is to arrive at a(More)
An approach that is common in the machine learning literature, known as active sensing, is applied to provide a method for managing agile sensors in a dynamic environment. We adopt an active sensing approach to scheduling sensors for multiple target tracking applications that combines particle filtering, predictive density estimation, and relative entropy(More)
This paper presents a sensor management scheme based on maximizing the expected Rényi Information Divergence at each sample , applied to the problem of tracking multiple targets. The underlying tracking methodology is a multiple target tracking scheme based on re-cursive estimation of a Joint Multitarget Probability Density (JMPD), which is implemented(More)
This paper considers the problem of sensor scheduling for the purposes of detection and tracking of " smart " targets. Smart targets are targets that can detect when they are under surveillance and react in a manner that makes future surveillance more difficult. We take a reinforcement learning approach to adaptively schedule a multi-modality sensor so as(More)
— This paper presents a particle filter approach to recursively estimating the joint multitarget probability density (JMPD) for the purposes of simultaneous multitarget detection and tracking. The JMPD is a conditional probability density that characterizes uncertainty in both target state and target number given the measurements. Estimation of the JMPD(More)
A method for managing agile sensors to optimize detection and classiication based on discrimination gain is presented. Expected discrimination gain is used to determine threshold settings and search order for a collection of discrete detection cells. This is applied in a low signal-to-noise environment where target-containing cells must be sampled many(More)
This paper develops two efficient methods of non-myopic (long-term) sensor management and investigates the benefit in the setting of multitarget tracking. The underlying tracking methodology is based on recursive estimation of a Joint Multitarget Probability Density (JMPD), which is implemented using particle filtering methods. The myopic sensor management(More)