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As evidenced by the works of many recent authors, the particle filtering (PF) framework has revolutionized probabilistic visual target tracking. In this paper, we present a new particle filter tracking algorithm that incorporates the switching multiple dynamic model and the technique of state partition with parallel filter banks. Traditionally, most tracking algorithms assume the target operates according...
State estimation is a central problem in many engineering applications. Traditionally, Kalman filters are widely used for linear systems with additive Gaussian noise. But the many dynamic systems are much more complex, usually involve nonlinear and non-Gaussian elements. Bearing the nature of sequential importance sampling (SIS) and Monte Carlo approach, particle filtering (PF) has emerged as a superior...
In this paper, we present a novel target tracking method applied to a distributed acoustic sensor network. The underlying tracking methodology is described as a multiple sensor tracking/fusion technique based on particle filtering (PF). As discussed in the most recent literature, particle filtering is defined as an emerging Monte-Carlo non-linear state estimation method. More specifically, in our...
The sequential importance sampling (SIS) method, also known as particle filtering (PF), has emerged as a promising filtering technique for nonlinear and non-Gaussian systems in which state estimation is the ultimate objective. In the framework of PFs, the accuracy of this estimation depends on the choice of the proposal distribution, which is the problem that is addressed in this paper. Here, we propose...
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