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The leader node particle filter is a partially distributed approach to tracking in a sensor network, in which the node performing the particle filter computations (the leader node) changes over time. The primary advantage is that the position of the leader node can follow the target, improving the efficiency of data collection. When the leader node changes, the particle filter must be communicated...
In recent years, the particle filter has become commonly accepted as the preferred tool for single target tracking in highly non-linear and non-Gaussian environments. This paper investigates the issues that arise when particle filters are integrated into a hierarchical data fusion system, in which the sensor-level tracking is performed using particle filters, but central-level track fusion is performed...
Different information theoretic sensor management approaches are compared in a Bayesian target-tracking problem. Specifically, the performance using the expected Renyi divergence with different parameter values is compared theoretically and experimentally. Included is the special case in which the expected Renyi divergence is equal to the expected Kullback-Leibler divergence, which is also equivalent...
Several nonlinear filtering techniques are investigated for nonlinear tracking problems. Experimental results show that for a weakly nonlinear tracking problem, the extended Kalman filter and the unscented Kalman filter are good choices, while a particle filter should be used for problems with strong nonlinearity. To quantitatively determine the nonlinearity of a nonlinear tracking problem, we propose...
Within the area of target tracking particle filters are the subject of consistent research and continuous improvement. The purpose of this paper is to present a novel method of fusing the information from multiple particle filters tracking in a multisensor multitarget scenario. Data considered for fusion is under the form of labeled particle clouds, obtained in the simulation from two probability...
In this paper, a two-tier hierarchical architecture is proposed to address the multi-target tracking problem using a particle probability hypothesis density filtering algorithm. According to a proposed cluster scheduling method, the base station selects active clusters at each time step and determines their order for the sequential data fusion in the second level of hierarchy. Within each active cluster,...
This paper addresses the efficient state estimation for mixed linear/nonlinear dynamic systems with noisy measurements. Based on a novel density representation - sliced Gaussian mixture density - the decomposition into a (conditionally) linear and nonlinear estimation problem is derived. The systematic approximation procedure minimizing a certain distance measure allows the derivation of (close to)...
We propose a particle filter based solution which uses auxiliary fixed point smoothers to the problem of out of sequence measurements. Three different cases, namely, auxiliary extended Kalman smoother, auxiliary unscented Kalman smoother and auxiliary particle smoother are considered for the auxiliary fixed point smoother. The proposed filter which can effectively combine out of sequence measurements...
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