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Consensus CPHD Filter for Distributed Multitarget Tracking
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AI
DOI：10.1109/JSTSP.2013.2250911.png)
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Abstract
En 中文
The paper addresses distributed multitarget tracking over a network of heterogeneous and geographically dispersed nodes with sensing, communication and processing capabilities. The contribution has been to develop a novel consensus Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filter that provides a fully distributed, scalable and computationally efficient solution to the problem. The effectiveness of the proposed approach is demonstrated via simulation experiments on realistic scenarios.
Keywords:
Cardinalized PHD filter
consensus
multitarget tracking
sensor networks
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13.7
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1.9K
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1.1W
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