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Multi-Sensor PHD: Construction and Implementation by Space Partitioning
Abstract : The Probability Hypothesis Density (PHD) is a well-known method for single-sensor multi-target tracking problems in a Bayesian framework, but the extension to the multi-sensor case seems to remain a challenge. In this paper, an extension of Mahler's work to the multi-sensor case provides an expression of the true PHD multi-sensor data update equation. Then, based on the configuration of the sensors' fields of view (FOVs), a joint partitioning of both the sensors and the state space provides an equivalent yet more practical expression of the data update equation, allowing a more effective implementation in specific FOV configurations.
Cited literature [9 references]
https://hal.inria.fr/hal-00639724
Contributor : Philippe Vanheeghe <>
Submitted on : Wednesday, November 9, 2011 - 6:25:59 PM
Last modification on : Tuesday, November 24, 2020 - 2:18:24 PM
Long-term archiving on: : Friday, February 10, 2012 - 2:40:12 AM
Contributor : Philippe Vanheeghe <>
Submitted on : Wednesday, November 9, 2011 - 6:25:59 PM
Last modification on : Tuesday, November 24, 2020 - 2:18:24 PM
Long-term archiving on: : Friday, February 10, 2012 - 2:40:12 AM
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ICASSP2011.pdf
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