Multi-sensor PHD by space partitioning: Computation of a true reference density within the PHD framework

Delande, Emmanuel; Duflos, Emmanuel; Vanheeghe, Philippe; Heurguier, Dominique

doi:10.1109/ssp.2011.5967695

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…The exact filter update equations of the general multisensor PHD filter are not computationally tractable except for a few simple cases. In [124,125] Delande et al simplify the filter update equations when the fields of view of different sensors have limited overlap. This reduces the computational complexity to some extent and a particle filter based implementation is discussed by the authors.…”

Section: Random Finite Set Based Filters: Multiple Sensorsmentioning

confidence: 99%

Multitarget, Multisensor Tracking

Ehrman¹

2012

Principles of Modern Radar: Advanced Techniques

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Section: Random Finite Set Based Filters: Multiple Sensorsmentioning

confidence: 99%

Multitarget, Multisensor Tracking

Ehrman¹

2012

Principles of Modern Radar: Advanced Techniques

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“…Because of their combinatorial nature the exact filter update equations of the general multisensor PHD filter are not computationally tractable in most cases. Simplified update equations with reduced computational complexity have been derived, 11,12 when the fields of view of different sensors have restricted overlap.…”

Section: Introductionmentioning

confidence: 99%

A distributed general multi-sensor cardinalized probability hypothesis density (CPHD) filter for sensor networks

et al. 2015

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We develop a distributed cardinalized probability hypothesis density (CPHD) filter that can be deployed in a sensor network to process the measurements of multiple sensors that make conditionally independent measurements. In contrast to the majority of the related work, which involves performing local filter updates and then exchanging data to fuse the local intensity functions and cardinality distributions, we strive to approximate the update step that a centralized multi-sensor CPHD filter would perform.

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Improved Iterated-corrector PHD with Gaussian mixture implementation

Liu

Fan

2015

Signal Processing

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Multi-sensor PHD by space partitioning: Computation of a true reference density within the PHD framework

Cited by 5 publications

References 4 publications

Multitarget, Multisensor Tracking

Multitarget, Multisensor Tracking

A distributed general multi-sensor cardinalized probability hypothesis density (CPHD) filter for sensor networks

Improved Iterated-corrector PHD with Gaussian mixture implementation

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