Multi-sensor PHD: Construction and implementation by space partitioning

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

doi:10.1109/icassp.2011.5947137

Cited by 25 publications

(22 citation statements)

References 5 publications

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“…Notice that Equations 13,14,19,20, and 21 are precisely the Kalman filtering equations. Also notice that there are no measurement-to-track assignments between the prediction and update steps.…”

Section: Linear Gaussian Modelsmentioning

confidence: 99%

MIMO radar target tracking using the probability hypothesis density filter

Glass

Lanterman

2012

2012 IEEE Aerospace Conference

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Target tracking in a widely spread multiple input multiple output (MIMO) radar system requires joint processing of several measurements from multiple sensors. The probability hypothesis density (PHD) filter provides a promising framework to process these measurements, since it does not require any measurement-to-track associations. Furthermore, the PHD filter naturally handles a multi-target environment because of the lack of explicit data association. We implement a PHD filter in the GTRI/ONR MIMO Benchmark, and compare results against the Benchmark's default solution. We assume a linear Gaussian target model so that the posterior target intensity at any time step is a Gaussian mixture (GM). Under this assumption, the PHD filter has closed-form recursions and target state extraction is simplified. This paper focuses on our implementation of the GM-PHD filter in the MIMO Benchmark, along with practical issues such as track labeling and applying the filter for the case of multiple sensors.

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Section: Linear Gaussian Modelsmentioning

confidence: 99%

MIMO radar target tracking using the probability hypothesis density filter

Glass

Lanterman

2012

2012 IEEE Aerospace Conference

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“…As illustrated on a simple scenario in [3], the "brute force" (5) and the partition method (9) both yield the true data updated density since (5) and (9) are equivalent, yet the partition method spares itself the computation of vanishing cross-terms and is therefore significantly lighter.…”

Section: Define the Equivalence Relation "Cross" (↔) Between Sensors Asmentioning

confidence: 99%

“…The authors proposed in [3] a multi-sensor data update equation constructed as a set differentiation of the cross-term β[g [1] , ..., g [N ] , h]:…”

Section: Data Update Equationmentioning

confidence: 99%

“…d (x) : a target is in state x, generated measurements z [1] and z [2] and was not detected by sensor 3; ] ,g [2] ,g [3] ,h]…”

Section: Cross-termsmentioning

confidence: 99%

“…In a previous paper ( [3]), the authors extended Mahler's work and provided a true multi-sensor data update equation in a derivative form. Here, an equivalent expression in a combinational form is given; it allows an intuitive intepretation of the data update equation and made easier the comparison with Mahler's own extension to the two-sensor case ( [4]), which turned to be conclusive.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Delande

Duflos

Vanheeghe

et al. 2011

2011 IEEE Statistical Signal Processing Workshop (SSP)

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Multi-target joint detection, tracking and classification based on random finite set for aerospace applications

Jing

Leung

2018

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Multi-target detection, tracking and classification are important problems in aerospace applications, such as reconnaissance, airborne and spaceborne sensing. These problems are correlated but are difficult to be solved simultaneously, especially for systems with multiple sensors. This paper summarized the existing work for multi-target joint detection, tracking and classification based on labeled random finite set. Furthermore, a new algorithm is proposed for multi-sensor multi-target joint detection, tracking and classification problem. A conditional multi-sensor multi-target state estimator is derived, and the optimal solution is then obtained based on the minimum Bayes risk criterion. The numerical simulations are performed, and the results are shown to be more accurate than that of the approximate solutions based on the unlabeled random finite set. It is observed that the labeled random finite set theory provides a good foundation for a joint solution for multi-target detection, tracking and classification.

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Multi-sensor PHD: Construction and implementation by space partitioning

Cited by 25 publications

References 5 publications

MIMO radar target tracking using the probability hypothesis density filter

MIMO radar target tracking using the probability hypothesis density filter

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

Multi-target joint detection, tracking and classification based on random finite set for aerospace applications

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