Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking

Fröhle, Markus; Granström, Karl; Wymeersch, Henk

doi:10.1109/access.2020.3008007

“…There is also an extension in [26] which fuses star-convex shapes by determining a new center and then a new radial function based on the input radial functions relative to the new center. In [27], estimated Random Finite Sets (RFS) from multiple sensors are combined using the Kullback-Leibler Divergence between them.…”

Section: B Related Workmentioning

confidence: 99%

Fusion of Elliptical Extended Object Estimates Parameterized With Orientation and Axes Lengths

Thormann

¹

,

Baum

²

2021

IEEE Trans. Aerosp. Electron. Syst.

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This article considers the fusion of target estimates stemming from multiple sensors, where the spatial extent of the targets is incorporated. The target estimates are represented as ellipses parameterized with center orientation and semi-axis lengths and width. Here, the fusion faces challenges such as ambiguous parameterization and an unclear meaning of the Euclidean distance between such estimates. We introduce a novel Bayesian framework for random ellipses based on the concept of a Minimum Mean Gaussian Wasserstein (MMGW) estimator. The MMGW estimate is optimal with respect to the Gaussian Wasserstein (GW) distance, which is a suitable distance metric for ellipses. We develop practical algorithms to approximate the MMGW estimate of the fusion result. The key idea is to approximate the GW distance with a modified version of the Square Root (SR) distance. By this means, optimal estimation and fusion can be performed based on the square root of the elliptic shape matrices. We analyze different implementations using, e.g., Monte Carlo methods, and evaluate them in simulated scenarios. An extensive comparison with state-of-the-art methods highlights the benefits of estimators tailored to the Gaussian Wasserstein distances.

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“…For computational efficiency, MBM may be approximated by a single MB distribution in a "best-fit-of-mixture" way [37], or simply by selecting a MB in the MBM that has the highest global hypothesis weight [39]. Then, the fusing of MB can be resorted to in the sense of AA fusion [9] or GA fusion [24]- [26], [39]. Obviously, MB is a special case of MBM when there is only one global hypothesis, namely |J| = 1.…”

Section: F Communication and Computation Considerationmentioning

confidence: 99%

“…Recently, the GA fusion has been exploited for PMB fusion in [39], [40], which fuses the PPP and MB separately and approximately by assuming all targets well spaced. PMBM fusion can be addressed similarly with regard to the PPP and MBM, respectively.…”

mentioning

confidence: 99%

Best Fit of Mixture For Distributed Poisson Multi-Bernoulli Mixture Filtering

Li¹,

Da²

2020

Preprint

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<div>The Poisson multi-Bernoulli mixture (PMBM) filter is extended for distributed implementation using a wireless sensor network. At the core of the proposed networking approach, the PMBM posterior is decomposed into two parts corresponding to the undetected and detected targets, respectively. Fusion is motivated to be performed with regard to the latter only which is represented by MBM based on a distributed flooding algorithm for internode communication, which iteratively shares the MBMs between neighbor sensors. Then, a suboptimal “best-fit-ofmixture” principle is followed at each local sensor to find a MBM that best fits the mixture of MBMs aggregated from distinct sensors, leading to an arithmetic average (AA) of these MBMs. We prove the exact closure of the MBM-AA fusion and discuss its sub-optimality and limitations. Simulation demonstrates the effectiveness and limitations of our approach. </div>

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“…There is also an extension in [38] which fuses star-convex shapes by determining a new center and then a new radial function based on the input radial functions relative to the new center. In [39], estimated Random Finite Sets (RFS) from multiple sensors are combined using the Kullback-Leibler Divergence between them.…”

Section: B Related Workmentioning

confidence: 99%

Fusion of Elliptical Extended Object Estimates Parameterized with Orientation and Axes Lengths

Thormann¹,

Baum²

2020

Preprint

0

View full text Add to dashboard Cite

This article considers the fusion of target estimates stemming from multiple sensors, where the spatial extent of the targets is incorporated. The target estimates are represented as ellipses parameterized with center orientation and semi-axis lengths and width. Here, the fusion faces challenges such as ambiguous parameterization and an unclear meaning of the Euclidean distance between such estimates. We introduce a novel Bayesian framework for random ellipses based on the concept of a Minimum Mean Gaussian Wasserstein (MMGW) estimator. The MMGW estimate is optimal with respect to the Gaussian Wasserstein (GW) distance, which is a suitable distance metric for ellipses. We develop practical algorithms to approximate the MMGW estimate of the fusion result. The key idea is to approximate the GW distance with a modified version of the Square Root (SR) distance. By this means, optimal estimation and fusion can be performed based on the square root of the elliptic shape matrices. We analyze different implementations using, e.g., Monte Carlo methods, and evaluate them in simulated scenarios. An extensive comparison with state-of-the-art methods highlights the benefits of estimators tailored to the Gaussian Wasserstein distances.

show abstract

Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking

Cited by 30 publications

References 40 publications

Fusion of Elliptical Extended Object Estimates Parameterized With Orientation and Axes Lengths

Fusion of Elliptical Extended Object Estimates Parameterized With Orientation and Axes Lengths

Best Fit of Mixture For Distributed Poisson Multi-Bernoulli Mixture Filtering

Fusion of Elliptical Extended Object Estimates Parameterized with Orientation and Axes Lengths

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