Minimum Covariance Bounds for the Fusion under Unknown Correlations

Reinhardt, Marc; Noack, Benjamin; Arambel, P.O.; Hanebeck, Uwe D.

doi:10.1109/lsp.2015.2390417

Cited by 94 publications

(48 citation statements)

References 20 publications

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“…Hence, the optimal distributed fusion with nonlinear model becomes one of the most important direction of signal processing. To our best knowledge, the most effective algorithms for fusion are based on covariance intersection (CI) [10,11]. These CI-based algorithms can be easily combined with nonlinear filers to form the state-of-the-art solutions to nonlinear problems, such as UKF-SCI [12] and DPF-ICI [13].…”

Section: Introductionmentioning

confidence: 99%

A novel distributed fusion algorithm for multi-sensor nonlinear tracking

Liu

Wang

2016

EURASIP J. Adv. Signal Process.

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The covariance intersection (CI), especially with feedback structure, can be easily combined with nonlinear filters to solve the distributed fusion problem of multi-sensor nonlinear tracking. However, this paper proves that the CI algorithm is sub-optimal, thus degrading the fusion accuracy. To avoid such an issue, a novel distributed fusion algorithm, namely Monte Carlo Bayesian (MCB) algorithm, is proposed. First, it builds a distributed fusion architecture based on the Bayesian tracking framework. Then, the Monte Carlo sampling is incorporated into this architecture to form a feasible solution to nonlinear tracking. Finally, the simulation results verify that our MCB algorithm advances the state-of-the-art distributed fusion of nonlinear tracking.

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Section: Introductionmentioning

confidence: 99%

A novel distributed fusion algorithm for multi-sensor nonlinear tracking

Liu

Wang

2016

EURASIP J. Adv. Signal Process.

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“…It has been shown that, without any assumptions about the correlations, CI provides the optimal fused estimate that guarantees that P f is consistent [15].…”

Section: Track-to-track Fusionmentioning

confidence: 99%

Decentralized Tracking in Sensor Networks with Varying Coverage

Nygårds

Deleskog

Hendeby

2018

2018 21st International Conference on Information Fusion (FUSION)

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The number of sensors used in tracking scenarios is constantly increasing, this puts high demands on the tracking methods to handle these data streams. Central processing (ideally optimal) puts high demands on the central node, is sensitive to inaccurate sensor parameters, and suffers from the single point of failure problem. Decentralizing the tracking can improve this, but may give considerable performance loss. The newly presented inverse covariance intersection method, proven to be consistent, even under unknown track cross-correlations, is benchmarked against alternatives. Different track-to-track methods, including smoothed association over a window, are compared. A scenario with objects tracked in multiple cameras, not necessarily optimized for tracking, are used to give realism to the evaluations.

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“…The Covariance Intersection (CI) algorithm is the most popular method in this first group of algorithms [36]. It is proven to deliver consistent estimates, but involves an optimization step that might be computationally demanding and leads to conservative fused estimates [38]. Among the faster, but for some situations, worse performing, alternatives are, e.g., the fast CI algorithm [39] and the improved fast CI [40] algorithm.…”

Section: Related Workmentioning

confidence: 99%

Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving

Elfring

Appeldoorn

Dries

et al. 2016

Sensors

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The number of perception sensors on automated vehicles increases due to the increasing number of advanced driver assistance system functions and their increasing complexity. Furthermore, fail-safe systems require redundancy, thereby increasing the number of sensors even further. A one-size-fits-all multisensor data fusion architecture is not realistic due to the enormous diversity in vehicles, sensors and applications. As an alternative, this work presents a methodology that can be used to effectively come up with an implementation to build a consistent model of a vehicle’s surroundings. The methodology is accompanied by a software architecture. This combination minimizes the effort required to update the multisensor data fusion system whenever sensors or applications are added or replaced. A series of real-world experiments involving different sensors and algorithms demonstrates the methodology and the software architecture.

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Minimum Covariance Bounds for the Fusion under Unknown Correlations

Cited by 94 publications

References 20 publications

A novel distributed fusion algorithm for multi-sensor nonlinear tracking

A novel distributed fusion algorithm for multi-sensor nonlinear tracking

Decentralized Tracking in Sensor Networks with Varying Coverage

Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving

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