Explicit Recursive Track-to-Track Fusion Rules for Nonlinear Multi-Sensor Systems

Zarei, Mahboubeh; Chhabra, Robin

doi:10.1109/lcsys.2021.3068703

Cited by 8 publications

(9 citation statements)

References 23 publications

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“…Furthermore, the methods of [18] and [19] may encounter computational difficulties when dealing with large systems, due to requirement of inverting large matrices and propagating the cross-covariance matrix between all tracks. To alleviate these drawbacks, [7] has proposed an explicit recursive fusion technique for a UKF-based network of two sensors that propagates the crosscovariance using the SLR, and employs it in the optimal fusion rule in [20].…”

Section: A Track-level Networked Estimationmentioning

confidence: 99%

“…The proposed fusion methodology is developed consistent with UKF track estimates, by employing the SLR technique to linearize the system and propagate the cross-covariance matrix. Accordingly, the optimal fusion rule of [7] is implemented to sequentially fuse multiple tracks. It is proved that the resulting track-to-track fusion methodology is commutative, and when dealing with sequential fusions only the information of the first and last tracks are required to propagate the correlations, at every step.…”

Section: Statement Of Contributionsmentioning

confidence: 99%

“…Explicit and implicit fusion approaches are employed to either propagate the cross-covariance or bound the optimal global estimate, respectively. Here, we briefly overview the UKF technique to set some notations and facilitate the explanation of our previously developed fusion rule for networks of two sensors [7] that is formally extended in this paper to include multi-sensor systems. We also briefly describe two prominent implicit methods for multi-sensor networks known as sequential CI and batch CI to which we rigorously compare our proposed fusion rule.…”

Section: Preliminariesmentioning

confidence: 99%

“…The proposed method in [7], which we denote by SLR/UKF, aims at finding the fused track with minimum covariance matrix for the nonlinear discrete-time system (1)-( 8), using a UKF-based network of two sensor agents. The fused estimate xF 1:2 (k) and its covariance matrix…”

Section: B Slr/ukf Track-to-track Fusion For Two Sensorsmentioning

confidence: 99%

“…To robustly estimate certain states of a system, networked estimation techniques have been proven effective. These techniques fall into two categories of sensor-level [5], [6] and track-level [7] estimation architectures. In this paper we show that a WMR exhibits a track-level networked estimation architecture, where wheels serve as agents of the network and track-to-track fusion algorithms can be used to perform estimation fusion.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Sequential Track Fusion in UKF-based Multi-Sensor Networks: A Case of Slip Estimation in Wheeled Mobile Robots

Zarei¹,

Chhabra²,

Mottaghi³

2023

Preprint

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Accurate wheel slip estimation facilitates Wheeled Mobile Robots (WMRs) with improved localization and traversability monitoring which are crucial to their autonomy in challenging environments. Although distributed track-level fusion offers better computational efficiency, often sensor-level fusion is adopted in slip estimators. Extending upon our earlier work, we develop a novel explicit track-to-track fusion algorithm for UKF-based multi-sensor networks that has immediate application to slip ratio estimation in WMRs. The algorithm demonstrates better consistency compared to the existing fusion techniques, since it implements an optimal fusion rule to sequentially combine local tracks. It also saves computational power due to the recursive propagation of cross-covariance matrices based on the statistical linearization technique, instead of performing online optimizations. We prove that this recursion only requires the information of the first and last tracks in a sequence if all local tracks are unbiased. We rigorously study various key properties of the developed fusion algorithm and show its superior level of confidence when compared to the two prominent fusion methods of sequential and batch covariance intersection. The slip ratio estimation in a six-wheel WMR is considered as a case study, where the steerable wheel sets act as a network of sensors. The proposed slip estimator works based on the rigid body kinematics and readings of purely proprioceptive sensors, i.e., an inertial measurement unit and encoders. The slip estimator's performance is evaluated in a high-fidelity software-in-the-loop simulation environment connecting MATLAB and CM Lab's Vortex Studio software. In a comparison study that considers four rival strategies, we show the superiority of the proposed fusion method offering a balance between consistency, accuracy, and speed in real-time slip estimations.

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