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Simplified augmented cubature information filtering and multi-sensor fusion for additive noise systems.

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Title: Simplified augmented cubature information filtering and multi-sensor fusion for additive noise systems.
Authors: Li, Shoupeng1 (AUTHOR) lishoupeng403@outlook.com, Mu, Rongjun1 (AUTHOR) murjun@hit.edu.cn, Cui, Naigang1 (AUTHOR) cui_naigang@163.com
Source: Aerospace Science & Technology. Apr2022, Vol. 123, pN.PAG-N.PAG. 1p.
Subjects: Kalman filtering, Information filtering, Multisensor data fusion, Monte Carlo method, Filters & filtration, Noise
Abstract: For a highly nonlinear system with additive process and observation noises, the non-augmented sigma-point nonlinear filter induces a loss of odd-order moment information (skewness), thereby resulting in a degradation of state estimation accuracy. To address this problem, we present a novel filtering algorithm, namely augmented cubature information filter (ACIF). The adopted augmentation strategy can facilitate the estimator to capture and propagate higher odd-order moment information of random variables. In addition, the covariance resulting from linearization errors is compensated based on the proposed filtering framework. Further, the ACIF is extended to the decentralized multi-sensor system. The employed modified state augmentation strategy can eliminate the inconsistency of covariance estimation induced by the state-mean samples. And the propagation of cubature points is simplified to reduce the computational cost associated with state augmentation. To validate the proposed algorithm, a comparative study is performed via Monte Carlo simulation in the scenario of tracking a maneuvering target. The results show that the simplified ACIF can improve the filtering efficiency without dramatically increasing the computational burden. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:For a highly nonlinear system with additive process and observation noises, the non-augmented sigma-point nonlinear filter induces a loss of odd-order moment information (skewness), thereby resulting in a degradation of state estimation accuracy. To address this problem, we present a novel filtering algorithm, namely augmented cubature information filter (ACIF). The adopted augmentation strategy can facilitate the estimator to capture and propagate higher odd-order moment information of random variables. In addition, the covariance resulting from linearization errors is compensated based on the proposed filtering framework. Further, the ACIF is extended to the decentralized multi-sensor system. The employed modified state augmentation strategy can eliminate the inconsistency of covariance estimation induced by the state-mean samples. And the propagation of cubature points is simplified to reduce the computational cost associated with state augmentation. To validate the proposed algorithm, a comparative study is performed via Monte Carlo simulation in the scenario of tracking a maneuvering target. The results show that the simplified ACIF can improve the filtering efficiency without dramatically increasing the computational burden. [ABSTRACT FROM AUTHOR]
ISSN:12709638
DOI:10.1016/j.ast.2022.107445