Data Fusion With Inverse Covariance Intersection for Prior Covariance Estimation of the Particle Flow Filter
- Authors
- Kang, Chang Ho; Kim, Sun Young; Song, Jin Woo
- Issue Date
- 2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Estimation; Covariance matrices; Mathematical model; Filtering algorithms; Trajectory; Convergence; Performance analysis; Particle flow filter; inverse covariance intersection; multiple target tracking; prior covariance estimation
- Citation
- IEEE ACCESS, v.8, pp.221203 - 221213
- Journal Title
- IEEE ACCESS
- Volume
- 8
- Start Page
- 221203
- End Page
- 221213
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/18561
- DOI
- 10.1109/ACCESS.2020.3041928
- ISSN
- 2169-3536
- Abstract
- The prior covariance estimation method based on inverse covariance intersection (ICI) is proposed to apply the particle flow filter. The proposed method has better estimate performance and guarantees consistent estimation results compared with previous works. ICI is the recently developed method of ellipsoidal intersection and is used to get the combined estimate of prior covariance. This method integrates the sample covariance estimate, which is unbiased but usually with high variance, together with a more structured but typically a biased target covariance through fusion gains. For verifying the performance of the proposed algorithm, analysis and simulations are performed. Through the simulations, the results are given to illustrate the consistency and accuracy of the proposed algorithm's estimation and target tracking performance.
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Collections - School of Mechanical System Engineering > 1. Journal Articles
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