Robust Cooperative Guidance Against Maneuvering Target Based on Distributed State Estimation

Zhang, Zheng; Yu, Juebang; Dong, Xiwang; Ren, Zhang

doi:10.1007/978-981-19-6613-2_326

Cited by 1 publication

(1 citation statement)

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“…In real-world scenarios, target tracking is challenged due to the maneuverability and noise characteristics of the target [1][2][3] , leading to model misalignment and outliers, which affect tracking accuracy and stability. Since single-model filtering methods cannot satisfy the accurate tracking of maneuvering targets, the researchers proposed an Interactive Multi-Model (IMM) algorithms [4][5][6][7] , which define the target state as a fusion of multiple motion models to estimate complex target motion. For example, Gao et al proposed an IMM extended Kalman filter time bias alignment method for maneuvering targets [8] , which improved the tracking results.…”

Section: Introductionmentioning

confidence: 99%

Research on a robust sensor maneuvering target tracking algorithm

Xu,

Wang,

Zhao

et al. 2024

Fourth International Conference on Digital Signal and Computer Communications (DSCC 2024)

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In multi-sensor tracking, tracking targets stably and accurately is one of the primary tasks. Aiming at the problems of outlier interference during measurement and unstable tracking filtering in complex scenarios, this paper proposes a robust maneuvering target tracking algorithm. The algorithm combines the probabilistic data association algorithm with the interactive multi-model algorithm, and utilizes the extended Kalman filter for the filtering operation. The accuracy of the algorithm's state estimation during target maneuvering is verified through simulation experiments, which proves that the algorithm can meet the needs of target tracking in complex scenes.

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Section: Introductionmentioning

confidence: 99%