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15 July 1999 Converted measurement Kalman filtering algorithm for radar target tracking
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Abstract
In this article, the filtering-algorithm for target tracking is studied in nonlinear systems. First, the converted measurement Kalman filtering algorithm (CMKFA) is inferred in 3D space. The statistics of the errors in converted measurements conditioned on target's true position is obtained, and the statistics of the errors in converted measurements conditioned on measurement is given to. Then it is proved that the CMKF is a linear unbiased least mean square estimator of debiased converted measurements under certain conditions. Finally, from simulations, it is proved that CMKF has a higher target tracking accuracy than EKF.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chunling Yang, Quan-Zhan Zheng, and Guo-Sui Liu "Converted measurement Kalman filtering algorithm for radar target tracking", Proc. SPIE 3692, Acquisition, Tracking, and Pointing XIII, (15 July 1999); https://doi.org/10.1117/12.352884
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