| Citation: | LIU Chang, YANG Suochang, WANG Liandong, et al. Target tracking algorithm based on adaptive strong tracking CQKF[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5): 982-990. doi: 10.13700/j.bh.1001-5965.2017.0312(in Chinese)
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As cubature quadrature Kalman filter (CQKF) is easily influenced by uncertainty of state-space model and need to know exactly noise statistics, a new type of adaptive CQKF algorithm with strong tracking behavior is proposed. Based on the theory of strong tracking filter, the new algorithm introduces fading factor to adapt to covariance matrix and reinforces residual sequence to be orthogonal, which effectively suppresses the filtering divergence caused by the model uncertainty. In the process of filtering, processing noise and measurement noise should be estimated online by the Sage-Husa noise statistics estimator, which will improve the filter precision under the circumstance of unknown time-varying noise. Simulations of target tracking demonstrate the efficiency and robustness of the algorithm.
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