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摘要:
针对容积积分卡尔曼滤波（CQKF）受模型不确定性影响较大及需要精确已知噪声统计特性的缺点，提出了一种自适应强跟踪CQKF算法。该算法根据强跟踪滤波原理，引入渐消因子调整状态预测协方差矩阵，强迫残差序列正交，有效抑制了模型不确定性引起的滤波发散。在滤波过程中，利用Sage-Husa时变噪声统计估值器对过程噪声及量测噪声实时估计，提高了算法在未知时变噪声环境下的滤波精度。目标跟踪仿真实验验证了算法的有效性和鲁棒性。
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关键词:
- 目标跟踪 /
- 容积积分卡尔曼滤波(CQKF) /
- 强跟踪滤波 /
- 噪声统计估值器 /
- 自适应滤波
Abstract: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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表 1 CQKF与AST-CQKF算法蒙特卡罗仿真计算时间
Table 1. Calculation time of Monte Carlo simulation in CQKF and AST-CQKF algorithm
仿真步数 仿真次数 仿真计算时间/s CQKF AST-CQKF 100 200 3.350 8 6.933 4 100 100 1.742 3 3.417 1 100 1 0.034 4 0.082 9 50 200 1.736 6 3.380 9 50 100 0.903 4 1.699 1 50 1 0.023 7 0.062 2 
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