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摘要:
目标跟踪过程中的模型误差会使得平方根容积卡尔曼滤波（SRCKF）性能下降，滤波精度降低；自适应滤波中的修正卡尔曼滤波（AKF）算法可以有效解决这一问题，但是难以应用到非线性滤波中。为了克服模型误差带来的不利影响，同时，进一步提高修正思想的应用范围，在SRCKF的基础上，基于最小化新息协方差准则推导了修正系数的向量形式，提出修正SRCKF（ASRCKF）算法。所提算法通过利用后期的测量数据，增加对测量值的信任度，从而达到对目标模型误差进行补偿的目的。仿真结果表明：与SRCKF和强跟踪SRCKF算法相比，所提ASRCKF算法能有效抑制模型误差，有着更优的滤波性能。
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关键词:
- 修正卡尔曼滤波 /
- 运动模型误差 /
- 平方根容积卡尔曼滤波 /
- 新息协方差 /
- 修正系数
Abstract:The model errors in the target tracking process will lead to the degraded performance and decreased filtering accuracy of the square-root cubature Kalman filter (SRCKF). Amended Kalman filter (AKF) can solve this problem effectively, but it is difficult to be applied to nonlinear filtering. To overcome the negative impact of the model errors and to further improve the application scope of the amendment thought, the vector form of the amendment coefficient is derived by minimizing innovation covariance on the basis of the SRCKF. Then, the amended SRCKF (ASRCKF) algorithm is proposed. By using posterior measurements, the ASRCKF algorithm can increase confidence level to measurement, so that the target model error can be compensated. The simulation results indicate the ASRCKF can suppress the model errors effectively with better filtering performance, compared with SRCKF and STF-SRCKF algorithms.
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表 1 场景1误差均值对比
Table 1. Comparison of mean error for each algorithm in case 1
算法 位置误差
均值/m速度误差
均值/(m·s−1)加速度误差
均值/(m·s−2)SRCKF 7.970 7 6.984 2 2.808 3 ASRCKF 4.752 0 4.119 1 2.320 1 STF-SRCKF 7.520 7 14.643 5 10.654 1 
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表 2 场景2误差均值对比
Table 2. Comparison of mean error for each algorithm in case 2
算法 位置误差
均值/m速度误差
均值/(m·s−1)加速度误差
均值/(m·s−2)SRCKF 12.5175 13.1114 5.6948 ASRCKF 6.4368 6.7186 4.2920 STF-SRCKF 8.2924 16.1961 12.1711
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