带遗忘因子的自适应迭代容积卡尔曼滤波算法
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摘要
自适应迭代滤波算法作为典型的滤波改进算法,有效提高了滤波精度,但旧数据影响过大,导致滤波发散;遗忘因子滤波算法虽然引进遗忘因子减少了旧数据的影响,但是其滤波算法本身的精度不高,难以处理高度非线性问题。基于此,本文借鉴遗忘因子的滤波算法和自适应迭代无迹卡尔曼滤波算法的思想,把遗忘因子与自适应迭代容积卡尔曼滤波相结合,这样既可以发挥遗忘因子的作用,减小历史数据对滤波结果的影响,又可以提高滤波算法本身精度和处理非线性问题的能力。仿真实验表明,该算法可以有效减小误差且提高滤波精度。
Adaptive iteration filter as the typical improvement filter algorithm effectively rises to the filter accuracy, but the old data once influences greatly lead to the filter's exhale. Although the filtering algorithm with forgetting factor reduces the influence of old data. But in fact, the filter's exhale is not high, and it is difficult to deal with the high degree of non-linear problem. So this passage learn from Kalman filter with forgetting factor and AIUKF a thought of calculate way, this can not only play the role of forgetting factor, reduce the impact of historical data on the filtering results, but also can improve the accuracy of the filter algorithm and the ability to deal with nonlinear problems. The simulation experiment results indicate that the algorithm effectively reduces the error and improves the filtering accuracy.
Adaptive iteration filter as the typical improvement filter algorithm effectively rises to the filter accuracy, but the old data once influences greatly lead to the filter's exhale. Although the filtering algorithm with forgetting factor reduces the influence of old data. But in fact, the filter's exhale is not high, and it is difficult to deal with the high degree of non-linear problem. So this passage learn from Kalman filter with forgetting factor and AIUKF a thought of calculate way, this can not only play the role of forgetting factor, reduce the impact of historical data on the filtering results, but also can improve the accuracy of the filter algorithm and the ability to deal with nonlinear problems. The simulation experiment results indicate that the algorithm effectively reduces the error and improves the filtering accuracy.
引文
[1] 虞波,周瞿和,赵庆涛,等.一种带遗忘因子的自适应卡尔曼滤波器及其在移动机器人中的应用[J].电气与自动化,2016,45(4):149-152.
[2] 胡家元,沙征东.遗忘因子在卡尔曼滤波自适应波束形成的应用[J].电波科学学报,1999,14(2):149-152.
[3] 王坚,刘超,高井祥,等.基于抗差EKF的GNSS/INS紧组合算法研究[J]. 武汉大学学报, 2011,36(5):169-200.
[4] 李建峰,张慧星,闫美辰.迭代扩展卡尔曼滤波在相对姿态估计中的应用[J].导弹与航天运载技术,2012,323(6):48-52.
[5] Julier S J, Uhlmann J K. Unscented filtering and nonlinear estimation[J]. Proceeding of the IEEE, 2004, 92(3): 401-422.
[6] 王路,刘忠.基于自适应迭代UKF的纯目标定位算法[J].中南大学学报,2016,47(2):504-507.
[7] Arasaratnam I, Haykin S. Cubayure Kalm-an filters[J].IEEE Transactions on Autom-atic Control, 2009, 54(6): 1254-1269.
[8] 穆静,蔡远利.迭代容积卡尔曼滤波算法及其应用[J].系统工程与电子技术,2011,33(7):1455-1590.
[9] Richard O N. Accuracy of angle estimation with monopulse processing using two beams[J].IEEE Trans. on Aerospace and Electronic System,2001,37(4):1419-1423.
[10] 吴本卿,钱良,林成浴. 一种可变遗忘因子的卡尔曼滤波信道跟踪[J].信息技术,2012(6):87-91.
[11] 章飞,周杏鹏,陈小惠.基于衰减记忆滤波的平方根UKF被动目标跟踪算法[J].测控技术,2010(4):22-26.
[12] 马瑞平,巍东,张明廉.一种改进的自适应卡尔曼滤波在组合导航中的应用[J].中国惯性技术学报,2008(6):37-40.
[13] He X, Le Y, Xiao W. MEMS IMU and two-antenna GPS integration navigation system using interval adaptive Kalman filter [J].IEEE Aerospace and Electronic System Magazine, 2013, 28(10): 22-28.
[14] Xia Q, Rao M, Ying Y, et al. Adaptive fading Kalman filter with an application[J].Automation,1994,30(8):1333-1338
[2] 胡家元,沙征东.遗忘因子在卡尔曼滤波自适应波束形成的应用[J].电波科学学报,1999,14(2):149-152.
[3] 王坚,刘超,高井祥,等.基于抗差EKF的GNSS/INS紧组合算法研究[J]. 武汉大学学报, 2011,36(5):169-200.
[4] 李建峰,张慧星,闫美辰.迭代扩展卡尔曼滤波在相对姿态估计中的应用[J].导弹与航天运载技术,2012,323(6):48-52.
[5] Julier S J, Uhlmann J K. Unscented filtering and nonlinear estimation[J]. Proceeding of the IEEE, 2004, 92(3): 401-422.
[6] 王路,刘忠.基于自适应迭代UKF的纯目标定位算法[J].中南大学学报,2016,47(2):504-507.
[7] Arasaratnam I, Haykin S. Cubayure Kalm-an filters[J].IEEE Transactions on Autom-atic Control, 2009, 54(6): 1254-1269.
[8] 穆静,蔡远利.迭代容积卡尔曼滤波算法及其应用[J].系统工程与电子技术,2011,33(7):1455-1590.
[9] Richard O N. Accuracy of angle estimation with monopulse processing using two beams[J].IEEE Trans. on Aerospace and Electronic System,2001,37(4):1419-1423.
[10] 吴本卿,钱良,林成浴. 一种可变遗忘因子的卡尔曼滤波信道跟踪[J].信息技术,2012(6):87-91.
[11] 章飞,周杏鹏,陈小惠.基于衰减记忆滤波的平方根UKF被动目标跟踪算法[J].测控技术,2010(4):22-26.
[12] 马瑞平,巍东,张明廉.一种改进的自适应卡尔曼滤波在组合导航中的应用[J].中国惯性技术学报,2008(6):37-40.
[13] He X, Le Y, Xiao W. MEMS IMU and two-antenna GPS integration navigation system using interval adaptive Kalman filter [J].IEEE Aerospace and Electronic System Magazine, 2013, 28(10): 22-28.
[14] Xia Q, Rao M, Ying Y, et al. Adaptive fading Kalman filter with an application[J].Automation,1994,30(8):1333-1338