自适应增量 Kalman 滤波方法
Adaptive incremental Kalman filter method
-
摘要: 提出自适应增量Kalman滤波(AIKF)的概念和定义,建立自适应增量Kalman滤波模型及其分析方法,给出主要的计算步骤.传统自适应Kalman滤波(AKF)方法能够对事先未知的系统噪声和量测噪声的统计量进行有效的估计.但是,传统自适应Kalman滤波方法也无法对由于环境因素(如深空探测)的影响、测量设备的不稳定性等原因产生的未知时变测量系统误差进行补偿和校正,从而产生较大的滤波误差,甚至导致发散.提出的自适应增量Kalman滤波方法不但能够对系统噪声和量测噪声的统计量进行估计,而且还能成功消除这种测量系统误差,有效地提高滤波精度.该方法计算简单,便于工程应用.
-
关键词:
- 自适应Kalman滤波 /
- 自适应增量滤波 /
- 系统误差 /
- 滤波精度 /
- 深空探测
Abstract: An adaptive incremental Kalman filter (AIKF) method was proposed, of which the concept, model, basic equations and key calculative steps were given. Classical adaptive Kalman filter(AKF)method can effectively estimate the prior knowledge on the statistical characteristics of state noise and measurement noise. Classical AKF method cannot compensate and correct the unknown time-varying system errors that due to environmental factors and the instability of measurement equipments in actual engineering (such as deep space exploration), which produced considerable filter errors and even led to diverge. The presented adaptive incremental Kalman filter method can estimate statistical characteristics of state noise and measurement noise, and also can successfully eliminate these measurement equation's system errors. The method can greatly improve the accuracy of incremental Kalman filter. The method is simple to calculate and easy to apply in engineering. -
[1] Grewal M S,Andrews A P.Kalman filtering,theory and practice using matlab[M].New York:John Wiley & Sons,2001. [2] Ding W,Wang J,Rizos C. Improving adaptive Kalman estimation in GPS/INS integration[J].Journal of Navigation,2007,60(3):517-529. [3] 邓自立.自校正滤波理论及其应用[M]. 哈尔滨:哈尔滨工业大学出版社,2003:161-193. [4] Jwo D J,Weng T P.An adaptive sensor fusion method with applications in integrated navigation[J].The Journal of Navigation,2008,61(4):705-721. [5] 付梦印,邓志红,闫莉萍.Kalman滤波理论及其在导航系统中的应用[M]. 北京:科学出版社, 2010: 108-120. [6] Sage A P,Husa G W.Adaptive filtering with unknown prior statistics //Proceedings of Joint American Control Conference.Washington,DC:American Automatic Council,1969:760-769. [7] LI Shuang,PENG Yuming.Radio beacons/IMU integrated navigation for Mars entry[J].Advances in Space Research,2011,47(7):1265-1279. [8] YANG Yuanxi, XU Tianhe. An adaptive Kalman filter based on sage windowing weights and variance components[J].The Journal of Navigation,2003,56(2):231-240. [9] 吴富梅,杨元喜.一种两步自适应抗差Kalman 滤波在GPS/ INS 组合导航中的应用[J]. 测绘学报,2010, 39(5): 522-527. WU Fumei,YANG Yuanxi.A new two-step adaptive robust Kalman filtering in GPS/ INS integrated navigation system[J].Acta Geodaetica et Cartographica Sinica,2010,39(5):522-527.(in Chinese) [10] GAO Shesheng,ZHONG Yongmin.Robust adaptive filtering method for SINS/SAR integrated navigation system[J].Aerospace Science and Technology,2011,15(6):425-430 [11] 傅惠民, 吴云章, 娄泰山. 欠观测条件下的增量Kalman滤波方法[J].机械强度,2012,34(1):43-47. FU Huimin, WU Yunzhang, LOU Taishan. Incremental Kalman filter method under poor observation condition[J].Journal of Mechanical Strength,2012,34(1):43-47.( in Chinese) [12] 傅惠民, 娄泰山, 吴云章. 欠观测条件下的扩展增量Kalman滤波方法[J]. 航空动力学报, 2012,27(4):777-781. FU Huimin,LOU Taishan,WU Yunzhang.Extended Incremental Kalman filter method under poor observation condition[J].Journal of Aerospace Power,2012,27(4):777-781.(in Chinese) [13] 傅惠民,吴琼. 线性独立增量过程分析方法[J].航空动力学报, 2010,25(4):930-935. FU Huimin,WU Qiong.Analysis method for linear process with independent increments[J] Journal of Aerospace Power,2010,25(4):930-935.(in Chinese)
点击查看大图
计量
- 文章访问数: 1769
- HTML浏览量: 5
- PDF量: 577
- 被引次数: 0