基于迭代观测更新滤波的单站无源定位算法

熊 露; 谌 剑; 毕晓文; 陈安全

doi:10.11993/j.issn.2096-3920.2019.04.007

基于迭代观测更新滤波的单站无源定位算法

doi: 10.11993/j.issn.2096-3920.2019.04.007

1.
陆军工程大学军械士官学校雷达系, 湖北武汉, 430075
2.
海军工程大学控制科学与工程博士后科研流动站, 湖北武汉, 430033
3.
海军士官学校信息与通信系, 安徽蚌埠, 233012

基金项目: 海军工程大学科研基金(20180795); 中国博士后科学基金(2018M643864)

详细信息

作者简介:
熊露(1986-), 女, 博士, 讲师, 主要研究方向为目标探测与跟踪.

中图分类号: TJ630.34; TN713
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- 文章访问数: 537
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- 被引次数: 0
出版历程
- 收稿日期: 2016-11-19
- 修回日期: 2016-12-18
- 刊出日期: 2019-08-31

Single Observer Passive Localization Algorithm Based on Iterated Measurement Updating Filter

1.
Department of Radar System, Ordnance NCO Academy of Army Engineering University of PLA, Wuhan 430075, China
2.
Post-doctoral Program on Control Science and Engineering, Naval University of Engineering, Wuhan 430033, China
3.
Department of Information & Communication, NCO Academy of PLA Navy, Bengbu 233012, China

摘要

摘要: 单站无源定位具有较强的隐蔽性, 能够避免多个观测平台之间数据的融合与同步等问题。针对单站无源定位算法性能易受测量误差一致性和初始状态误差等因素影响的问题, 文中从线性估计理论出发, 提出了一种基于迭代观测更新滤波(IMUF)的单站无源定位算法。首先, 将经典的一步离散线性估计器观测更新改写为连续时间上的逐步更新过程, 推导了连续逐步状态及其误差矩阵的演化方程, 然后进一步离散化得到迭代观测更新方程, 并采用Sigma点方法对其中的高斯矩进行近似计算, 得到了一种具有类似Kalman滤波运算形式, 适用于单站无源定位问题的迭代观测更新滤波算法。仿真试验证明, 较之传统算法, 该算法能够有效处理非一致性观测误差和大初始状态误差下的性能恶化问题, 在滤波收敛性与估计准确性方面更有优势。
- 单站无源定位 /
- 迭代观测更新滤波 /
- 线性估计器 /
- Sigma点方法
Abstract: Single observer passive localization has strong concealment performance, and can avoid the problem of data fusion and synchronization among multiple observing platforms. To address the performance degeneration of single observer passive localization affected by the factors such as measurement error consistency and initial state error, a single observer passive localization algorithm based on iterated measurement updating filter(IMUF) is proposed in this paper. Firstly, based on the theory of linear estimation, the classical one-step discrete linear estimator update is rewritten as the step-by-step updating process in continuous time. Secondly, the evolution equations of continuous stepwise state and its error matrix are deduced and the iterated measurement updating equation is obtained by discretization. And then, the Sigma point method are used to approximate calculate the Gaussian matrix included in updating equations, and the IMUF is obtained, which has the Kalman filter-like computation form and is suitable for single observer passive localization. Finally, compared with the classical method, the experimental results show that the IMUF algorithm can effectively deal with the performance degradation problem under non-uniform measurement error and large initial state error, with better filtering convergence and estimation accuracy.
- single observer passive localization /
- iterated measurement updating filter(IMUF) /
- linear estimator /
- Sigma point method

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参考文献(15)

[1]	孙仲康, 郭福成, 冯道旺, 等. 单站无源定位跟踪技术[M]. 北京: 国防工业出版社, 2008.
[2]	Shalom Y, Li X R, Thiagalingam K. Estimation with Applications to Tracking and Navigation[M]. New York: Wiley, 2001: 381-394.
[3]	Arasaratnam I, Haykin S. Cubature Kalman Filters[J]. IEEE Transactions on Automatic Control, 2009, 54(6): 1254-1269.
[4]	Rudolph M. Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models[D]. Oregon: Oregon Health & Science University, 2004: 251-256.
[5]	Garcia-Fernandez A F, Morelande M R, Grajal J. Truncated Unscented Kalman Filtering[J]. IEEE Transactions on Signal Processing, 2012, 60(7): 3372-3386.
[6]	Chang L, Hu B, Li A, et al. Transformed Unscented Kalman Filter[J]. IEEE Transactions on Automatic Control, 2013, 58(1): 252-257.
[7]	Gustafsson F, Hendeby G. Some Relations Between Extened Kalman Filter and Unscented Kalman Filter[J]. IEEE Transactions on Signal Processing, 2013, 60(2): 545-555.
[8]	Zanetti R. Adaptable Recursive Update Filter[J]. Journal of Guidance Control & Dynamics, 2015, 38(7): 1-5.
[9]	Perea L, How J, Breger L. Nonlinearity in Sensor Fusion: Divergence Issues in EKF, Modified Truncated SOF, and UKF[C]//Proceedings of AIAA Conference of Guidance, Navigation, and Control, South Carolina, USA: IEEE, 2007: 3489-3499.
[10]	Morelande M R, Garcia-Fernandez A F. Analysis of Kalman Filter Approximations for Nonlinear Measurements[J]. IEEE Transactions on Signal Processing, 2013, 61(12): 5477-5484.
[11]	李宗华, 肖予钦, 周一宇, 等. 利用频域和空域信息的单站无源定位跟踪算法[J]. 系统工程与电子技术, 2004, 26(5): 613-616. Li Zong-hua, Xiao Yu-qing, Zhou Yi-yu, et al. Single Station Localization Algorithm for Source Free Targets Using Frequent and Spatial Information[J]. System Engineering and Electronic Technology, 2004, 26(5): 613-616.
[12]	Jazwinski A. Stochastic Process and Filtering Theory[M]. New York: Academic Press, 1970.
[13]	Sarkka S. On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems[J]. IEEE Transactions on Automatic Control, 2007, 52(9): 1631- 1641.
[14]	袁罡, 陈鲸. 基于UKF的单站无源定位与跟踪算法[J]. 电子与信息学报, 2008, 30(9): 2120-2123. Yuan Gang, Chen Jing. Single Station Localization and Tracking Algorithm for Source Free Targets Based on UKF[J]. Journal of Electronics and Information, 2008, 30(9): 2120-2123.
[15]	胡振涛, 张勇, 刘先省. 基于量测迭代更新集合卡尔曼滤波的机动目标跟踪算法[J]. 控制理论与应用, 2014, 31(11): 1517-1523. Hu Zhen-tao, Zhang Yong, Liu Xian-xing. Maneuver Target Tracking Algorithm Based on Iterated Measurement Updated Ensemble Kalman Filter[J]. Control Theory & Application, 2014, 31(11): 1517-1523.