基于交互多模型的被动多传感器机动目标跟踪算法研究
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摘要
被动多传感器的机动目标跟踪是目标跟踪领域的一个重要研究方面,受到越来越多国内外学者专家的关注。随着现代科学技术的日益发展,目标的机动性能不断提高,使得现代战争环境日益复杂,对目标跟踪提出越来越高的要求。因而,研究被动多传感器系统下的机动目标跟踪,对于提高我国防御系统的能力,具有十分重要的意义。针对机动目标的机动特性,本文重点研究了基于交互多模型(IMM)的机动目标跟踪算法。
本文首先针对被动多传感器的非线性观测问题,研究了最近新提出的基于确定性采样的高斯滤波算法,详细阐述了利用拉格朗日乘数法得到更加逼近状态先验概率密度函数采样点的过程。利用IMM算法典型的模块化设计的优点,本文提出了一种基于交互多模型高斯滤波(IMMGF)的被动多传感器目标跟踪算法,该算法能够有效地提高机动目标跟踪的精度。
其次,针对由于IMM算法采用标量权值更新模型概率而导致的目标的速度、加速度等估计不精确问题,研究了基于对角矩阵的交互多模型(DIMM)算法,该算法利用最优信息融合理论,将各模型状态估计的每一维向量元素(如：位置、速度、加速度等)实现优化融合,使得融合后状态的误差协方差矩阵最小。本文结合无迹卡尔曼滤波(UKF)算法,提出了DIMMUKF算法,该算法有效地提高了机动目标跟踪的精度,尤其对目标速度、加速度等的估计更加精确。
再次,研究了基于模型切换时间的交互多模型(STC-IMM)算法,该算法在传感器的采样频率大于目标的机动频率的情况下,机动跟踪性能明显优于IMM算法。本文将STC-IMM算法与扩展卡尔曼滤波(EKF)算法相结合,应用于被动多传感器系统中,提出了一种新的STC-IMM-EKF算法,实现了只测角条件下对机动目标的跟踪。
最后,针对机动目标跟踪中模型难以匹配的问题,本文提出了一种基于曲线模型自适应的机动目标跟踪算法。该算法将已有的曲线模型自适应跟踪算法作了一些改进,有效地避免了由于模型估计不精确导致的滤波器发散的现象。仿真实验表明,本文提出算法比现有算法取得更高精度的同时,扩大了其应用范围。
Maneuvering target tracking based on multiple passive sensors is one of important aspects in the field of target tracking, which has been paid attention by domestic and foreign scholars and experts. With the development of modern science and technology and the unceasing enhancement of the target maneuver performance, modern war environment is increasingly complex, which put forward higher requirement to target tracking. Therefore, the research of the maneuvering target tracking based on multiple passive sensors is of great significance to enhancing the capacity of China's defense system. Aiming at maneuvering targets maneuvering characteristic, this dissertation focuses on Interacting Multiple Model (IMM) algorithm.
Firstly, aiming at the nonlinear observation problem of multiple passive sensors, this dissertation involves researches about the recently proposed Gaussian Filter algorithm based on deterministic sampling, and elaborates in detail on utilizing Lagrange multiplier method so as to attain sampling points which is close to the state a priori probability density function. Combined with IMM algorithm modular design, the Interacting Multiple Model Gaussian filter algorithm (IMMGF) based on multiple passive sensors is proposed, which can effectively improve the accuracy of maneuvering target tracking.
Secondly, in view of the imprecise estimated problem of the target's velocity and acceleration resulting from the scalar weight to update model in the IMM algorithm, this dissertation involves researches about diagonal interacting multiple model (DIMM) algorithm, which utilizes the optimal information fusion theory so as that each dimension element of the state estimation vector (such as:position, velocity, acceleration, etc.) achieves optimal integration and state fusion error covariance matrix is minimized. In combination with the Unscented Kalman filter (UKF) algorithm, the DIMMUKF algorithm is proposed, which effectively improved the accuracy of maneuvering target tracking, especially for the target speed, acceleration, etc.
Thirdly, this dissertation involves researches about a new Interacting Multiple Model with Switch Time Conditions (STC-IMM). When the sampling rate of the underlying continuous process is high compared to the target dynamics, the maneuver tracking performance of STC-IMM algorithm is significantly superior to the IMM algorithm. The IMM algorithm in combination with Extended Kalman Filter (EKF) applied to the passive multi-sensor maneuvering target tracking, a novel STC-IMM-EKF algorithm is proposed, which can achieve maneuvering target tracking in multi-sensor bearing-only tracking.
Finally, to solve the problem that the model for maneuvering target tracking is difficult to match, an adaptive tracking algorithm based on curve model for maneuvering target tracking is proposed. It makes some improvements to the present curve model adaptive tracking algorithm, and then effectively avoids the filter divergence resulting from the imprecise estimates to the model. Simulation results show that the proposed algorithm has higher precision and broader scope of application than the existing algorithm.
本文首先针对被动多传感器的非线性观测问题,研究了最近新提出的基于确定性采样的高斯滤波算法,详细阐述了利用拉格朗日乘数法得到更加逼近状态先验概率密度函数采样点的过程。利用IMM算法典型的模块化设计的优点,本文提出了一种基于交互多模型高斯滤波(IMMGF)的被动多传感器目标跟踪算法,该算法能够有效地提高机动目标跟踪的精度。
其次,针对由于IMM算法采用标量权值更新模型概率而导致的目标的速度、加速度等估计不精确问题,研究了基于对角矩阵的交互多模型(DIMM)算法,该算法利用最优信息融合理论,将各模型状态估计的每一维向量元素(如：位置、速度、加速度等)实现优化融合,使得融合后状态的误差协方差矩阵最小。本文结合无迹卡尔曼滤波(UKF)算法,提出了DIMMUKF算法,该算法有效地提高了机动目标跟踪的精度,尤其对目标速度、加速度等的估计更加精确。
再次,研究了基于模型切换时间的交互多模型(STC-IMM)算法,该算法在传感器的采样频率大于目标的机动频率的情况下,机动跟踪性能明显优于IMM算法。本文将STC-IMM算法与扩展卡尔曼滤波(EKF)算法相结合,应用于被动多传感器系统中,提出了一种新的STC-IMM-EKF算法,实现了只测角条件下对机动目标的跟踪。
最后,针对机动目标跟踪中模型难以匹配的问题,本文提出了一种基于曲线模型自适应的机动目标跟踪算法。该算法将已有的曲线模型自适应跟踪算法作了一些改进,有效地避免了由于模型估计不精确导致的滤波器发散的现象。仿真实验表明,本文提出算法比现有算法取得更高精度的同时,扩大了其应用范围。
Maneuvering target tracking based on multiple passive sensors is one of important aspects in the field of target tracking, which has been paid attention by domestic and foreign scholars and experts. With the development of modern science and technology and the unceasing enhancement of the target maneuver performance, modern war environment is increasingly complex, which put forward higher requirement to target tracking. Therefore, the research of the maneuvering target tracking based on multiple passive sensors is of great significance to enhancing the capacity of China's defense system. Aiming at maneuvering targets maneuvering characteristic, this dissertation focuses on Interacting Multiple Model (IMM) algorithm.
Firstly, aiming at the nonlinear observation problem of multiple passive sensors, this dissertation involves researches about the recently proposed Gaussian Filter algorithm based on deterministic sampling, and elaborates in detail on utilizing Lagrange multiplier method so as to attain sampling points which is close to the state a priori probability density function. Combined with IMM algorithm modular design, the Interacting Multiple Model Gaussian filter algorithm (IMMGF) based on multiple passive sensors is proposed, which can effectively improve the accuracy of maneuvering target tracking.
Secondly, in view of the imprecise estimated problem of the target's velocity and acceleration resulting from the scalar weight to update model in the IMM algorithm, this dissertation involves researches about diagonal interacting multiple model (DIMM) algorithm, which utilizes the optimal information fusion theory so as that each dimension element of the state estimation vector (such as:position, velocity, acceleration, etc.) achieves optimal integration and state fusion error covariance matrix is minimized. In combination with the Unscented Kalman filter (UKF) algorithm, the DIMMUKF algorithm is proposed, which effectively improved the accuracy of maneuvering target tracking, especially for the target speed, acceleration, etc.
Thirdly, this dissertation involves researches about a new Interacting Multiple Model with Switch Time Conditions (STC-IMM). When the sampling rate of the underlying continuous process is high compared to the target dynamics, the maneuver tracking performance of STC-IMM algorithm is significantly superior to the IMM algorithm. The IMM algorithm in combination with Extended Kalman Filter (EKF) applied to the passive multi-sensor maneuvering target tracking, a novel STC-IMM-EKF algorithm is proposed, which can achieve maneuvering target tracking in multi-sensor bearing-only tracking.
Finally, to solve the problem that the model for maneuvering target tracking is difficult to match, an adaptive tracking algorithm based on curve model for maneuvering target tracking is proposed. It makes some improvements to the present curve model adaptive tracking algorithm, and then effectively avoids the filter divergence resulting from the imprecise estimates to the model. Simulation results show that the proposed algorithm has higher precision and broader scope of application than the existing algorithm.
引文
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[2]陈利斌.机动目标跟踪理论和算法的研究.博士学位论文,西北工业大学,2000.
[3]丁维福.多传感器信息融合理论及其在机动目标跟踪中的应用.硕士学位论文,西北工业大学,2007.
[4]Pasupathy, S., Alford, W.J. Range and Bearing Estimation in Passive Sonar. IEEE Trans. on AES, March 1980,16(2):255-249.
[5]Grindon, J. An Optimal Multisensor Source Location Algorithm for Passive Sonar with Moving Source or Sensors. IEEE Transactions on Acoustics, Speech, and Signal Processing,1981,29(3):617-619.
[6]De Vlieger, J.H., R.H.J.G. Maximum Likelihood Estimation for Long-range Target Tracking using Passive Sonar Measurements. IEEE Transactions on Signal Processing,1992,40(5):1216-1225.
[7]B. Ristic and M. S. Arulampalam. Tracking a Maneuvering Target using Angel-only Measurements:Algorithms and Performance. Signal Processing, 2003,83(6):1223-1238.
[8]F. Dufour and M. Mariton. Tracking a 3D Maneuvering Target with Passive Sensors. IEEE Trans. on AES, July 1991,27(4):725-739.
[9]李娟,李斯娜,陈新亿.多传感器数据融合技术综述.云南大学学报(自然科学版),2008,30(S2)：241-246.
[10]何友,彭应宁.多传感器数据融合模型综述.清华大学学报(自然科学版),1996,36(9)：14-20.
[11]宋骊平.被动多传感器目标跟踪方法研究.博士学位论文,西安电子科技大学,2008.
[12]Leigh A. Johnston and Vikram Krishnamurthy. An Improvement to the Interacting Multiple Model (IMM) Algorithm. IEEE Transactions on Signal Processing,2001,49(12):2909-2923.
[13]Xu Jian-juan, He You, Chezhi-yu. Three-Dimensional Passive Location Algorithm of the OTHT. In 3rd International Conference on Computational Electromagnetics and Its Applications Proceedings,2004.
[14]Dersan, Aysegul, Tanik, Yalcin. Passive Radar Localization by Time Difference of Arrival. Proceedings-IEEE Military Communications Conference MILCOM, 2002, v2,pp:1251-1257.
[15]王成,李少洪,王鑫全,黄槐.测时差被动定位算法的研究.系统工程与电子技术,2001,23(11)：9-12.
[16]Li Wenhua, Liu Peiguo.3D AOA/TDOA Emitter Location by Integrated Passive Radar/GPS/INS Systems. Proceedings of the 2005 IEEE International Workshop on VLSI Design and Video Technology, IWVDVT 2005.
[17]王杰贵,罗景清.对机动辐射源单站无源定位IMM算法.现代雷达,2002,24(6)：9-12.
[18]Bishop Adrian N., Pathirana Pubudu N. Localization of Emitters via the Intersection of Bearing Lines:A Ghost Elimination Approach. IEEE Transactions on Vehicular Technology,2007,56(5):3106-3110.
[19]Ni Zhong-De, Sun Yong-Kan, Dong Zhi-Gang, Jiang Can-shu. A Distributed Multiple Warships Bearings-only Tracking Algorithm.2005 International Conference on Communications, Circuits and Systems-Proceedings,2005.
[20]邹卫军,薄煜明.差分线性化EKF滤波方法研究.计算机工程与应用,2009,25(9)：64-66.
[21]Simon Julier, Jeffrey Uhlmann, and Hugh F. Durrant-Whyte. A New Method for the Nonlinear Transformation of Means and Covariances in Filters and Estimators. IEEE Transactions on Automatics Control, March 2000,45(3): 477-482.
[22]吴玲,卢发兴,刘忠.UKF算法及其在目标被动跟踪中的应用.系统工程与电子技术,2005,27(1)：49-51.
[23]Simon Julier, Jeffrey K. Uhlmann. Unscented Filtering and Nonlinear Estimation. Proceedings of the IEEE, March 2004,92(3):401-422.
[24]Fredrik Gustafsson, et al. Particle filters for Positioning, Navigation, and Tracking. IEEE Trans. on Signal Processing,2002,50(2):425-437.
[25]N. J. Gordon, D. J. Salmond, A. F. M. Simth. Novel Approach to Nonlinear/ Non-Gaussian Bayesian State Estimation, IEE Proceedings-F,1993, 140(2):107-113
[26]X. Rong Li, Vesselin P. Jilkov. Survey of Maneuvering Target Tracking. Part I: Dynamic Models. IEEE Trans. on AES,2003,39(4):1333-1364
[27]Blom H A P. An Efficient Filter for Abruptly Changing Systems. IEEE Proceedings of 23rd Conference on Decision and Control, Vol.23, Las Vegas, Dec.1984,pp:656-658.
[28]Blom H A, Bar-Shalom, Y. The Interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficient, IEEE Trans, on Automatic Control, Vol.33(8),1988:780～783.
[29]赵敏.机动目标跟踪理论的研究及其应用.硕士学位论文,西北工业大学,2006.
[30]M. F. Huber and U. D. Hanebeck. Gaussian Filter Based on Deterministic Sampling for High Quality Nonlinear Estimation. In Proceedings of the 17th IFAC World Congress (IFAC 2008),2008.
[31]赵欣.基于随机集理论的被动多传感器多目标跟踪技术.硕士学位论文,西安电子科技大学,2009.
[32]姚剑敏.粒子滤波跟踪方法研究.博士学位论文,中国科学院.2004
[33]沈慧峰.基于粒子滤波的多目标跟踪算法研究.硕士学位论文,浙江大学,2006
[34]Kalman R E. A New Approach to Linear Filtering and Prediction Problems. Trans. ASME. Journal of Basic Eng,1960,82D:35-46.
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