战车主动防护系统中的目标跟踪算法研究
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摘要
在现代局部战争中,装甲战车仍承担着重要的角色。装甲车辆主动防护系统为提高其在战场上的生存能力提供了重要的保障,许多军事强国都对其展开积极的研究。目标跟踪技术的研究对主动防护系统的研制至关重要,因此,深入研究近程或超近程机动目标跟踪算法及其关键技术,具有重要的理论和应用价值。
为了精确地跟踪来袭目标,本文着重研究带径向速度量测的近程目标跟踪问题,并提出相应的滤波算法。通过对典型目标跟踪算法进行系统的分析和研究,提出了一些改进或优化的方法,并进行了仿真验证。本文主要成果和创新点包括以下三个方面：
首先,为解决主动防护系统要求目标跟踪精度高、反应速度快等问题,考虑引入径向速度量测且设计了带径向速度量测的EKF、UKF、DCMKF滤波器,并通过仿真表明正确应用径向速度量测可以显著的提高跟踪性能。
其次,提出了一种推广至三维的改进的带径向速度的DCMKF算法即基于坐标变换的非传统扩展卡尔曼滤波(AEKF),对其进行理论上的推导、证明与仿真验证。接着,基于CV模型、Singer模型将其与其它带径向速度的滤波器针对近程目标进行跟踪性能和运算速度的比较,证实本算法的有效性、适用性,优于其它几种算法。
最后,分析相关系数、采样频率、量测误差方差等对改进算法跟踪性能的影响且辅以仿真图例,并基于工程实例对本文提出的算法进行验证、分析,为工程实际应用提供一定的理论上和工程上的参考。
The armored vehicles will play an indispensable role in the modern regional war. Many military powerful countries have started the works on active protection systems for armored vehicle so that the survivability of armored vehicles will be improved on the battlefield. Target tracking technology is essential to active protection system researches. Therefore, it is very useful in theory and application to study short-range or ultra-short-range target tracking algorithms and techniques thoroughly in developing the active protection systems of armored vehicles.
In order to track the coming targets accurately, this paper focuses on short-range target tracking with the radial velocity measurements and corresponding filter algorithms. Through systematic analysis and research, some filter algorithms are improved and some typical target tracking algorithms are analyzed with numerical simulations. Main results and improvements are listed in the following.
Firstly, radial velocity measurements are introduced for designing the EKF, UKF, DCMKF filter so that they meet the performance indices such as high precision and quick response for target tracking of active protection system. The simulation results show that the tracking performance can be improved significantly by using the designed algorithm properly.
Secondly, improved EKF algorithm with radial velocity is proposed by extending the results in two-dimensional case to that of three-dimension, namely alternative extended Kalman filter(AEKF). Based on CV and the Singer model for short-range or ultra-short-range target, the difference between this algorithm and others results on tracking performance and calculation speed are analyzed. It is verified that this algorithm more feasible than other methods.
Finally, based on engineering and application background, this paper analyses the effect of the parameter changes of the correlation coefficient, the sampling frequency and measurement error variance to the tracking performance of the improved algorithms with simulation methods. This algorithm is also verified and analyzed by examples from practical project, which may be useful to theory and engineering application.
为了精确地跟踪来袭目标,本文着重研究带径向速度量测的近程目标跟踪问题,并提出相应的滤波算法。通过对典型目标跟踪算法进行系统的分析和研究,提出了一些改进或优化的方法,并进行了仿真验证。本文主要成果和创新点包括以下三个方面：
首先,为解决主动防护系统要求目标跟踪精度高、反应速度快等问题,考虑引入径向速度量测且设计了带径向速度量测的EKF、UKF、DCMKF滤波器,并通过仿真表明正确应用径向速度量测可以显著的提高跟踪性能。
其次,提出了一种推广至三维的改进的带径向速度的DCMKF算法即基于坐标变换的非传统扩展卡尔曼滤波(AEKF),对其进行理论上的推导、证明与仿真验证。接着,基于CV模型、Singer模型将其与其它带径向速度的滤波器针对近程目标进行跟踪性能和运算速度的比较,证实本算法的有效性、适用性,优于其它几种算法。
最后,分析相关系数、采样频率、量测误差方差等对改进算法跟踪性能的影响且辅以仿真图例,并基于工程实例对本文提出的算法进行验证、分析,为工程实际应用提供一定的理论上和工程上的参考。
The armored vehicles will play an indispensable role in the modern regional war. Many military powerful countries have started the works on active protection systems for armored vehicle so that the survivability of armored vehicles will be improved on the battlefield. Target tracking technology is essential to active protection system researches. Therefore, it is very useful in theory and application to study short-range or ultra-short-range target tracking algorithms and techniques thoroughly in developing the active protection systems of armored vehicles.
In order to track the coming targets accurately, this paper focuses on short-range target tracking with the radial velocity measurements and corresponding filter algorithms. Through systematic analysis and research, some filter algorithms are improved and some typical target tracking algorithms are analyzed with numerical simulations. Main results and improvements are listed in the following.
Firstly, radial velocity measurements are introduced for designing the EKF, UKF, DCMKF filter so that they meet the performance indices such as high precision and quick response for target tracking of active protection system. The simulation results show that the tracking performance can be improved significantly by using the designed algorithm properly.
Secondly, improved EKF algorithm with radial velocity is proposed by extending the results in two-dimensional case to that of three-dimension, namely alternative extended Kalman filter(AEKF). Based on CV and the Singer model for short-range or ultra-short-range target, the difference between this algorithm and others results on tracking performance and calculation speed are analyzed. It is verified that this algorithm more feasible than other methods.
Finally, based on engineering and application background, this paper analyses the effect of the parameter changes of the correlation coefficient, the sampling frequency and measurement error variance to the tracking performance of the improved algorithms with simulation methods. This algorithm is also verified and analyzed by examples from practical project, which may be useful to theory and engineering application.
引文
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[2]孔繁清,薛军楼.主动防护系统的发展[J].坦克装甲车辆.2000,(4)：2-5
[3]王雄高.“软硬”兼施—战车主动防护系统[J].综合述评.2006,(3)：15～19
[4]阐隐枫.轻型装甲车辆防护忧喜谈[J].世界军事.2005,(10)：73～77
[5]文昱.主动防护系统的研制动向.国外坦克.2004年第2期5～7
[6]陈理凯.轻型装甲车辆主动防护系统拦截效率研究[D].硕士论文,南京理工大学,2008
[7]方正.主动防护系统龙虎斗一俄罗斯“竞技场”vs以色列“战利品”[J].国外坦克.2007,(1)：30
[8]李补莲.坦克车辆主动防护系统新发展.国外坦克[J].2009年第9期37～40
[9]德国公司推出革命性车辆主动防护系统[N].现代军事.2008.8
[10]李鑫译.主动防护装甲战斗车辆的灵巧屏蔽[J].国外坦克.2000,(1)：31～39
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[28]Duan Zhansheng, Han Chongzhao, Li X R. Sequential Nonlinear Tracking Filter with Range Rate Measurements in Spherical Coordinates [C] Proc of the 7~(th) International Conference on Information Fusion, Stockholm, Sweden:[s.n.].2004:599～605
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[32]Bar-Shalom, Li X. R. Negative Correlation and Optimal Tracking with Doppler Measurement[J]. IEEE Tans. Aerospace and Electronic System.2001,37(3):1117～1120
[33]王建国,龙腾,何佩琨.一种在Kalman滤波中引入径向速度测量的新方法[J].信号处理.2002,18(5)：414～417
[34]王建国,何佩琨,龙腾.径向速度测量在Kalman滤波中的应用[J].北京理工大学学报.2003,22(2)：225～227
[35]张怀根,张林让,吴顺君.利用径向速度观测值提高目标跟踪性能[J].西安电子科技大学学报(自然科学版).2005,32(5)：667～610
[36]韩崇昭,朱洪艳,段战胜等.多源信息融合[M].北京：清华大学出版社,2006
[37]周宏仁,敬忠良,王培德.机动目标跟踪[M].北京：国防工业出版社,1991
[38]Singer Robert A. Estimating Optimal Tracking Filter Performance for Manned Maneuvering[J]. IEEE Transactions on Aerospace and Electronic Systems.1970,6(4): 473～483
[39]E. mazor, J. Dayan, Bar-Shalom, Y. Interacting Multiple Model in Target Tracking:a Survey[J]. IEEE Transactions on Aerospace and Electronic.1998,103～124
[40]高嵩,潘泉.有效的平方根分解交互式多模型滤波器[J].西安工业学院学报.1996,16(1)：20～25
[41]何友,修建娟,张晶炜,关欣等.雷达数据处理及应用(第二版)[M].北京：电子工业出版社,2009
[42]Phanenf R. J. Approximate Nonlinear Estimation[D]. Ph. D Dissertation, Cambridge, MII,1968
[43]Julier S. J., Uhlmann J K. A New Extension of Kalman Filtering to Nonlinear Systems[J]. SPIE,1997,3068:182～193
[44]黄铫,张天骐,李越雷,刘燕丽.一种扩维无迹卡尔曼滤波[J].电子测量与仪器学报.2009增刊：56～60
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[2]孔繁清,薛军楼.主动防护系统的发展[J].坦克装甲车辆.2000,(4)：2-5
[3]王雄高.“软硬”兼施—战车主动防护系统[J].综合述评.2006,(3)：15～19
[4]阐隐枫.轻型装甲车辆防护忧喜谈[J].世界军事.2005,(10)：73～77
[5]文昱.主动防护系统的研制动向.国外坦克.2004年第2期5～7
[6]陈理凯.轻型装甲车辆主动防护系统拦截效率研究[D].硕士论文,南京理工大学,2008
[7]方正.主动防护系统龙虎斗一俄罗斯“竞技场”vs以色列“战利品”[J].国外坦克.2007,(1)：30
[8]李补莲.坦克车辆主动防护系统新发展.国外坦克[J].2009年第9期37～40
[9]德国公司推出革命性车辆主动防护系统[N].现代军事.2008.8
[10]李鑫译.主动防护装甲战斗车辆的灵巧屏蔽[J].国外坦克.2000,(1)：31～39
[11]Kalman R E. A new approach to linear filtering and prediction problems. Journal of Basic Engineering[J].1960:35～46
[12]Sunahara Y An. Approximate method of state estimation for nonlinear dynamical systems[C]. Joint Automatic Control Conf. Univ. of Colorado.1969
[13]Julier S. J., Uhlmann J. K.. A new approach for filtering nonlinear system[C]. Proc of the American Control Conf. Washington Seattle.1995:1628～1632
[14]Gordon N, Salmond D. J.. Smith A. F. M. Novel approach to nonlinear and non-gaussian Bayesian state estimation[J]. IEEE Proceedings on Radar and Signal Processing.1993,140(2):107～113
[15]Li X R, Jilkov V P.. A Survey of Maneuvering Target Tracking Approximation Techniques for Nonlinear Filtering[C] Proc of 2004 SPIE Conference on Signal and Data Processing of Small Target s, Bellingham, WA:[s. n.].2004:537～550
[16]Lerro Don, Bar-Shalom Y. Tracking With Debiased Consistent Converted Measurements Versus EKF[J]. IEEE Transactions on Aerospace and Electronic Systems.1993,29(3): 1015～1019
[17]P. Suchomski. Explicit Expressions for Debiased Statistics of 3D Converted Measurements[J]. IEEE Trans. Aerospace and Electronic Systems.1999,35(1): 368-370
[18]Mo L, Song X, Zhou Y, Sun Z. Unbiased converted measurements for Target Tracking [J]. IEEE Trans. Aerospace and Electronic Systems,1998,34(3):1023～1027
[19]Zhao Zhanlue, Li X. R., Jikov V.P., Zhu Yunming. Optimal Linear Unbiased Filtering with Polar Measurements for Tracking[C]. The Fifth International Conference on Information Fusion, Annapolis, MD.2002:1527～1534
[20]Park S.E., Lee J.G. Improved Kalman Filter Design for Three-Dimensional Radar Tracking[J]. IEEE. Trans. Aerospace and Electronic System,2001,37(2):727～739
[21]Farina A, Studer F A. Radar Data Processing[M], Vol I:Introduction and Tracking, Vol II:Advanced Topics and Applications. Letchworth:Research Studies Press,1985
[22]Dai Y P, Jin C Z, Hu J L, et al. A Target Tracking Algorithm with Range Rate Under the Color Measurement Environment [C] Proc of the 38th SICE Annual Conference, Morioka, Japan:[s. n.].1999:1145～1148
[23]段战胜,韩崇昭,陶唐飞.球坐标系下多普勒雷达目标跟踪滤波器[J].信号处理.2005,21(4)：355～358
[24]张倩,景占荣,羊彦.简化的UKF方法在多普勒雷达对目标跟踪中的应用[J].火力与控制.2008,33(5)：82～84
[25]Bizup David Francis. Improving Radar Target Tracking with the Range Rate Measure-ment [D], Doctor dissertation.2003
[26]王建国,商艳海,龙腾,何佩琨.利用径向速度观测值提高目标跟踪性能[J].北京理工大学学报.2002,22(6)：750～753
[27]Wang Jianguo, He Peikun, Long Teng. Use of the Radial Velocity Measurement in Target Tracking[J], IEEE Trans on Aerospace and Electronic Systems.2003.39(2): 401～411
[28]Duan Zhansheng, Han Chongzhao, Li X R. Sequential Nonlinear Tracking Filter with Range Rate Measurements in Spherical Coordinates [C] Proc of the 7~(th) International Conference on Information Fusion, Stockholm, Sweden:[s.n.].2004:599～605
[29]段战胜,韩崇昭.极坐标系中带多普勒量测的目标跟踪[J].系统仿真学报.2004,16(4)：2860～2863
[30]Duan Zhansheng, Li X R, Han Chongzhao, et al. Sequential Unscented Kalman Filter for Radar Target Tracking with Range Rate Measurements[C]. Proc of the 8th International Conference on Information Fusion, Franklin Plaza, Philadelphia, USA:[s.n.].2005:130～138
[31]祝依龙,范红旗,付强.视线坐标系带多普勒观测的雷达目标跟踪[J].雷达科学与技术.2009,7(3),200～203
[32]Bar-Shalom, Li X. R. Negative Correlation and Optimal Tracking with Doppler Measurement[J]. IEEE Tans. Aerospace and Electronic System.2001,37(3):1117～1120
[33]王建国,龙腾,何佩琨.一种在Kalman滤波中引入径向速度测量的新方法[J].信号处理.2002,18(5)：414～417
[34]王建国,何佩琨,龙腾.径向速度测量在Kalman滤波中的应用[J].北京理工大学学报.2003,22(2)：225～227
[35]张怀根,张林让,吴顺君.利用径向速度观测值提高目标跟踪性能[J].西安电子科技大学学报(自然科学版).2005,32(5)：667～610
[36]韩崇昭,朱洪艳,段战胜等.多源信息融合[M].北京：清华大学出版社,2006
[37]周宏仁,敬忠良,王培德.机动目标跟踪[M].北京：国防工业出版社,1991
[38]Singer Robert A. Estimating Optimal Tracking Filter Performance for Manned Maneuvering[J]. IEEE Transactions on Aerospace and Electronic Systems.1970,6(4): 473～483
[39]E. mazor, J. Dayan, Bar-Shalom, Y. Interacting Multiple Model in Target Tracking:a Survey[J]. IEEE Transactions on Aerospace and Electronic.1998,103～124
[40]高嵩,潘泉.有效的平方根分解交互式多模型滤波器[J].西安工业学院学报.1996,16(1)：20～25
[41]何友,修建娟,张晶炜,关欣等.雷达数据处理及应用(第二版)[M].北京：电子工业出版社,2009
[42]Phanenf R. J. Approximate Nonlinear Estimation[D]. Ph. D Dissertation, Cambridge, MII,1968
[43]Julier S. J., Uhlmann J K. A New Extension of Kalman Filtering to Nonlinear Systems[J]. SPIE,1997,3068:182～193
[44]黄铫,张天骐,李越雷,刘燕丽.一种扩维无迹卡尔曼滤波[J].电子测量与仪器学报.2009增刊：56～60
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