相位阵列雷达信号目标识别算法的研究
|
摘要
本文讨论了传统α－β，α－β－γ滤波器引入采样间隔可变概念后的三种新
型采样间隔自适应变化雷达目标跟踪算法，通过仿真，对各算法的滤波性能及效果
进行了比较分析，算法中采样间隔的变化与预测误差的平方根或立方根的大小成
反比。结果表明，当跟踪目标运动机动性较大时，α－β－γ滤波器跟踪效果好，
在同样的跟踪误差水平下其平均采样间隔明显优于α－β滤波，而α－β滤波则
更适于跟踪机动性不大的运动目标。另外，当综合考虑采样点数与跟踪误差时，
同样使用α－β－γ滤波，采用采样间隔与预测误差立方根大小成反比可获得更
好的滤波效果：而同样在使用α－β滤波时，则采用采样间隔与误差平方根成反
比时的滤波性能好。
此外，本文还研究了一种基于连续状态Hopfield神经网络的状态估计算法
用于目标跟踪，并对该算法中某些参数的选择变化对跟踪滤波效果的影响进行
了详细的分析。仿真结果表明在一定条件下，这种新型神经网络目标跟踪算法的
滤波性能与Kalman滤波器相仿，但由于其具有很强的并行数据处理能力，因此
从计算时间角度讲，较各传统方法更适用于对实时性要求很高的应用领域，如
雷达目标跟踪。
The effects of incorporating a time-varying update time into three target-tracking
algorithms based on the a and a -filters are presented and compared. The
update times are chosen according to the inverse of either the square root or cube root
of the position residual. Simulation results show that the a 13 v -filter yields larger
update times during the manoeuvre than those obtained with the a 13 -filter, as the
residuals obtained with the former filter are smaller during the manoeuvre. It is found
that the best compromise between the number of updates and estimation errors is
obtained when the update time is chosen according to the inverse of the square root of
the residual when the a -filter is being used, and the inverse of the cube root of the
residual when the a -filter is being used.
Besides that, a state estimator based on continuos-state Hopfield net applying to
phased array tracking is discussed in this paper, we also show how to chose some of
the estimator design parameters. Simulation results show that the new approach
performs similar to Kalman filter. Due to the parallel computational mode of neural
net, the new approach is more attractive for real time implementation, such as radar
target tracking, from the computational time point of view, than classical trackers.
型采样间隔自适应变化雷达目标跟踪算法，通过仿真，对各算法的滤波性能及效果
进行了比较分析，算法中采样间隔的变化与预测误差的平方根或立方根的大小成
反比。结果表明，当跟踪目标运动机动性较大时，α－β－γ滤波器跟踪效果好，
在同样的跟踪误差水平下其平均采样间隔明显优于α－β滤波，而α－β滤波则
更适于跟踪机动性不大的运动目标。另外，当综合考虑采样点数与跟踪误差时，
同样使用α－β－γ滤波，采用采样间隔与预测误差立方根大小成反比可获得更
好的滤波效果：而同样在使用α－β滤波时，则采用采样间隔与误差平方根成反
比时的滤波性能好。
此外，本文还研究了一种基于连续状态Hopfield神经网络的状态估计算法
用于目标跟踪，并对该算法中某些参数的选择变化对跟踪滤波效果的影响进行
了详细的分析。仿真结果表明在一定条件下，这种新型神经网络目标跟踪算法的
滤波性能与Kalman滤波器相仿，但由于其具有很强的并行数据处理能力，因此
从计算时间角度讲，较各传统方法更适用于对实时性要求很高的应用领域，如
雷达目标跟踪。
The effects of incorporating a time-varying update time into three target-tracking
algorithms based on the a and a -filters are presented and compared. The
update times are chosen according to the inverse of either the square root or cube root
of the position residual. Simulation results show that the a 13 v -filter yields larger
update times during the manoeuvre than those obtained with the a 13 -filter, as the
residuals obtained with the former filter are smaller during the manoeuvre. It is found
that the best compromise between the number of updates and estimation errors is
obtained when the update time is chosen according to the inverse of the square root of
the residual when the a -filter is being used, and the inverse of the cube root of the
residual when the a -filter is being used.
Besides that, a state estimator based on continuos-state Hopfield net applying to
phased array tracking is discussed in this paper, we also show how to chose some of
the estimator design parameters. Simulation results show that the new approach
performs similar to Kalman filter. Due to the parallel computational mode of neural
net, the new approach is more attractive for real time implementation, such as radar
target tracking, from the computational time point of view, than classical trackers.
引文
[1] 楼顺天等，MATLAB程序设计语言，西安电子科技大学出版社，1998． 12
[2] 张立明，人工神经网络的模型及其应用，复旦大学出版社，1993． 7
[3] 杨宏晖，基于神经网络的弹载雷达目标检测与识别的研究，西北工业大学 硕士论文，1998，11
[4] 罗发龙，李衍达，神经网络信号处理，电子工业出版社，1993． 10
[5] 施阳，楼顺天，基于MATLAB的系统分析与设计----神经网络，西安电子科 技大学出版社，1998． 9
[6] S.A.Cohen, Adaptive variable update rate algorithm for tracking targets with a phased array radar, Proceedings. Pt. F. 133． 3 (1986) . 277-280
[7] Wilkin.D. J.. Harrison. I.and Woolfson. M. S(1991) , Target tracking algorithms for phased array radar, IEE Proceedings. Pt.F.138． 3 (1991) , 255-262
[8] Gardner, L. A, and Mullen, R. J(1988) , Constant gain tracker with variable frame time, IEEE Trans.Aerosp.Electron.Syst (1988) AES-24,pp. 322-326
[9] Munu. M.. Harrison. I and Woolson.M. S(1992) , Comparison of adaptive target tracking algorithms for phased array radar, IEE Proceedings. Pt. F. 139． 5 (1992) , 336-342
[10] Sun. Q.. Alouani. A. T.. Rice. T. R and Gray. J. E. (1992) A neural network computation algorithm for discrete-time linear system state estimation, In Proceedings of International Joint Conference on Neural Network. I(1992) . 443-458
[11] Henry Leung. (1997) , A Hopfield neural tracker for phased array antenna, IEEE Trans. Aerosp. Electron. Syst (1997) Vol. 33,No. 1． pp. 301-307
[12] Elanayar. S.. and Shin. Y. C. (1994) Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems. IEEE Transactions on Neural networks. 5． (1994) . 594-603
[13] Narendra. K. S and Parthasarthy. K. (1990) ,Identification and control of dynamical systems using neural network, IEEE Transactions on Neural Networks. 1． (1990) . 4-27
[14] Hopf ield. J. J.. and Tank. D. W. (1985) , Neural computation of decisions in optimization problems, Biological Cybernetics. 52 (1985) , 141-152．
[15] Jumarie. G, (1990) , Nonlinear filtering weighted mean squares approach and Bayesian one via the maximum entropy principle. Signal Proceedings, 21 (1990) 323-338．
[16] Hayakawa, Y., Marumoto, A. and Sawada. Y. (1995) , Effects of the chaotic noise on the performance of a neural network model for optimization problems, Physical review. E. 51． (1995) , R2693-R2696．
[17] Sun, Q., Alouani. A. T.. Rice, T. R., and Gray, J. E. (1992) Linear system state estimation: A neural computing approach. In Proceedings of the American Control Conference. 1992． 550-554．
[18] Barrett. M.. and Aronott. R. (1994) , Adaptive antennas for mobile communications, Electronics and Communication Engineering Journal. (Aug. 1994) . 203-214．
[19] 程云鹏，矩阵论，西北工业大学出版社，1989，6
[20] 浙江大学数学系高等数学教研组编，概率论与数理统计，高等教育出版 社，1989
[21] 封建湖，数值分析，西北工业大学出版社，1988，7
[22] Henery Leung．(1997) ，A Hopfield Neural Tracker for Phased Array Antenna．，IEEE Transactions on Aerospace and Electronic Systems Vol．33，No．1，(Jan 1997) 301-307
[23] 刘有恒，信号检测与估计，人民邮电出版社，1989，4
[24] 吕杨生，随机信号处理导论天津大学出版社1988年5月
[25] 胡寿松，自动控制原理，国防工业出版社，1994年5月
[26] Catin，Donald E．(1989) Kalman filtering．Springer-Verlag New York Inc．
[2] 张立明，人工神经网络的模型及其应用，复旦大学出版社，1993． 7
[3] 杨宏晖，基于神经网络的弹载雷达目标检测与识别的研究，西北工业大学 硕士论文，1998，11
[4] 罗发龙，李衍达，神经网络信号处理，电子工业出版社，1993． 10
[5] 施阳，楼顺天，基于MATLAB的系统分析与设计----神经网络，西安电子科 技大学出版社，1998． 9
[6] S.A.Cohen, Adaptive variable update rate algorithm for tracking targets with a phased array radar, Proceedings. Pt. F. 133． 3 (1986) . 277-280
[7] Wilkin.D. J.. Harrison. I.and Woolfson. M. S(1991) , Target tracking algorithms for phased array radar, IEE Proceedings. Pt.F.138． 3 (1991) , 255-262
[8] Gardner, L. A, and Mullen, R. J(1988) , Constant gain tracker with variable frame time, IEEE Trans.Aerosp.Electron.Syst (1988) AES-24,pp. 322-326
[9] Munu. M.. Harrison. I and Woolson.M. S(1992) , Comparison of adaptive target tracking algorithms for phased array radar, IEE Proceedings. Pt. F. 139． 5 (1992) , 336-342
[10] Sun. Q.. Alouani. A. T.. Rice. T. R and Gray. J. E. (1992) A neural network computation algorithm for discrete-time linear system state estimation, In Proceedings of International Joint Conference on Neural Network. I(1992) . 443-458
[11] Henry Leung. (1997) , A Hopfield neural tracker for phased array antenna, IEEE Trans. Aerosp. Electron. Syst (1997) Vol. 33,No. 1． pp. 301-307
[12] Elanayar. S.. and Shin. Y. C. (1994) Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems. IEEE Transactions on Neural networks. 5． (1994) . 594-603
[13] Narendra. K. S and Parthasarthy. K. (1990) ,Identification and control of dynamical systems using neural network, IEEE Transactions on Neural Networks. 1． (1990) . 4-27
[14] Hopf ield. J. J.. and Tank. D. W. (1985) , Neural computation of decisions in optimization problems, Biological Cybernetics. 52 (1985) , 141-152．
[15] Jumarie. G, (1990) , Nonlinear filtering weighted mean squares approach and Bayesian one via the maximum entropy principle. Signal Proceedings, 21 (1990) 323-338．
[16] Hayakawa, Y., Marumoto, A. and Sawada. Y. (1995) , Effects of the chaotic noise on the performance of a neural network model for optimization problems, Physical review. E. 51． (1995) , R2693-R2696．
[17] Sun, Q., Alouani. A. T.. Rice, T. R., and Gray, J. E. (1992) Linear system state estimation: A neural computing approach. In Proceedings of the American Control Conference. 1992． 550-554．
[18] Barrett. M.. and Aronott. R. (1994) , Adaptive antennas for mobile communications, Electronics and Communication Engineering Journal. (Aug. 1994) . 203-214．
[19] 程云鹏，矩阵论，西北工业大学出版社，1989，6
[20] 浙江大学数学系高等数学教研组编，概率论与数理统计，高等教育出版 社，1989
[21] 封建湖，数值分析，西北工业大学出版社，1988，7
[22] Henery Leung．(1997) ，A Hopfield Neural Tracker for Phased Array Antenna．，IEEE Transactions on Aerospace and Electronic Systems Vol．33，No．1，(Jan 1997) 301-307
[23] 刘有恒，信号检测与估计，人民邮电出版社，1989，4
[24] 吕杨生，随机信号处理导论天津大学出版社1988年5月
[25] 胡寿松，自动控制原理，国防工业出版社，1994年5月
[26] Catin，Donald E．(1989) Kalman filtering．Springer-Verlag New York Inc．