基于先验知识及其定量评估的自适应杂波抑制研究
收稿日期: 2012-05-02
修回日期: 2013-01-23
网络出版日期: 2013-02-02
基金资助
国家自然科学基金(61201379, 61179036);安徽省自然科学基金(1208085QF103)
Adaptive Clutter Suppression Research Based on Priori Knowledge and Its Accuracy Evaluation
Received date: 2012-05-02
Revised date: 2013-01-23
Online published: 2013-02-02
Supported by
National Natural Science Foundation of China (61201379, 61179036); Natural Science Foundation of Anhui Province (1208085QF103)
为了改善训练样本数受限的非均匀杂波环境中的系统检测性能,研究了基于先验知识及其定量评估的自适应杂波抑制算法。提出了使用经真实杂波信息白化后的先验杂波协方差矩阵与单位矩阵之差的谱范数,来定量评估杂波先验知识的准确程度,并给出了真实杂波协方差矩阵未知时的矩阵谱范数估计方法。结合先验知识定量评估结果,获得了具有先验知识约束时的杂波协方差矩阵最大似然估计方法。分别基于多脉冲相参雷达以及空时自适应雷达进行了杂波建模,在此基础之上分析了算法性能。仿真结果证实了该算法优于使用样本协方差矩阵及先验杂波信息形成杂波抑制权值的性能。
唐波 , 张玉 , 李科 . 基于先验知识及其定量评估的自适应杂波抑制研究[J]. 航空学报, 2013 , 34(5) : 1174 -1180 . DOI: 10.7527/S1000-6893.2013.0078
To overcome the limitation on the number of homogeneous samples in heterogeneous clutter environments, an algorithm is proposed for suppressing clutter based on a priori knowledge and its accuracy evaluation. First, the spectral norm of the priori clutter covariance matrix, which is whitened by the true clutter covariance matrix and then subtracted by an identity matrix, is computed as a measure for the accuracy of the priori knowledge. Given that the true clutter covariance matrix is unknown, a method for adaptively estimating the spectral norm is proposed. Then with the evaluation result of the accuracy of the priori knowledge, a maximum likelihood estimation of the true clutter covariance matrix is obtained under a knowledge constraint. Finally, after constructing the clutter model of the coherent-pulse radar and space time adaptive radar respectively, the proposed algorithm is analyzed through numerical simulation. Simulation results show that the performance of the proposed algorithm is superior to the method in which the adaptive weight is formulated either by the sample covariance matrix or the priori covariance matrix.
[1] Brennan L E, Reed I S. Theory of adaptive radar. IEEE Transactions on Aerospace and Electronic Systems, 1973, 9(2): 237-252.
[2] Ward J. Space-time adaptive processing for airborne radar. MIT Lincoln Laboratory Technical Report 1015, 1994.
[3] Guerci J R. Space-time adaptive processing for radar. Norwood: Artech House, 2003.
[4] Tang B, Tang J, Peng Y N. Clutter model and space time adaptive processing for truncated cone array. Acta Aeronautica et Astronautica Sinica, 2010, 31(3): 587-592. (in Chinese) 唐波, 汤俊, 彭应宁. 圆台阵列杂波模型及空时二维自适应处理. 航空学报, 2010, 31(3): 587-592.
[5] Reed S, Mallett J D, Brennan L E. Rapid convergence rate in adaptive arrays. IEEE Transactions on Aerospace and Electronic Systems, 1974, 10(6): 853-863.
[6] Melvin W L. Space-time adaptive radar performance in heterogeneous clutter. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(2): 621-633.
[7] Guerci J R, Baranoski E J. Knowledge-aided adaptive radar at DARPA: an overview. IEEE Signal Processing Magazine, 2006, 23(1): 41-50.
[8] Wicks M C, Rangaswamy M, Adve R, et al. Space-time adaptive processing: a knowledge-based perspective for airborne radar. IEEE Signal Processing Magazine, 2006, 23(1): 51-65.
[9] Melvin W L, Guerci J. Knowledge-aided signal processing: a new paradigm for radar and other advanced sensors. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(3): 983-996.
[10] Bergin J S, Teixeira C M, Techau P M, et al. Improved clutter mitigation performance using knowledge-aided space-time adaptive processing. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(3): 997-1009.
[11] Stoica P, Li J, Zhu X M, et al. On using a priori knowledge in space-time adaptive processing. IEEE Transactions on Signal Processing, 2008, 56(6): 2598-2602.
[12] Besson O, Bidon S, Tourneret J Y. Covariance matrix estimation with heterogeneous samples. IEEE Transactions on Signal Processing, 2008, 56(3): 909-920.
[13] Bidon S, Besson O, Tourneret J Y. A Bayesian approach to adaptive detection in nonhomogeneous environments. IEEE Transactions on Signal Processing, 2008, 56(1): 205-217.
[14] de Maio A, Farina A, Foglia G. Knowledge-aided Bayesian radar detectors & their application to live data. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(1): 170-183.
[15] de Maio A, de Nicola S, Landi L, et al. Knowledge-aided covariance matrix estimation: a MAXDET approach. IET Radar, Sonar & Navigation, 2009, 3(4): 341-356.
[16] Zhu X M, Li J, Stoica P. Knowledge-aided space-time adaptive processing. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(2): 1325-1336.
[17] Tang B, Tang J, Peng Y N. Performance of knowledge aided space time adaptive processing. IET Radar, Sonar & Navigation, 2011, 5(3): 331-340.
[18] Horn R A, Johnson C R. Matrix analysis. New York: Cambridge University Press, 1985.
[19] Bhatia R. Matrix analysis. New York: Springer, 1997.
[20] Löfberg J. YALMIP: a toolbox for modeling and optimization in MATLAB. 2004 IEEE International Symposium on Computer Aided Control Systems Design, 2004: 1-4.
[21] Toh K C, Todd M J, Tutuncu R H. SDPT3—a MATLAB software for semidefinite programming. Optimization Method Software, 1999, 11(1-4): 545-581.
[22] Robey F C, Fuhrmann D R, Kelly E J, et al. A CFAR adaptive matched filter detector. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(1): 208-216.
/
| 〈 |
|
〉 |
CJA