ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Robustness analysis of knowledge aided detector under prior mismatched conditions
Received date: 2014-04-14
Revised date: 2014-07-22
Online published: 2015-03-31
Supported by
National Natural Science Foundation of China (61271292); Aeronautical Innovation Foundation
Smart use of the prior information of detection environment is one of effective approaches to improve the performance of radar detection. Prior information is given at the design stage of radar detector, and the resultant difference between the prior model and current detection environment may be existed. In this paper, the knowledge aided (KA) detector using the texture component prior information in compound Gaussian clutter is considered. The quantized relationship between the detector performance and the mismatched prior model parameters is proposed, and the impact of the mismatched prior model on the detector performance is analyzed, with given detection environment model. The analyzed results show that, the robustness of knowledge aided detector is related to the model parameters of current detection environment. Furthermore, we propose the allowable region of the prior model mismatches, and when the prior model parameters are located in this region, the knowledge aided detector outperforms the convention detector without using prior information.
ZOU Kun , ZHANG Bin , LIU Zifu . Robustness analysis of knowledge aided detector under prior mismatched conditions[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015 , 36(3) : 939 -948 . DOI: 10.7527/S1000-6893.2014.0169
[1] Berger J O. Statistical decision theory and Bayesian analysis[M]. 2nd ed. New York: Springer-Varlag, 1985: 1-50.
[2] Moya J C, Maio A D. Experimental performance analysis of distributed targets coherent radar detector [J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(3): 2216-2238.
[3] Sangston K J, Gini F, Greco M S. Coherent radar target detection in heavy-tailed compound Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(1): 64-77.
[4] Carretero-Moya J, Menoyo J G, Lopze A A. Small target detection in high resolution heterogeneous sea clutter: an empirical analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(3): 1880-1888.
[5] Ollila E, Tyler D E, Koivunen V, et al. Compound Gaussian clutter modeling with an inverse Gaussian texture distribution[J]. IEEE Signal Processing Letters, 2012, 19(12): 876-879.
[6] Gao Y, Liao G S, Zhu S, et al. A persymmetric GLRT for adaptive detection in compound Gaussian clutters with random texture[J]. IEEE Signal Processing Letters, 2013, 20(6): 615-618.
[7] Abdelaziz M E M, Chonavel T, Aissa-El-Bey A, et al. Sea clutter texture estimation: exploiting decorrelation and cyclostationarity[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(2): 726-742.
[8] Gini F, Rangaswamy M. Knowledge-based radar detection, tracking, and classification [M]. New York: John Wiley & Sons, 2008: 14-55.
[9] Gini F, Greco M V, Farina A, et al. Optimum and mismatched detection against K-distribution plus Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(3): 860-876.
[10] Greco M, Stinco P, Gini F. Impact of sea clutter nonstationarity on disturbance covariance matrix estimation and CFAR detector performance[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(3): 1502-1513.
[11] Bandiera F, Orlando D, Ricci G. Advanced radar detection schemes under mismatched signal model[M]. New York: Morgan & Claypool Publishers, 2009: 1-55.
[12] Tang B, Tang J, Peng Y N. Performance of knowledge aided space time adaptive processing[J]. IET Radar, Sonar & Navigation, 2011, 5(3): 331-340.
[13] Tang B, Zhang Y, Li K. Adaptive clutter suppression research based on priori knowledge and its accuracy evaluation[J]. Acta Aeronoutica et Astronautica Sinica, 2013, 34(5): 1174-1180 (in Chinese). 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.
[14] Balleri A, Nehorai A, Wang J. Maximum likelihood estimation for compound-Gaussian clutter with inverse gamma texture[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(2): 775-779.
[15] Shang X, Song H. Radar detection based on compound Gaussian model with inverse gamma texture[J]. IET Radar, Sonar & Navigation, 2011, 5(3): 315-321.
[16] Bandiera F, Besson O, Ricci G. Adaptive detection of distributed targets in compound-Gaussian noise without secondary data: a Bayesian approach[J]. IEEE Transactions on Signal Processing, 2011, 59(12): 5698-5708.
[17] Bandiera F, Besson O, Ricci G, Knowledge-aided covariance matrix estimation and adaptive detection in compound Gaussian noise [J]. IEEE Transactions on Signal Processing, 2011, 58(10): 5390-5396.
[18] Zhao Y N, Li F C, Yin B. Adaptive polarimetric detection of targets in heavy-tailed compound-Gaussian clutter[J]. Journal of Electronics & Information Technology, 2013, 35(2): 376-380(in Chinese). 赵宜楠, 李风从, 尹彬. 严重拖尾复合高斯杂波中目标的自适应极化检测[J].电子与信息学报,2013, 35(2):376-380.
[19] Zou K, Liao G S, Li J, et al. Robust detection in compound Gaussian clutter based on Bayesian framework[J]. Journal of Electronics & Information Technology, 2013, 35(7): 1551-1560 (in Chinese). 邹鲲, 廖桂生, 李军, 等.基于Bayesian框架的复合高斯杂波下稳健检测[J].电子与信息学报,2013, 35(7):1551-1560.
[20] Zou K, Liao G S, Li J, et al. Sensitivity analysis of knowledge aided detector in non-Gaussian clutter[J]. Journal of Electronics & Information Technology, 2014, 36(1): 181-186 (in Chinese). 邹鲲, 廖桂生, 李军, 等. 非高斯杂波下知识辅助检测器敏感性分析[J]. 电子与信息学报, 2014, 36(1): 181-186.
[21] Zou K, Liao G S, Li J, et al. Cognitive method for know-ledge aided detection in non-Gaussian clutter[J]. Acta Electronica Sinica, 2014, 42(6): 1047-1054 (in Chinese). 邹鲲, 廖桂生, 李军, 等,非高斯杂波下的知识辅助检测的认知方法[J]. 电子学报, 2014, 42(6): 1047-1054.
/
〈 |
|
〉 |