海雜波背景下的組合自適應(yīng)GLRT-LTD
doi: 10.11999/JEIT150588 cstr: 32379.14.JEIT150588
基金項(xiàng)目:
國家自然科學(xué)基金(61271295)
Combined Adaptive GLRT-LTD against Sea Clutter
Funds:
The National Natural Science Foundation of China (61271295)
-
摘要: 為了檢測海雜波背景下的微弱運(yùn)動(dòng)目標(biāo)，相干檢測器通常需要作長時(shí)間的累積。然而，長時(shí)累積條件下的目標(biāo)多普勒頻率的擴(kuò)散和幅度的起伏以及海雜波空間非均勻性對參考單元數(shù)目的限制導(dǎo)致傳統(tǒng)的自適應(yīng)檢測器沒法工作。注意到逆伽馬(IG)紋理的復(fù)合高斯分布(CGD)可以很好地描述海雜波和目標(biāo)的瞬時(shí)頻率是時(shí)間的慢變函數(shù)，該文提出一種組合自適應(yīng)檢測器，即組合自適應(yīng)廣義似然比線性門限檢測器(CA-GLRT-LTD)，它由自適應(yīng)GLRT-LTD在幾個(gè)連續(xù)的短的累積間隔上的最大響應(yīng)的乘積的構(gòu)成。由于GLRT-LTD對IG紋理的復(fù)合高斯雜波的最優(yōu)性，該檢測器相比組合自適應(yīng)歸一化匹配濾波(CANMF)檢測器具有更好的檢測性能。
-
關(guān)鍵詞:
- 目標(biāo)檢測 /
- 長時(shí)累積 /
- 逆伽馬紋理 /
- 海雜波 /
- 組合自適應(yīng)廣義似然比線性門限檢測器
Abstract: Long integration is often required to detect weak moving target in sea clutter. However, the Doppler frequency spread and amplitude fluctuation in long integration and limited reference cells resulting from spatial non-homogeneity of sea clutter make the traditional adaptive detector work badly. By observing that the Compound Gaussian Distribution (CGD) with Inverse Gamma (IG) texture gives a good fit to sea clutter and the instantaneous frequency is slowly varying, a combined adaptive detector, namely the Combined Adaptive Generalized Likelihood Ratio Test-Linear Threshold Detector (CA-GLRT-LTD), is proposed in the paper, which consists of the product of the maximal response of the adaptive GLRT-LTD in several continuous short integration intervals. Owing to the optimality of the GLRT-LTD for CG clutter with IG texture, the proposed detector obtains better performance than the Combined Adaptive Normalized Matched Filter (CANMF) detector. -
Moya J C, Menoyo J G, Lopez A A, et al.. Small target detection in high-resolution heterogeneous sea-clutter: an empirical analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(3): 1880-1898. Chen Xiao-long, Cai Yong, and Cai Fu-qing. Application of the sparse decomposition to micro-motion target detection embedded in sea clutter[C]. Proceedings of the 2013 International Conference on Radar, Adelaide, Canada, 2013: 163-166. Chen Si-jia, Kong Ling-jiang, and Yang Jian-yu. Adaptive detection in compound-Gaussian clutter with partially correlated texture[C]. Proceedings of the 2013 IEEE Radar Conference, Ottawa, Canada, 2013: 1-5. Chen Xiao-long, Guan Jian, He You, et al.. Detection and extraction of target with micro-motion in spiky sea clutter via short-time fractional Fourier transform[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 1002-1018. Ward K D, Tough R J A, and Watts S. Sea Clutter: Scattering, the K Distribution and Radar Performance[M]. London, United Kingdom: The Institution of Engineering and Technology, 2006: 230-266. Sangston K J and Gerlach K R. Coherent detection of radar targets in a non-Gaussian background[J]. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(1): 208-216. Greco M V, Gini F, and Rangaswamy M. Statistical analysis of measured polarimetric clutter data at different resolutions [J]. IEE Proceedings-Radar, Sonar and Navigation, 2006, 153(6): 473-481. 張玉石, 許心瑜, 尹雅磊, 等. L波段小擦地角海雜波幅度統(tǒng)計(jì)特性研究[J]. 電子與信息學(xué)報(bào), 2014, 36(5): 1044-1048. Zhang Yu-shi, Xu Xin-yu, Yin Ya-lei, et al.. Research on amplitude statistics of L-band low grazing angle sea clutter [J]. Journal of Electronics Information Technology, 2014, 36(5): 1044-1048. Moya J C, Menoyo J G, Lopez A A, et al.. Statistical analysis of a high-resolution sea-clutter database[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(4): 2024-2037. Rangaswamy M, Weiner D, and Ozturk A. Computer generation of correlated non-Gaussian radar clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1995, 31(1): 106-116. Gini F, Greco M V, Farina A, et al.. Optimum and mismatched detection against K-distributed plus Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(3): 860-876. Yang D. Optimal coherent radar detection in a K-distributed clutter environment[J]. IEE Proceedings-Radar, Sonar and Navigation, 2012, 6(5): 283-292. Sangston K J, Gini F, and Greco M V. Coherent radar target detection in heavy-tailed compound-Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(1): 64-77. Balleri A, Nehorai A, and Wang J. Maximum likelihood estimation for compound-Gaussian clutter with inverse gamma texture[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(2): 775-780. Greco M V and Gini F. Cramer-Rao lower bounds for covariance matrix estimation of complex elliptically symmetric distributions[J]. IEEE Transactions on Signal Processing, 2013, 61(24): 6401-6409. 鄒鯤, 廖桂生, 李軍, 等. 非高斯雜波下知識輔助檢測器敏感性分析[J]. 電子與信息學(xué)報(bào), 2014, 36(1): 181-186. Zou Kun, Liao Gui-sheng, Li Jun, et al.. Sensitivity analysis of knowledge aided detector in non-Gaussian clutter[J]. Journal of Electronics Information Technology, 2014, 36(1): 181-186. Zhao Yi-nan, Pang Xiao-yu, and Yin Bin. Adaptive radar detection for targets in compound-Gaussian clutter with inverse gamma texture[C]. Proceedings of the IET International Radar Conference, Xian, China, 2013: 1-4. Shui Peng-lang, Li Dong-chen, and Xu Shu-wen. Tri-feature- based detection of floating targets in sea clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1416-1430. -
計(jì)量
- 文章訪問數(shù): 1380
- HTML全文瀏覽量: 106
- PDF下載量: 591
- 被引次數(shù): 0
下載:
