基于知識(shí)的海雜波背景下距離擴(kuò)展目標(biāo)檢測(cè)

許述文; 薛健; 水鵬朗

doi:10.11999/JEIT160905

基于知識(shí)的海雜波背景下距離擴(kuò)展目標(biāo)檢測(cè)

doi: 10.11999/JEIT160905

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61201296, 61372136)，中央高?；究蒲袠I(yè)務(wù)費(fèi)(JB160224)，陜西省科協(xié)青年人才托舉計(jì)劃(20160205)

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出版歷程
- 收稿日期: 2016-09-07
- 修回日期: 2016-11-09
- 刊出日期: 2016-12-19

Adaptive Detection of Range-spread Targets Based on Knowledge in Sea Clutter Background

Funds:

The National Natural Science Foundation of China (61201296, 61372136), The Fundamental Research Funds for the Central Universities (JB160224), Young Talent Fund of University Association for Science and Technology in Shaanxi Province (20160205)

摘要

摘要: 該文針對(duì)在輔助數(shù)據(jù)有限的情況下自適應(yīng)檢測(cè)器出現(xiàn)檢測(cè)性能損失，提出基于雜波的先驗(yàn)知識(shí)分布的距離擴(kuò)展目標(biāo)自適應(yīng)檢測(cè)算法。復(fù)合高斯雜波的紋理和散斑的協(xié)方差矩陣分別被建模為服從逆伽瑪分布的隨機(jī)變量和逆復(fù)Wishart分布的隨機(jī)矩陣。利用先驗(yàn)知識(shí)推導(dǎo)了紋理分量的最大后驗(yàn)估計(jì)，并結(jié)合廣義似然比檢驗(yàn)設(shè)計(jì)了不依賴輔助數(shù)據(jù)的距離擴(kuò)展目標(biāo)自適應(yīng)檢測(cè)器。仿真結(jié)果表明，提出的檢測(cè)器在參數(shù)失配條件下具有好的魯棒性，而且在輔助數(shù)據(jù)有限情況下檢測(cè)性能優(yōu)于傳統(tǒng)的廣義似然比檢測(cè)器的檢測(cè)性能。
- 距離擴(kuò)展目標(biāo) /
- 先驗(yàn)知識(shí) /
- 復(fù)合高斯雜波 /
- 逆伽瑪分布 /
- 逆復(fù)Wishart分布
Abstract: For the problem of detection performance loss of adaptive detectors on the condition that the secondary data are limited, the adaptive detection method of range-spread target based on the prior knowledge of clutter is proposed. The texture and the covariance matrix of speckle of clutter are respectively modeled as the random variable which follows the inverse Gamma distribution and the random matrix which follows the inverse complex Wishart distribution. Based on the prior knowledge, the Maximum A Posteriori (MAP) estimation of texture component is obtained and the adaptive detector of range spread target which does not need the secondary data is designed via utilizing the generalized likelihood ratio test. Finally, the detection performances of the proposed detector are evaluated and the experimental results illustrate that the proposed detector is robust in parameters mismatched situation and outperforms the conventional generalized likelihood ratio test detector for range-spread target in limited secondary data scenarios.
- Range-spread target /
- Prior knowledge /
- Compound Gaussian clutter /
- Inverse Gamma distribution /
- Inverse complex Wishart distribution

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參考文獻(xiàn)(21)

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