發(fā)布時(shí)間：2018-07-03 05:49

  本文選題：非均勻強(qiáng)雜波 + 微弱目標(biāo)檢測(cè)��； 參考：《電子科技大學(xué)》2014年博士論文

【摘要】：雜波背景中目標(biāo)檢測(cè)是雷達(dá)系統(tǒng)的基本任務(wù)之一。隨著現(xiàn)代隱身技術(shù)的發(fā)展,目標(biāo)的雷達(dá)截面積顯著減小,回波信號(hào)變得十分微弱,信雜比顯著下降;同時(shí),城市、山地、和海浪等背景產(chǎn)生的雜波,強(qiáng)度大,均勻性差。這兩方面因素的共同作用的結(jié)果,導(dǎo)致基于均勻雜波假設(shè)的傳統(tǒng)檢測(cè)器性能明顯下降,尤其是在低信雜比或強(qiáng)雜波情況下,性能下降更為顯著,這使得非均勻強(qiáng)雜波下的目標(biāo)檢測(cè)面臨嚴(yán)峻挑戰(zhàn),已成為現(xiàn)代雷達(dá)信號(hào)與數(shù)據(jù)處理必須解決的難點(diǎn)問題。本文圍繞非均勻強(qiáng)雜波下的目標(biāo)檢測(cè)問題,開展了雜波建模、檢測(cè)器設(shè)計(jì)、仿真檢驗(yàn)和實(shí)測(cè)數(shù)據(jù)驗(yàn)證等研究工作,主要內(nèi)容如下:1.針對(duì)內(nèi)海、湖泊等逆高斯(IG)調(diào)制復(fù)合高斯(CG)(IG-CG)非均勻強(qiáng)雜波,提出了基于兩步廣義似然比(GLRT)準(zhǔn)則的自適應(yīng)檢測(cè)器,克服現(xiàn)有非均勻檢測(cè)器雜波模型失配的缺點(diǎn),檢測(cè)性能得到改善。2.針對(duì)紋理分量部分相關(guān)的非均勻地/海強(qiáng)雜波,根據(jù)兩步廣義似然比(GLRT)準(zhǔn)則,提出根據(jù)紋理分量和斑點(diǎn)分量估計(jì)雜波協(xié)方差矩陣,以改善檢測(cè)器的雜波自適應(yīng)性能,能夠降低雜波模型失配對(duì)檢測(cè)性能的影響。3.針對(duì)城市、海洋、植被等復(fù)合高斯非均勻強(qiáng)雜波,提出了GLRT-MSD,Rao-MSD和Wald-MSD等三種自適應(yīng)多幀檢測(cè)器,以期有效利用目標(biāo)雜波的幀間相關(guān)性差異,提升分辨單元內(nèi)運(yùn)動(dòng)小目標(biāo)的檢測(cè)性能。4.針對(duì)循環(huán)平穩(wěn)的非均勻海雜波,提出了M-NHD和SV-NHD非均勻多幀檢測(cè)器,可以避免非均勻參考數(shù)據(jù)對(duì)分辨單元內(nèi)運(yùn)動(dòng)目標(biāo)檢測(cè)性能的不利影響。5.針對(duì)城市、海洋、植被等復(fù)合高斯非均勻強(qiáng)雜波,提出VL-HSCD,VL-HKelly和VL-HAMF等自適應(yīng)非均勻多幀檢測(cè)器,結(jié)合混合協(xié)方差矩陣估計(jì)方法和Viterbi-like(VL)幀間積累方法,可以提升跨分辨單元運(yùn)動(dòng)目標(biāo)的檢測(cè)性能。上述的檢測(cè)算法,已通過仿真數(shù)據(jù)或?qū)崪y(cè)數(shù)據(jù)的驗(yàn)證,其中,實(shí)測(cè)雜波數(shù)據(jù)為國際通用的IPIX雷達(dá)數(shù)據(jù),仿真中的雜波參數(shù)主要來自于對(duì)實(shí)測(cè)數(shù)據(jù)的估計(jì)。
[Abstract]:Target detection in clutter background is one of the basic tasks of radar system. With the development of modern stealth technology, the radar cross section of the target decreases significantly, the echo signal becomes very weak, and the signal-to-clutter ratio decreases significantly. At the same time, the clutter produced by the background of city, mountain, wave and so on is of great intensity and poor uniformity. As a result of the combined action of these two factors, the performance of traditional detectors based on the assumption of uniform clutter is significantly reduced, especially in the case of low signal-to-clutter ratio or strong clutter. This makes the target detection under non-uniform strong clutter face a severe challenge and has become a difficult problem that must be solved in modern radar signal and data processing. Focusing on the problem of target detection under non-uniform strong clutter, this paper has carried out research work on clutter modeling, detector design, simulation and verification of measured data. The main contents are as follows: 1. In this paper, an adaptive detector based on two-step generalized likelihood ratio (GLRT) criterion is proposed for inhomogeneous strong clutter modulated by inverse Gao Si (IG) modulation in lakes and lakes, which overcomes the shortcomings of existing heterogeneous detector clutter models. Detection performance improved. 2. 2. Based on the two-step generalized likelihood ratio (GLRT) criterion, the clutter covariance matrix is estimated based on texture component and speckle component to improve the adaptive performance of the detector. It can reduce the influence of clutter model mismatch detection performance. In this paper, three adaptive multi-frame detectors, GLRT-MSD Rao-MSD and Wald-MSD, are proposed to improve the detection performance of moving small targets in the resolution unit by effectively utilizing the inter-frame correlation difference of target clutter and improving the detection performance of moving small targets in the resolution unit. For cyclic stationary heterogeneous sea clutter, M-NHD and SV-NHD non-uniform multi-frame detectors are proposed, which can avoid the adverse effect of non-uniform reference data on the detection performance of moving targets in the resolution unit. For urban, oceanic, vegetation and other complex Gao Si heterogeneous strong clutter, an adaptive nonuniform multi-frame detector, such as VL-HSCDNF-VL-HKelly and VL-HAMF, is proposed, which combines the mixed covariance matrix estimation method and the Viterbi-like (VL) inter-frame accumulation method. It can improve the detection performance of moving targets. The above detection algorithms have been verified by simulation data or measured data. Among them, the measured clutter data are international IPIX radar data, and the clutter parameters in the simulation mainly come from the estimation of the measured data.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN957.52
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本文編號(hào)：2092632