發(fā)布時(shí)間：2018-06-28 21:36

  本文選題：運(yùn)動(dòng)平臺(tái) + 前視掃描雷達(dá)　； 參考：《電子科技大學(xué)》2016年博士論文

【摘要】：運(yùn)動(dòng)平臺(tái)載雷達(dá)實(shí)波束掃描圖像,受天線孔徑限制,方位角分辨率難以滿足精確末制導(dǎo)、地形回避和自主著陸等應(yīng)用的要求。而現(xiàn)有的前視單脈沖銳化技術(shù)雖然改善了圖像顯示效果,但不能提高圖像分辨能力。因此,開(kāi)展運(yùn)動(dòng)平臺(tái)雷達(dá)前視成像方法研究,提出改善角分辨率的超分辨方法,突破實(shí)波束分辨率的制約,具有重要的應(yīng)用價(jià)值。本文圍繞運(yùn)動(dòng)平臺(tái)載雷達(dá)前視超分辨成像問(wèn)題,開(kāi)展了回波建模、超分辨算法和飛行實(shí)驗(yàn)驗(yàn)證等研究工作,主要內(nèi)容如下:1.導(dǎo)出了掃描雷達(dá)的方位類卷積信號(hào)模型,為采用卷積反演方法實(shí)現(xiàn)前視超分辨提供了理論支撐。還研究了超分辨性能的信噪比約束條件,為工程應(yīng)用中確定雷達(dá)工作參數(shù)提供了理論依據(jù)。2.提出了稀疏目標(biāo)超分辨成像算法,可有效抑制卷積反演過(guò)程中的噪聲和雜波放大效應(yīng),避免出現(xiàn)虛假目標(biāo)。顯著改善了噪聲和雜波背景中孤立多目標(biāo)的分辨能力。3.提出了面目標(biāo)超分辨成像算法,以廣義高斯分布作為目標(biāo)先驗(yàn)信息,并通過(guò)調(diào)節(jié)其分散度參數(shù)確定目標(biāo)函數(shù),可實(shí)現(xiàn)噪聲和雜波背景下的面目標(biāo)超分辨成像,同時(shí)改善面目標(biāo)圖像紋理信息。4.提出了等效陣列前視超分辨成像算法,將前視掃描轉(zhuǎn)化為陣列模型,并以最小二乘和最小均方誤差準(zhǔn)則進(jìn)行陣列超分辨�？梢栽谏倭糠轿粧呙璐螖�(shù)條件下,實(shí)現(xiàn)孤立目標(biāo)個(gè)數(shù)和位置的準(zhǔn)確估計(jì)。上述模型和超分辨成像算法,已通過(guò)仿真或?qū)崪y(cè)數(shù)據(jù)進(jìn)行了驗(yàn)證,結(jié)果表明,論文提出的超分辨成像算法,可實(shí)現(xiàn)實(shí)際運(yùn)動(dòng)平臺(tái)載雷達(dá)前視超分辨成像。
[Abstract]:The real beam scanning images of radar on moving platform are limited by antenna aperture and the azimuth resolution is difficult to meet the requirements of precise terminal guidance terrain avoidance and autonomous landing. Although the existing forward looking monopulse sharpening technology can improve the image display effect, it can not improve the image resolution. Therefore, it is of great value to research forward imaging method of moving platform radar, to improve the angle resolution and to break through the constraints of real beam resolution. In this paper, the echo modeling, super-resolution algorithm and flight experiment verification are carried out around the problem of super-resolution imaging of moving platform-borne radar. The main contents are as follows: 1. The azimuth type convolution signal model of scanning radar is derived, which provides theoretical support for using convolution inversion method to realize forward looking super resolution. The signal-to-noise ratio (SNR) constraint condition of superresolution performance is also studied, which provides a theoretical basis for determining the operational parameters of radar in engineering applications. A super-resolution imaging algorithm for sparse targets is proposed, which can effectively suppress the noise and clutter amplification in convolution inversion and avoid the appearance of false targets. The resolution ability of isolated multi-target in noise and clutter background is improved significantly. In this paper, a super-resolution imaging algorithm for surface targets is proposed. The generalized Gao Si distribution is used as the prior information of the targets, and the target function is determined by adjusting the dispersion parameters. The super-resolution imaging of surface targets under noise and clutter background can be realized. At the same time, improve the face target image texture information. 4. In this paper, an equivalent array forward super-resolution imaging algorithm is proposed. The forward scan is transformed into an array model, and the array superresolution is carried out by using the least square method and the least mean square error criterion. The accurate estimation of the number and position of isolated targets can be realized under the condition of a few azimuth scanning times. The above model and super-resolution imaging algorithm have been verified by simulation or measured data. The results show that the super-resolution imaging algorithm proposed in this paper can realize the super-resolution imaging of radar on the actual moving platform.
【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】：2016
【分類號(hào)】：TN957.52

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 李悅麗;梁甸農(nóng);黃曉濤;;一種單脈沖雷達(dá)多通道解卷積前視成像方法[J];信號(hào)處理;2007年05期

2 張杰,廖桂生,王玨;對(duì)角加載對(duì)信號(hào)源數(shù)檢測(cè)性能的改善[J];電子學(xué)報(bào);2004年12期

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本文編號(hào)：2079437