本文選題：合成孔徑雷達(dá) + 運動目標(biāo)檢測�。� 參考：《西安電子科技大學(xué)》2015年碩士論文

【摘要】：運動目標(biāo)的檢測和參數(shù)估計是雷達(dá)信號處理的重點方向。基于多通道合成孔徑雷達(dá)(SAR)系統(tǒng)的運動目標(biāo)檢測方法能利用空域自由度實現(xiàn)有效的雜波抑制,完成地面慢速目標(biāo)的檢測(GMTI)。然而隨著通道數(shù)目的增加,數(shù)據(jù)量急劇增大,造成雷達(dá)系統(tǒng)數(shù)據(jù)處理負(fù)擔(dān)變大。壓縮感知(CS)理論可以在欠采樣條件下實現(xiàn)信號的無失真重構(gòu),并且運動目標(biāo)信號在空間上通常具有稀疏性,因此,研究稀疏信號體制下的SAR動目標(biāo)檢測技術(shù)對提升雷達(dá)系統(tǒng)空間監(jiān)視能力具有重要意義。本文主要研究基于稀疏重構(gòu)的多通道SAR系統(tǒng)的動目標(biāo)檢測方法,主要內(nèi)容如下:稀疏重構(gòu)算法一般存在計算量過大的問題,難以直接應(yīng)用于實際系統(tǒng)。本文借鑒零空間調(diào)整(NST)思想,提出一種快速的雙通道SAR動目標(biāo)檢測方法。該方法聯(lián)合雙通道數(shù)據(jù)進(jìn)行雜波抑制,利用NST算法實現(xiàn)動目標(biāo)檢測,提高了目標(biāo)檢測的精度和穩(wěn)健性,并減小了算法的計算復(fù)雜度。動目標(biāo)位置需標(biāo)定在高分辨SAR圖像上,盡管運動目標(biāo)在空間上滿足稀疏性,場景是非稀疏的,因此不能同時實現(xiàn)高分辨率SAR成像與動目標(biāo)檢測定位。針對該問題,首先對滿采樣通道采用交替方向法(ADM)重構(gòu)場景和目標(biāo)的SAR圖像,利用所成SAR圖像對稀疏采樣通道進(jìn)行雜波抑制,最后基于NST技術(shù)實現(xiàn)運動目標(biāo)檢測。仿真結(jié)果說明本算法具有良好的動目標(biāo)檢測性能。針對各通道稀疏重構(gòu)時重組誤差的不一致性,給出一種適用于稀疏采樣模型的多通道動目標(biāo)檢測方法,大大提高了算法對重組誤差的穩(wěn)健性;在此基礎(chǔ)上,提出一種稀疏采樣多通道SAR的運動目標(biāo)徑向速度估計方法,該方法首先對各通道進(jìn)行稀疏重構(gòu)得到SAR圖像,再通過對雙通道數(shù)據(jù)進(jìn)行ATI幅相聯(lián)合檢測實現(xiàn)運動目標(biāo)定位,最終通過陣列DOA方法實現(xiàn)目標(biāo)的高精度徑向速度估計。
[Abstract]:Detection and parameter estimation of moving targets are the key points of radar signal processing. The moving target detection method based on multi-channel synthetic aperture radar (SAR) system can effectively suppress clutter by using spatial freedom and complete ground slow target detection (GMTI). However, with the increase of the number of channels, the amount of data increases rapidly, which makes the data processing burden of radar system become larger. Compression sensing (CS) theory can realize the distortion free reconstruction of the signal under the condition of under-sampling, and the moving target signal is usually sparse in space, so, It is very important to study the SAR moving target detection technology in sparse signal system to improve the space surveillance capability of radar system. In this paper, the moving target detection method of multi-channel SAR system based on sparse reconstruction is studied. The main contents are as follows: the sparse reconstruction algorithm is difficult to be directly applied to practical systems because of its large computational complexity. Based on the idea of null space adjustment (NST), a fast dual channel SAR moving target detection method is proposed in this paper. This method combines dual-channel data for clutter suppression and uses NST algorithm to realize moving target detection. It improves the accuracy and robustness of target detection and reduces the computational complexity of the algorithm. Moving targets need to be calibrated on high resolution SAR images. Although moving targets satisfy sparsity in space, the scene is non-sparse, so high resolution SAR imaging and moving target detection and localization cannot be realized at the same time. To solve this problem, alternate direction method (ADM) is used to reconstruct the scene and target SAR images, and the sparse sampling channel is suppressed by the SAR images. Finally, the moving target detection is realized based on NST technology. Simulation results show that the algorithm has good performance in moving target detection. Aiming at the inconsistency of recombination error in sparse reconstruction of every channel, a multi-channel moving target detection method suitable for sparse sampling model is presented, which greatly improves the robustness of the algorithm to the recombination error. A method for estimating the radial velocity of moving targets based on sparse sampling multi-channel SAR is proposed. Firstly, the SAR images are obtained by sparse reconstruction of each channel, and then the moving target location is realized by ATI amplitude-phase joint detection of two-channel data. Finally, high precision radial velocity estimation is realized by array DOA method.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN957.51

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