【摘要】：當(dāng)前各種雷達(dá)體制如相控陣?yán)走_(dá)、寬帶/超寬帶雷達(dá)、合成孔徑與逆合成孔徑雷達(dá)等,都采用數(shù)字化處理技術(shù)從回波中提取目標(biāo)參數(shù)信息。壓縮感知理論能夠用遠(yuǎn)低于奈奎斯特(Nyquist)理論的采樣速率采集信號(hào)、獲取離散數(shù)據(jù),然后通過非線性重構(gòu)算法重建信號(hào),是信號(hào)處理領(lǐng)域的一個(gè)革命性突破。本文對(duì)壓縮感知在雷達(dá)信號(hào)處理中的應(yīng)用展開研究,擬解決目前雷達(dá)信號(hào)處理中存在的若干問題,主要內(nèi)容有： (1)遠(yuǎn)程雷達(dá)及多目標(biāo)跟蹤的相控陣?yán)走_(dá)對(duì)目標(biāo)跟蹤數(shù)據(jù)率較低,多普勒頻率的估計(jì)存在模糊問題。本文采用了一種基于壓縮感知的隨機(jī)稀疏脈沖多普勒解模糊新方法。雷達(dá)系統(tǒng)只需隨機(jī)發(fā)射稀疏的探測(cè)脈沖,通過設(shè)計(jì)相應(yīng)的感知矩陣,運(yùn)用壓縮感知重構(gòu)算法進(jìn)行信號(hào)重建,從而獲得無模糊的多普勒頻率值。研究分析得出,應(yīng)用中隨機(jī)稀疏脈沖的發(fā)射時(shí)刻設(shè)置并非完全隨機(jī),還應(yīng)考慮目標(biāo)的觀測(cè)數(shù)目和無模糊距離的影響。仿真表明,該方法可以解決遠(yuǎn)程雷達(dá)的多普勒模糊問題,而且大大節(jié)省了相控陣?yán)走_(dá)的時(shí)間資源。 (2)寬帶雷達(dá)信號(hào)的應(yīng)用需要高速的模擬數(shù)字轉(zhuǎn)換器,造成雷達(dá)數(shù)據(jù)量劇增。本文通過采用寬帶逆合成孔徑雷達(dá)成像信號(hào)的方位向稀疏脈沖和距離向壓縮采樣相結(jié)合的方法,既節(jié)省了雷達(dá)時(shí)間資源,同時(shí)降低了信號(hào)采樣率和存儲(chǔ)傳輸代價(jià)。在成像時(shí)運(yùn)用壓縮感知重構(gòu)算法重建原始信號(hào),再對(duì)重構(gòu)后的成像數(shù)據(jù)在距離向和方位向分別作信號(hào)預(yù)測(cè),從而獲得超分辨率的雷達(dá)圖像。 (3)低目標(biāo)跟蹤數(shù)據(jù)率也給遠(yuǎn)程雷達(dá)的微多普勒測(cè)量帶來較大困難。本文對(duì)低脈沖重復(fù)頻率條件下的微多普勒提取問題進(jìn)行研究,提出了一種基于壓縮感知的低PRF微多普勒提取方法。該方法僅需對(duì)雷達(dá)時(shí)間資源調(diào)度進(jìn)行微調(diào),通過發(fā)射隨機(jī)探測(cè)脈沖串,然后對(duì)回波進(jìn)行壓縮感知信號(hào)重構(gòu)和時(shí)頻分析,獲得雷達(dá)的微動(dòng)特征曲線。仿真表明,壓縮感知應(yīng)用于微多普勒特征提取是可行的。
[Abstract]:At present, various radar systems such as phased array radar, wideband / ultra-wideband radar, synthetic aperture radar and inverse synthetic aperture radar all use digital processing technology to extract target parameter information from echo. Compression sensing theory can acquire discrete data at a sampling rate much lower than that of Nyquist (Nyquist) theory, and then reconstruct signals by nonlinear reconstruction algorithm, which is a revolutionary breakthrough in the field of signal processing. In this paper, the application of compressed sensing in radar signal processing is studied, and some problems existing in radar signal processing are solved. The main contents are as follows: (1) the tracking data rate of remote radar and multi-target tracking phased array radar is low, and the estimation of Doppler frequency is fuzzy. In this paper, a new method of random sparse pulse Doppler ambiguity based on compressed sensing is proposed. The radar system only needs to transmit sparse detection pulse randomly. By designing the corresponding sensing matrix and using the compression perception reconstruction algorithm to reconstruct the signal, the Doppler frequency value is obtained without ambiguity. It is concluded that the emission time of the random sparse pulse is not completely random in application, and the effect of the number of observations and the non-fuzzy distance should be taken into account. Simulation results show that this method can solve the Doppler ambiguity problem of remote radar and save the time resource of phased array radar greatly. (2) the application of wideband radar signals requires high speed analog to digital converters, resulting in a sharp increase in radar data. In this paper, the method of combining azimuth sparse pulse and range compression sampling of wideband inverse synthetic aperture radar imaging signal is used to save radar time resource and reduce signal sampling rate and storage transmission cost. The original signal is reconstructed by compression perception reconstruction algorithm, and then the reconstructed image is predicted in the range and azimuth directions respectively, and the super-resolution radar image is obtained. (3) low target tracking data rate also brings great difficulty to micro Doppler measurement of remote radar. In this paper, the problem of micro-Doppler extraction under low pulse repetition rate is studied, and a low PRF micro-Doppler extraction method based on compression sensing is proposed. This method only needs to fine-tune the time resource scheduling of radar and obtain the fretting characteristic curve of radar by transmitting a random detection pulse train and then reconstructing the echo by compressed sensing signal and time-frequency analysis. Simulation results show that compression sensing is feasible for feature extraction of micro Doppler.
【學(xué)位授予單位】：廈門大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN957.51

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