本文選題：DOA估計(jì) + 單快拍　； 參考：《電子科技大學(xué)》2014年碩士論文

【摘要】：在現(xiàn)代軍事或衛(wèi)星通信中,由于跳頻通信與突發(fā)短時(shí)通信技術(shù)的廣泛應(yīng)用,以及復(fù)雜多變的電磁環(huán)境影響,陣列接收的有效數(shù)據(jù)往往不足。對(duì)于基于統(tǒng)計(jì)理論的經(jīng)典測(cè)向算法來(lái)說(shuō),上述情況導(dǎo)致可利用快拍數(shù)據(jù)不足以準(zhǔn)確估計(jì)樣本協(xié)方差矩陣,進(jìn)而導(dǎo)致子空間相互泄漏,算法性能下降甚至失效。本文分析了短快拍對(duì)傳統(tǒng)子空間類測(cè)向算法的性能影響,將短快拍陣列測(cè)向技術(shù)分為極限的單快拍和非極限的少快拍兩種情況分別進(jìn)行了分析和探討,并在原有算法的基礎(chǔ)上做了如下工作:提出了一種無(wú)需已知信源個(gè)數(shù)基于空域平滑理論的單快拍測(cè)向算法。該算法基于有效提高陣列孔徑的空域平滑理論,利用子空間的旋轉(zhuǎn)不變特性,在較少的先驗(yàn)條件下,可先對(duì)信源個(gè)數(shù)進(jìn)行比較準(zhǔn)確的估計(jì),再應(yīng)用樣本協(xié)方差矩陣的冪來(lái)近似子空間,進(jìn)而完成DOA估計(jì)。實(shí)驗(yàn)結(jié)果表明其測(cè)向性能與現(xiàn)有空域平滑理論下的SSIA ESPRIT算法相當(dāng)。與其他現(xiàn)有單快拍測(cè)向算法如相關(guān)域單快拍重構(gòu)偽協(xié)方差矩陣的Power ESPRIT相比測(cè)向精度更高,適用于對(duì)目標(biāo)信號(hào)已知條件較少的非合作通信偵察中。提出一種在短快拍下基于偽噪聲重采樣技術(shù)并結(jié)合酉Root-MUSIC算法的快速DOA估計(jì)算法。該算法借助偽噪聲重采樣技術(shù)提高測(cè)向算法性能使其適用于短快拍。由于利用計(jì)算復(fù)雜度較低的QR分解來(lái)求解噪聲子空間,故不需計(jì)算復(fù)雜的特征分解。同時(shí)利用酉Root-MUSIC算法來(lái)進(jìn)行DOA估計(jì),將復(fù)數(shù)運(yùn)算轉(zhuǎn)化為實(shí)數(shù)運(yùn)算,降低了算法的計(jì)算復(fù)雜度。與只使用PR技術(shù)的酉Root-MUSIC算法相比,性能相當(dāng),但計(jì)算復(fù)雜度較低;且相比調(diào)整子空間類算法及離網(wǎng)稀疏貝葉斯算法等現(xiàn)有的短快拍測(cè)向算法具有更低的計(jì)算復(fù)雜度,適用于信號(hào)處理中的實(shí)時(shí)系統(tǒng)。
[Abstract]:In modern military or satellite communication, due to the wide application of frequency-hopping communication and burst short-time communication technology, as well as the complex and changeable electromagnetic environment, the effective data received by array are often insufficient. For the classical direction-finding algorithm based on statistical theory, the situation mentioned above leads to insufficient estimation of the sample covariance matrix by using the rapid-beat data, which leads to the leakage of subspace, and the performance of the algorithm even fails. In this paper, the effect of short-shot on the performance of traditional subspace-like direction-finding algorithm is analyzed, and the short-shot array direction-finding technology is divided into two cases, which are limited single-shot and non-limit low-shot respectively. On the basis of the original algorithm, the following work is done: a single beat direction finding algorithm based on spatial smoothing theory without known number of sources is proposed. This algorithm is based on the spatial smoothing theory which can effectively improve the aperture of the array. By using the rotation invariance of the subspace, the number of sources can be estimated accurately under the condition of less prior information. Then the power of the sample covariance matrix is used to approximate the subspace and the DOA estimation is completed. The experimental results show that the direction finding performance is comparable to the SSIA Esprit algorithm based on the existing spatial smoothing theory. Compared with other existing single shot direction-finding algorithms such as Power Esprit with pseudo-covariance matrix reconstruction in correlation domain, it is suitable for non-cooperative communication reconnaissance with less known conditions for target signals. A fast DOA estimation algorithm based on pseudo-noise resampling technique and unitary Root-music algorithm is proposed in this paper. The algorithm improves the performance of the direction finding algorithm by using pseudo-noise resampling technique and makes it suitable for short shot. Because the QR decomposition with low computational complexity is used to solve the noise subspace, there is no need to compute the complex characteristic decomposition. At the same time, the unitary Root-music algorithm is used to estimate the DOA, and the complex operation is transformed into real operation, which reduces the computational complexity of the algorithm. Compared with the unitary Root-MUSIC algorithm which only uses PR technique, the performance of the algorithm is comparable, but the computational complexity is lower, and the computational complexity is lower than the existing short-shot direction-finding algorithm, such as adjusting subspace class algorithm and off-network sparse Bayesian algorithm. It is suitable for real-time system in signal processing.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7

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