本文選題：陣列綜合 + 稀疏��； 參考：《電子科技大學》2014年碩士論文

【摘要】：雷達誕生至今已接近80年,在此期間各種不同體制的雷達不斷涌現(xiàn),其功能、體積、重量、可靠性以及生存能力等亦發(fā)生了相當大的變化。毫無疑問,雷達在國民經濟和軍事應用領域正扮演著越來越重要的角色。本文主要就雷達信號處理中的陣列綜合以及參數(shù)估計這兩項技術展開了研究。全文分為三個部分:第一部分就稀疏陣列綜合展開討論。首先,介紹了一種基于連續(xù)凸優(yōu)化的稀疏陣列綜合方法,其對方向圖的功率進行約束,然后將陣列優(yōu)化問題轉換為二階錐規(guī)劃(Second Order Cone Program,SOCP)問題并使用SeDuMi進行求解。之后在上述方法模型的基礎上,提出了一種基于加權l(xiāng)1范數(shù)的稀疏陣列綜合方法,在整個觀測角度上對波形進行約束,并采用復數(shù)求導結合啟發(fā)式近似方法來求解。將仿真結果與已有的結論相比較,該方法可以得到孔徑更短,稀疏程度更高的陣列。第二部分詳細闡述了基于稀疏表示的波達方向(Direction Of Arrival,DOA)估計方法。首先簡單介紹了三種常見的譜估計方法并指出它們的局限性。然后,通過引入過完備集,將陣列接收模型轉化為稀疏表示的DOA估計問題,在此基礎上詳細介紹了l1-SVD算法。最后,引入一個酉變換矩陣,通過該變換矩陣將上述基于稀疏表示的復接收信號模型轉換為實數(shù)模型,大幅度降低了l1-SVD算法的計算復雜度。第三部分在多輸入多輸出(Multiple-Input Multiple-Output,MIMO)雷達體制下討論了穩(wěn)健的參數(shù)估計方法。首先對MIMO雷達系統(tǒng)進行建模,同時將Capon方法和APES方法在MIMO雷達體制下進行了推導并分析了其性能的優(yōu)劣;之后考慮雷達陣列部分校準的情況,提出了一種可以對目標的幅度、方位以及陣列的擾動進行準確估計的方法,同時分析了信噪比(Signal to Noise Ratio,SNR)對該方法性能的影響。最后考慮陣列完全失配的情況,首先介紹了一種RCB方法并對其存在的局限性進行了分析。然后在RCB方法的基礎上提出了一種IRCB方法,該方法無需事先獲知流形矢量的不確定程度,其性能與流形矢量的不確定水平確定已知時的RCB方法相近。
[Abstract]:Radar has been born for nearly 80 years. During this period, various kinds of radars of different systems have been emerging, and their functions, volume, weight, reliability and survivability have also changed a lot. There is no doubt that radar is playing an increasingly important role in national economy and military applications. In this paper, array synthesis and parameter estimation in radar signal processing are studied. The thesis is divided into three parts: the first part is about sparse array synthesis. Firstly, a sparse array synthesis method based on continuous convex optimization is introduced, in which the power of the opposite direction graph is constrained, and then the array optimization problem is transformed into the second order cone programming (SOCP) problem and solved by SeDuMi. Then a sparse array synthesis method based on weighted L 1 norm is proposed based on the above method model. The waveform is constrained from the whole observation angle and solved by complex derivation combined with heuristic approximation. Comparing the simulation results with the existing results, the proposed method can obtain arrays with shorter aperture and higher sparsity. In the second part, the direction of arrival (DOA) estimation method based on sparse representation is described in detail. Firstly, three common spectral estimation methods are introduced and their limitations are pointed out. Then, by introducing over-complete sets, the array reception model is transformed into a sparse representation DOA estimation problem, and then the l1-SVD algorithm is introduced in detail. Finally, a unitary transformation matrix is introduced, by which the complex received signal model based on sparse representation is transformed into a real number model, which greatly reduces the computational complexity of the l1-SVD algorithm. In the third part, the robust parameter estimation method is discussed in the Multiple-Input Multiple-Output MIMO (MIMO) radar system. Firstly, the MIMO radar system is modeled. At the same time, the Capon method and the apes method are deduced and analyzed under MIMO radar system. The influence of signal to noise ratio (SNR) on the performance of the method is analyzed. Finally, a RCB method is introduced and its limitations are analyzed. Then an IRCB method is proposed based on the RCB method. This method does not need to know the degree of uncertainty of the manifold vector in advance, and its performance is similar to that of the RCB method when the uncertainty level of the manifold vector is known.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN957.51

【參考文獻】

相關博士學位論文 前1條

1 陳客松;稀布天線陣列的優(yōu)化布陣技術研究[D];電子科技大學;2006年

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本文編號：2079350