本文選題：空時自適應(yīng)處理技術(shù) + 壓縮感知�。� 參考：《南京理工大學(xué)》2017年碩士論文

【摘要】：空時自適應(yīng)處理(STAP)技術(shù)利用空域和時域的信息有效抑制了雜波,與稀疏恢復(fù)算法相結(jié)合可以減少所需的樣本數(shù)據(jù),但是,在樣本數(shù)量嚴(yán)重不足時,該方法恢復(fù)出的雜波只能得到大概位置,與真實譜相差甚遠(yuǎn)。由于STAP信號中的非零值不僅具有稀疏特性,而且顯著值還具有聚類的特性,即具有塊稀疏特性,因此可以考慮利用STAP信號特有的塊稀疏特性恢復(fù)雜波譜,從而提高抑制雜波的性能。本文將塊稀疏重構(gòu)理論應(yīng)用于STAP技術(shù),在樣本量嚴(yán)重不足時,較大地提高了重構(gòu)精度與雜波抑制性能,并且計算時間在可接受范圍內(nèi)。本文所做的工作具體如下:1.介紹STAP的基本原理,分析雜波譜特性與性能指標(biāo)。同時介紹了信號稀疏重構(gòu)的原理與方法,選擇正交匹配追蹤(OMP)算法與光滑l0(SLO)算法這兩種經(jīng)典方法梳理算法過程,并且將這兩種算法應(yīng)用于STAP技術(shù)中,分析其性能上的不足。2.介紹塊稀疏重構(gòu)的原理,并在分析了 STAP雜波空時譜具有塊稀疏特性的基礎(chǔ)上,創(chuàng)新性地將塊稀疏重構(gòu)算法與STAP技術(shù)相結(jié)合,提出了基于塊稀疏恢復(fù)算法重構(gòu)STAP雜波譜的算法流程,并對算法步驟進(jìn)行了詳細(xì)研究。3.將OMP算法推廣為塊稀疏情況下的塊正交匹配追蹤(BOMP)算法并通過重構(gòu)STAP雜波譜仿真分析得出,在BOMP應(yīng)用于STAP技術(shù)時,由于STAP雜波子塊邊界未知,塊稀疏分塊的不準(zhǔn)確會使貪婪算法本身易陷入局部最優(yōu)解的缺陷放大。因此本文提出了一種BOMP修正算法,對要選擇的最優(yōu)原子塊進(jìn)行修正,設(shè)能量閾值對該子塊邊界進(jìn)行判斷,避免上述的重構(gòu)誤差,提高了重構(gòu)精度與抑制雜波性能。同時,用Matlab仿真數(shù)據(jù)和MountainTop實測數(shù)據(jù)分別重構(gòu)雜波譜進(jìn)行比較,驗證了提出算法的有效性。4.將SL0算法推廣為塊稀疏情況下的塊光滑l0(BSL0)算法并用Matlab仿真數(shù)據(jù)和MountainTop實測數(shù)據(jù)分別重構(gòu)雜波譜進(jìn)行比較分析并驗證:用塊稀疏恢復(fù)算法處理STAP信號性能要優(yōu)于普通稀疏恢復(fù)算法應(yīng)用于STAP的性能。同時,在相同條件下橫向比較兩類算法,證明上述結(jié)論。
[Abstract]:The space-time adaptive processing (STAP) technique can effectively suppress clutter by using spatial and temporal information. Combining with sparse recovery algorithm, the required sample data can be reduced. However, when the number of samples is seriously insufficient, The clutter recovered by this method can only get the approximate position, which is far from the true spectrum. Because the non-zero values in STAP signal have not only sparse characteristics, but also significant values have clustering characteristics, that is, block sparsity, so we can use the block sparsity characteristic of STAP signal to recover clutter spectrum. In order to improve the performance of clutter suppression. In this paper, the block sparse reconstruction theory is applied to STAP technology. When the sample size is seriously insufficient, the reconstruction accuracy and clutter suppression performance are greatly improved, and the computation time is within the acceptable range. The work done in this paper is as follows: 1. The basic principle of STAP is introduced, and the characteristics and performance of clutter spectrum are analyzed. At the same time, the principle and method of signal sparse reconstruction are introduced. Two classical algorithms, orthogonal matching tracking algorithm (OMP) and smooth l0sloo algorithm, are selected to sort out the process, and the two algorithms are applied to STAP technology to analyze their performance deficiency. 2. This paper introduces the principle of block sparse reconstruction, and on the basis of analyzing the block sparse characteristic of STAP clutter space-time spectrum, innovatively combines block sparse reconstruction algorithm with STAP technology. The algorithm flow of reconstruction of STAP clutter spectrum based on block sparse recovery algorithm is proposed, and the algorithm steps are studied in detail. The OMP algorithm is extended to block orthogonal matching tracking (Bomp) algorithm in the case of block sparsity, and the simulation analysis of reconstructed STAP clutter spectrum shows that when BOMP is applied to STAP technology, the STAP clutter sub-block boundary is unknown. The inaccuracy of block sparse partitioning makes the greedy algorithm itself prone to the defect amplification of the local optimal solution. In this paper, a BOMP correction algorithm is proposed, which modifies the optimal atomic block to be selected, determines the boundary of the sub-block by energy threshold, avoids the reconstruction error mentioned above, and improves the reconstruction accuracy and clutter suppression performance. At the same time, the Matlab simulation data and the MountainTop measured data are compared to reconstruct the clutter spectrum respectively, which verifies the effectiveness of the proposed algorithm. 4. In this paper, the SL0 algorithm is extended to block smooth l0 BSL0 algorithm in the case of block sparsity. By comparing and analyzing the reconstructed clutter spectrum with Matlab simulation data and MountainTop measured data, the performance of block sparse restoration algorithm in STAP signal processing is better than that in common STAP signal processing. Sparse recovery algorithm is applied to the performance of STAP. At the same time, the above conclusion is proved by comparing two kinds of algorithms horizontally under the same conditions.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN911.7

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