一種自動匹配的分布式非圓信號二維DOA快速估計方法
doi: 10.11999/JEIT171058
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解放軍信息工程大學信息系統(tǒng)工程學院 ??鄭州 ??450001
基金項目: 國家自然科學基金(61401513)
Fast Two-dimensional DOA Estimation for Coherently Distributed Noncircular Signals with Automatic Pairing
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Institute of Information System Engineering, The PLA Information Engineering University, Zhengzhou 450001, China
Funds: The National Natural Science Foundation of China (61401513)
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摘要: 在相干分布式非圓信號2維波達方向(DOA)估計中,針對利用非圓特性后維數(shù)擴展帶來的較大復雜度問題,且現(xiàn)有的低復雜度算法均需要額外的參數(shù)匹配,該文提出一種基于互相關(guān)傳播算子的自動匹配2維DOA快速估計算法。該算法考慮L型陣列,在建立相干分布式非圓信號擴展陣列模型的基礎(chǔ)上,首先證明了L陣中兩個子陣的廣義方向矢量(GSV)均具有近似旋轉(zhuǎn)不變特性,然后通過陣列輸出信號的互相關(guān)運算消除了額外噪聲,最終利用子陣GSV的近似旋轉(zhuǎn)不變關(guān)系通過傳播算子方法得到中心方位角與俯仰角估計。理論分析和仿真實驗表明,所提算法無須譜峰搜索和協(xié)方差矩陣特征分解運算,具有較低的計算復雜度,并且能夠?qū)崿F(xiàn)2維DOA估計的自動匹配;同時,相比于現(xiàn)有的相干分布式非圓信號傳播算子算法,所提算法以較小的復雜度代價獲得了性能的較大提升。
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關(guān)鍵詞:
- 陣列信號處理 /
- 相干分布式非圓信號 /
- 互相關(guān)傳播算子 /
- 自動匹配
Abstract: In the two-dimensional Direction Of Arrival (DOA) estimation of coherently distributed noncircular sources, the problem of large complexity is caused by dimension expansion after exploiting noncircular property, meanwhile the existing low-complexity algorithms all require additional parameter pairing procedure. To solve these problems, a rapid DOA estimation algorithm with automatic pairing is proposed for coherently distributed noncircular sources based on cross-correlation propagator. The L-shaped array is considered. Firstly, the extended array manifold model is established by exploiting the noncircularity of the signal, and then it is proved that there are approximate rotational invariance relationships in the Generalized Steering Vectors (GSVs) of two subarrays of the L array. At the same time, the extra noise can be eliminated by the cross-correlation matrix of the array output signals. Finally, on the basis of the approximate rotational invariance relationships of the sub-arrays, the center azimuth and elevation DOAs can be obtained by propagator method. Theoretical analysis and simulation experiments show that without the spectrum searching and eigenvalue decomposition of the sample covariance matrix, the proposed algorithm has low computational complexity. Moreover, it can automatically pair the estimated central azimuth and central elevation DOAs. In addition, compared with the existing propagation method for coherently distributed noncircular sources, the proposed algorithm can achieve higher estimation accuracy with the small complexity cost. -
表 1 計算復雜度對比
算法 計算量 SOS $O\left(8{M^3} + 4{M^2}N + L({K^3} + 2{K^2}M)\right)$ TLS-ESPRIT $O\left({(2M + 1)^3} + {(2M + 1)^2}N + 2M{K^2} + 2{K^3}\right)$ CDNC $O\left(64{M^3} + 16{M^2}N + \left(\frac{{11}}{9}M - 4\right){K^2} + 2{K^3}\right)$ NC-PM $O\left(2(4M - 1)KN + 2{K^3} + {K^2}\right)$ 本文算法 $O\left(4{M^2}N + 22{M^2}K + 3{K^3} - 12{K^2}\right)$ 下載: 導出CSV
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