基于空域平滑稀疏重構(gòu)的DOA估計(jì)算法
doi: 10.11999/JEIT150538
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1.
(西安電子科技大學(xué)電子工程學(xué)院 西安 710071) ②(中國(guó)人民解放軍95037部隊(duì) 武漢 430074)
基金項(xiàng)目:
國(guó)家自然科學(xué)基金(61405150, 61271300), 中央高?；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金(JB140229)
DOA Estimation Via Sparse Representation of theSmoothed Array Covariance Matrix
Funds:
The National Natural Science Foundation of China (61405150, 61271300), The Fundamental Research Funds for the Central Universities (JB140229)
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摘要: 該文提出一種基于空域平滑稀疏重構(gòu)的DOA估計(jì)算法，利用空域平滑理論對(duì)協(xié)方差矩陣進(jìn)行處理，然后通過KR積變換改變其結(jié)構(gòu)，并對(duì)變換后的矩陣進(jìn)行稀疏重構(gòu)獲得角度估計(jì)。此外，該文還給出了兩種不同的目標(biāo)函數(shù)誤差求解方法。從仿真實(shí)驗(yàn)可以看出，該算法與傳統(tǒng)的基于壓縮感知理論的DOA估計(jì)算法對(duì)比，明顯降低了運(yùn)算量，且對(duì)于相干和非相干信號(hào)的處理性能均有所提高，在低角度間隔、低信噪比和低采樣數(shù)條件下優(yōu)勢(shì)更為突出。Abstract: A novel Direction-Of-Arrival (DOA) estimation algorithm based on spatial smoothing and sparse reconstruction is proposed in this paper. Firstly, the covariance matrix is processed using spatial smoothing theory, and it is converted with the Khatri-Rao transformation, then DOA estimation is achieved by sparse reconstruction of the converted matrix. Furthermore, two different kinds of methods are given to deal with the error of the objective function. Experimental results show that the proposed algorithm can reduce the amount of computation, and exhibit better performance on both coherent and non-coherent signals compared with the other DOA algorithms based on compressed sensing, especially under the conditions of low angle interval, low signal-to-noise ratio and low sampling number.
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Key words:
- Compressed sensing /
- Spatial smoothing /
- Sparse reconstruction /
- DOA estimation
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