低信噪比條件下寬帶欠定信號(hào)高精度DOA估計(jì)

馮明月; 何明浩; 徐璟; 李少東

doi:10.11999/JEIT160921

低信噪比條件下寬帶欠定信號(hào)高精度DOA估計(jì)

doi: 10.11999/JEIT160921

1.
(空軍預(yù)警學(xué)院武漢 430019) ②(解放軍94969部隊(duì) 上海 200040)

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61401504)，軍內(nèi)計(jì)劃科研項(xiàng)目(2015XXX)，湖北省自然科學(xué)基金(2016CFB288)

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出版歷程
- 收稿日期: 2016-09-12
- 修回日期: 2017-01-24
- 刊出日期: 2017-06-19

High Accuracy DOA Estimation Under Low SNR Conditionfor Wideband Underdetermined Signals

1.
(Air Force Early Warning Academy, Wuhan 430019, China)
2.
(94969 Unit the PLA, Shanghai 200040, China)

Funds:

The National Natural Science Foundation of China (61401504), The Military Plan of Scientific Research Project (2015XXX), The Natural Science Foundation of Hubei Province (2016CFB288)

摘要

摘要: 為提高低信噪比條件下寬帶欠定信號(hào)DOA估計(jì)精度，該文提出基于網(wǎng)格失配迭代最小化稀疏學(xué)習(xí)的寬帶DOA估計(jì)方法。該方法首先對(duì)頻域協(xié)方差矩陣進(jìn)行矢量化處理實(shí)現(xiàn)虛擬陣列擴(kuò)展，將欠定信號(hào)轉(zhuǎn)換為超定信號(hào)。其次利用線性變換濾除含有噪聲項(xiàng)的虛擬陣元，并對(duì)協(xié)方差估計(jì)誤差進(jìn)行了白化處理，抑制了信號(hào)中的干擾項(xiàng)。最后建立了包含不同頻點(diǎn)聯(lián)合稀疏參數(shù)和網(wǎng)格失配參數(shù)的貝葉斯層次架構(gòu)，推導(dǎo)了聯(lián)合稀疏參數(shù)、網(wǎng)格失配參數(shù)的最小稀疏表達(dá)式并進(jìn)行了迭代學(xué)習(xí)。較傳統(tǒng)方法，該方法不依賴任何先驗(yàn)信息，更好地抑制了虛擬陣元中的噪聲和干擾，降低了網(wǎng)格失配對(duì)DOA估計(jì)的影響，在低信噪比條件下具有更高的DOA估計(jì)精度和分辨率。仿真實(shí)驗(yàn)驗(yàn)證了該方法的有效性。
- 寬帶信號(hào)到達(dá)角估計(jì) /
- 低信噪比條件 /
- 欠定信號(hào) /
- 網(wǎng)格失配 /
- 最小稀疏迭代
Abstract: In order to improve underdetermined wideband signals DOA estimation accuracy under low Signal to Noise Ratio (SNR) condition, an off-grid sparse learning via iterative minimization algorithm is proposed. Firstly, the novel algorithm vectorizes the covariance matrix in frequency domain to realize visual array extension, as a result, underdetermined wideband signals are transformed into overdetermined signals. Then linear transform is used to eliminate the noise contained virtual array elements, whitening process is utilized to the estimation error of covariance matrix, as a result, the interference in signals is suppressed. Finally, a Bayesian structure containing the joint sparsity parameter of different frequencies and off-grid parameter is built, the minimization sparse expressions of joint sparsity parameter and off-grid parameter are deduced and corresponding parameters are learned iteratively. Compared with other methods, the proposed method does not rely on any prior information, suppresses the inference in virtual array elements more efficiently, reduces the effects of off-grid problem, and gets higher DOA estimation accuracy and resolution under low SNR condition. Simulation experiments verify the validity of the novel algorithm.
- Wideband signals DOA estimation /
- Under low SNR condition /
- Underdetermined signals /
- Off-grid problem; Sparse Learning via Iterative Minimization (SLIM) /

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