低信噪比条件下宽带欠定信号高精度DOA估计

冯明月; 何明浩; 徐璟; 李少东

doi:10.11999/JEIT160921

低信噪比条件下宽带欠定信号高精度DOA估计

doi: 10.11999/JEIT160921

1.
(空军预警学院武汉 430019) ②(解放军94969部队上海 200040)

基金项目:

国家自然科学基金(61401504)，军内计划科研项目(2015XXX)，湖北省自然科学基金(2016CFB288)

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出版历程
- 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: 为提高低信噪比条件下宽带欠定信号DOA估计精度，该文提出基于网格失配迭代最小化稀疏学习的宽带DOA估计方法。该方法首先对频域协方差矩阵进行矢量化处理实现虚拟阵列扩展，将欠定信号转换为超定信号。其次利用线性变换滤除含有噪声项的虚拟阵元，并对协方差估计误差进行了白化处理，抑制了信号中的干扰项。最后建立了包含不同频点联合稀疏参数和网格失配参数的贝叶斯层次架构，推导了联合稀疏参数、网格失配参数的最小稀疏表达式并进行了迭代学习。较传统方法，该方法不依赖任何先验信息，更好地抑制了虚拟阵元中的噪声和干扰，降低了网格失配对DOA估计的影响，在低信噪比条件下具有更高的DOA估计精度和分辨率。仿真实验验证了该方法的有效性。
- 宽带信号到达角估计 /
- 低信噪比条件 /
- 欠定信号 /
- 网格失配 /
- 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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