| 改进的贝叶斯压缩感知目标方位估计 |
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| 引用本文： | 周明阳,郭良浩,闫超.改进的贝叶斯压缩感知目标方位估计[J].声学学报,2019,44(6):961-969. |
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| 作者姓名： | 周明阳 郭良浩 闫超 |
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| 作者单位： | 1. 中国科学院声学研究所 声场声信息国家重点实验室 北京 100190; |
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| 基金项目： | 国家自然科学基金项目(61571436)资助 |
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| 摘 要： | 针对基于高斯先验模型的贝叶斯压缩感知在目标方位(Direction Of Arrival,DOA)估计中可能出现明显随机伪峰的问题,改进了高斯先验模型,并在此基础上提出了一种贝叶斯压缩感知目标方位估计方法。通过波束输出噪声背景预估与二值指示变量标记,并引入基于信号先验方差的噪声方差估计方法,与变分贝叶斯推断相结合改进目标方位估计性能和优化迭代收敛过程。利用32元线阵对改进算法进行数值仿真处理和分析结果表明,该改进方法不仅可以准确估计目标信号的方位,而且可以显著地减少空间谱中伪峰的数量。实际海上实验数据处理结果表明,使用改进后的贝叶斯压缩感知方法进行DOA估计,可以显著地抑制空间谱中随机的伪峰,提高波束输出峰值背景比,具有更强的目标检测能力。
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| 关 键 词： | 目标方位估计 压缩感知 变分贝叶斯推断 |
| 收稿时间： | 2018-01-05 |
Improved Bayesian compressive sensing-based direction of arrival estimation |
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| Affiliation: | 1. State Key Laboratory of Acoustics.Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190;2. University of Chinese Academy of Sciences, Beijing 100049 |
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| Abstract: | The spurious peaks of Bayesian compressive sensing with Gaussian prior model are considered when used in Directions Of Arrival(DOA)estimation.In order to suppress spurious peaks,we improve a Bayesian prior model and propose a kind of DO A estimation method where we estimate the noise background of the beam output,mark directions of targets using binary indicator variables and bring in a signal variance based noise power estimation method,and then combine the model with variational Bayesian inference to improve the performance of DOA estimation.Numerical simulation and experimental results show that,compared with the state-of-the-art nethods,the improved method yields fewer spurious peaks,higher peak-background ratio and superior detection performance. |
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