Yan Huichao, Chen Ting, Wang Peng, Zhang Linmei, Cheng Rong, Bai Yanping
School of Information and Communication Engineering, North University of China, Taiyuan 030051, China.
Department of Mathematics, School of Science, North University of China, Taiyuan 030051, China.
Sensors (Basel). 2021 Mar 21;21(6):2191. doi: 10.3390/s21062191.
Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters; Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate; Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR).
到达方向（DOA）估计一直是研究人员的热门话题。复杂多变的环境使得在小快照和强噪声环境下估计DOA极具挑战性。基于压缩感知（CS）的到达方向估计方法是近年来提出的一种新方法。由于它能在小快照下实现到达方向估计，因此受到了广泛关注。然而，该方法在强噪声环境下会导致严重失真。为解决这一问题，本文提出了一种基于CS原理和基于密度的空间聚类（DBSCAN）的DOA估计算法。首先，为了使估计精度更高，本文选择了基于基追踪去噪（BPDN）的信号重构策略。针对该策略中正则化参数选择的挑战，提出了功率谱熵来表征信号的噪声强度，以便为正则化参数的选择提供合理建议；然后，本文通过分析发现基于CS原理的DOA估计在小快照条件下会在真实角度附近得到更密集的估计，因此提出使用DBSCAN方法对上述数据进行处理以获得最终的DOA估计；最后，计算每个聚类的聚类中心值，聚类数即为信号源数，聚类中心值即为最终的DOA估计。将所提方法应用于仿真实验和微机电系统（MEMS）矢量水听器湖试实验，结果表明所提方法在小快照和低信噪比（SNR）条件下能获得良好的DOA估计结果。
