Abstract
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).
Highlights
Direction of arrival (DOA) estimation has been an important research field for scientific researchers because of its wide application scenarios
The specific steps of the algorithm are as follows: Step1: The power spectrum entropy of the signal is calculated, and the regularization parameter range is determined; Step2: Based on the principle of compressed sensing (CS), the DOA of small snapshots data was estimated, and the first 15 maximum peaks data coordinates of each snapshot were saved for the further processing; Step3: Cluster the peaks data saved in step 2 through DBSCAN to obtain the number of clusters C, which are the number of signal sources; Step4: Calculate the cluster center value of each cluster, which is the final DOA estimation
Low signal-to-noise ratio (SNR), a method based on CS and DBSCAN was proposed in this paper
Summary
Direction of arrival (DOA) estimation has been an important research field for scientific researchers because of its wide application scenarios. The representative article is Capon’s minimum variance method [8] This kind of method assumes that the signal is a spatially stationary random process, and this assumption is not valid in real signals, the algorithm has a large error or even failure in low SNR environment, which leads to some limitations in application. In the process of grid fission, sparse Bayesian learning method is used to correct the position of spatial spectrum, so as to achieve fast and accurate DOA estimation These above algorithms based on CS cannot guarantee the estimation performance at low SNR. Under the same conditions of low SNR and small snapshots, the proposed method can get effective DOA estimation, which has more advantages than the traditional super-resolution algorithms. A conclusion of this paper is given in the sixth section
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