Abstract
Target position estimation is one of the important research directions in array signal processing. In recent years, the research of target azimuth estimation based on graph signal processing (GSP) has sprung up, which provides new ideas for the Direction of Arrival (DoA) application. In this article, by extending GSP-based DOA to joint azimuth and distance estimation and constructing a fully connected graph signal model, a multi-target joint azimuth and distance estimation method based on GSP is proposed. Firstly, the fully connection graph model is established related to the phase information of a linear array. For the fully connection graph, the Fourier transform method is used to solve the estimated response function, and the one-dimensional estimation of azimuth and distance is completed, respectively. Finally, the azimuth and distance estimation information are combined, and the false points in the merging process are removed by using CLEAN algorithm to complete the two-dimensional estimation of targets. The simulation results show that the proposed method has a smaller mean square error than the Multiple Signal Classification (MUSIC) algorithm in azimuth estimation under the condition of a low signal-to-noise ratio and more accurate response values than the MUSIC algorithm in distance estimation under any signal-to-noise ratio in multi-target estimation.
Highlights
Which makes the elements of the adjacency matrix AθR prone to periodic repetition, and the linear relationship between QA and Xrec of echo signal after graph Fourier transform is weakened, resulting in the poor performance of graph signal processing (GSP) algorithm than Multiple Signal Classification (MUSIC) algorithm under high SNR condition
The derivation and analysis showed that the estimation method based on fully connected graph signal makes full use of the phase information between array elements and has better performance than nofull GSP algorithm and MUSIC
The experimental results show that, in azimuth estimation, the GSP algorithm has a 45.6% performance improvement compared with the MUSIC algorithm under the condition of low SNR
Summary
Some new methods of Direction of Arrival (DoA) estimation based on graph signal processing have emerged. Graph signal processing uses a new data structure that studies the connection of things, which has shown excellent performance in many fields such as graph neural network and graph cuts [3]. Some related works can be found focusing on the use of graph signals to deal with DoA estimation problems in the radar array system [4,5,6], microphone and speakers [7,8], and the sonar array system [9]. Experiments show that the graph signal processing-based DoA methods have better performance than traditional algorithms such as Multiple Signal Classification (MUSIC) in a low signal-to-noise ratio environment [10,11]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
