Joint Estimation of Azimuth and Distance for Far-Field Multi Targets Based on Graph Signal Processing

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.

Automated summary

This article proposes a method for joint azimuth and distance estimation of multiple targets based on graph signal processing (GSP), utilizing a fully connected graph signal model and applying the Fourier transform method to solve the estimated response function. The simulation results show that this method outperforms the MUSIC algorithm in azimuth estimation under low signal-to-noise ratio conditions and provides more accurate distance estimation results under any signal-to-noise ratio.