Direction-of-Arrival Estimation Using Signal Processing on Graphs

Alcantara, Eldridge; Atlas, Les; Abadi, Shima

doi:10.1109/ssp49050.2021.9513776

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…σ 2 i refers to the intensity coefficient of the i-th target echo signal. The significance of finding the covariance matrix for the echo signal is because the expression R xx contains information about multiple targets [9]. Multi-angle information and eigenvalue decomposition can be carried out.…”

Section: Echo Signal Covariance Matrixmentioning

confidence: 99%

“…Initialization: Transmit orthogonal signal vector S according to (9). Transmit steering vector C according to (8).…”

Section: Algorithm 1: General Estimation Solution Of Graph Signal Alg...mentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Liao

Xie

et al. 2022

Remote Sensing

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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.

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Section: Echo Signal Covariance Matrixmentioning

confidence: 99%

“…Initialization: Transmit orthogonal signal vector S according to (9). Transmit steering vector C according to (8).…”

Section: Algorithm 1: General Estimation Solution Of Graph Signal Alg...mentioning

confidence: 99%