Graph-based Matched Field Localization for an Underwater Source

Abstract

Matched Field Processing (MFP) locates the underwater sources by matching the received data with the replica vectors, which could be regarded as a generalized beamformer. In this paper, the MFP method is combined with a recently developed framework-Graph Signal Processing (GSP) method. Following the paradigm of GSP, a spatial adjacency matrix is constructed for the arbitrary distributed sensors based on the Green's function, then the source is located by utilizing the graph Fourier transform. The simulation results illustrate that the Graph-based MFP outperforms the the conventional MFP processors-the Bartlett processor and the Minimum Variance processor-for its good accuracy and robustness.

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

“…Figure 4b,d are the dimensionality reduction plan views of Figure 4a,c, respectively. The blue curve is the result of the azimuth estimation of the full GSP algorithm; the red curve is the result of the estimation of the nofull GSP algorithm according the previous work [23]; and the yellow curve is the original azimuth parameter. From Figure 4b of the dual target estimation, it can be seen that the blue line represented by the fully GSP can achieve the −3 db sidelobe effect at the −40 db response strength and can accurately estimate the target point {−30 • , 45 • }; compared with the red line of the nofull GSP algorithm, the full GSP has lower side lobes and more accurate results.…”

“…This article is different from the previous work [23], we use a fully connected way to construct the graph signal. The proposed graph signal matrix structure diagram is shown in Figure 3.…”