Source number estimation based on orthogonal matching pursuit and signal subspace matching

In complex underwater acoustic environments, factors such as low signal-to-noise ratio, colored noise, coherent sources, and limited snapshots often significantly degrade the performance of conventional source number estimation methods. Nevertheless, the orthogonal matching pursuit method can still estimate support vectors with considerable accuracy. To robustly estimate the number of sources, this paper constructs two subspaces using the orthogonal matching pursuit method. The two subspaces are generated by the support vectors iteratively solved by the orthogonal matching pursuit method and the eigenvector corresponding to the maximum eigenvalue of the residual. Finally, the two subspaces are used for signal subspace matching criteria to estimate the number of sources. Simulations show that, compared to existing source number estimation methods, the proposed method requires a lower signal-to-noise ratio. It performs better under conditions with a small number of snapshots and is also insensitive to coherent sources and colored noise. The lake trial data processing result shows the method can effectively estimate the number of fixed and moving underwater acoustic targets.