Underwater Target Bearing Estimation Performance of Bottom-Mounted Extended Coprime Sparse Array

Extended coprime sparse array (ECSA) has been widely used in signal processing, which realizes direction-of-arrival (DOA) estimation with fewer sensors. Accordingly, via spatial smoothing technique, an augmented sample covariance matrix was constructed. Then, the MUltiple SIgnal Classification (MUSIC) algorithm was applied in DOA estimation with higher-resolution ability, which is comparable to that of a uniform full array sharing the same aperture. As far as the application of underwater bottom-mounted horizontal ECSA, due to the property of multipath arrival, the estimated source bearing lies in between the true source bearing and the broadside direction of ECSA. Basically, for the problem of identifying a specific target in the presence of interferers, with regard to both bearing resolution performance and corresponding estimation accuracy, a relationship with the true source positions was established. It was proven that the MUSIC algorithm suffers from an intrinsic error, which is driven by energy-dominant modes in the waveguide. Based on the multimode phenomenon, this paper reveals the influence of source position, especially the angular arrangement. Additionally, on the premise of successful target resolution, reference sound speed substitution was carried out to achieve improved bearing estimation accuracy. The results are herein demonstrated with both numerical simulation and experimental data.