Adaptive Beamforming Applied to a Cylindrical Sonar Array Using an Interpolated Array Transformation

In applications such as fishery sonar and navigation, cylindrical or spherical arrays are often used because of the need for a 360 field of view. However, adaptive beamforming methods, known for their high angular resolution and interference rejection capabilities, often rely on a Vandermonde structure of the steering vectors. This is generally not the case for nonlinear arrays. In this paper, we use an interpolated array transformation to map the data to a virtual linear array before adaptive beamforming. We evaluate the performance of two different adaptive beamformers using simulations as well as experimental data from the SX90 fish finding sonar. We show that the adaptive minimum variance (MV) and amplitude and phase estimation of a sinusoid (APES) beamformers offer a significant improvement in azimuth resolution compared to the conventional delay-and-sum (DAS) beamformer. The APES beamformer offers slightly more reliable amplitude estimates in the direction of interest compared to the MV beamformer, at the cost of a somewhat lower azimuth resolution. When applied to data from the SX90 fish finding sonar, the MV beamformer offers a 40%-50% improvement in resolution, while the APES beamformer offers an improvement of 20%-30%.