Comparison of sparse arrays that maximize detection and optimize beampatterns

Abstract

In a standard uniform linear array (ULA), the sensors are uniformly spaced on a grid with half wavelength intersensor spacing. The beampattern of an array shows the gain applied by the conventional beamformer to a plane wave arriving at the array from various possible directions. The main lobe width, peak side lobe height, and the side lobe area are important beampattern parameters. Many sparse arrays have been widely studied because of their ability to provide one or more of the same beampattern parameters as a full array using fewer sensors. Examples of such arrays are coprime [Vaidyanathand and Pal 2011] and nested [Pal and Vaidyanathan 2010] arrays. Recently, an exact way to design sparse sampling schemes to maximize detection performance of a known signal in first-order autoregressive noise was introduced [Adhikari and Kay 2023]. In this research, we compare the beampatterns and detection performances of these detection maximizing arrays with the sparse arrays that are designed to optimize beampattern metrics. We analyze beampatterns and detection performances under various conditions using simulated and real underwater acoustic data.