Neural beamforming for signal detection and location

Abstract

The goal of neural beamforming is to design neural processing algorithms which adapt to low cost phased array antennas, even when they behave non-linearly, are imperfectly manufactured, or become degraded. Neural beamforming techniques can decrease antenna manufacturing and maintenance costs, and increase mission time and performance before repair. In this paper, we present a neural network architecture which performs signal detection and direction finding despite antenna degradations and non-linear behavior. We present the network's detection and direction-finding (DF) performance at various signal-to-noise ratios (SNRs) and compare it's DF accuracy to a monopulse technique, with and without calibration.<>