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. >
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