Direction finding in phased arrays with a neural network beamformer

Abstract

Adaptive neural network processing of phased-array antenna received signals promises to decrease antenna manufacturing and maintenance costs while increasing mission uptime and performance between repair actions. We introduce one such neural network which performs aspects of digital beamforming with imperfectly manufactured, degraded, or failed antenna components. This paper presents measured results achieved with an adaptive radial basis function (ARBF) artificial neural network architecture which learned the single source direction finding (DF) function of an eight-element X-band array having multiple, unknown failures and degradations. We compare the single source DF performance of this ARBF neural network, whose internal weights are computed using a modified gradient descent algorithm, with another radial basis function network, Linnet, whose weights are calculated using linear algebra. Both networks are compared to a traditional DF approach using monopulse.