A neural network-based smart antenna for multiple source tracking

TLDR

The study addresses multiple‑source tracking using neural network‑based smart antennas in wireless terrestrial and satellite mobile communications. The authors propose the neural multiple‑source tracking (N‑MUST) algorithm, which employs a family of radial basis function neural networks partitioned by spatial angular sectors to detect sources and estimate their directions of arrival in a two‑stage process. Simulations demonstrate that N‑MUST performs robustly across varying angular separations, random signal‑to‑noise ratios, and Doppler spread.

Abstract

This paper considers the problem of multiple-source tracking with neural network-based smart antennas for wireless terrestrial and satellite mobile communications. The neural multiple-source tracking (N-MUST) algorithm is based on an architecture of a family of radial basis function neural networks (RBFNN) to perform both detection and direction of arrival (DOA) estimation. The field of view of the antenna array is divided into spatial angular sectors, which are in turn assigned to a different pair of RBFNNs. When a network detects one or more sources in the first stage, the corresponding second stage network(s) are activated to perform the DOA estimation. Simulation results are performed to investigate the performance of the algorithm for various angular separations, with sources of random relative signal-to-noise ratio and when the system suffers from Doppler spread.