Deep Semantic Segmentation for Multi-Source Localization Using Angle of Arrival Measurements

TL;DR

This paper introduces a deep learning framework using semantic segmentation models for multi-source localization with angle of arrival measurements, addressing challenges in dynamic environments.

Contribution

It presents a novel deep learning approach employing UNet models for multi-source localization using AOA data, filling a research gap in dynamic scenarios.

Findings

Performs comparably to traditional methods in single source localization.

Achieves accurate multi-source localization under high noise conditions.

Effective in environments with multiple sources and platform movement.

Abstract

This paper presents a solution for multi source localization using only angle of arrival measurements. The receiver platform is in motion, while the sources are assumed to be stationary. Although numerous methods exist for single source localization, many relying on pseudo-linear formulations or non convex optimization techniques, there remains a significant gap in research addressing multi source localization in dynamic environments. To bridge this gap, we propose a deep learning-based framework that leverages semantic segmentation models for multi source localization. Specifically, we employ UNet and UNetPP as backbone models, processing input images that encode the platform's positions along with the corresponding direction finding lines at each position. By analyzing the intersections of these lines, the models effectively identify and localize multiple sources. Through simulations, we evaluate both single- and multi-source localization scenarios. Our results demonstrate that while the proposed approach performs comparably to traditional methods in single source localization, it achieves accurate source localization even in challenging conditions with high noise levels and an increased number of sources.