# Source Localization and Tracking for Dynamic Radio Cartography using Directional Antennas

## TL;DR

This paper introduces a unified compressive sensing framework combining RSS and DoA for source localization using directional antennas, improving accuracy and efficiency in dynamic radio cartography.

## Contribution

It proposes a novel formulation that unifies RSS and DoA in a CS framework, optimizing antenna beams for better localization and spectrum sensing.

## Key findings

- Significant improvement in source localization accuracy.
- Enhanced spectrum sensing efficiency with directional antennas.
- Effective dynamic RF map estimation over time.

## Abstract

Utilization of directional antennas is a promising solution for efficient spectrum sensing and accurate source localization and tracking. Spectrum sensors equipped with directional antennas should constantly scan the space in order to track emitting sources and discover new activities in the area of interest. In this paper, we propose a new formulation that unifies received-signal-strength (RSS) and direction of arrival (DoA) in a compressive sensing (CS) framework. The underlying CS measurement matrix is a function of beamforming vectors of sensors and is referred to as the propagation matrix. Comparing to the omni-directional antenna case, our employed propagation matrix provides more incoherent projections, an essential factor in the compressive sensing theory. Based on the new formulation, we optimize the antenna beams, enhance spectrum sensing efficiency, track active primary users accurately and monitor spectrum activities in an area of interest. In many practical scenarios there is no fusion center to integrate received data from spectrum sensors. We propose the distributed version of our algorithm for such cases. Experimental results show a significant improvement in source localization accuracy, compared with the scenario when sensors are equipped with omni-directional antennas. Applicability of the proposed framework for dynamic radio cartography is shown. Moreover, comparing the estimated dynamic RF map over time with the ground truth demonstrates the effectiveness of our proposed method for accurate signal estimation and recovery.