Abstract

The primary goal of this paper is to propose a new factor graph (FG) technique for the direction-of-arrival (DOA)-based three-dimensional (3D) multi-target geolocation. The proposed FG detector uses only the mean and the variance of the DOA measurement including both the azimuth and the elevation, assuming that they are suffering from errors following a Gaussian probability density function (PDF). Therefore, both the up-link (UL) transmission load and the detection complexity can be significantly reduced. The Cramer-Rao lower bound (CRLB) of the proposed DOA-based 3D geolocation system is mathematically derived. According to the root mean square error (RMSE) results obtained by simulations, the proposed FG algorithm is found to outperform the conventional linear least square (LS) approach, which achieves a very close performance to the derived CRLB. Moreover, we propose a sensor separation algorithm to solve the target-DOAs matching problem such that the DOAs, measured by each sensor, can be matched to their corresponding targets. With this technique, additional target identification is not needed, and the multi-target geolocation can be decomposed into multiple independent single-target detections.