Weighted Least-square Method for A Novel 3D Localization Scheme Using 1D AOA Measurements

Abstract

Traditional Angle of Arrival (AOA) based localization methods require both the azimuth and elevation angle measurements to locate the source in 3D space. For that reason, sensors have to equip different kinds of 2D arrays. However, in some practical situations, the sensors are only allowed to be equipped with linear arrays for its portability or the space limitation. In those cases, traditional Angle of Arrival based localization methods turn to locate a target in 2D plane by using the azimuth angles measured by several linear arrays. In this paper, we propose a novel 3D localization technique using 1D AOA measured from linear arrays. The localization problem is formulated as a weighted least squares (WLS) optimization problem. In order to reduce the computation complexity, pseudo-linear approximation is used to deal with the nonlinearity of the problem. The Cramer-Rao Lower Bound (CRLB) is analyzed to evaluate the performance. To show the robustness, random positions of sensors and directions of linear arrays are considered in this paper. Simulation results show that the proposed method is very close to the CRLB with a certain number of sensors when the measurement noise is small.