Angle of arrival (AOA)-based Cross-localization Algorithm using Orientation Angle for Improved Target Estimation in Far-field Environments

Passive positioning systems with a small aperture array exhibit poor accuracy of target estimation in far-field environments. To improve this accuracy, this paper presents a novel angle-of-arrival (AOA)-based cross-localization algorithm for direction finding using orientation angle. Improved geometric and numerical target-positioning models are constructed after analyzing the mechanism of the conventional AOA-based cross-positioning algorithm. The target predication equation is then derived using the constructed models, and the equation for nonlinear estimation is linearized using the Taylor series. An unbiased estimation of the target is obtained by optimizing the control of the iteration process, thus achieving an accurate positioning of the target. The performance of the proposed algorithm was evaluated in terms of its effectiveness and positioning accuracy under varying signal-to-noise conditions and orientation angle-measurement errors. Simulation results shows that the proposed algorithm is capable of positioning the target effectively, and offers better positioning accuracy than traditional algorithms under conditions of large AOA measurement errors or high-level background noise.