Systematical Sensor Path Optimization Solutions for AOA Target Localization Accuracy Improvement With Theoretical Analysis

This paper focuses on improving estimation accuracy for angle-of-arrival (AOA) target localization using the sensor path optimization technique. Firstly, the AOA target localization problem and a classical estimation method are formulated and presented. Secondly, the impacts of sensor path optimization and different cost functions are explored. Thirdly, we systematically develop the FIM- and covariance-based path optimization algorithms using gradient- and grid-based methods to improve the target localization accuracy. In addition, the detailed accuracy differences between the diverse path optimization methods are analyzed for the first time to the best of our knowledge. Fourthly, The proposed method is further extended to the 3D and mobile target scenarios. Finally, simulation and experimental examples using real unmanned aerial vehicles (UAVs) demonstrate the effectiveness and correctness of the proposed method and theoretical findings. In summary, the optimal solution is the FIM-based grid search optimization with a large number of searching waypoints, meanwhile, the other solutions have different merits.