UAV Path Planning for AOA-based Source Localization with Distance-Dependent Noises
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摘要: 该文研究到达角度(AOA)协同定位下无人机路径优化问题。考虑实际AOA量测噪声方差是目标-传感器距离的函数，距离相关噪声特性使得AOA定位难度增加。为了更好地适应量测噪声随距离变化特性，该文提出一种变增益无迹卡尔曼滤波算法。随后，给出了距离相关噪声AOA定位下广义克劳美罗下界(GCRLB)。在此基础上理论分析了无约束最优传感器位置分布和约束条件下最优传感器位置分布。以GCRLB的迹最小化为目标函数建立AOA协同定位下多无人机路径规划问题，采用罚函数和LM算法优化求解，仿真验证了所提算法的有效性。Abstract: An optimal path planning problem is investigated for Angle-Of-Arrival (AOA) source localization using Unmanned Aerial Vehicles (UAVs) equipped with passive sensors. The more realistic model is considered where the variance of AOA measurement noises is a function of the source-to-sensor distances, which complicates AOA-based source localization. A modified Variable Gain Unscented Kalman Filter (VG-UKF)is developed to adapt to distance-dependent variance of AOA measurement noises. The Generalized Cramer-Rao Lower Bound (GCRLB) of AOA localization is calculated. Further, the unconstrained optimal sensor placement and constrained optimal sensor placement are theoretically analyzed. Then a path planning model for UAVs is constructed with minimizing the trace of GCRLB, which is solved optimally with penalty function and LM (Levenberg-Marquardt) algorithm. The effectiveness of the proposed algorithm is illustrated with simulation results.
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