城市环境下单无人机测向定位航迹优化算法

为解决单架无人机在城市环境中对辐射源目标的定位问题,提出了一种基于环境预测法的单无人机测向定位航迹优化算法。使用交互多模型-扩展卡尔曼滤波进行视距和非视距信号混合环境下的目标估计。结合估计的目标位置和城市地理信息模型,基于视线追踪法求解信号遮挡区域和多径信号干扰区域。在滚动时域控制算法框架下生成无人机预测轨迹,以最大化Fisher信息矩阵行列式为测向定位评价准则,考虑建筑物障碍以及其对信号的遮挡和反射效应对无人机测向定位航迹的影响,控制无人机选择最优航向飞行。仿真结果表明,该方法能够使无人机在存在障碍、信号遮挡和多径干扰的环境下实现对目标的高精度测向定位,为解决城市环境下的单架无人机测向定位问题提供了新思路。

Trajectory optimization of single unmanned aerial vehicle for bearings-only target localization in urban environments

To solve the problem of radiation-source target localization for a single UAV(unmanned aerial vehicle) in an urban environment, a new trajectory optimization algorithm for bearings-only target localization based on the environment prediction method was proposed. Interacting multiple model methods coupled with the extended Kalman filter was used to estimate the target localization in the line-of-sight and non-line-of-sight mixed environment. Based on the estimated target location and urban geographic information system, the electromagnetic signal occlusion region and the multipath interference region were calculated by using the line of sight tracking method. Under the framework of receding horizon method, the UAV prediction trajectory was generated, so as to maximize the Fisher information matrix determinant as the orientation positioning evaluation criterion. Considering the influence of building obstacles and their occlusion and reflection effects in the localization process, the UAV was controlled to choose the optimal heading flight.The numerical simulation results show that the trajectory optimization algorithm enables the UAV to perform high-precision bearings-only target localization in the complex environment containing obstacles, signal occlusion, and multipath interference. The algorithm provides a new way to solve the problem of bearings-only target localization for single UAV in an urban environment.