运动参数辅助的无人机单站机会信号定位方法

Research on the drones single station opportunistic signal positioning method aided by motion parameters

当前,无人机的位置估计严重依赖于卫星导航系统。针对此问题,提出了一种利用无人机动力学参数辅助的射频机会信号单站定位算法。给出了系统运动状态转移方程和位置估计输出方程建立的详细步骤;推导了无人机位置估计的可观性条件;通过设置可变自适应平滑窗口,达到了运动参数估计和误差抑制的效果,并给出了窗口设置的判决门限;最终依靠运动状态递推和单测距射频信号站,实现了对不同运动状态无人机的位置估计。理论仿真表明,在卫星导航定位系统拒止环境下,利用动力学参数辅助的射频机会信号单站定位算法,能够实现对不同运动状态无人机的鲁棒定位,满足无人机巡航、侦查和投送等常规任务需求;利用超宽带机会信号信标的实际测试,进一步验证了所提方法的精确性和有效性。相较于卫星导航定位系统,所提算法的定位均方根误差约为2.7 m,验证了在拒止环境下无人机利用机会信号实现较高精度定位的可行性,降低了无人机对传统导航定位系统传感器的依赖性。

The current estimation of the position of drones relies heavily on the Global Navigation Satellite System(GNSS).To solve this problem,a Single Station Positioning of Opportunity Signals Aided by Kinetic Parameters(SPoKP)is proposed.This paper gives the detailed steps of establishing the motion state transfer equation and position estimation output equation,deduces the observability condition for drones position estimation,and gives the method for motion parameter estimation and error suppression by setting the variable adaptive smoothing window,with the decision threshold for window setting given.Finally by relying on the recursion of the motion state and a single ranging radio frequency signal station,the estimation of positions of the drones in different motion states is realized.Theoretical simulation shows that in the environment where the navigation satellite system refuses,the single-station positioning algorithm for the radio frequency opportunistic signal assisted by dynamic parameters can achieve robust positioning of drones in different motion states,which can meet routine tasks such as delivery,drones cruise and reconnaissance and that the actual test using the ultra-wideband(UWB)signal beacon further verifies the accuracy and effectiveness of the proposed method.Compared with the navigation satellite system,the positioning root mean square error of the proposed algorithm is only 2.7 m,which verifies the feasibility of drones using opportunistic signals to achieve higher-precision positioning in a denial environment,and reduces the drones’dependence on sensors of traditional navigation and positioning systems.