基于惯导/数据链的动态相对定位方法

Dynamic relative positioning method based on inertial navigation and data link

摘要:

针对卫星导航定位在复杂环境不可靠情况下如何实现无人机机间相对定位问题，提出一种基于机载惯性导航系统与机间数据链测距相结合的动态相对定位算法。该方法利用机载数据链通信测距能力与机载惯性导航系统输出的无人机速度矢量信息结合，建立机间相对定位模型，通过最小二乘法对无人机之间的相对位置进行估计，实现无人机机间的实时相对定位能力。由于通过最小二乘法解算出的相对定位结果依然存在误差，针对最小二乘法相对定位误差，提出秩亏网平差算法对无人机机群间的相对定位误差进行校正。仿真结果表明：基于最小二乘法的相对定位方法可以减缓惯性导航系统相对定位误差发散速度并且将惯导相对定位精度提高到3倍左右，通过秩亏网平差算法校正将最小二乘相对定位精度提高2倍。

Abstract:

Aiming at the problem of how to realize the relative positioning between unmanned aerial vehicles (UAV) when satellite navigation and positioning are unreliable in complex environment, a dynamic relative positioning algorithm based on the combination of airborne inertial navigation system and data link ranging is proposed. The method using airborne data link communications ranging capacity and output of airborne inertial navigation system of UAV combined with the velocity vector information, relative positioning between the machine model is established, by the least squares method to estimate the relative position between the UAV, realizing the real-time relative positioning between UAV machine capacity. There is still an error in the relative location of the relative location that is calculated from the least squares, Aimed at least squares relative positioning error, the rank-deficit adjustment algorithm is proposed to correct the relative positioning error between uav groups. Simulation results indicate: The relative positioning method based on the least square method can slow down the divergence of relative positioning error of inertial navigation system and improve relative positioning accuracy of inertial navigation system to about 3 times, the rank-deficit net adjustment algorithm can improve the accuracy of least square relative positioning by 2 times.