Abstract In recent years, polarized light navigation has been widely used for the application of the autonomous heading measurement of small unmanned combat platforms to satellite navigation rejection. However, current techniques of polarized light navigation yield low accuracy when measuring the heading angle in dynamic environments, and the use of a single source of navigation has some shortcomings as well. In the environment of satellite navigation rejection, a method to measure the heading angle using angle compensation based on three-dimensional image geometric correction is proposed in this paper. A model of polarized light/inertial combination based on the Cubature Kalman Filter (CKF) is established using this, and was verified by outdoor experiments that employed the polarized light/inertial combination heading measurement system. The results showed that the overall stability of the heading angle as calculated by the data fusion algorithm of polarized light and the inertial system based on the CKF followed the trend of the polarized light, which adequately suppressed the angle drift caused by data of the inertial system. Following the application of the CKF filtering algorithm, the root mean square error of the heading angle was 1.0719°, which improved the accuracy of calculating the heading angle of the polarized light/inertial integrated system in non-horizontal road environments.

中文翻译：

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基于偏振光/惯性组合的航向测量系统算法设计与实验验证

摘要 近年来，偏振光导航被广泛应用于小型无人作战平台自主航向测量对卫星导航抑制的应用。然而，目前的偏振光导航技术在动态环境下测量航向角时精度较低，使用单一导航源也存在一些不足。在卫星导航抑制环境下，提出了一种基于三维图像几何校正的角度补偿测量航向角的方法。以此建立了基于Cupature卡尔曼滤波器(CKF)的偏振光/惯性组合模型，并通过采用偏振光/惯性组合航向测量系统的户外实验进行了验证。结果表明，基于CKF的偏振光与惯性系统数据融合算法计算的航向角整体稳定性跟随偏振光的趋势，充分抑制了惯性系统数据引起的角漂移。 . 应用CKF滤波算法后，航向角均方根误差为1.0719°，提高了偏光/惯性集成系统在非水平道路环境下航向角的计算精度。