Abstract

The paper presents a comparative analysis of the extended Kalman filter (EKF) and the sigma-point Kalman filter (SPKF) applied to solve the problem of SINS/GNSS integration based on a loosely-coupled integration scheme. Complete stochastic measurement models of MEMS inertial sensors are considered. The efficiency of the EKF and the SPKF is evaluated using real experimental data on complex motion from an SINS based on MEMS technology and a GNSS receiver with a double antenna. The estimation accuracy of navigation parameters using the EKF and the SPKF in the presence of the GNSS signal and during the GNSS outages is analyzed. The results of the statistical analysis of the errors in estimating navigation parameters for different periods of GNSS signal outage are considered.

中文翻译：

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基于实际数据处理的松耦合组合导航系统融合算法比较分析

摘要

本文对扩展卡尔曼滤波器（EKF）和西格玛点卡尔曼滤波器（SPKF）进行了比较分析，以解决基于松耦合积分方案的SINS / GNSS积分问题。考虑了MEMS惯性传感器的完整随机测量模型。使用来自基于MEMS技术的SINS和带有双天线的GNSS接收器的复杂运动的真实实验数据，评估EKF和SPKF的效率。分析了存在GNSS信号时以及在GNSS中断期间使用EKF和SPKF的导航参数的估计精度。考虑了在GNSS信号中断的不同时段中估计导航参数时的误差统计分析结果。