基于全区间航向误差预测与校正的惯性行人导航算法研究

doi:10.12305/j.issn.1001-506X.2025.05.28

摘要/Abstract

摘要：

针对惯性行人导航中存在的航向角发散问题, 提出一种基于全区间航向误差预测与校正的算法。利用零速区间内航向稳定的特性计算航向误差, 并结合陀螺仪短期稳定性来预测非零速区间的航向误差。通过卡尔曼滤波对导航误差进行全面校正, 显著提升行人导航的精确度。实验结果显示, 在非闭合凹形路径中, 所提算法的平均导航轨迹误差仅为0.94 m, 相比零速修正算法降低了75.33%, 较仅对零速阶段进行航向误差处理的算法减少了48.91%。在400 m闭合路径测试中, 终点位置误差仅为2.53%, 解算路径最符合实际运动轨迹, 验证了本算法能够显著提高行人导航的精度。

关键词: 惯性行人导航, 零速区间, 非零速区间, 航向误差预测与校正, 卡尔曼滤波

Abstract:

Aiming at the problem of heading angle divergence in inertial pedestrian navigation, an algorithm based on heading error prediction and correction across the entire area is proposed. Using the stable heading characteristics within the zero-velocity interval to calculate heading errors, and combining with the short-term stability of the gyroscope to predict heading errors in non-zero-velocity interval. By using Kalman filtering to comprehensively correct navigation errors, the accuracy of pedestrian navigation is significantly improved. The experimental results show that in non closed concave paths, the average navigation trajectory error of the proposed algorithm is only 0.94 m, which is 75.33% lower than the zero speed correction algorithm and 48.91% lower than the algorithm that only processes heading errors during the zero speed phase. In the 400 m closed path test, the endpoint position error is only 2.53%, and the calculated path best matched the actual motion trajectory, verifying that the proposed algorithm can significantly improve the accuracy of pedestrian navigation.

Key words: inertial pedestrian navigation, zero-velocity interval, non-zero-velocity interval, heading error prediction and correction, Kalman filtering

中图分类号:

V249.32+2

戴洪德, 于佳炜, 郑百东, 张笑宇, 田密. 基于全区间航向误差预测与校正的惯性行人导航算法研究[J]. 系统工程与电子技术, 2025, 47(5): 1663-1670.

Hongde DAI, Jiawei YU, Baidong ZHENG, Xiaoyu ZHANG, Mi TIAN. Research on inertial pedestrian navigation algorithm based on prediction and correction of heading error through full-interval[J]. Systems Engineering and Electronics, 2025, 47(5): 1663-1670.

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算法	参考点坐标/m	对应点坐标/m	Δr/m	ΔR/m	ΔS/m
算法1	(0, 5.5)	(-0.48, 4.86)	0.80	3.81	2.77
	(11, 5.5)	(10.22, 5.34)	0.80	3.81	2.77
	(11, -11)	(10.33, -10.79)	0.70	3.81	2.77
	(-29.32, -11)	(-28.47, -16.47)	5.54	3.81	2.77
	(-29.32, 5.5)	(-33.31, -1.36)	7.94	3.81	2.77
	(-18.32, 5.5)	(-22.9, 1.58)	6.03	3.81	2.77
	(-18.32, 0)	(-21.73, -3.46)	4.86	3.81	2.77
算法2	(0, 5.5)	(-0.48, 4.86)	0.80	1.84	1.05
	(11, 5.5)	(10.28, 5.58)	0.72	1.84	1.05
	(11, -11)	(10.87, -10.42)	0.59	1.84	1.05
	(-29.32, -11)	(-28.41, -13.63)	2.78	1.84	1.05
	(-29.32, 5.5)	(-30.42, 2.28)	3.40	1.84	1.05
	(-18.32, 5.5)	(-19.95, 3.52)	2.56	1.84	1.05
	(-18.32, 0)	(-19.45, -1.65)	2.00	1.84	1.05
算法3	(0, 5.5)	(-0.48, 4.86)	0.80	1.30	0.56
	(11, 5.5)	(10.27, 5.77)	0.78	1.30	0.56
	(11, -11)	(11.18, -10.34)	0.68	1.30	0.56
	(-29.32, -11)	(-28.12, -12.6)	2.00	1.30	0.56
	(-29.32, 5.5)	(-29.2, 3.33)	2.17	1.30	0.56
	(-18.32, 5.5)	(-18.45, 4.02)	1.49	1.30	0.56
	(-18.32, 0)	(-18.23, -1.16)	1.16	1.30	0.56
算法4	(0, 5.5)	(-0.48, 4.86)	0.80	0.94	0.17
	(11, 5.5)	(10.29, 5.85)	0.79	0.94	0.17
	(11, -11)	(11.22, -10.24)	0.79	0.94	0.17
	(-29.32, -11)	(-28.25, -10.87)	1.08	0.94	0.17
	(-29.32, 5.5)	(-28.22, 4.89)	1.26	0.94	0.17
	(-18.32, 5.5)	(-17.4 6, 4.92)	1.04	0.94	0.17
	(-18.32, 0)	(-17.55, -0.25)	0.81	0.94	0.17

算法	起点坐标/m	终点坐标/m	Δr/m	相对位置误差/%
算法1	(0, 0)	(97.57, -131.21)	163.51	40.88
算法2	(0, 0)	(51.76, -13.81)	53.57	13.40
算法3	(0, 0)	(26.42, -19.61)	32.90	8.22
算法4	(0, 0)	(-3.71, 9.43)	10.13	2.53

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