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

Abstract

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

CLC Number:

V249.32+2

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.

Figures/Tables 10

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References 30

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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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Research on inertial pedestrian navigation algorithm based on prediction and correction of heading error through full-interval

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