| Pedestrian navigation is an important branch in the navigation field.Pedestrian navigation systems based on micro-inertial measurement units can provide short-term autonomous navigation services when satellite navigation signals are weak or missing due to external interference,such as indoors and tunnels.It can be used in personal navigation services,fire rescue scenes and other fields,and has important theoretical research and social application value.A zero-speed detection and error correction method are proposed based on motion mode constraints to improve the navigation and positioning accuracy of pedestrian navigation in an environment without external auxiliary information.The main research contents and results of this paper are as follows:(1)The typical error correction method based on the zero-speed interval in the inertial pedestrian navigation system is studied.Based on the analysis of the pedestrian navigation error correction method based on Kalman filtering,a complementary filtering algorithm is introduced to correct the pedestrian's static posture.Experiments have verified that the Kalman filter-based zero-speed/zero-angle rate correction algorithm can significantly reduce the positioning error of the inertial navigation system.The advantages and disadvantages of this algorithm and the complementary filter attitude correction algorithm in attitude correction are compared and analyzed.(2)Designed a foot zero speed detection algorithm based on motion mode constraints.The pattern recognition of the human body motion state based on a single MIMU is used as a constraint condition for zero-speed detection;based on the analysis of the performance differences of several zero-speed interval detection algorithms in different motion modes,a zero-speed zone suitable for the five main motion modes is designed.The speed detection algorithm independently adjusts the parameters of the zero-speed detection algorithm according to the results of the motion pattern recognition to improve the detection accuracy of the zerospeed interval.Experiments verify that a single MIMU can complete motion pattern recognition and the effectiveness of the zero-speed zone detection algorithm based on motion pattern constraints.(3)A zero-speed correction algorithm based on motion mode constraints is designed.Based on the analysis of the influence of different human motion states on the filter error,a zero-speed correction algorithm based on motion mode constraints is proposed.The algorithm adjusts the error parameter matrix Q/R in the Kalman filter according to the motion mode to reduce the influence of the human motion state on navigation.The influence of the algorithm.Experiments have verified that the algorithm can effectively suppress the positioning error of the navigation system in each motion mode.(4)A height correction algorithm based on motion mode constraints is designed.In indoor sports scenes,the height change model in each sports mode is established,and the height correction algorithm of the motion mode constraint is proposed,and the calculated height information is corrected using the motion mode as a parameter to suppress the accumulation of height errors.Experiments show that the algorithm can improve the high positioning accuracy of the navigation system.Finally,multiple sets of comprehensive experiments were carried out.In the walking experiment of about 370 m,the horizontal error of the start and end points reached 1.60m;in the regular rectangular repetitive motion experiment,the heading could be effectively corrected,and the error of the start and end points was reduced to 0.03m;indoor motion simulation in the experiment,a combination of five motion modes is included.After the motion mode constraint is added,the horizontal error of the start and end points is reduced from 2.33 m by 0.30 m,and the height error is significantly reduced.The above experiments verify the excellent performance of the error correction algorithm proposed. |
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