传感技术学报2017，Vol.30Issue(6)：878-885,8.DOI:10.3969/j.issn.1004-1699.2017.06.013
基于行人运动模态辨识的室内外无缝导航航向算法研究
Research on Indoor and Outdoor Seamless Navigation Heading Algorithm Based on Pedestrian Modal Identification
摘要
Abstract
Nowadays,heading algorithm in pedestrian navigation is mostly based on the fixed mode of navigation sen-sor on the pedestrian body,or relies on other auxiliary radio frequency information,which greatly reduces the porta-bility of sensors. Hence,based on the sensitivity of the gyroscope to the low frequency noise and the stability of the accelerometer,this paper puts forward one solution to determine the steady-state and non-steady state of the hand held mobile based on the modal identification and proposes complementary filter to achieve optimal attitude based on time domain. This paper uses modified complementary filter to weaken the interference caused by pedestrian movement and improves the accuracy of the carrier attitude measurement. In addition,the adaptive kalman filter al-gorithm is designed by using the magnetic sensor calibration data to restrain the divergence of heading angle error. The test results show that this algorithm can ensure that the accuracy of heading angle measurement in indoor and outdoor pedestrians can be improved by 80% and greatly improve the adaptability and portability of sensors simulta-neously,which meets the demand of practical engineering.关键词
惯性传感器/改进型互补滤波/磁异常辨识/自适应卡尔曼滤波/行人运动模态辨识Key words
inertial sensors/modified complementary filter/identification of magnetic anomaly/adaptive Kalman filter/model identification of pedestrian movement分类
航空航天引用本文复制引用
黄欣,许建新,张苗,熊智..基于行人运动模态辨识的室内外无缝导航航向算法研究[J].传感技术学报,2017,30(6):878-885,8.基金项目
国家自然科学基金项目(61533008,61673208,61533009,61374115) （61533008,61673208,61533009,61374115）
江苏省六大人才高峰项目(2013-JY-013) （2013-JY-013）
江苏高校优势学科建设工程项目和江苏省"物联网与控制技术"重点实验室基金项目 （）
中央高校基本科研业务费专项资金项目(NZ2016104,NS2017016) （NZ2016104,NS2017016）
江苏省" 333 工程" 科研立项项目( BRA2016405) （ BRA2016405）
留学人员择优项目( 2016年) （ 2016年）
航空科学基金项目(20165552043) （20165552043）
江苏省普通高校研究生科研创新计划项目(KYLX150264) （KYLX150264）
江苏省自然科学基金项目(BK20141453) （BK20141453）
