Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-24T03:47:20.968Z Has data issue: false hasContentIssue false

An Online Smoothing Method Based on Reverse Navigation for ZUPT-Aided INSs

Published online by Cambridge University Press:  21 October 2016

Qingzhong Cai*
Affiliation:
(School of Instrument Science and Opto-electronics Engineering, Beihang University, Beijing, China)
Gongliu Yang
Affiliation:
(School of Instrument Science and Opto-electronics Engineering, Beihang University, Beijing, China)
Ningfang Song
Affiliation:
(School of Instrument Science and Opto-electronics Engineering, Beihang University, Beijing, China)
Jianye Pan
Affiliation:
(School of Instrument Science and Opto-electronics Engineering, Beihang University, Beijing, China)
Yiliang Liu
Affiliation:
(School of Instrument Science and Opto-electronics Engineering, Beihang University, Beijing, China)
*

Abstract

Zero velocity update (ZUPT) is widely discussed for error restriction in land vehicle Inertial Navigation Systems (INSs) and wearable pedestrian INSs to overcome the problems of Global Positioning System (GPS) unavailability in urban canyons or indoor scenarios. In this paper, an online smoothing method for ZUPT-aided INSs is presented. By introducing the Rauch–Tung–Striebel (RTS) smoothing method into the ZUPT-aided INS, position errors can be effectively restrained not only at stop points but during the whole trajectory. By integrating reverse navigation with a ZUPT smoother, the method realises forward and real-time processing. Compared with existing approaches, it can improve the position accuracy in real time without any other sensors, which is well suited for applications on high-accuracy navigation in GPS-challenging environments. Accuracy test results with different Inertial Measurement Units (IMUs) show that the developed method can significantly decrease position errors from hundreds or thousands of metres to below ten metres. During the whole trajectory, the online smoothing method ensures the maximum position errors at non-stop points can reach the same level of accuracy at stop points. A delay test result proves that the delay of the reverse online smoothing method proposed in this paper is much shorter than existing online smoothing methods.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Aloi, D. and Korniyenko, O. (2007). Comparative performance analysis of a Kalman filter and a modified double exponential filter for GPS-only position estimation of automotive platforms in an urban-canyon environment. IEEE Transactions on Vehicular Technology, 56(5), 28802892.Google Scholar
Bebek, O., Suster, M.A., Rajgopal, S., Fu, M.J., Huang, X., Cavusoglu, M.C., Young, D.J., Mehregany, M., van den Bogert, A.J. and Mastrangelo, C.H. (2010). Personal navigation via high-resolution gait-corrected inertial measurement units. IEEE Transactions on Instrumentation and Measurement, 59(11), 30183027.Google Scholar
Ben, Y.Y., Yin, G., Gao, W. and Sun, F. (2009). Improved filter estimation method applied in zero velocity update for SINS. IEEE International Conference on Mechatronics and Automation, Changchun, China, 3375–3380.Google Scholar
Collin, J. (2015). MEMS IMU Carouseling for Ground Vehicles. IEEE Transactions on Vehicular Technology, 64(6), 22422251.Google Scholar
Colomar, D., Nilsson, J. and Händel, P. (2012). Smoothing for ZUPT-aided INSs. International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia, DOI: 10.1109/IPIN.2012.6418869.Google Scholar
Correa, A., Barcelo, M., Morell, A. and Vicario, J. (2016). Indoor pedestrian tracking by on-body multiple receivers. IEEE Sensors Journal, DOI: 10.1109/JSEN.2016.2518872.Google Scholar
Elhoushi, M., Georgy, J., Noureldin, A. and Korenberg, M. (2016). Motion mode recognition for indoor pedestrian navigation using portable devices. IEEE Transactions on Instrumentation and Measurement, 65(1), 208221.Google Scholar
Fourati, H. (2015). Heterogeneous data fusion algorithm for pedestrian navigation via foot-mounted inertial measurement unit and complementary filter”. IEEE Transactions on Instrumentation and Measurement, 64(1), 221229.Google Scholar
Godha, S. and Lachapelle, G. (2008). Foot mounted inertial system for pedestrian navigation. Measurement Science. Technology, 19(7), 697703.Google Scholar
Gong, X., Zhang, R. and Fang, J. (2013). Application of unscented R-T-S smoothing on INS/GPS integration system post processing for airborne earth observation. Measurement, 46(3), 10741083.CrossRefGoogle Scholar
Grejner-Brzezinska, D.A., Yi, Y., and Toth, C.K. (2001). Bridging GPS gaps in urban canyons: Benefits of ZUPT. Navigation, 48(4), 217225.CrossRefGoogle Scholar
Huddle, J.R. (1986). Historical perspective on estimation techniques for position and gravity survey with inertial systems. Journal of Guidance Control and Dynamics, 9(3), 257267.Google Scholar
Li, L., Pan, Y., Lee, J., Ren, C., Liu, Y., Grejner-Brzezinska, D.A. and Toth, C.K. (2012). Cart-Mounted geolocation system for unexploded ordnance with adaptive ZUPT assistance. IEEE Transactions on Instrumentation and Measurement, 61(4), 974979.Google Scholar
Liu, H., Nassar, S. and El-Sheimy, N. (2010). Two-filter smoothing for accurate INS/GPS land-vehicle navigation in urban centers. IEEE Transactions on Vehicular Technology, 59(9), 42564267.Google Scholar
Ojeda, L. and Borenstein, J. (2007). Non-GPS navigation for security personnel and first responders. The Journal of Navigation, 60(3), 391407.Google Scholar
Ramanandan, A., Chen, A. and Farrell, J. (2012). Inertial navigation aiding by stationary updates. IEEE Transaction on Intelligent Transportation Systems, 13(1), 235248.Google Scholar
Suh, Y.S. (2012). A smoother for attitude and position estimation using inertial sensors with zero velocity intervals. IEEE Sensors Journal, 12(5), 12551262.CrossRefGoogle Scholar
Tian, Y., Hamel, W. and Tan, J. (2014). Accurate human navigation using wearable monocular visual and inertial sensors. IEEE Transactions on Instrumentation and Measurement, 63(1), 203213.Google Scholar
Titterton, D.H. and Weston, J.L. (1997), Strapdown Inertial Navigation Technology. The Institution of Electrical Engineers, Stevenage, U.K. Google Scholar
Wang, Z., Zhao, H., Qiu, S. and Gao, Q. (2015). Stance-phase detection for ZUPT-aided foot-mounted pedestrian navigation system. IEEE Transactions on Instrumentation and Measurement, 20(6), 30703081.Google Scholar
Yan, G., Yan, W. and Xu, D. (2008). On reverse navigation algorithm and its application to SINS gyro-compass in-movement Alignment. 27th Chinese Control Conference, Kunming China, 724–729.Google Scholar
Yang, C., Gao, Z. and Li, D. (2010). Hybrid filter combining with ZUPT for vehicle MINS. 2nd International Asia Conference on Informatics in Control, Automation and Robotics, IEEE, Wuhan, China, 370–374.Google Scholar
Yun, X., Calusdian, J., Bachmann, E. and McGhee, R. (2012). Estimation of human foot motion during normal walking using inertial and magnetic sensor measurements. IEEE Transactions on Instrumentation and Measurement, 61(7), 20592072.Google Scholar
Zhang, H., Yuan, W., Shen, Q., Li, T., and Chang, H. (2015). A handheld inertial pedestrian navigation system with accurate step modes and device poses recognition. IEEE Sensors Journal, 15(3), 14211429.Google Scholar
Zhou, C., Downey, J., Stancil, D. and Mukherjee, T. (2010). A low-power shoe-embedded radar for aiding pedestrian inertial navigation. IEEE Transactions on Microwave Theory and Techniques, 58(10), 25212528.Google Scholar
Zihajehzadeh, S., Lee, T., Lee, J., Hoskinson, R. and Park, E. (2015). Integration of MEMS inertial and pressure sensors for vertical trajectory determination. IEEE Transactions on Instrumentation and Measurement, 64(3), 804814.Google Scholar