Skip to main content
SpringerLink
Log in

Research on the UWB/IMU fusion positioning of mobile vehicle based on motion constraints

  • Original Study
  • Published:
Acta Geodaetica et Geophysica Aims and scope Submit manuscript

Abstract

In a non-line-of-sight (NLOS) environment, high accuracy ultra-wideband (UWB) positioning has been one of the hot topics in studying indoor positioning. Aiming at the UWB and inertial measurement unit (IMU) fusion vehicle positioning, a constraint robust iterate extended Kalman filter (CRIEKF) algorithm has been proposed in this paper. It has overcome the innate defect of the extended Kalman filter against non-Gaussian noise and the shortcoming of the robust extended Kalman filter algorithm, which has just processed the non-Gaussian noise solely based on the prior information. Our algorithm can update the observation covariance based on the posteriori estimate of the system in each iteration, and then update the posteriori distribution of the system based on the obtained covariance to significantly reduce the influence of non-Gaussian noise on positioning accuracy. Also, with the introduction of motion constraints, such as zero velocity, pseudo velocity and plane constraints, it can achieve a smoother positioning result. The experimental result proves that through the CRIEKF-based UWB/IMU fusion robot positioning method, a mean positioning accuracy of around 0.21 m can be achieved in NLOS environments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Ascher C, Zwirello L, Zwick T, Trommer G (2011) Integrity monitoring for UWB/INS tightly coupled pedestrian indoor scenarios. In: International conference on indoor positioning and indoor navigation, pp 1–6

  • Benini A, Mancini A, Longhi S (2013) An IMU/UWB/vision-based extended Kalman filter for mini-UAV localization in indoor environment using 802.15.4a wireless sensor network. J Intell Robot Syst 70(1):461–476

    Article  Google Scholar 

  • Blanco JL, Galindo C, Ortiz-De-Galisteo A, Moreno FA (2009) Mobile robot localization based on ultra-wide-band ranging: a particle filter approach. Robot Auton Syst 57(5):496–507

    Article  Google Scholar 

  • Fan Q, Wu Y, Jing H, Lei W, Yu Z, Zhou L (2014) Integrated navigation fusion strategy of INS/UWB for indoor carrier attitude angle and position synchronous tracking. Sci World J 2014:215–303

    Google Scholar 

  • Fan Q, Sun B, Sun Y, Zhuang X (2017a) Performance enhancement of mems-based ins/UWB integration for indoor navigation applications. IEEE Sens J 17(10):3116–3130

    Article  Google Scholar 

  • Fan Q, Sun B, Sun Y, Wu Y, Zhuang X (2017b) Data fusion for indoor mobile robot positioning based on tightly coupled ins/UWB. J Navig 70(5):1–19

    Article  Google Scholar 

  • Fischer C, Sukumar PT, Hazas M (2013) Tutorial: implementing a pedestrian tracker using inertial sensors. IEEE Pervasive Comput 12(2):17–27

    Article  Google Scholar 

  • García E, Poudereux P, Álvaro Hernández Ureña J, Gualda D (2015) A robust UWB indoor positioning system for highly complex environments. In: IEEE international conference on industrial technology. IEEE, pp 3386–3391

  • Hol JD, Dijkstra F, Luinge H, Schon TB (2009) Tightly coupled UWB/IMU pose estimation. In: IEEE international conference on ultra-wideband, pp 688–692

  • Johnson ME, Sathyan T (2011) Improved orientation estimation in complex environments using low-cost inertial sensors. In: Proceedings of the, international conference on information fusion. IEEE, pp 1–8

  • Li X, Wang J, Liu C (2015) A bluetooth/pdr integration algorithm for an indoor positioning system. Sensors 15(10):24862–24885

    Article  Google Scholar 

  • Li X, Wang J, Liu C, Zhang L, Li Z (2016) Integrated WiFi/PDR/smartphone using an adaptive system noise extended Kalman filter algorithm for indoor localization. ISPRS Int J Geo-Inf 5(2):8

    Article  Google Scholar 

  • Li X, Wang Y, Khoshelham K (2019) Comparative analysis of robust extended kalman filter and incremental smoothing for uwb/pdr fusion positioning in nlos environments. Acta Geodaetica Geophys 54(5):387

    Google Scholar 

  • Nummiaro K, Koller-Meier E, Gool LV (2003) An adaptive color-based particle filter. Image Vis Comput 21(1):99–110

    Article  Google Scholar 

  • Santoso F, Redmond SJ (2015) Indoor location-aware medical systems for smart homecare and telehealth monitoring: state-of-the-art. Physiological Measurement 36(10):R53

    Article  Google Scholar 

  • Savioli A, Goldoni E, Savazzi P, Gamba P (2013) Low complexity indoor localization in wireless sensor networks by UWB and inertial data fusion. Comput Sci 52(4):723–732

    Google Scholar 

  • Sczyslo S, Schroeder J, Galler S, Kaiser T (2008) Hybrid localization using UWB and inertial sensors. In: IEEE international conference on ultra-wideband, vol 3. IEEE Xplore, pp 89–92

  • Titterton DH, Weston JL (2004) Strapdown inertial navigation technology. Institution of Electrical Engineers, London

    Book  Google Scholar 

  • Wang Y, Li X (2017) The IMU/UWB fusion positioning algorithm based on a particle filter. ISPRS Int J Geo-Inf 6(8):235

    Article  Google Scholar 

  • Wang J, Gao Y, Li Z, Meng X, Hancock CM (2016) A tightly-coupled GPS/INS/UWB cooperative positioning sensors system supported by v2i communication. Sensors 16(7):E944

    Article  Google Scholar 

  • Wang X, Hoseinnezhad R, Gostar AK, Rathnayake T, Xu B, Bab-Hadiashar A (2018) Multi-sensor control for multi-object bayes filters. Sig Process 142:260–270

    Article  Google Scholar 

  • Xu Y, Chen X (2016) Range-only UWB/ins tightly integrated navigation method for indoor pedestrian. Chin J Sci Instru 37(8):142–148

    Google Scholar 

  • Xu Y, Chen X, Cheng J, Zhao Q, Wang Y (2016) Improving tightly-coupled model for indoor pedestrian navigation using foot-mounted IMU and UWB measurements. In: Instrumentation and measurement technology conference proceedings. IEEE

  • Zhang J, Shen C (2016) Research on UWB indoor positioning in combination with TDOA improved algorithm and Kalman filtering. Mod Electron Tech 39(13):1–5

    Google Scholar 

  • Zwirello L, Ascher C, Trommer GF, Zwick T (2011) Study on UWB/INS integration techniques. In: Positioning navigation and communication. IEEE Xplore, pp 13–17

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under grant number 41674030 and China Postdoctoral Science Foundation under grant number 2016M601909 and the grand of China Scholarship Council.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, People’s Republic of China

    Xin Li & Yang Wang

  2. Mine Digitization Engineering Research Center of Ministry of Education, Xuzhou, People’s Republic of China

    Xin Li

Authors
  1. Xin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Wang, Y. Research on the UWB/IMU fusion positioning of mobile vehicle based on motion constraints. Acta Geod Geophys 55, 237–255 (2020). https://doi.org/10.1007/s40328-020-00291-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40328-020-00291-8

Keywords

Search

Navigation