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References

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1 1 Bowditch, N. (2002). The American Practical Navigator, Bicentennial Edition. Bethesda, MD: National Imagery and Mapping Agency.

2 2 Sobel, D. (2005). Longitude: The True Story of a Lone Genius Who Solved the Greatest Scientific Problem of His Time. Macmillan.

3 3 Hofmann‐Wellenhof, B., Lichtenegger, H., and Wasle, E. (2007). GNSS‐Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and More. Springer Science & Business Media.

4 4 Titterton, D. and Weston, J. (2004). Strapdown Inertial Navigation Technology, 2e, vol. 207. AIAA.

5 5 Woodman, O.J. (2007). An Introduction to Inertial Navigation. No. UCAM‐CL‐TR‐696. University of Cambridge Computer Laboratory.

6 6 Wagner, J. and Trierenberg, A. (2010). The machine of Bohnenberger: bicentennial of the gyro with cardanic suspension. Proceedings in Applied Mathematics and Mechanics 10 (1): 659–660.

7 7 Prikhodko, I.P., Zotov, S.A., Trusov, A.A., and Shkel, A.M. (2012). Foucault pendulum on a chip: rate integrating silicon MEMS gyroscope. Sensors and Actuators A: Physical 177: 67–78.

8 8 Tazartes, D. (2014). An historical perspective on inertial navigation systems. IEEE International Symposium on Inertial Sensors and Systems (ISISS), Laguna Beach, CA, USA (25–26 February 2014).

9 9 Ma, M., Song, Q., Li, Y., and Zhou, Z. (2017). A zero velocity intervals detection algorithm based on sensor fusion for indoor pedestrian navigation. IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China (15–17 December 2017).

10 10 Perlmutter, M. and Robin, L. (2012). High‐performance, low cost inertial MEMS: a market in motion!. IEEE/ION Position, Location and Navigation Symposium, Myrtle Beach, SC, USA (23–26 April 2012).

11 11 Jopling, P.F. and Stameris, W.A. (1970). Apollo guidance, navigation and control‐design survey of the Apollo inertial subsystem.

12 12 Foxlin, E. (2005). Pedestrian tracking with shoe‐mounted inertial sensors. IEEE Computer Graphics and Applications 25 (6): 38–46.

13 13 Harle, R. (2013). A survey of indoor inertial positioning systems for pedestrians. IEEE Communications Surveys & Tutorials 15 (3): 1281–1293.

14 14 Díez, L.E., Bahillo, A., Otegui, J., and Otim, T. (2018). Step length estimation methods based on inertial sensors: a review. IEEE Sensors Journal 18 (17): 6908–6926.

15 15 Köse, A., Cereatti, A., and Della Croce, U. (2012). Bilateral step length estimation using a single inertial measurement unit attached to the pelvis. Journal of Neuroengineering and Rehabilitation 9 (1): 1–10.

16 16 Miyazaki, S. (1997). Long‐term unrestrained measurement of stride length and walking velocity utilizing a piezoelectric gyroscope. IEEE Transactions on Biomedical Engineering 44 (8): 753–759.

17 17 Bishop, E. and Li, Q. (2010). Walking speed estimation using shank‐mounted accelerometers. IEEE International Conference on Robotics and Automation, Anchorage, AK, USA (3–7 May 2010).

18 18 Omr, M. (2015). Portable navigation utilizing sensor technologies in wearable and portable devices. PhD dissertation. Department of Electrical and Computer Engineering, Queens University.

19 19 Renaudin, V., Susi, M., and Lachapelle, G. (2012). Step length estimation using handheld inertial sensors. Sensors 12 (7): 8507–8525.

20 20 Munoz Diaz, E. (2015). Inertial pocket navigation system: unaided 3D positioning. Sensors 15 (4): 9156–9178.

21 21 Beauregard, S. (2006). A helmet‐mounted pedestrian dead reckoning system. VDE International Forum on Applied Wearable Computing, Bremen, Germany (15–16 March 2006).

22 22 Park, J.‐G., Patel, A., Curtis, D. et al. (2012). Online pose classification and walking speed estimation using handheld devices. ACM Conference on Ubiquitous Computing, New York City, NY, USA (September 2012).

23 23 Wang, Y., Jao, C.‐S., and Shkel, A.M. (2021) Scenario‐dependent ZUPT‐aided pedestrian inertial navigation with sensor fusion. Gyroscopy and Navigation 12 (1).

24 24 Laverne, M., George, M., Lord, D. et al. (2011). Experimental validation of foot to foot range measurements in pedestrian tracking. ION GNSS Conference, Portland, OR, USA (19–23 September 2011).

25 25 Wang, Y., Lin, Y.‐W., Askari, S. et al. (2020). Compensation of systematic errors in ZUPT‐aided pedestrian inertial navigation. IEEE/ION Position Location and Navigation Symposium (PLANS), Portland, OR, USA (20–23 April 2020).

26 26 TDK InvenSense (2020). ICP‐10100 Barometric Pressure Sensor Datasheet.

27 27 Cui, X., Gulliver, T.A., Li, J., and Zhang, H. (2016). Vehicle positioning using 5G millimeter‐wave systems. IEEE Access 4: 6964–6973.

28 28 Gersdorf, B. and Freese, U. (2013). A Kalman filter for odometry using a wheel mounted inertial sensor. International Conference on Informatics in Control, Automation and Robotics (ICINCO) (1), 388–395.

29 29 Jimenez, A.R., Seco, F., Prieto, J.C., and Guevara, J. (2010). Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot‐mounted IMU. IEEE Workshop on Positioning Navigation and Communication (WPNC), Dresden, Germany (11–12 March 2010).

30 30 Mezentsev, O. and Collin, J. (2019). Design and performance of wheel‐mounted MEMS IMU for vehicular navigation. IEEE International Symposium on Inertial Sensors & Systems, Naples, FL, USA (1–5 April 2019).

31 31 Zheng, Y., Liu, Q., Chen, E. et al. (2014). Time series classification using multi‐channels deep convolutional neural networks. International Conference on Web‐Age Information Management, Macau, China (16–18 June 2014), pp. 298–310.

32 32 Hannink, J., Kautz, T., Pasluosta, C.F. et al. (2017). Mobile stride length estimation with deep convolutional neural networks. IEEE Journal of Biomedical and Health Informatics 22 (2): 354–362.

33 33 Wagstaff, B., Peretroukhin, V., and Kelly, J. (2017). Improving foot‐mounted inertial navigation through real‐time motion classification. IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan (18–21 September 2017).

34 34 Fan, L., Wang, Z., and Wang, H. (2013). Human activity recognition model based on decision tree. IEEE International Conference on Advanced Cloud and Big Data, Nanjing, China (13–15 December 2013).

35 35 Wang, Y. and Shkel, A.M. (2021) Learning‐based floor type identification in ZUPT‐aided pedestrian inertial navigation. IEEE Sensors Conference 5.

36 36 Askari, S., Jao, C.‐S., Wang, Y., and Shkel, A.M. (2019). Learning‐based calibration decision system for bio‐inertial motion application. IEEE Sensors Conference, Montreal, Canada (27–30 October 2019).

37 37 Anguita, D., Ghio, A., Oneto, L. et al. (2012). Human activity recognition on smartphones using a multiclass hardware‐friendly support vector machine. In: International Workshop on Ambient Assisted Living, 216–223. Berlin, Heidelberg: Springer‐Verlag.

38 38 Ordóñez, F.J. and Roggen, D. (2016). Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16 (1): 115.

39 39 Olsson, F., Rantakokko, J., and Nygards, J. (2014). Cooperative localization using a foot‐mounted inertial navigation system and ultrawideband ranging. IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN), Busan, Korea (27–30 October 2014).

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