gait reconstruction. Recognition of gait pattern can help to reduce the navigation error obtained from a single IMU.
Machine Learning (ML) has also been applied to pedestrian inertial navigation. ML has mostly been explored in the field of Human Activity Recognition (HAR) [31], stride length estimation [32], and stance phase detection [33]. However, few studies used the ML approach to directly solve the pedestrian navigation problem. Commonly used techniques include Decision Trees (DT) [34], Artificial Neural Network (ANN) [35], Convolutional Neural Network (CNN) [36], Support Vector Machine (SVM) [37], and Long Short‐Term Memory (LSTM) [38].
Sensor fusion method can be used in a self‐contained way, where multiple self‐contained sensors are used in a single system, and their readouts are fused in the system to obtain a navigation result. For example, self‐contained ranging technique is one possibility. In this technique, the transmitter sends out a signal (can be ultrasonic wave or electromagnetic wave) which is received by the receiver. This technique can be categorized as self‐contained if both the transmitter and the receiver are within the system whose state is to be estimated. For example, in the foot‐to‐foot ranging, the transmitter and receiver are placed on two feet of a person to keep track of the distance between them [24]. In the cooperative localization, ranging technique is applied to measure the distance between multiple agents as a network to improve the overall navigation accuracy of each of the agent [39].
1.5 Outline of the Book
The topic of this book is about the pedestrian inertial navigation and related self‐contained aiding techniques. In Chapter , we first introduce the technological basis of inertial navigation – inertial sensors, and IMUs. Their basic principles of operation, technology background, and state‐of‐the‐art are included. Next, in Chapter , basic implementation and algorithm of strapdown inertial navigation are presented as a basis of the following analysis. Then, we demonstrate how the navigation errors are accumulated in the navigation process in Chapter , with a purpose of pointing out the importance of aiding in the pedestrian inertial navigation. Chapter introduces one of the most commonly used aiding technique in pedestrian inertial navigation: ZUPT aiding algorithm. It is followed by an analysis on navigation error propagation in the ZUPT‐aided pedestrian inertial navigation in Chapter , relating the navigation error to the IMU errors. Chapter presents some of the limitations of the ZUPT‐aided pedestrian inertial navigation, and methods have been proposed and demonstrated to be able to reduce the majority part of the errors caused by the ZUPTs. Chapter discusses efforts in improving the adaptivity of the pedestrian inertial navigation algorithm. Approaches including ML and Multiple‐Model (MM) methods are introduced. Chapter discusses other popular self‐contained aiding techniques, such as magnetometry, barometry, computer vision, and ranging techniques. Different ranging types, mechanisms, and implementations are covered in this chapter. Finally, in Chapter , the book concludes with a technological perspective on self‐contained pedestrian inertial navigation with an outlook for development of the Ultimate Navigation Chip (uNavChip).
References
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