Pedestrian Inertial Navigation with Self-Contained Aiding. Andrei M. Shkel
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).
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