Research On GPS/self-contained Sensors Based Seamless Outdoor/Indoor Pedestrian Positioning Algorithm

As navigation function becomes a more and more common application in mobile phones and LBS (Location-Based Service) grows rapidly, requirements are increasingly urgent on the accuracy, reliability and continuity of pedestrian navigation and positioning technology. However, as a primary positioning means in pedestrian navigation, the U.S. GPS (Global Positioning System) receiver can't provide accurate results and even fails in positioning under complex urban canyons and indoor environments due to signal attenuation, interference or blockage, etc, in which most of personal users'activities exactly happen. In order to obtain continuous navigation, GPS must be augmented by other positioning technologies, such as self-contained sensors, WLAN (Wireless Local Area Network), mobile cellular networks, RFID (Radio Frequency IDentification), Pseudolites, etc. Integrating GPS with self-contained DR sensors is autonomous and doesn't need extra infrastructure, or a fingerprint database, so that it attracts extensive attentions and intensive investigation.Based on a self-developed low-cost Multi-Sensor Positioning platform (MSP), which includes a GPS receiver and two self-contained sensors (a 2-axis digital compass and a 3-axis accelerometer), this dissertation investigates if the GPS receiver can't provide accurate and continuous positioning information, how to obtain a pedestrian's speed and heading from self-contained sensors and calculate his/her position through combined Kalman filters, for achieving a seamless outdoor/indoor pedestrian positioning solution.After deeply studying on various seamless pedestrian positioning algorithms, taking into account that the performance of low-cost sensors in MSP can't meet the accuracy requirement of traditional inertial navigation mechanism, the dissertation selected Pedestrian Dead Reckoning (PDR) algorithm to assist the positioning in the complex environment that GPS performance is poor, and focused on the following three aspects:1. Step detection and step length estimation algorithms: based on physiological characteristics of a pedestrian gait, referring to traditional step detection and step length estimation methods, a step detection algorithm is realized using accelerometer's signals, combing three methods: sliding-window, zero-crossing and peak detection, and a 1-parameter step length estimation model is chosen. In addition, a novel biomedical signal, Electromyography (EMG) is firstly introduced into personal navigation, for detecting the pedestrian's step occurrences and corresponding step lengths, and the details of the proposed method are presented, including setup of EMG electrodes, signal pre-processing procedure, algorithms of stride detection and stride length estimation. Several experiments were carried out to validate step detection methods and evaluate the precision of step length estimation models separately.2. Digital compass heading calibration algorithm: it's always a key issue in PDR algorithms to obtain accurate heading, as well as a long-term problem in pedestrian positioning research. Since a 2-axis digital compass can't compensate tilt error by itself, and is vulnerable to various errors, such as hard and soft iron effects, biases, scale factors and oscillation of the pedestrian's body when walking, a simulation method is utilized to study the impact of each error on outputs of digital compasses. Fully considering the easy-to-use requirement on heading calibration methods, a unified heading error model is proposed, which includes all the possible and predictable errors. Two different algorithms are used to solve the error model's parameters. One of these algorithms requires an independent calibration procedure using least square method, and the other one is non-independent using Knowledge-based and Kalman filter for online training the model's parameters. The application condition and feasibility of the calibration approach has been discussed and validated through extensive field tests.3. Seamless pedestrian positioning algorithm: to achieve a seamless outdoor/ indoor pedestrian positioning solution, this dissertation proposes a three-mode positioning mechanism: GPS mode- when GPS signal is good, final positioning results derive from GPS receiver directly; PDR mode- when there is no GPS signal, final positioning information are obtained from PDR results; GPS/PDR hybrid mode- when GPS signal can be received but not good, the final results are the ones combined by both GPS and PDR positioning outputs through Kalman filter. When GPS signal is not available, the acceleration-based and EMG-based PDR algorithms are realized separately with the aid of the heading from digital compass, and verified in several field tests conducted in the environments including less magnetic disturbances. The results demonstrate that the typical positioning performance of the MSP can meet and even exceed the common level in most of existing pedestrian positioning systems. Based on a set of data collected in a field test conducted in the urban canyon, the GPS/PDR hybrid positioning algorithm has been developed, including quality evaluation of GPS positioning performance and design of fusion filter, and the potential of combining the algorithm with other techniques is explored in order to further improve the positioning accuracy.