Abstract
Abstract The demand for navigating pedestrian by using a hand-held mobile device increased remarkably over the past few years, especially in GPS-denied scenario. We propose a new pedestrian dead reckoning (PDR)-based navigation algorithm by using magnetic, angular rate, and gravity (MARG) sensors which are equipped in existing commercial smartphone. Our proposed navigation algorithm consists of step detection, stride length estimation, and heading estimation. To eliminate the gauge step errors of the random bouncing motions, we designed a reliable algorithm for step detection. We developed a BP neural network-based stride length estimation algorithm to apply to different users. In response to the challenge of magnetic disturbance, a quaternion-based extended Kalman filter (EKF) is introduced to determine the user's heading direction for each step. The performance of our proposed pedestrian navigation algorithm is verified by using a smartphone in providing accurate, reliable, and continuous location tracking services.
Highlights
A large number of navigation systems, such as GPS, can only be applied to the outdoor and open sky scenarios since the microwaves are blocked by the buildings and ground
The magnetic, angular rate, and gravity (MARG) sensors involved in MEMS technology have been widely used in smartphones for pedestrian inertial navigation and are expected to become one of the key components of a variety of localization and tracking systems [5,6]
This paper shows a new pedestrian dead reckoning-based MARG navigation algorithm, which is highly accurate and is easy to be implemented on smartphones
Summary
A large number of navigation systems, such as GPS, can only be applied to the outdoor and open sky scenarios since the microwaves are blocked by the buildings and ground. To obtain a stable and reliable attitude angle, the gyroscope should be integrated with accelerometer and magnetometer To this end, the complementary filter, Kalman filter, and gradient descent algorithm are widely used to conduct data fusion. There is significant accuracy deterioration by using the aforementioned conventional algorithms when a large linear acceleration occurs or the magnetometer is seriously interfered by the surrounding noise, such as the blocking by iron products To solve this problem, this paper shows a new pedestrian dead reckoning-based MARG navigation algorithm, which is highly accurate and is easy to be implemented on smartphones. Considering the nonlinear relations of the proportion coefficient C, pedestrian height, and stride length, we use a back propagation (BP) neural network to obtain this nonlinear mapping for the sake of predicting the value C accurately and real timely.
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