Low-Cost MEMS-Based Pedestrian Navigation Technique for GPS-Denied Areas

Hindawi Publishing Corporation Journal of Sensors Volume 2013, Article ID 197090, 10 pages http://dx.doi.org/10.1155/2013/197090 Research Article Low-Cost MEMS-Based Pedestrian Navigation Technique for GPS-Denied Areas Abdelrahman Ali and Naser El-Sheimy Department of Geomatics Engineering, The University of Calgary, 2500 University Drive N.W., Calgary, AB, Canada T2N 1N4 Correspondence should be addressed to Abdelrahman Ali; Received 10 April 2013; Accepted 22 July 2013 Academic Editor: Kai-Wei Chiang Copyright © 2013 A. Ali and N. El-Sheimy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The progress in the micro electro mechanical system (MEMS) sensors technology in size, cost, weight, and power consumption allows for new research opportunities in the navigation field. Today, most of smartphones, tablets, and other handheld devices are fully packed with the required sensors for any navigation system such as GPS, gyroscope, accelerometer, magnetometer, and pressure sensors. For seamless navigation, the sensors’ signal quality and the sensors availability are major challenges. Heading estimation is a fundamental challenge in the GPS-denied environments; therefore, targeting accurate attitude estimation is considered significant contribution to the overall navigation error. For that end, this research targets an improved pedestrian navigation by developing sensors fusion technique to exploit the gyroscope, magnetometer, and accelerometer data for device attitude estimation in the different environments based on quaternion mechanization. Results indicate that the improvement in the traveled distance and the heading estimations is capable of reducing the overall position error to be less than 15 m in the harsh environments. 1. Introduction Personal navigation requires technologies that are immune to signal obstructions and fading. One of the major challenges is obtaining a good heading solution in different environments and for different user positions without external absolute reference signals. Part of this challenge arises from the complexity and freedom of movement of a typical handheld user where the heading observability considerably degrades in low-speed walking, making this problem even more challenging. However, for short periods, the relative attitude and heading information is quite reliable. Self-contained systems requiring minimal infrastructure, for example, inertial measurement units (IMUs), stand as a viable option, since pedestrian navigation is not only focused on outdoor navigation but also on indoor navigation. Nowadays, most of the smartphones are programmable and equipped with self-contained, low cost, small size, and power-efficient sensors, such as magnetometers, gyroscopes, and accelerometers. Hence, integrating IMUs navigation solution with a magnetometer-based heading can play an important role in pedestrian navigation in all environments. In the current state of the art in MEMS technology, the accuracy of gyroscopes is not good enough for deriving an absolute heading or relative heading over longer durations of time. However, for short periods, the relative attitude information is quite reliable. Magnetometers, on the other hand, provide absolute heading information once calibrated. However, they can easily be disturbed by ferrous objects nearby, making them unreliable for brief intervals. This calls for the investigation of possible sources of heading error in complementary sensors such as a gyroscope and a magnetometer and improving the accuracy of the result based on an improved Kalman filter design. Much research towards the heading estimation for personal positioning applications has been conducted in the recent years. Some approaches use magnetometers exclusively for heading estimation [1] while others integrate it tightly with an IMU [2, 3]. One commercially available personal locator system based on this principle is 2 the Dead Reckoning Module DRM-4000 made by Honeywell [4]. A quaternion-based method to integrate IMU with magnetometer is presented by [5]. Three body angular rates and four quaternion elements were used to express attitude and were selected as the states of the Kalman filter. The method needs to model the angular motion of the body. In [6], a linear system error model based on the Euler angles errors expressing the local frame errors is developed, and the corresponding system observation model is derived. The proposed method does not need to model the system angular motion and also avoids the nonlinear problem which is inherent in the customarily used methods. A similar technique is proposed by [7] where the angular rates were modeled to be a constant. A nonlinear derivative equation for the Euler angle integration kinematics is investigated in [8]. Work in [9, 10] presented an Euler angle error based method to integrate IMU with magnetometer data where three Euler angle errors and three gyroscope biases were used as states for the Kalman filter. The estimated states were used to correct the Euler angles and to compensate gyroscope drifts, respectively. The work at [11] presented a mathematical model for compass deviation by creating an a priori look-up table for heading corrections. A Kalman filtering approach was investigated by [12] to estimate the angular rotation from the input of a magnetometer compass and three gyroscopes. References [13, 14] presented a least squares technique with improvement which is used for the estimation of the compass deviation model. In addition, much research has been conducted to use the 3D magnetometer-based heading for personal navigation applications in the recent years [15]. The magnetometer cannot be used as standalone source for heading information in the harsh environments, especially indoor [16]. In addition, it is required to have knowledge about the preexisted magnetic anomalies resulted from some of the man-made infrastructure [17]. Using magnetic field measurements in heading estimation for indoor navigation also has some limitations as the magnetic field signal needs to be strong enough. Also, the mobile navigation device should be away from any source of disturbances to avoid any perturbation effect [18]. Besides that, the magnetic field during the indoor environment is not completely constant due to the presence of the electronic and electrical devices everywhere. To avoid the problem of magnetometer anomaly, arising out of ferrous materials in the vicinity of the magnetometers, a perturbation detection technique is required. In such scenario, the filter works only in the propagation mode without any update for the attitude. Also the gyroscope bias drifts with time and temperature can be compensated by magnetometers. In this paper, a method is presented to obtain seamless attitude information by inte (...truncated)