%0 Journal Article
%T Robust Modeling of Low-Cost MEMS Sensor Errors in Mobile Devices Using Fast Orthogonal Search
%A M. Tamazin
%A A. Noureldin
%A M. J. Korenberg
%J Journal of Sensors
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/101820
%X Accessibility to inertial navigation systems (INS) has been severely limited by cost in the past. The introduction of low-cost microelectromechanical system-based INS to be integrated with GPS in order to provide a reliable positioning solution has provided more wide spread use in mobile devices. The random errors of the MEMS inertial sensors may deteriorate the overall system accuracy in mobile devices. These errors are modeled stochastically and are included in the error model of the estimated techniques used such as Kalman filter or Particle filter. First-order Gauss-Markov model is usually used to describe the stochastic nature of these errors. However, if the autocorrelation sequences of these random components are examined, it can be determined that first-order Gauss-Markov model is not adequate to describe such stochastic behavior. A robust modeling technique based on fast orthogonal search is introduced to remove MEMS-based inertial sensor errors inside mobile devices that are used for several location-based services. The proposed method is applied to MEMS-based gyroscopes and accelerometers. Results show that the proposed method models low-cost MEMS sensors errors with no need for denoising techniques and using smaller model order and less computation, outperforming traditional methods by two orders of magnitude. 1. Introduction Presently, GPS-enabled mobile devices offer various positioning capabilities to pedestrians, drivers, and cyclists. GPS provides absolute positioning information, but when signal reception is attenuated and becomes unreliable due to multipath, interference, and signal blockage, augmentation of GPS with inertial navigation systems (INS) or the like is needed. INS is inherently immune to the signal jamming, spoofing, and blockage vulnerabilities of GPS, but the accuracy of INS is significantly affected by the error characteristics of the inertial sensors it employs [1]. GPS/INS integrated navigation systems are extensively used [2], for example, in mobile devices that require low-cost microelectromechanical System (MEMS) inertial sensors (gyroscopes and accelerometers) due to their low cost, low power consumption, small size, and portability. The inadequate long-term performance of most commercially available MEMS-based INS limits their usefulness in providing reliable navigation solutions. MEMSs are challenging in any consumer navigation system because of their large errors, extreme stochastic variance, and quickly changing error characteristics. According to [3], the inertial sensor errors of a low-cost INS consist of
%U http://www.hindawi.com/journals/js/2013/101820/