Sensor Error Compensation Methods for Performance Enhancement of a Low-cost INS/GPS Navigation Algorithm used in Severe Urban Environments

P. Lavoie, R. Jr. Landry

Abstract:	Today’s commercial navigation solutions used for automotive applications mostly rely exclusively on Global Positioning System (GPS). In order to provide a ubiquitous and reliable navigation solution to the users, GPS receivers require optimal operating conditions which cannot be fulfilled at all time, especially when driving in severe urban environments. To reduce the impact of multipath, GPS is often combined to an Inertial Navigation System (INS) which, as opposed to GPS, is immune to signal jamming/blocking. However, the performance of low-cost Microelectromechanical systems (MEMS) based inertial sensors is strongly affected by complex errors of both deterministic and stochastic nature, thus contributing to the rapid divergence of the navigation solution when the INS is used in standalone (i.e. during GPS outages). To improve the performance of such low-cost INS/GPS integrated navigation algorithms, this paper proposes the following methodology: 1) development of a robust real-time calibration procedure to compensate for deterministic errors and 2) compensation for stochastic errors of low-cost inertial sensors by using an autoregressive (AR) based Gauss-Markov (GM) model. The first method consists of using a KF-based real-time calibration procedure to compensate for deterministic errors inherent to low-cost MEMS-based inertial sensors (i.e. scale factor, misalignment and bias errors). In contrast to traditional calibration algorithms which use iterative optimization methods such as Gauss-Newton or Levenberg-Marquardt (LM) algorithms, the proposed approach is based on an incremental algorithm and thus is easy to implement in any real-time application (i.e. it does not require laborious pre/post-processing of the sensor measurements). Real measurements from a MEMSense nIMU sensor unit have been used to validate the proposed algorithm and results shown that the KF-based method offers similar performance than a classical LM-based calibration algorithm. Results from a road test performed in a severe urban environment (using Novatel SPAN technology as a reference trajectory) also shows that the application of the proposed calibration procedure can help significantly improve the overall performance of the algorithm by approximately 55%. The second method proposed by this study consists of augmenting the conventional INS/GPS integration model with a first-order GM model in order to dynamically estimate and compensate the stochastic errors contained in the low-cost inertial sensors. In this paper, two different identification methods were used for the identification of the first-order GM model parameters namely the conventional method based on the analysis of an experimental autocorrelation function and another method based on the analysis of a first-order autoregressive process model. Results obtained during road tests using the nIMU sensor unit shown that the latter method outperforms the conventional identification method in any evaluated situation. Results have also shown that using the first-order AR-based GM model to estimate and compensate the stochastic errors can help furthermore improve the overall performance of the algorithm by approximately 20%.
Published in:	Proceedings of the ION 2013 Pacific PNT Meeting April 23 - 25, 2013 Marriott Waikiki Beach Resort & Spa Honolulu, Hawaii
Pages:	207 - 220
Cite this article:	Lavoie, P., Landry, R. Jr., "Sensor Error Compensation Methods for Performance Enhancement of a Low-cost INS/GPS Navigation Algorithm used in Severe Urban Environments," Proceedings of the ION 2013 Pacific PNT Meeting, Honolulu, Hawaii, April 2013, pp. 207-220.
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