The Assessment of the Impact of Stochastic Error Modeling, Signal Denoising and Improved Gravity Compensation on the Navigation Performance of the Multi-IMU/GPS Sensor Assembly

Dorota A. Grejner-Brzezinska, Yudan Yi and Charles Toth

Abstract:	"This paper is an abbreviated version of the original paper by Grejner-Brzezinska, D. A., Toth, C. and Yi, Y., ?On Improving Navigation Accuracy of GPS/INS Systems,? published in Photogrammetirc Engineering and Remote Sensing, April 2005, Vol. 71, No. 4 pp 377-389. Section 2 herein is new. With the increasing use of multisensor mapping and intelligence-collecting platforms, data fusion has become a crucial step in the design of these systems and an essential component of many spatial data processing algorithms. The fundamental step of any data integration process is georeferencing or geometric fusion (time-space registration), provided by GPS/IMU, considered a fundamental technology of modern mobile mapping systems (MMS). Imaging sensors, most frequently supported by direct georegistration by GPS/IMU, are digital cameras, LiDAR systems (Light Detection and Ranging), multi-spectral or hyper-spectral scanners, or InSAR (Interferometric Synthetic Aperture Radar); their performance in object space is strongly depended on the quality of the GPS/IMU sensor registration. In recent years, a substantial research effort has been devoted to extensive algorithmic developments, performance analysis and practical implementations of GPS/IMU in direct sensor orientation, with a special emphasis on the higher-end inertial systems. However, since the market price of the navigation grade systems is still rather high, and the performance of the OTS consumer-grade IMU sensors still does not meet the high accuracy requirements of the majority of mapping projects, the current challenge is to examine the applicability of the lower-end IMU sensors to direct georeferencing, under the assumption that special signal processing algorithms and extended error models are applied. The primary objective of this paper is to discuss and demonstrate several algorithmic methods enabling improvements in the GPS/IMU system?s performance, with a special emphasis on the enhancement of standalone inertial navigation with medium and low-end IMUs. To this end, the effects of the IMU signal denoising using wavelet decomposition, and careful stochastic modeling of the IMU sensor errors in the extended Kalman filter architecture will be assessed and validated for the consumer and tactical grade sensors. In addition, the possibility of improving inertial navigation accuracy of the higher-end sensors using an improved gravity compensation procedure will be discussed. From the MMS perspective, our primary objective is to improve the long-term integrated system?s performance and stability, and to achieve better sensor georegistration accuracy in various environments. Several IMU sensors (navigation grade Northrop Grumman LN-100 and Honeywell H764G, tactical grade Honeywell HG1700, and the consumer grade MEMS IMU400CC from Crossbow) will be tested in tight integration with dualfrequency GPS carrier phase data and the results of the performance analysis based on kinematic land-based data sets will be presented. The multi-IMU/GPS integrated system enables integrity monitoring and internal quality checking for the system?s components; the high-end sensors in conjunction with differential GPS provide the reference truth to the lower-end inertial components of the multi-sensor assembly."
Published in:	Proceedings of the 61st Annual Meeting of The Institute of Navigation (2005) June 27 - 29, 2005 Royal Sonesta Hotel Cambridge, MA
Pages:	988 - 999
Cite this article:	Grejner-Brzezinska, Dorota A., Yi, Yudan, Toth, Charles, "The Assessment of the Impact of Stochastic Error Modeling, Signal Denoising and Improved Gravity Compensation on the Navigation Performance of the Multi-IMU/GPS Sensor Assembly," Proceedings of the 61st Annual Meeting of The Institute of Navigation (2005), Cambridge, MA, June 2005, pp. 988-999.
Full Paper:	ION Members/Non-Members: 1 Download Credit Sign In