Tightly coupled MEMS based INS/GNSS performance evaluation during extended GNSS outages

Abstract Ubiquitous positioning using combined lowcost, Micro-electro-mechanical-systems (MEMS) Inertial Navigation Systems (INS) and Global Navigation Satellite System (GNSS) systems remains a challenge in difficult GNSS environments. This is due to the GNSS signals being partially or completely obstructed by building structures and consequently no information being available to calibrate the MEMS INS errors. In these environments although there may be insufficient signals for a two or three dimensional GNSS solution, some signals can still be received, particularly when utilizing high sensitivity GNSS receivers. These signals can be used to aid the INS/GNSS integrated system but can only be realized when it utilizes a tightly coupled (TC) as opposed to a loosely coupled (LC) integration architecture. This is particularly useful for MEMS based INS/GNSS integrated system as its performance can degrade rapidly over short period of GNSS outages. This paper aims to evaluate the capabilities of MEMS based INS/GNSS using TC integration architecture during extended period of GNSS outages. The results obtained show that TC integration architecture does help limit the error growth even when INS/GNSS integrated system experienced relatively long period of partial GNSS outages. commercially available MEMS INSs. Two independent datasets were collected and used to demonstrate the performance of the integrated system architecture (LC vs. TC) during periods of complete and partial GNSS outage. The results of these experiments conducted show that considerable improvements are observed when TC integration architecture is employed compared to LC. The experiment platform, integrated processing architecture and results obtained will be fully presented in this paper.

[1]  Salah Sukkarieh,et al.  Tightly Coupled INS/GPS with Bias Estimation for UAV Applications , 2004 .

[2]  Azmir Hasnur Rabiain Performance evaluation of MEMS based INS/GPS integration , 2010 .

[3]  Nima Alam,et al.  Collaborative navigation with ground vehicles and personal navigators , 2012, 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[4]  Kefei Zhang,et al.  A new GPS/RFID integration algorithm based on iterated reduced sigma point kalman filter for vehicle navigation , 2009 .

[5]  Saurabh Godha,et al.  Performance evaluation of low cost MEMS-based IMU integrated with GPS for land vehicle navigation application , 2006 .

[6]  John,et al.  Strapdown Inertial Navigation Technology - 2nd Edition , 2005 .

[7]  Dogan Ibrahim,et al.  Real-time GPS based outdoor WiFi localization system with map display , 2010, Adv. Eng. Softw..

[8]  Xiaoji Niu,et al.  Analysis and Modeling of Inertial Sensors Using Allan Variance , 2008, IEEE Transactions on Instrumentation and Measurement.

[9]  Peter F. Swaszek,et al.  Performance Trials of an Integrated Loran/GPS/IMU Navigation System, Part I , 2005 .

[10]  P. Groves Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Second Edition , 2013 .

[11]  Mohammed El-Diasty,et al.  Calibration and Stochastic Modelling of Inertial Navigation Sensor Erros , 2008 .

[12]  Jay A. Farrell,et al.  Aided Navigation: GPS with High Rate Sensors , 2008 .

[13]  SU Bao-ku Improved particle filter algorithm for INS/GPS integrated navigation system , 2010 .

[14]  Dong-Hwan Hwang,et al.  A Deeply Coupled GPS/INS Integrated Kalman Filter Design Using a Linearized Correlator Output , 2006, 2006 IEEE/ION Position, Location, And Navigation Symposium.