A reliable modeless mobile multi-sensor integration technique based on RLS-lattice

Abstract

The last two decades have witnessed an increasing trend in integrating different navigation sensors for the overall purpose of overcoming the limitation of stand-alone systems. An example of this integration is the fusion of the global positioning system (GPS) and inertial navigation system (INS) for several navigation and positioning applications. Both systems have their unique features and shortcomings. Therefore, their integration offers a robust navigation solution. This paper introduces a novel multi-sensor system integration using a recursive least-squares lattice (RLSL) filter. The proposed system has a similar structure to the widely used KF. However, it has the major advantage of working without the need of either dynamic or stochastic models. Furthermore, no prior information about the covariance information of INS and GPS is required. The proposed RLSL filter parameters, similar to Kalman gain, are tuned recursively in the update mode utilizing the GPS velocity components. The RLSL, in turn, can filter out the high frequency noise associated with the INS. To test the capabilities of the proposed architecture, a field test was conducted in a land vehicle using a tactical grade INS system (the Honeywell HG1700) integrated with differential GPS measurements collected by a NovAtel OEM4 GPS receiver. The proposed system is examined during the availability of the GPS signal and with intentionally introduced GPS signal outages. The results indicate that the proposed RLSL system is robust in providing a reliable modeless INS/GPS integration module.

A wavelet-based bootstrap method applied to inertial sensor stochastic error modelling using the Allan variance

“…The application investigated in this paper aims at determining the accuracy of Allan spectrum estimators which can be used to characterize inertial sensor stochastic errors, including bias drifts and electronic noise. In practice, longterm stability data are needed to create stochastic models which can be used in the development of filtering algorithms, e.g., for estimating the attitude of the body to which the IMU is strapped [4,6,21,33]. It is known that integration of the gyro signals provides estimates of the roll, pitch and yaw angles that determine the attitude of the body.…”

Methods for in-field user calibration of an inertial measurement unit without external equipment

“…This IMU can also be combined with position sensors to form hybrid, six degrees of freedom (6DOF) trackers, which can measure both position and orientation. Examples of hybrid 6DOF trackers with IMUs are presented by Titterton and Weston (1997), Foxlin and Naimark (2003), Niu and El-Sheimy (2005), Semeniuk and Noureldin (2006), as well as El-Gizawy et al (2006).…”