After reviewing the two main approaches of adaptive Kalman filtering, namely, innovation-based adaptive estimation (IAE) and multiple-model-based adaptive estimation (MMAE), the detailed development of an innovation-based adaptive Kalman filter for an integrated inertial navigation system/global positioning system (INS/GPS) is given. The developed adaptive Kalman filter is based on the maximum likelihood criterion for the proper choice of the filter weight and hence the filter gain factors. Results from two kinematic field tests in which the INS/GPS was compared to highly precise reference data are presented. Results show that the adaptive Kalman filter outperforms the conventional Kalman filter by tuning either the system noise variance–covariance (V–C) matrix `Q' or the update measurement noise V–C matrix `R' or both of them.

Adaptive Kalman Filtering for INS/GPS

A navigation or motion tracking system includes components associated with particular sensors, which are decoupled from a tracking component that takes advantage of information in the sensor measurements. The architecture of this system enables development of sensor-specific components independently of the tracking component, and enables sensors and their associated components to be added or removed without having to re-implement the tracking component. In a software implementation of the system, sensor-specific software components may be dynamically incorporated into the system and the tracking component is then automatically configured to take advantage of measurements from the corresponding sensors without having to modify the tracking component.

Tracking, auto-calibration, and map-building system

............................................................................................................................................... III ACKNOWLEDGEMENTS.........................................................................................................................V DEDICATION............................................................................................................................................ VI TABLE OF CONTENTS..........................................................................................................................VII LIST OF TABLES ..................................................................................................................................... XI LIST OF FIGURES ................................................................................................................................ XIII LIST OF SYMBOLS............................................................................................................................... XVI LIST OF ABBREVIATIONS................................................................................................................. XIX CHAPTER ONE : INTRODUCTION.........................................................................................................1 1.1 BACKGROUND .......................................................................................................................................1 1.2 LOW COST INERTIAL SENSORS ..............................................................................................................5 1.2.1 MEMS Inertial Sensors .................................................................................................................5 1.2.2 Performance Characteristics ........................................................................................................6 1.3 PREVIOUS RESEARCH AND THEIR LIMITATIONS.....................................................................................8 1.3.1 Integration Strategy ......................................................................................................................8 1.3.2 Initial Alignment .........................................................................................................................10 1.3.3 Sensor Error Calibration/Modeling............................................................................................11 1.3.4 Rapid Degradation in Solution during GPS Outage...................................................................13 1.3.5 Operational Environment ...........................................................................................................16 1.4 OBJECTIVES.........................................................................................................................................17 1.5 RESEARCH METHODOLOGY SUMMARY ...............................................................................................19

Performance evaluation of low cost MEMS-based IMU integrated with GPS for land vehicle navigation application

Method and apparatus for locating position of a satellite signal receiver is described. In one example, pseudoranges are obtained that estimate the range of a satellite signal receiver to a plurality of satellites. An absolute time and a position are computed using the pseudoranges at a first time. The absolute time is then used to compute another position at a subsequent time. In another example, a plurality of states associated with a satellite signal receiver are estimated, where the plurality of states includes a time tag error state. A dynamic model is then formed relating the plurality of states, the dynamic model operative to compute position of the satellite signal receiver.

Method and apparatus for processing of satellite signals without time of day information

A receiver that uses a single processor to control a GPS sub-system and an inertial ('INS') sub-system and, through software integration, shares GPS and INS position and covariance information between the sub-systems. The receiver time tags the INS measurement data using a counter that is slaved to GPS time, and the receiver then uses separate INS and GPS filters to produce GPS and INS position information that is synchronized in time. The GPS/INS receiver utilizes GPS position and associated covariance information in the updating of an INS Kalman filter, which provides updated system error information that is used in propagating inertial position, velocity and attitude. Whenever the receiver is stationary after initial movement, the INS sub-system performs 'zero-velocity updates', to more accurately compensate in the Kalman filter for component measurement biases and measurement noise. Further, if the receiver loses GPS satellite signals, the receiver utilizes the inertial position, velocity and covariance information provided by the Kalman filter in the GPS filters, to speed up GPS satellite signal re-acquisition and associated ambiguity resolution operations.

An inertial/gps navigation system

This paper addresses the question: "Why aren't tightly-coupled OPS/INS systems everywhere, on aircraft, ships and land vehicles?" Two barriers to the widespread use are cited. One is the high cost of the INS, and the other is the cost and complexity of tightly-coupled OPS/INS integration. One of those two barriers has recently been diminished drastically with the development of a standardized software package for tightly-coupled integration. In the past, only the largest corporations have been able to pay the initial development cost for tightly-coupled OPS/INS integration, usually with funding from a large defense program. Using the new software package, integration and van test can be accomplished in a matter of days, and this has been demonstrated with field trials. The package is intended primarily for small companies that otherwise would not be able to build tightly-coupled OPS/INS systems at all. What would have been a prohibitive 3- or 4-man year development effort is reduced to a few man weeks. To accomplish an integration, the system integrator has to find a way, through serial interfaces or by some other means, to get the INS measurements of acceleration (accumulated velocity change /spl Delta/V) and attitude rate (accumulated angle change /spl Delta//spl theta/) into a processor, along with the raw data of a GPS receiver. He also has to find a way to time tag the INS /spl Delta/V, /spl Delta//spl theta/ with GPS time. The rest of tightly-coupled OPS/INS integration is predominately accomplished in the standardized software package. That leaves the cost of the INS as the only remaining barrier to the very widespread use of OPS/INS, and invites new development of low cost inertial sensors. The focus of this paper is on the software package, and how it achieves standardization and ease of use while retaining the flexibility to produce optimal results with a variety of INS and GPS receiver types.

D.T. Knight

Papers

Rapid development of tightly-coupled GPS/INS systems