Fundamentals of Satellite Navigation. GPS Systems Segments. GPS Satellite Signal Characteristics and Message Formats. Satellite Signal Acquisitions and Tracking. Effects of RF Interference on GPS Satellite Signal Receiver Tracking. Performance of Standalone GPS. Differential GPS. Integration of GPS with other Sensors. Galileo. The Russian GLONASS, Chinese Bediou, and Japanese QZSS Systems. GNSS Markets and Applications.

Understanding GPS : principles and applications

This paper gives an evaluation of a low-cost MEMS accelerometer The accelerometer is intended for the distance measurement of a mobile robot or platform in short duration The distance traveled is obtained by double integration of the sensor signal with time Bias offset drift exhibited in the acceleration signal is accumulative and the accuracy of the distance measurement can deteriorate with time due to the integration This problem can be fixed by periodic recalibration with the help of external measurements on position, velocity and attitude These external signals can be calculated by an inertial system A Kalman filter can use the differences between these values to provide an optimum estimate of the system error The random bias drift of the accelerometer was found by experiment to be 25 mg The bias drift rate due to temperature was 0108 μg/s when the accelerometer was placed at room temperature With proper compensation on gravitation, the accelerometer can be a viable solution as a short duration distance-measuring device for a mobile robot

Evaluation of a Low-cost MEMS Accelerometer for Distance Measurement

An evaluation of a low-cost small-sized solid-state accelerometer is described in this paper. The sensor is intended for positioning of a mobile robot or platform. The acceleration signal output by the sensor is doubly integrated with time, which yields the travelled distance. Bias offset drift exhibited in the acceleration signal is accumulative and the accuracy of the distance measurement deteriorates with time due to the integration. This problem can be fixed by periodic recalibration by the use of external measurements on position, velocity, and attitude which can be compared with the corresponding quantities calculated by the inertial system. A Kalman filter could use the differences between those values to provide an optimum estimate of the system error. The random bias drift of the accelerometer was found experimentally to be 2.5 mg. The accelerometer was moved back and forth three times for a distance of 40 cm with an acceleration of 8 m/s/sup 2/. The final distance error accumulated was -1.08 cm. The bias drift rate due to temperature was 0.108 /spl mu/g/s when the accelerometer was placed at room temperature. The results show that the accelerometer could be a viable solution as a short-duration distance-measuring device for a mobile robot or platform.

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Accelerometer for mobile robot positioning

This research aims at enhancing the accuracy of land vehicular navigation systems by integrating GPS and Micro-Electro-Mechanical-System (MEMS) based inertial measurement units (IMU). This comprises improving the MEMS-based inertial output signals as well as investigating the limitations of a conventional Kalman Filtering (KF) solution for MEMS-IMU/GPS integration. These limitations are due to two main reasons. The first is that a KF suppresses the effect of inertial sensor noise using GPS-derived position and velocity as updates but within a limited band of frequency. The second reason is that a KF only works well under certain predefined dynamic models and convenient input data that fit these models, which are not attainable with the utilization of MEMS-based inertial technology. Therefore, if the GPS reference solutions are lost, the accuracy of standalone MEMS-IMU navigation will drastically degrade over time. The Wavelet Multi-Resolution Analysis (WMRA) technique is proposed in this thesis as an efficient pre-filter for MEMS-based inertial sensors outputs. Applying this prefiltering process successfully improves the sensors’ signal-to-noise ratios, removes shortterm errors mixed with motion dynamics, and provides more reliable data to the KFbased MEMS-INS/GPS integration module. The results of experimental validation show the effectiveness of the proposed WMRA method in improving the accuracy of KF estimated navigation states particularly position. Moreover, the Adaptive-Neuro-Fuzzyinference-system (ANFIS)-based algorithm is suggested and assessed to model the variations of the MEMS sensors’ performance characteristics with temperature. The

Accuracy Enhancement of Integrated MEMS-IMU/GPS Systems for Land Vehicular Navigation Applications

A fully differential CMOS readout circuit for SOI resonant accelerometer is reported. The readout circuit is essentially an oscillator, consisting of an oscillator and a low noise automatic amplitude control (AAC) loop. A differential sense resonator is proposed to facilitate fully differential circuit topology and improves the SNR under a 3.3-V supply. A second-order AAC loop filter and a novel chopper stabilized rectifier are employed in the AAC loop to remove the noises, in particular, the 1/f noise, and to minimize the phase noise caused by the amplitude stiffening effect. The strong driving feedthrough is avoided by separating the drive and sense operation in the time domain, while using the same electrodes. The complete resonant accelerometer operates under a 3.3-V supply and achieves 140-Hz/g scaling factor, 20 mug/radicHz resolution and 4 mug bias stability. The readout circuit draws 7 mA under 3.3-V supply.

A CMOS Readout Circuit for SOI Resonant Accelerometer With 4- $\mu \rm g$ Bias Stability and 20- $ \mu\rm g/\sqrt{{\hbox{Hz}}}$ Resolution

The paper presents the initial results of AlliedSignal Inc. (formerly Sundstrand Data Control) Instrument System's, development of a medium accuracy aircraft inertial navigation grade silicon accelerometer. This effort focused on developing a monolithic bulk-micromachined vibrating beam accelerometer. The accelerometer is designed such that an acceleration along the positive input axis places the vibrating beams in tension. Thus, the resonant frequency of the vibrating beams increase with applied acceleration. An electronic oscillator capacitively couples energy into the vibrating beams to keep them oscillating at their resonant frequency. The acceleration signal is then output from the oscillator as a frequency modulated square wave that can be sent directly to a digital interface. Performance data from the first AlliedSignal silicon vibrating beam accelerometer development program is presented. The data presented includes noise level, short term stability and tumble over temperature tests. This accelerometer is targeted to be incorporated into a system with one nautical-mile-per-hour navigational accuracy. This ambitious goal translates to an accelerometer with 50 /spl mu/g, one sigma bias, and 50 ppm, one sigma long term scale factor accuracy. The data presented demonstrate the feasibility of meeting one-nautical-mile-per-hour accuracy with a vibrating beam micro-accelerometer. The cost savings offered by a micro-machined silicon accelerometer ensure that this technology will be pushed to its limit for application in inertial guidance systems. >

G. Gassner

Papers

A navigation grade micro-machined silicon accelerometer