USA As Represented by Secretary of the Navy

Kvh Industries, Inc.

Ahmed S Zaki

Timothy B Straw

Michael J Kroger

David J Bamford

Michael J Obara

John G Miskimins

William H Slater

Peter A Child

Richard Vaccaro

Christopher C Moll

Eric J Zell

A method for reducing bias drift in electrical-mechanical systems sensor such as an accelerometer is provided. The method includes the steps of arranging at least one accelerometer in a chamber and initiating a plurality of temperature cycle sets. Each temperature cycle set has a minimum temperature, a maximum temperature, and a ramp rate. The conditioning method monitors the accelerometers until a bias drift is stabilized within a desired bias drift range. The minimum temperature and the maximum temperature limit a temperature range in which the accelerometer is cycled. Use of temperature limiting can quickly accelerate stabilization of the bias drift while also reducing degradation.

Method for reducing bias drift in a microelectromechanical systems sensor

A method and system is provided for estimating and compensating for gyro bias in gyro-stabilized systems. The method includes comparing an output of a gyroscope to a reference measurement; estimating a bias of the gyroscope based on the comparison using a Kalman filter; and adjusting a control output of the gyro-stabilized system with the estimated bias to maintain a position of the gyro-stabilized system.

Bias corrected inertial navigation system

The disclosed invention provides a method that exploits the correlations in the statistical properties of one inertial sensor to another. Among the dominant noise sources, the additive noise terms for different sensors are uncorrelated, however, there may be correlations between the random drift of different sensors. The method of the present invention estimates these correlations from a finite amount of calibration data in order to calculate coefficients for the optimal linear combination of multiple sensor signals. The method is a means of combining the multiple sensor outputs to obtain a virtual sensor signal with reduced drift, and in a far more time efficient and less processor intensive way than the prior art.

Method of reducing random drift in the combined signal of an array of inertial sensors

A high performance solid-state heading sensor for underwater towed arrays is taught that combines high quality MEMS sensor components with data processing filters to resolve performance limitations present in prior art heading sensors. The heading sensor employs rate gyroscopes, accelerometers, magnetometers and a customized Kalman filter formulation implemented on a micro-controller to improve performance in determining the roll, pitch and heading of a towed array.

High accuracy heading sensor for an underwater towed array

A device for use with a gyroscopic stabilizer for use in an assembly that is moveably connected to a vehicle. The gyroscopic stabilizer has a gyroscope, a skew motor, and a skew motor velocity servo-control system. The gyroscopic stabilizer can control a skew of the assembly based on a torque signal to maintain the assembly in an intended position. The skew motor velocity servo-control system can control the skew motor. The skew motor can move the gyroscope. The gyroscope can provide the torque signal. The device comprises an assembly position determining system, a detector, a state estimator and an output portion. The assembly position determining system can generate an assembly position signal based on one of a position of the assembly, a position of the gyroscopic stabilizer, a position of the gyroscope, a position of the skew motor and a position of the skew motor velocity servo-control system. The detector can detect a skew of one of the assembly, the gyroscopic stabilizer, the gyroscope, the skew motor and the skew motor velocity servo-control system and to generate a skew signal. The state estimator can provide an error signal based on the skew signal. The output portion can provide an output signal to the gyroscopic stabilizer. The output signal is based on the assembly position signal and the error signal.

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Kingston, RI, United States of America

Ahmed S Zaki