Kalman filtering approach for reducing gyroscope noise effects in stabilized platforms

Marcelo C. Algrain

doi:10.1117/12.138192

25 November 1992 Kalman filtering approach for reducing gyroscope noise effects in stabilized platforms

Marcelo C. Algrain

Proceedings Volume 1697, Acquisition, Tracking, and Pointing VI; (1992) https://doi.org/10.1117/12.138192
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

Stabilization systems use gyroscopes typically mounted along side sensing or imaging devices being isolated from external rotations in inertial space. The gyroscopes measure the residual angular motion of the platform stabilized member. The better the stabilization the smaller the sensed residual motion is. Conceivably, the residual motion (angular jitter) could be as small as the noise in the gyroscope outputs. This represents a low signal-to-noise ratio case. Under such conditions, lower noise gyroscopes would be required to further reduce angular jitter, at considerably higher expense. Alternatively, the performance of the stabilization system can be significantly enhanced through noise reducing optimal filtering techniques. The latter approach uses angular velocity estimates that are more accurate than raw measurements to stabilize the platform, without resorting to more precise and costly lower-noise gyroscopes. This paper presents a new Kalman filtering technique that reduces the mean-square-error (MSE) between actual angular velocity values and estimated ones by an order of magnitude (when compared to the MSE resulting from direct measurements) even under extremely low signal-to-noise ratio conditions. The electronically improved angular motion measurements can be fed into the platform stabilization control system (instead of raw measurements) considerably reducing stabilization jitter.

Citation Download Citation

Marcelo C. Algrain "Kalman filtering approach for reducing gyroscope noise effects in stabilized platforms", Proc. SPIE 1697, Acquisition, Tracking, and Pointing VI, (25 November 1992); https://doi.org/10.1117/12.138192

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KEYWORDS

Error analysis

Gyroscopes

Filtering (signal processing)

Signal to noise ratio

Electronic filtering

3D acquisition

3D modeling

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