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Bias drift estimation for mems gyroscope used in inertial navigation

Czechowicz R.

Acta Mechanica et Automatica

2017

Vol. 11, no. 2

104--110

MEMS gyroscopes can provide useful information for dead-reckoning navigation systems if suitable error compensation algorithm is applied. If there is information from other sources available, usually the Kalman filter is used for this task. This work focuses on improving the performance of the sensor if no other information is available and the integration error should be kept low during periods of still (no movement) operation. A filtering algorithm is proposed to follow bias change during sensor operation to reduce integration error and extend time between successive sensor calibrations. The advantage of the proposed solution is its low computational complexity which allows implementing it directly in the micro-controller of controlling the MEMS gyroscope. An intelligent sensor can be build this way, suitable for use in control systems for mobile platforms. Presented results of a simple experiment show the improvement of the angle estimation. During the 12 hours experiment with a common MEMS sensor and no thermal compensation, the maximum orientation angle error was below 8 degrees.

Exploiting Stein's paradox in analysing sparse data from genome-wide association studies

Valenta Z., Kalina J.

Biocybernetics and Biomedical Engineering

2015

Vol. 35, no. 1

64--67

Unbiased estimation appeared to be an accepted golden standard of statistical analysis ever until the Stein's discovery of a surprising phenomenon attributable to multivariate spaces. So called Stein's paradox arises in estimating the mean of a multivariate standard normal random variable. Stein showed that both natural and intuitive estimate of a multivariate mean given by the observed vector itself is not even admissible and may be improved upon under the squared-error loss when the dimension is greater or equal to three. Later Stein and his student James developed so called 'James–Stein estimator', a shrunken estimate of the mean, which had uniformly smaller risk for all values in the parameter space. The paradox first appeared both unintuitive and even unacceptable, but later it was recognised as one of the most influential discoveries of all times in statistical science. Today the 'shrinkage principle' literally permeates the statistical technology for analysing multivariate data, and in its application is not exclusively confined to estimating the mean, but also the covariance structure of multivariate data. We develop shrinkage versions of both the linear and quadratic discriminant analysis and apply them to sparse multivariate gene expression data obtained at the Centre for Biomedical Informatics (CBI) in Prague.