Off-line estimation of trajectory in discrete state space using the minimal-covariance adaptive FIR smoothing with extended output vector

Abstract

The new tool for off-line estimation of the state of discrete linear systems is presented. The algorithm of Finite Impulse Response (FIR) smoother is described and its optimality is proven. The optimality of this smoother means that error covariance matrix of the estimation is minimal. It places this method in the Least Square Estimation (LSE) methods group, which are much better than frequently used Least Mean Square (LMS) methods. This method can also be used as FIR filter or predictor. In the paper the simple simulation, showing the mode of actions of described smoother, is presented. It is compared with others presented in the literature.

The Measurement of Displacement with the Use of MEMS Sensors: Accelerometer, Gyroscope and Magnetometer

“…However it should be remembered that each sensor has different accuracy described as the noise of measurement covariance matrix and his uniqueness should be included while gauging the real location of arm. The references to Kalman filtration can be seen in bibliography [5][6][7][8]. It includes all of foregoing factors and sets the estimate of the state vector having the smallest covariance error.…”

Data Fusion Using Fuzzy Logic Techniques Supported by Modified Weighting Factors (FLMW)

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