Miniaturized Inertial Sensors' Noise Reduction by using Redundant Linear Configurations

Lucian T. Grigorie and Ruxandra M. Botez


statistical filtering, miniaturized inertial sensors, inertial navigation, minimum variance method


An adaptive algorithm for the statistical filtering of the miniaturized inertial sensors noise is presented. The algorithm uses the minimum variance method to perform a best estimate calculation of the accelerations or angular speeds appearing on each of the three axes of an Inertial Measurement Unit (IMU), by using the information from some accelerometers and gyros arrays placed along the IMU axes. In order to see how works the algorithm, its numerical simulation is performed. In this way, an accelerometer sensor model and an array of four sensors (unbiased and with different noise densities) are used. For simulation, three cases of the ideal input acceleration are considered: 1) a null signal; 2) a step signal with a no-null time step; and 3) a low frequency sinusoidal signal.

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