Improved EKF Method of Estimating Locations with Sudden High Jumps in the Measurement Noise

Range and bearing navigation is commonly used in many autonomous systems such as guided vehicles and freely moving robots. The obtained measurements are accompanied by noise and possible errors, which may lead to wrong decisions of the control and guidance system. An improved method, over Kalman filter with standard gates, of filtering unexpected high measurement noise due to clutter, glittering, or shaking of the measuring system, featuring minimum computational effort and minimum time, is suggested. Optimal variable correlation gates around the predicted values of the signal states – the Autonomous Guided Vehicle or its target/obstacle position – reduce the unexpected noise effect. Values of measurements out of these gates are not considered and the integration of the prediction model for the tracked signal continues until a new measurement is received within the gate opening. The dimensions of the correlation gates are determined by filter predictions and measurement error variances that are related to the probability of the unexpected high measurement noise and its approximated covariances.

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