| Summary: | 碩士 === 國立臺灣海洋大學 === 通訊與導航工程系 === 95 === Abstract
The well-known Kalman filtering is a form of optimal estimation characterized by recursive evaluation, which has been widely applied to the integrated navigation designs. The Kalman filter requires that all the plant dynamics and noise processes are completely known, and the noise process is zero mean white noise. If the theoretical becavior of a filter and its actual behavior do not agree, divergence problems will tend to occur. The adaptive algorithm has been one of the approaches to prevent divergence problem of the Kalman filter when precise knowledge on the system models are not available.
One of the adaptive methods is called the adaptive fading Kalman filter (AFKF), which incorporates the covariance scaling factor, when the innovation exceeds a prescribed threshold. Traditional approach for selecting the factor may rely on personal experience or computer simulation. In order to resolve this shortcoming, a novel scheme called the fuzzy adaptive fading Kalman filter (FAFKF) is presented. In the FAFKF, the fuzzy logic reasoning approach is incorporated into the AFKF. By monitoring parameters based on the innovation information, the fuzzy logic adaptive system (FLAS) is designed for dynamically adjusting the threshold and accordingly, the fading factor, according to the change in vehicle dynamics. Integrated navigation processing using the FAFKF will be conducted to validate the effectiveness of the proposed strategy. The performance of the proposed FAFKF scheme will be assessed and compared to those of conventional EKF and AFKF.
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