Abstract
A novel scheme using fuzzy logic based interacting multiple model (IMM) unscented Kalman filter (UKF) is employed in which the Fuzzy Logic Adaptive System (FLAS) is utilized to address uncertainty of measurement noise, especially for the outlier types of multipath errors for the Global Positioning System (GPS) navigation processing. Multipath is known to be one of the dominant error sources, and multipath mitigation is crucial for improvement of the positioning accuracy. It is not an easy task to establish precise statistical characteristics of measurement noise in practical engineering applications. Based on the filter structural adaptation, the IMM nonlinear filtering provides an alternative for designing the adaptive filter in the GPS navigation processing for time varying satellite signal quality. The uncertainty of the noise can be described by a set of switching models using the multiple model estimation. An UKF employs a set of sigma points by deterministic sampling, which avoids the error caused by linearization as in an extended Kalman filter (EKF). For enhancing further system flexibility, the fuzzy logic system is introduced. The use of IMM with FLAS enables tuning of appropriate values for the measurement noise covariance so as to obtain improved estimation accuracy. Performance assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing.
Highlights
Multipath [1,2,3,4] is known to be one of the dominant error sources in high accuracy global navigation satellite systems (GNSS) positioning systems, such as the Global Positioning System (GPS)[5]
Since multipath errors are among uncorrelated errors that are not cancelled out during observation differencing, the performance of high precision GPS receivers are mostly limited by the multipath induced errors
It can be seen that the IMMUKF does provide improved accuracy than the unscented Kalman filter (UKF), it doesn't mitigate the multipath error remarkably
Summary
Multipath [1,2,3,4] is known to be one of the dominant error sources in high accuracy global navigation satellite systems (GNSS) positioning systems, such as the Global Positioning System (GPS)[5]. The adaptive Kalman filter [9,10] algorithm has been one of the strategies considered for estimating the state vector to prevent divergence problem due to modeling errors. To deal with the noise uncertainty and system nonlinearity simultaneously, the IMMUKF can be introduced In the approach, these multiple models are developed to describe various dynamic behaviors. The interacting multiple model (IMM) [11,12,13,14] algorithm has the configuration that runs in parallel several model-matched state estimation filters, which exchange information (interact) at each sampling time. By monitoring the innovation information, the FLAS is employed for dynamically adjusting the process noise based on the fuzzy rules so as to enhance the estimation accuracy and tracking capability.
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