Navigation Performance Enhancement Using IMM Filtering for Time Varying Satellite Signal Quality

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.

[1]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[2]  Emmanuel Seignez,et al.  Outlier Detection in GNSS Pseudo-Range/Doppler Measurements for Robust Localization , 2016, Sensors.

[3]  Dah-Jing Jwo,et al.  Fuzzy Adaptive Interacting Multiple Model Nonlinear Filter for Integrated Navigation Sensor Fusion , 2011, Sensors.

[4]  Hermie Hermens,et al.  Sedentary Behaviour Profiling of Office Workers: A Sensitivity Analysis of Sedentary Cut-Points , 2015, Sensors.

[5]  Marcus Obst,et al.  Multipath mitigation in GNSS-based localization using robust optimization , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[6]  Dah Jing Jwo,et al.  GPS/INS Integration Accuracy Enhancement Using the Interacting Multiple Model Nonlinear Filters , 2013 .

[7]  Cong Lin,et al.  Hybrid particle filtering algorithm for GPS multipath mitigation , 2014 .

[8]  Zang Rong-chun.  Integrated navigation algorithm based on IMM-UKF , 2007 .

[9]  Bradford W. Parkinson,et al.  Global Positioning System , 1995 .

[10]  Dah-Jing Jwo,et al.  Unscented Kalman filter with nonlinear dynamic process modeling for GPS navigation , 2008 .

[11]  Gérard Lachapelle,et al.  Characterization of Signal Quality Monitoring Techniques for Multipath Detection in GNSS Applications , 2017, Sensors.

[12]  A. H. Mohamed,et al.  Adaptive Kalman Filtering for INS/GPS , 1999 .

[13]  M. Harigae,et al.  Using IMM adaptive estimator in GPS positioning , 2001, SICE 2001. Proceedings of the 40th SICE Annual Conference. International Session Papers (IEEE Cat. No.01TH8603).

[14]  Lan Cheng,et al.  Multipath estimation using an intelligent optimization algorithm with non-Gaussian noise , 2017, 2017 23rd International Conference on Automation and Computing (ICAC).

[15]  Javier Roales,et al.  Optical Gas Sensing of Ammonia and Amines Based on Protonated Porphyrin/TiO2 Composite Thin Films , 2016, Sensors.