scholarly journals The Particle Filter and Extended Kalman Filter methods for the structural system identification considering various uncertainties

2020 ◽  
Vol 4 (3) ◽  
pp. 42-58
Author(s):  
M. Ebrahimi ◽  
R. Karami Mohammadi ◽  
F. Sharafi ◽  
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...  
Keyword(s):  
Kalman Filter ◽  
System Identification ◽  
Particle Filter ◽  
Extended Kalman Filter ◽  
Structural System ◽  
Filter Methods ◽  
Structural System Identification

Improved Filter Strategies for Precise Geolocation of Unexploded Ordnance using IMU/GPS Integration

2009 ◽  
Vol 62 (3) ◽  
pp. 365-382 ◽  
Author(s):  
Jong Ki Lee ◽  
Christopher Jekeli
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Extended Kalman Filter ◽  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Detection Systems ◽  
Filter Methods ◽  
Unscented Particle Filter ◽  
Measurement Units ◽  
Non Gaussian

Efficient and precise geolocation can be achieved by integrating a ranging system, such as GPS, with inertial sensors in order to bridge short outages, enhance accuracy degradation, and increase the temporal resolution in the ranging system. Optimal integration depends on appropriate filter methods that can accommodate the particular short-term dynamics experienced by platforms, such as UXO ground-based detection systems. The traditional extended Kalman filter was designed to integrate data from a linearized system excited by Gaussian noise. We compared this filter to modern filters that obviate these prerequisites, including the unscented Kalman filter, the particle filter, and adaptive variations thereof, using simulated IMU/ranging systems that follow a typical trajectory with both straight and curved segments. The unscented filter performed significantly better than the extended Kalman filter, particularly over the curved segments, yielding up to 50% improvement in the position accuracy using medium-grade inertial measurement units. Similar improvement was obtained for the unscented particle filter, and its adaptive variant, over the unscented Kalman filter (which performed comparably to the extended Kalman filter) when the statistical distribution of the IMU noise was non-symmetric (i.e., essentially non-Gaussian). While the few-centimetre geolocation accuracy goal for highly dynamic UXO characterization applications remains a challenge if tactical grade IMUs are integrated with a significantly degraded ranging system, using filters appropriate to the inherent nonlinear dynamics and potential non-Gaussian nature of the sensor noise tend to reduce overall errors compared to the traditional filter.

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