Extension of a Particle Filter for Bioprocess State Estimation using Invasive and Non-Invasive IR Measurements

2019 ◽  
pp. 1417-1422 ◽  
Author(s):  
Julian Kager ◽  
Vladimir Berezhinskiy ◽  
Robert Zimmerleiter ◽  
Markus Brandstetter ◽  
Christoph Herwig
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
Non Invasive

scholarly journals Constrained Unscented Particle Filter for SINS/GNSS/ADS Integrated Airship Navigation in the Presence of Wind Field Disturbance

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 471 ◽  
Author(s):  
Zhaohui Gao ◽  
Dejun Mu ◽  
Yongmin Zhong ◽  
Chengfan Gu
Keyword(s):  
Wind Speed ◽  
Particle Filter ◽  
State Estimation ◽  
Wind Field ◽  
Satellite System ◽  
Integrated Navigation ◽  
Unscented Particle Filter ◽  
Field Disturbance ◽  
Global Navigation Satellite ◽  
Optimal State Estimation

Due to the disturbance of wind field, it is difficult to achieve precise airship positioning and navigation in the stratosphere. This paper presents a new constrained unscented particle filter (UPF) for SINS/GNSS/ADS (inertial navigation system/global navigation satellite system/atmosphere data system) integrated airship navigation. This approach constructs a wind speed model to describe the relationship between airship velocity and wind speed using the information output from ADS, and further establishes a mathematical model for SINS/GNSS/ADS integrated navigation. Based on these models, it also develops a constrained UPF to obtain system state estimation for SINS/GNSS/ADS integration. The proposed constrained UPF uses the wind speed model to constrain the UPF filtering process to effectively resist the influence of wind field on the navigation solution. Simulations and comparison analysis demonstrate that the proposed approach can achieve optimal state estimation for SINS/GNSS/ADS integrated airship navigation in the presence of wind field disturbance.

scholarly journals Iterative Truncated Unscented Particle Filter

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 214
Author(s):  
Yanbo Wang ◽  
Fasheng Wang ◽  
Jianjun He ◽  
Fuming Sun
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Process Models ◽  
Filter Method ◽  
Proposal Distribution ◽  
Truncation Method ◽  
Unscented Particle Filter

The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state estimation. This paper introduces an iterative truncated unscented particle filter, which provides a state estimation method with inequality constraints. In this method, the proposal distribution is generated by an iterative unscented Kalman filter that is supplemented with a designed truncation method to satisfy the constraints. The detailed iterative unscented Kalman filter and truncation method is provided and incorporated into the particle filter framework. Experimental results show that the proposed algorithm is superior to other similar algorithms.

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