Abstract
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.
Highlights
State estimation of a nonlinear dynamic system is of great importance in a variety of fields including computer vision, target tracking, machine learning, geophysics, bioengineering, control systems and econometrics [1,2,3]
In order to further demonstrate the performance of the proposed iterative truncated unscented particle filter (ITUPF), we considered a problem of tracking a vehicle
unscented particle filter (UPF), IUPF, and UPF-MCMC showed a similar trend to the generic particle filter (PF). Such a trend was less evident than in the generic PF. When it comes to truncated unscented particle filter (TUPF) and proposed ITUPF, we can see from this figure that the root mean square error (RMSE) of the two methods decreased when the particle number grew from 100 to 500
Summary
State estimation of a nonlinear dynamic system is of great importance in a variety of fields including computer vision, target tracking, machine learning, geophysics, bioengineering, control systems and econometrics [1,2,3]. The iterated EKF is used for designing the proposal distribution in [11] providing better estimation accuracy compared to UPF. In order to solve the constrained nonlinear state estimation problems, Fernandez et al [15] devise a truncated unscented Kalman filter (TUKF) to approximate the first two moments of the posterior PDF. Straka et al [18] proposed to design the proposal distribution using the TUKF in the particle filter framework for generally nonlinear inequality-constrained filtering problems. In [21], the authors present a truncated unscented particle filter which uses the TUKF to generate the proposal distribution in order to incorporate both observations and constraint information. Kalman filter (IUKF) to generate a proposal distribution for efficient sampling for state estimation with general inequality constraints.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
