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Estimation Performance for the Cubature Particle Filter under Nonlinear/Non-Gaussian Environments

Dah-Jing Jwo, Chien-Hao Tseng
2021 Computers Materials & Continua  
This paper evaluates the state estimation performance for processing nonlinear/non-Gaussian systems using the cubature particle lter (CPF), which is an estimation algorithm that combines the cubature Kalman  ...  In Bayesian ltering, the nonlinear lter performs well when all conditional densities are assumed Gaussian.  ...  Assessment of the nonlinear ltering approaches to the nonlinear/non-Gaussian state estimation performance has been carried out.  ... 
doi:10.32604/cmc.2021.014875 fatcat:ksogt3q4brdzlbmhut7vtbyqem

MMSE-Based Filtering for Linear and Nonlinear Systems in the Presence of Non-Gaussian System and Measurement Noise [chapter]

I. Bilik, J. Tabriki
2009 Kalman Filter Recent Advances and Applications  
The nonlinearity of the measurement model leads to non-Gaussian, multi-modal PDF of the system state, even when the system and the measurement noises are Gaussian.  ...  The IMM algorithm with greater number of modes was proposed in [31] for non-Gaussian system and measurement  ...  NL-GMKF In this section, the NL-GMKF performance is evaluated using the following nonlinear DSS model with non-Gaussian driving and measurement noise distributions.  ... 
doi:10.5772/6800 fatcat:4rv53ifnqrak7jzhy22wbgoih4

Conditional Posterior Cramér–Rao Lower Bounds for Nonlinear Sequential Bayesian Estimation

Long Zuo, Ruixin Niu, Pramod K. Varshney
2011 IEEE Transactions on Signal Processing  
Therefore, it is adaptive to the particular realization of the underlying system state and provides a more accurate and effective online indication of the estimation performance than the unconditional  ...  Further, a general sequential Monte Carlo solution is proposed to compute the conditional PCRLB recursively for nonlinear non-Gaussian sequential Bayesian estimation problems.  ...  Mohan, and Dr. O. Ozdemir for helpful discussions and suggestions and the anonymous reviewers for their constructive comments.  ... 
doi:10.1109/tsp.2010.2080268 fatcat:h6a2vjq6xjhdval2felasglo6q

Mid-State Kalman Filter for Nonlinear Problems

Zhengwei Liu, Ying Chen, Yaobing Lu
2022 Sensors  
In order to verify the filter performance in comparison, an iterative formulation of Cramér-Rao Low Bound for the nonlinear system is further derived and given in this paper.  ...  Simulation results show that the proposed method has excellent performance of high filtering accuracy and fast convergence by comparing the filter state estimation accuracy and consistency.  ...  Nonlinear Filter Performance Evaluation In order to better verify the performance of nonlinear filters, it is necessary to analyze and evaluate the filtering accuracy, credibility, stability, and other  ... 
doi:10.3390/s22041302 pmid:35214203 pmcid:PMC8963023 fatcat:xmtlg24gzrc33fyjfcvmallf4y

A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment

Zhu Xiao, Vincent Havyarimana, Tong Li, Dong Wang
2016 Sensors  
In this paper, a novel nonlinear framework of smoothing method, non-Gaussian delayed particle smoother (nGDPS), is proposed, which enables vehicle state estimation (VSE) with high accuracy taking into  ...  account the non-Gaussianity of the measurement and process noises.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s16050692 pmid:27187405 pmcid:PMC4883383 fatcat:kyskbg2ujzg6fmzzq2lowcxtki

Use of the Extended Kalman Filter for State Dependent Drift Estimation in Weakly Nonlinear Sensors

Arsenia Chorti, Dimosthenis Karatzas, Neil M. White, Chris J. Harris
2006 Sensor Letters  
We compare their performance by simulation and demonstrate their validity by estimating the drift of an accelerometer, modeled as a weakly nonlinear system.  ...  In this letter, we discuss three approaches for the identification of reversible state dependent drift in sensors through the use of the Extended Kalman Filter.  ...  We will evaluate the performance of the three algorithms in the case of simulated Gaussian state variables and experimental sinusoidal state variables. III.  ... 
doi:10.1166/sl.2006.051 fatcat:e25aqfqlmfcxbgqksrp6jgze6u

Robust Huber-Based Cubature Kalman Filter for GPS Navigation Processing

Chien-Hao Tseng, Sheng-Fuu Lin, Dah-Jing Jwo
2016 Journal of navigation  
In GPS navigation, the filter-based estimation of the position and velocity states can be severely degraded due to contaminated measurements caused by outliers or deviation from a Gaussian distribution  ...  For the signals contaminated with non-Gaussian noise or outliers, a robust scheme combining the Huber M-estimation methodology and the CKF framework is beneficial where the Huber M-estimation methodology  ...  (3) When the GPS pseudorange observables are contaminated with non-Gaussian measurement errors, the CKF can adequately capture the non-Gaussianity and demonstrate noticeably better performance.  ... 
doi:10.1017/s0373463316000692 fatcat:j2om63vxq5gdpnplyaaq5bkbui

Comparative Study of Two Localization Approaches for Mobile Robots in an Indoor Environment

Eman Alhamdi, Ramdane Hedjar, L. Fortuna
2022 Journal of Robotics  
The simulations results showed better estimation performance achieved by the particle filter being compared to the extended Kalman filter when the sensors are subject to non-Gaussian noises.  ...  In the last years, mobile robot localization has been developed significantly due to the need for accurate solutions to determine the position and orientation of the wheeled mobile robot (WMR) in a given  ...  Performance evaluation of the estimation algorithms utilizes different criteria. ese criteria depend on the type of evaluation (performance optimization, estimation, and analysis).  ... 
doi:10.1155/2022/1999082 fatcat:m76tbwlygff6lavbnsacxaphou

2D and 3D Angles-Only Target Tracking Based on Maximum Correntropy Kalman Filters

Asfia Urooj, Aastha Dak, Branko Ristic, Rahul Radhakrishnan
2022 Sensors  
The performance of all these estimators is evaluated in terms of root-mean-square error (RMSE) in position and % track loss.  ...  of non Gaussian measurement noise.  ...  This makes state estimation a very challenging problem in the presence of nonlinear models and non Gaussian noise.  ... 
doi:10.3390/s22155625 fatcat:ie7pt5vxcfcgpg7jvdvhtg7yaa

Robust Power System Dynamic State Estimator with Non-Gaussian Measurement Noise: Part II--Implementation and Results [article]

Junbo Zhao, Lamine Mili
2017 arXiv   pre-print
This paper is the second of a two-part series that discusses the implementation issues and test results of a robust Unscented Kalman Filter (UKF) for power system dynamic state estimation with non-Gaussian  ...  In addition, when the power measurement noises obey a Cauchy distribution, our GM-UKF converges to a state estimate vector that exhibits a much higher statistical efficiency than that of the GM-IEKF; by  ...  Zhenyu Huang from PNNL for providing us with real PMU data for analyzing the statistical probability distributions of the PMU measurement errors.  ... 
arXiv:1703.05991v1 fatcat:ybdv5kuvk5flbmxvfjyolot7ty

Adaptive Gaussian sum squared-root cubature Kalman filter with split-merge scheme for state estimation

Yu Liu, Kai Dong, Haipeng Wang, Jun Liu, You He, Lina Pan
2014 Chinese Journal of Aeronautics  
The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency.  ...  The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement.  ...  Acknowledgments The authors thank the associate editor and the anonymous reviewers for their valuable comments. This work was supported by the National Natural Science Foundation of China  ... 
doi:10.1016/j.cja.2014.09.007 fatcat:q2cymzoxcjbwpfgw6twyhgyxwe

Adaptive Extended Kalman Filter with Correntropy Loss for Robust Power System State Estimation

Zhiyu Zhang, Jinzhe Qiu, Wentao Ma
2019 Entropy  
In the actual power system however, these measurements are usually disturbed by non-Gaussian noises in practice.  ...  Firstly, correntropy is used to improve the robustness of the EKF algorithm in the presence of non-Gaussian noises and outliers.  ...  Acknowledgments: The authors would like to thank the Guest Editor, Assistant editor, and the reviewers for their valuable comments and suggestions, which helped us improve the quality of the manuscript  ... 
doi:10.3390/e21030293 pmid:33267008 fatcat:xjj42rjfubemrh54d6ynknl3de

Non-Gaussian Filters for Nonlinear Continuous-Discrete Models

2017 SICE Journal of Control Measurement and System Integration  
Since the nonlinear problems generally make the states non-Gaussian as time proceeds, these non-Gaussian filters are promising for improving estimation accuracy.  ...  Their filtering performance is evaluated using two benchmark simulation models and compared with the performance of existing Gaussian filters, such as extended and unscented Kalman filters, and with that  ...  Evaluation Results Equilibrium models We compared the performance of the following Gaussian and non-Gaussian filters: • Gaussian filters: EKF, UKF • Non-Gaussian filters: EnKF, BF, EKPF, EKPF-IS These  ... 
doi:10.9746/jcmsi.10.53 fatcat:rz24rtnjk5hmhb6ixrlyazxqqy

Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach

Feng Lu, Jinquan Huang, Yiqiu Lv
2013 Energies  
and non-Gaussian problems.  ...  Recent investigations have focused on the particle filter (PF) based on Monte Carlo sampling algorithms for tackling strong nonlinear and non-Gaussian models.  ...  NZ2012110), and the National Nature Science Foundation of China (No. 51276087).  ... 
doi:10.3390/en6010492 fatcat:f6layatwbfhfxegljf2kecozia

Nonlinear estimation framework in target tracking

Ondrej Straka, Miroslav Flidr, Jindrich Dunik, Miroslav Simandl, E Blasch
2010 2010 13th International Conference on Information Fusion  
The framework was designed with the aim to facilitate implementation, testing and use of various nonlinear state estimation methods.  ...  The goal of the article is to describe a software framework designed for nonlinear state estimation of discrete-time dynamic systems.  ...  Acknowledgement The work was supported by the Ministry of Education, Youth and Sports of the Czech Republic, project 1M0572, and by the Czech Science Foundation, project GACR 102/08/0442.  ... 
doi:10.1109/icif.2010.5712076 fatcat:64ukxfm2u5eqdojonp7lzzv6uu
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