scholarly journals An Unscented Rauch-Tung-Striebel Smoother for a Vehicle Localization Problem

2011 ◽  
Vol 15 (7) ◽  
pp. 860-868 ◽  
Author(s):  
Saifudin Razali ◽  
◽  
Keigo Watanabe ◽  
Shoichi Maeyama ◽  
Kiyotaka Izumi ◽  
...  
Keyword(s):  
Unscented Kalman Filter ◽  
Localization Problem ◽  
Unscented Transformation ◽  
Vehicle Localization ◽  
Deterministic Sampling ◽  
Derivative Free ◽  
Nonlinear Smoothing ◽  
Estimation Problems ◽  
A New Technique ◽  
Filtering Problem

The Unscented Kalman Filter (UKF) has become relatively a new technique used in a number of nonlinear estimation problems to overcome the limitation of Taylor series linearization. It uses a deterministic sampling approach known as sigma points to propagate nonlinear systems and has been discussed in many literature. However, a nonlinear smoothing problem has received less attention than the filtering problem. Therefore, in this article an unscented smoother based on Rauch-Tung-Striebel formis examined for discretetime dynamic systems. It has advantages available in unscented transformation over approximation by Taylor expansion as well as its benefit in derivative free. To show the effectiveness of the proposed method, the unscented smoother is implemented and evaluated through a vehicle localization problem.

scholarly journals The Onsager–Machlup functional for data assimilation

2017 ◽  
Vol 24 (4) ◽  
pp. 701-712 ◽  
Author(s):  
Nozomi Sugiura
Keyword(s):  
Data Assimilation ◽  
Prior Distribution ◽  
Numerical Experiments ◽  
Time Estimation ◽  
Model Error ◽  
Theoretical Studies ◽  
Drift Term ◽  
Estimation Problems ◽  
Large Systems ◽  
A New Technique

Abstract. When taking the model error into account in data assimilation, one needs to evaluate the prior distribution represented by the Onsager–Machlup functional. Through numerical experiments, this study clarifies how the prior distribution should be incorporated into cost functions for discrete-time estimation problems. Consistent with previous theoretical studies, the divergence of the drift term is essential in weak-constraint 4D-Var (w4D-Var), but it is not necessary in Markov chain Monte Carlo with the Euler scheme. Although the former property may cause difficulties when implementing w4D-Var in large systems, this paper proposes a new technique for estimating the divergence term and its derivative.

M-estimator based robust unscented Kalman filter through statistical linearization

2018 ◽  
Vol 41 (7) ◽  
pp. 2016-2025 ◽  
Author(s):  
Guobin Chang ◽  
Tianhe Xu ◽  
Haitao Wang
Keyword(s):  
Kalman Filter ◽  
Gaussian Distribution ◽  
Unscented Kalman Filter ◽  
Weighted Least Squares ◽  
Point Of View ◽  
Tuning Parameter ◽  
Statistical Linearization ◽  
Unscented Transformation ◽  
Actual Distribution ◽  
M Estimator

The robust unscented Kalman filter (UKF) is revisited in this paper from a new point of view, namely the statistical linear regression (SLR) perspective of the unscented transformation (UT). When the actual distribution of the observation noise deviates from the assumed Gaussian distribution, the performance of the filter may degrade significantly owing to lacking robustness. After linearizing the nonlinear observation equation using the SLR perspective, the M-estimator is employed to replace the weighted least-squares one, resulting in the robust UKF. Besides providing new theoretical aspects, the advantageous performance of the proposed filter is re-emphasized in terms of the following three: (1) robust, the proposed filter is robust against deviations from the assumed Gaussian distribution; (2) efficient, by elaborately selecting the tuning parameter of the M-estimator, the robust filter will have a slight efficiency loss compared with the UKF when the Gaussian assumption does hold; (3) accurate, the proposed filter achieves robustness without compromising the accuracy of the UT.

scholarly journals Extended Particle-Aided Unscented Kalman Filter Based on Self-Driving Car Localization

2020 ◽  
Vol 10 (15) ◽  
pp. 5045 ◽  
Author(s):  
Ming Lin ◽  
Byeongwoo Kim
Keyword(s):  
Kalman Filter ◽  
Real World ◽  
Gaussian Noise ◽  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Two Dimensions ◽  
Localization Problem ◽  
Simulation Results ◽  
Non Gaussian ◽  
Self Driving Cars

The location of the vehicle is a basic parameter for self-driving cars. The key problem of localization is the noise of the sensors. In previous research, we proposed a particle-aided unscented Kalman filter (PAUKF) to handle the localization problem in non-Gaussian noise environments. However, the previous basic PAUKF only considers the infrastructures in two dimensions (2D). This previous PAUKF 2D limitation rendered it inoperable in the real world, which is full of three-dimensional (3D) features. In this paper, we have extended the previous basic PAUKF’s particle weighting process based on the multivariable normal distribution for handling 3D features. The extended PAUKF also raises the feasibility of fusing multisource perception data into the PAUKF framework. The simulation results show that the extended PAUKF has better real-world applicability than the previous basic PAUKF.

scholarly journals Train Localization using Unscented Kalman Filter – Based Sensor Fusion

2018 ◽  
Vol 1 (2) ◽  
pp. 35-41
Author(s):  
I Faruqi ◽  
M. B. Waluya ◽  
Y. Y. Nazaruddin ◽  
T. A. Tamba ◽  
◽  
...  
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Precision Measurement ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Experimental Results ◽  
Fusion Method ◽  
Localization Problem ◽  
Rail Systems ◽  
Fusion Methods

This paper presents an application of sensor fusion methods based on Unscented Kalman filter (UKF) technique for solving train localization problem in rail systems. The paper first reports the development of a laboratory-scale rail system simulator which is equipped with various onboard and wayside sensors that are used to detect and locate the train vehicle movements in the rail track. Due to the low precision measurement data obtained by each individual sensor, a sensor fusion method based on the UKF technique is implemented to fuse the measurement data from several sensors. Experimental results which demonstrate the effectiveness of the proposed UKF-based sensor fusion method for solving the train localization problem is also reported.

scholarly journals An Improved Unscented Kalman Filter for Discrete Nonlinear Systems with Random Parameters

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yue Wang ◽  
Zhijian Qiu ◽  
Xiaomei Qu
Keyword(s):  
Nonlinear Systems ◽  
Unscented Kalman Filter ◽  
Taylor Series Expansion ◽  
Superior Performance ◽  
Random Parameters ◽  
Mobile Source ◽  
Unscented Transformation ◽  
Sensor Position ◽  
Discrete Nonlinear Systems ◽  
True Values

This paper investigates the nonlinear unscented Kalman filtering (UKF) problem for discrete nonlinear dynamic systems with random parameters. We develop an improved unscented transformation by incorporating the random parameters into the state vector to enlarge the number of sigma points. The theoretical analysis reveals that the approximated mean and covariance via the improved unscented transformation match the true values correctly up to the third order of Taylor series expansion. Based on the improved unscented transformation, an improved UKF method is proposed to expand the application of the UKF for nonlinear systems with random parameters. An application to the mobile source localization with time difference of arrival (TDOA) measurements and sensor position uncertainties is provided where the simulation results illustrate that the improved UKF method leads to a superior performance in comparison with the normal UKF method.

scholarly journals The Nonsequential Fusion Method for Localization from Unscented Kalman Filter by Multistation Array Buoys

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Gou Yanni ◽  
Wang Qi
Keyword(s):  
Kalman Filter ◽  
Nonlinear Filtering ◽  
Target Location ◽  
Unscented Kalman Filter ◽  
Research Field ◽  
Measuring Error ◽  
Engineering Practice ◽  
Relevant Parameter ◽  
Unscented Transformation ◽  
Target Track

Based on special features of array buoy and the research field of location and tracking of underwater target, the research combines the highly adaptive nonlinear filtering algorithm unscented Kalman filter with the nonlinear programming of multistation array buoy positioning system. In accordance with the model of nonsequential target location, the research utilizes Unscented Transformation to update the measuring error and covariance matrix of state error, aiming at estimating the filtering of state variable and acquiring the object’s current state of motion. The research analyzes the positioning performance of algorithm, pursuit path, astringency, and other performance indexes of target-relevant parameter through numerical simulation experiment. From the result, the conclusion that multistation array buoy can complete the task of tracing target track very well can be reached, which provides theoretical foundation for putting the algorithm into engineering practice.

Unscented Kalman Filtering for Nonlinear Systems with Colored Measurement Noises and One-Step Randomly Delayed Measurements

2019 ◽  
Vol 23 (2) ◽  
pp. 165-174
Author(s):  
Xinmei Wang ◽  
Zhenzhu Liu ◽  
Feng Liu ◽  
Wei Liu ◽  
◽  
...  
Keyword(s):  
Nonlinear Systems ◽  
Kalman Filtering ◽  
Sampling Method ◽  
Unscented Transformation ◽  
Markov Sequence ◽  
One Step ◽  
Delayed Measurements ◽  
Measurement Noises ◽  
Symmetric Sampling ◽  
Filtering Problem

Traditional unscented Kalman filtering (UKF) cannot solve the filtering problem for nonlinear systems with colored measurement noises and one-step randomly delayed measurements. To fix this problem, a new UKF algorithm is proposed in this paper. First, a system model with one-step randomly delayed measurements and colored measurement noises is established, wherein a first order Markov sequence model for whitening colored noises and an independently identical distributed Bernoulli variable for modeling one-step randomly delayed measurements is introduced. Second, an UKF is proposed for the above established models through unscented transformation by calculating the nonlinear states posterior mean and covariance based on the Bayesian filter framework. Specially, the proportional symmetric sampling method is used in the new UKF algorithm. Finally, the effectiveness and superiority of the proposed method is verified via simulation.

Sign in / Sign up

Export Citation Format

Share Document