Dual Station Based Bearings-Only Tracking Using Unscented Filter

2014 ◽  
Vol 989-994 ◽  
pp. 3526-3529
Author(s):  
Hong Wei Quan ◽  
Jun Hua Li ◽  
Ming Shu Chen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Measurement System ◽  
Strong Nonlinearity ◽  
Matlab Simulation ◽  
Unscented Transformation ◽  
Filtering Algorithm ◽  
Simulation Scenario ◽  
Unscented Filter

This paper investigated the bearings-only tracking approach based on dual station detection. In order to solve the problem of strong nonlinearity in bearings –only measurement system, we introduced the unscented filter which is based on the idea of unscented transformation. Compared with traditional extended Kalman filter, unscented filter has shown better convergence and stabiltiy, especially in stabiltiy of tracking waveform. Matlab simulation scenario is taken for illustrating the effectiveness and validity of the unscented filtering algorithm.

scholarly journals Quadrature Kalman filter–based state of charge estimation for lithium-ion battery

2020 ◽  
Vol 12 (7) ◽  
pp. 168781402094269
Author(s):  
Mengtao Huang ◽  
Chao Wang ◽  
Bao Liu ◽  
Fan Wang ◽  
Jingting Wang
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Quadrature Rule ◽  
State Of Charge ◽  
Gauss Hermite Quadrature ◽  
State Of Charge Estimation ◽  
Unscented Filter

This article presents an approach to lithium-ion battery state of charge estimation based on the quadrature Kalman filter. Among the existing state of charge estimation approaches, the extended Kalman filter–based state of charge and unscented filter–based state of charge algorithms are influenced by the linearization or the solution of sigma points. The proposed quadrature Kalman filter–based state of charge algorithm avoids these problems. Specifically, the battery system equations are built based on the second-order resistance–capacitance equivalent circuit model, and the parameters are identified according to the hybrid pulse power characterization discharging test. Then, the quadrature points and corresponding weights are defined by the Gauss–Hermite quadrature rule, and the Kronecker tensor product is adopted to solve the points of multivariate. In addition, the stability of quadrature Kalman filter–based state of charge is verified. Finally, the simulation is carried out under the discharging and urban dynamometer driving schedule condition, which demonstrates that the quadrature Kalman filter–based state of charge algorithm has a better performance compared with extended Kalman filter–based state of charge and unscented filter–based state of charge.

scholarly journals A Discussion Related to Orbit Determination Using Nonlinear Sigma Point Kalman Filter

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
Paula Cristiane Pinto Mesquita Pardal ◽  
Helio Koiti Kuga ◽  
Rodolpho Vilhena de Moraes
Keyword(s):  
Kalman Filter ◽  
Dynamic Model ◽  
Extended Kalman Filter ◽  
Orbit Determination ◽  
Unscented Kalman Filter ◽  
Satellite Orbit ◽  
Observation Model ◽  
Gps Measurements ◽  
Unscented Transformation ◽  
Sigma Point

Herein, the purpose is to present a Kalman filter based on the sigma point unscented transformation development, aiming at real-time satellite orbit determination using GPS measurements. First, a brief review of the extended Kalman filter will be done. After, the sigma point Kalman filter will be introduced as well as the basic idea of the unscented transformation, in which this filter is based. Following, the unscented Kalman filter applied to orbit determination will be explained. Such explanation encloses formulations about the orbit determination through GPS; the dynamic model; the observation model; the unmodeled acceleration estimation; also an application of this new filter approaches on orbit determination using GPS measurements discussion.

An Application of the Kalman Filter in a Laser Tracking Measurement System

2005 ◽  
Vol 295-296 ◽  
pp. 245-252
Author(s):  
X.R. Chen ◽  
P. Cai ◽  
Wen Ku Shi
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Measurement System ◽  
Tracking System ◽  
Measuring System ◽  
Filter Method ◽  
Laser Tracking ◽  
Filtering Algorithm ◽  
Kalman Filtering Algorithm ◽  
Tracking Measurement

Flexible coordinate measuring system based on laser tracking measurement system (LTS) is an effective method to detect 3D coordinates of moving target. However, the system suffers from various interferences resulting in low accuracy. This paper extends the application of a Kalman filtering algorithm in LTS to solve the problem. The laser tracking system is introduced. The state model of the laser tracking measurement system is developed and the linearization method of the model is analyzed. A Kalman filtering algorithm is used for the system. The result of the simulation shows that the proposed Kalman filter method works well for the improvement of accuracy of LTS.

scholarly journals Attitude Estimation Algorithm of Portable Mobile Robot Based on Complementary Filter

Micromachines ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1373
Author(s):  
Mei Liu ◽  
Yuanli Cai ◽  
Lihao Zhang ◽  
Yiqun Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Computational Cost ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Attitude Motion ◽  
Extended Kalman Filtering ◽  
Filtering Algorithm ◽  
Complementary Filter

In robot inertial navigation systems, to deal with the problems of drift and noise in the gyroscope and accelerometer and the high computational cost when using extended Kalman filter (EKF) and particle filter (PF), a complementary filtering algorithm is utilized. By combining the Inertial Measurement Unit (IMU) multi-sensor signals, the attitude data are corrected, and the high-precision attitude angles are obtained. In this paper, the quaternion algorithm is used to describe the attitude motion, and the process of attitude estimation is analyzed in detail. Moreover, the models of the sensor and system are given. Ultimately, the attitude angles are estimated by using the quaternion extended Kalman filter, linear complementary filter, and Mahony complementary filter, respectively. The experimental results show that the Mahony complementary filtering algorithm has less computational cost than the extended Kalman filtering algorithm, while the attitude estimation accuracy of these two algorithms is similar, which reveals that Mahony complementary filtering is more suitable for low-cost embedded systems.

GPS/INS Integrated Navigation Based on Unscented Kalman Filter

2013 ◽  
Vol 380-384 ◽  
pp. 3429-3433
Author(s):  
Wan Li Xu ◽  
Zhun Liu ◽  
Jun Hui Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Error Reduction ◽  
Effective Algorithm ◽  
Integrated Navigation ◽  
Unscented Transformation ◽  
Recursive Bayesian Estimation ◽  
Navigation Method ◽  
Optimal Implementation

[Purpose] In GPS/INS integrated navigation, which is widely used in high precision of the real-time navigation, the Extended Kalman Filter (EKF) has become one of the most widely used algorithms. Unfortunately, the EKF is based on a sub-optimal implementation of the recursive Bayesian estimation framework applied to Gaussian random variables. This can seriously affect the accuracy or even lead to divergence of the system. In order to improve the accuracy, we apply the Unscented Transformation to GPS/INS integrated navigation. [Method] This paper optimizes GPS/INS integrated navigation by applying the Unscented Kalman Filter (UKF) algorithm which is based on the Unscented Transformation. [Results] The experimental results show that the UKF has an error reduction of over 10% in every estimator relative to the EKF. [Conclusions] Consequently, the UKF is an effective algorithm to improve the accuracy of GPS/INS integrated navigation.

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