scholarly journals Road Tracking from High resolution IRS And IKONOS Images Using Unscented Kalman Filtering

2011 ◽  
pp. 5-11
Author(s):  
Jenita Subash ◽  
Madhan Kumar K
Keyword(s):  
Kalman Filter ◽  
Satellite Images ◽  
Unscented Kalman Filter ◽  
Image Interpretation ◽  
Estimation Algorithm ◽  
The Road ◽  
Median Axis ◽  
Interpretation System ◽  
Road Maps ◽  
Map Data

A typical way to update map is to compare recent satellite images with existing map data, detect new roads and add them as cartographic entities to the road layer. At present image processing and pattern recognition are not robust enough to automate the image interpretation system feasible. For this reason we have to develop an image interpretation system that rely on human guidance. More importantly road maps require final checking by a human due to the legal implementations of error. Our proposed technique is applied to IRS and IKONOS images using Unscented Kalman Filter(UKF) . UKF is used for tracing the median axis of the single road segment. The Extended Kalman Filter (EKF) is probably the most widely used estimation algorithm for road tracking. However, more than 35 years of experience in the estimation community has shown that is difficult to implement and is difficult to tune. To overcome this limitation,UKF is introduced in road tracking which is more accurate, easier to implement, and uses the same order of calculations as linearization. The principles and algorithm of EKF and UKF were also discussed. The core of our system is based on profile matching.UKF traces the roadbeyond obstacles and tries to find the continuation of the road finding all road branches initializing at the road junction.The completeness and correctness of road tracking from the IRS and IKONOS images were also compared.

State of Charge Estimation Algorithm Based on Fractional Order Adaptive Extended Kalman Filter and Unscented Kalman Filter

2021 ◽  
pp. 1-32
Author(s):  
Weijie Liu ◽  
Hongliang Zhou ◽  
Zeqiang Tang ◽  
Tianxiang Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Fractional Order ◽  
Lithium Battery ◽  
Unscented Kalman Filter ◽  
Estimation Algorithm ◽  
Internal Resistance ◽  
Second Order ◽  
State Of Charge ◽  
Adaptive Extended Kalman Filter

Abstract Accurate estimation of battery state of charge (SOC) is the basis of battery management system. the fractional order theory is introduced into the second-order resistance-capacitance (RC)model of lithium battery and adaptive genetic algorithm is used to identify the parameters of the second-order RC model based on fractional order. Considering the changes of internal resistance and battery aging during battery discharge, the battery health state (SOH) is estimated based on unscented Kalman filter (UKF), and the values of internal resistance and maximum capacity of the battery are obtained. Finally, a novel estimation algorithm of lithium battery SOC based on SOH and fractional order adaptive extended Kalman filter (FOAEKF) is proposed. In order to verify the effectiveness of the proposed algorithm, an experimental system is set up and the proposed method is compared with the existing SOC estimation algorithms. The experimental results show that the proposed method has higher estimation accuracy, with the average error lower than 1% and the maximum error lower than 2%.

scholarly journals Maneuvering Acceleration Estimation Algorithm Using Doppler Radar Measurement

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Hongqiang Liu ◽  
Zhongliang Zhou ◽  
Lei Yu
Keyword(s):  
Kalman Filter ◽  
Doppler Radar ◽  
Unscented Kalman Filter ◽  
Estimation Algorithm ◽  
Multiple Model ◽  
State Variables ◽  
Radar Measurement ◽  
Direction Angle ◽  
Kalman Filter Algorithm ◽  
Tangential Acceleration

An algorithm to estimate the tangential and normal accelerations directly using the Doppler radar measurement in an online closed loop form is proposed. Specific works are as follows: first, the tangential acceleration and normal acceleration are taken as the state variables to establish a linear state transition equation; secondly, the decorrelation unbiased conversion measurement Kalman filter (DUCMKF) algorithm is proposed to deal with the strongly nonlinear measurement equation; thirdly, the geometric relationship between the range rate and the velocity direction angle is used to obtain two estimators of the velocity direction angle; finally, the interactive multiple model (IMM) algorithm is used to fuse the estimators of the velocity direction angle and then the adaptive IMM of current statistical model based DUCMKF (AIMM-CS-DUCMKF) is proposed. The simulation experiment results show that the accuracy and stability of DUCMKF are better than the sequential extended Kalman filter algorithm, the sequential unscented Kalman filter algorithm, and converted measurement Kalman filter algorithms; on the other hand they show that the AIMM-CS-DUCMKF can obtain the high accuracy of the tangential and normal accelerations estimation algorithm.

An Efficient Method for Road Tracking from Satellite Images Using Hybrid Multi-Kernel Partial Least Square Analysis and Particle Filter

2017 ◽  
Vol 26 (11) ◽  
pp. 1750181 ◽  
Author(s):  
K. Madhan Kumar ◽  
A. Velayudham ◽  
R. Kanthavel
Keyword(s):  
Particle Filter ◽  
Satellite Images ◽  
Partial Least Square ◽  
Least Square ◽  
Road Extraction ◽  
The Road ◽  
Remotely Sensed Imagery ◽  
Partial Least Square Analysis ◽  
Gis Data ◽  
Road Maps

The Road extraction from the remotely sensed imagery is highly realistic for the quick road updating in the Geographic Information System (GIS) data collection. The particle filter (PF) was earlier employed to track the road maps in satellite images. In our previous work, we have introduced an efficient Gauss–Hermite Kalman Filter with Locally Excitatory Globally Inhibitory Oscillator Networks (GHKF–LEGION)-based road extraction, even though it does not properly extract the road from the complex region. In order to recover the track of the road beyond obstacles, in this work, we proposed a novel hybrid multi-kernel partial least squares (PLS) with PF approach. Here, at first, we estimate the initial leader point of the road employing the K-means clustering technique. Subsequently, the PF traces a road till a stopping benchmark is satisfied. Thereafter, without finishing the process, the outcomes are furnished to the hybrid kernel PLS technique which attempts to locate the continuance of the road after several potential road blocks or to locate the entire feasible road branches which are on the other side of the road junction. The outcomes are offered for five satellite images. The experimental results show our proposed road tracking method is better compared to other existing works.

scholarly journals A Novel Unscented Kalman Filter Strategy to Enhance Navigation System Performance

2020 ◽  
Vol 8 (5) ◽  
pp. 2446-2453
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Ground Truth ◽  
Estimation Algorithm ◽  
Large Error ◽  
Loosely Coupled ◽  
Grade Span ◽  
Complementary Nature ◽  
Navigation Information

The Extended Kalman Filter (EKF) is the most widely estimation algorithm used for nonlinear system such as a navigation system to fuse an inertial navigation system (INS) with Global Positioning System (GPS) which its information has complementary nature to get more accurate navigation information. Unfortunately, the performance of INS/GPS fusion using EKF is degraded due to the linearization error and GPS error. Therefore, a new algorithm is developed to overcome these issues. This algorithm uses the sampling-based Unscented Kalman Filter (UKF) to solve the linearization problem, and ignore the GPS reading when there is a large error in its measurements. The new algorithm is named Adaptive Loosely Coupled Unscented Kalman Filter (ALCUKF). The ALCUKFbased INS/GPS systems are presented for two different datasets. The first dataset is acquired using a high-end tactical-grade SPAN unit featuring Novatel HG1700 IMU module. The second dataset is acquired from a MEMS-based SCC1300-D04 IMU unit from VTI. The results of the new method are compared against reference ground truth trajectories and measured quantitatively using the Root Mean Square Error (RMSE). The ALCUKF increasedthe navigation system performancesignificantly when compared with EKF for both datasets as shown in the paper.

scholarly journals A Modified Unscented Kalman Filter Combined with Ant Lion Optimization for Vehicle State Estimation

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Yixi Zhang ◽  
Jian Ma ◽  
Xuan Zhao ◽  
Xiaodong Liu ◽  
Kai Zhang
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
The Road ◽  
Experiment Platform ◽  
Ant Lion Optimization ◽  
Ant Lion

Accurate estimation of vehicle states is extremely crucial for vehicle stability control. As a reliable estimation methodology, the unscented Kalman filter (UKF) has been widely utilized in vehicle control. However, the estimation accuracy still needs to be improved caused by the unpredictable measurement and process noise. In this paper, a novel modified UKF state estimation methodology combined with the ant lion optimization (ALO) is proposed for the stability control of a four in-wheel motor independent drive electric vehicle (4WIDEV). First, the optimal performance of the ALO algorithm is analyzed, where both unimodal and multimodal optimization test functions are selected and optimized by GA, PSO, and ALO, respectively. The results indicate that the ALO algorithm has good global optimization capability and applicability. Second, the ALO algorithm is merged into the UKF to adjust the statistical properties of noise information for the ALOUKF estimator design without extra sensor signals. At last, the simulations on the Matlab/Simulink-CarSim co-simulation platform and the road test based on an A&D 5435 rapid prototyping experiment platform (RPP) are carried out to verify the proposed method. The simulation and experiment results demonstrate that the ALOUKF estimator can improve state estimation accuracy and resist the vehicle nonlinearity even in the case of the complicated and emergency maneuvers.

scholarly journals Ship tracking based on the fusion of Kalman filter and particle filter

2021 ◽  
Vol 2113 (1) ◽  
pp. 012017
Author(s):  
Wanjin Xu ◽  
Jiying Li ◽  
Junjie Bai ◽  
Yingying Zhang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
State Estimation ◽  
Particle Filtering ◽  
Unscented Kalman Filter ◽  
Optimal Estimation ◽  
Estimation Algorithm ◽  
Tracking Accuracy ◽  
Gps Navigation ◽  
Degradation Degree

Abstract Aiming at the problem of low filtering accuracy and even divergence caused by model mismatch when using extended Kalman filter in ship GPS navigation and positioning state estimation, a positioning ship state estimation algorithm based on the fusion of improved unscented Kalman filter and particle filter is proposed. Compared with the traditional particle filtering algorithm, the algorithm has two improvements: first, the algorithm uses untraced Kalman as the main framework, and uses the optimal estimation of particle updating state by particle algorithm; Secondly, in the resampling process, a resampling algorithm based on weight optimization is proposed to increase the diversity of particles. The simulation results show that not only the particle degradation degree of the particle filter is reduced, but also the particle tracking accuracy is improved.

scholarly journals A Novel Adaptive Function—Dual Kalman Filtering Strategy for Online Battery Model Parameters and State of Charge Co-Estimation

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2268
Author(s):  
Yongcun Fan ◽  
Haotian Shi ◽  
Shunli Wang ◽  
Carlos Fernandez ◽  
Wen Cao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Initial Period ◽  
Lithium Ion ◽  
Estimation Algorithm ◽  
Maximum Error ◽  
Internal Resistance ◽  
State Of Charge ◽  
Model Parameters ◽  
The Difference

This paper aims to improve the stability and robustness of the state-of-charge estimation algorithm for lithium-ion batteries. A new internal resistance-polarization circuit model is constructed on the basis of the Thevenin equivalent circuit to characterize the difference in internal resistance between charge and discharge. The extended Kalman filter is improved through adding an adaptive noise tracking algorithm and the Kalman gain in the unscented Kalman filter algorithm is improved by introducing a dynamic equation. In addition, for benignization of outliers of the two above-mentioned algorithms, a new dual Kalman algorithm is proposed in this paper by adding a transfer function and through weighted mutation. The model and algorithm accuracy is verified through working condition experiments. The result shows that: the errors of the three algorithms are all maintained within 0.8% during the initial period and middle stages of the discharge; the maximum error of the improved extension of Kalman algorithm is over 1.5%, that of improved unscented Kalman increases to 5%, and the error of the new dual Kalman algorithm is still within 0.4% during the latter period of the discharge. This indicates that the accuracy and robustness of the new dual Kalman algorithm is better than those of traditional algorithm.

scholarly journals A New State of Charge Estimation Algorithm for Lithium-Ion Batteries Based on the Fractional Unscented Kalman Filter

Energies ◽  
2017 ◽  
Vol 10 (9) ◽  
pp. 1313 ◽  
Author(s):  
Yixing Chen ◽  
Deqing Huang ◽  
Qiao Zhu ◽  
Weiqun Liu ◽  
Congzhi Liu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
Estimation Algorithm ◽  
State Of Charge ◽  
State Of Charge Estimation
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