A new multi-sensor hierarchical data fusion algorithm based on unscented Kalman filter for the attitude observation of the wave glider

2021 ◽  
Vol 109 ◽  
pp. 102562
Author(s):  
Fen Liu ◽  
Yubing Liu ◽  
Xiujun Sun ◽  
Hongqiang Sang
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Unscented Kalman Filter ◽  
Hierarchical Data ◽  
Fusion Algorithm ◽  
Wave Glider

scholarly journals Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Huadong Wang ◽  
Shi Dong
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Observational Data ◽  
Measurement System ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Fusion Algorithm ◽  
Multisensor Data Fusion ◽  
Multiple Sensors ◽  
Multisensor Data

In order to improve the reliability of measurement data, the multisensor data fusion technology has progressed greatly in improving the accuracy of measurement data. This paper utilizes the real-time, recursive, and optimal estimation characteristics of unscented Kalman filter (UKF), as well as the unique advantages of multiscale wavelet transform decomposition in data analysis to effectively integrate observational data from multiple sensors. A new multiscale UKF-based multisensor data fusion algorithm is proposed by combining the UKF with multiscale signal analysis. Firstly, model-based UKF is introduced into the multiple sensors, and then the model is decomposed at multiple scales onto the coarse scale with wavelets. Next, signals decomposed from fine to coarse scales are adjusted using the denoised observational data from corresponding sensors and reconstructed with wavelets to obtain the fused signals. Finally, the processed data are fused using adaptive weighted fusion algorithm. Comparison of simulation and experimental results shows that the proposed method can effectively improve the antijamming capability of the measurement system and ensure the reliability and accuracy of sensor measurement system compared to the use of data fusion algorithm alone.

Model aided airborne integrated navigation system based on an improved square-root unscented H∞ filter

2018 ◽  
Vol 41 (5) ◽  
pp. 1290-1300
Author(s):  
Jieliang Shen ◽  
Yan Su ◽  
Qing Liang ◽  
Xinhua Zhu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Statistical Characteristics ◽  
Small Scale ◽  
Strong Nonlinearity ◽  
Square Root ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System

An inertial navigation system (INS) aided with an aircraft dynamic model (ADM) is developed as a novel airborne integrated navigation system, coping with the absence of a global navigation satellite system. To overcome the shortcomings of the conventional linear integration of INS/ADM based on an extended Kalman filter, a nonlinear integration method is proposed. Fast-update ADM makes it possible to utilize a direct filtering method, which employs nonlinear INS mechanics as system equations and a nonlinear ADM as observation equations, substituting the indirect filtering based on linear error equations. The strong nonlinearity generally calls for an unscented Kalman filter to accomplish the fusion process. Dealing with the model uncertainty, the inaccurate statistical characteristics of the noise and the potential nonpositive definiteness of the covariance matrix, an improved square-root unscented H∞ filter (ISRUHF) is derived in the paper, in which the robust factor [Formula: see text] is further expanded into a diagonal matrix [Formula: see text], to improve the accuracy and robustness of the integrated navigation system. Corresponding simulations as well as real flight tests based on a small-scale fixed-wing aircraft are operated and ISRUHF shows superiority compared with the commonly used fusion algorithm.

Unscented Kalman Filter for INS/GNSS Data Fusion with Time Delay

2021 ◽  
Author(s):  
Kanishke Gamagedara ◽  
Taeyoung Lee ◽  
Murray R. Snyder
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
Data Fusion ◽  
Unscented Kalman Filter ◽  
Gnss Data

scholarly journals Data Fusion of UWB and IMU Based on Unscented Kalman Filter for Indoor Localization of Quadrotor UAV

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 64971-64981 ◽  
Author(s):  
Weide You ◽  
Fanbiao Li ◽  
Liqing Liao ◽  
Meili Huang
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Indoor Localization ◽  
Unscented Kalman Filter ◽  
Quadrotor Uav

scholarly journals Robust Data Fusion of UAV Navigation Measurements with Application to the Landing System

2020 ◽  
Vol 12 (23) ◽  
pp. 3849
Author(s):  
Kirill Kolosov ◽  
Alexander Miller ◽  
Boris Miller
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Measurement Errors ◽  
High Sensitivity ◽  
False Alarm Probability ◽  
Navigation Systems ◽  
Fusion Algorithm ◽  
Chi Square ◽  
Filtering Algorithm ◽  
Anomalous Errors

To perform precise approach and landing concerning an aircraft in automatic mode, local airfield-based landing systems are used. For joint processing of measurements of the onboard inertial navigation systems (INS), altimeters and local landing systems, the Kalman filter is usually used. The application of the quadratic criterion in the Kalman filter entails the well-known problem of high sensitivity of the estimate to anomalous measurement errors. During the automatic approach phase, abnormal navigation errors can lead to disaster, so the data fusion algorithm must automatically identify and isolate abnormal measurements. This paper presents a recurrent filtering algorithm that is resistant to anomalous errors in measurements and considers its application in the data fusion problem for landing system measurements with onboard sensor measurements—INS and altimeters. The robustness of the estimate is achieved through the combined use of the least modulus method and the Kalman filter. To detect and isolate failures the chi-square criterion is used. It makes possible the customization of the algorithm in accordance with the requirements for false alarm probability and the alarm missing probability. Testing results of the robust filtering algorithm are given both for synthesized data and for real measurements.

Information Fusion for State Estimation of Power Battery in Electric Vehicle Based on Unscented Kalman Filter

2013 ◽  
Vol 303-306 ◽  
pp. 975-978
Author(s):  
Hong Yu Zheng ◽  
Chang Fu Zong
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Information Fusion ◽  
Unscented Kalman Filter ◽  
Accurate Estimate ◽  
Fusion Algorithm ◽  
Power Battery ◽  
Battery State Of Charge ◽  
New Generation

The power battery state of charge (SOC) in electric vehicles is not easy to measure accurately or apply a sensor but the expense is increased. However the variable of SOC is great importance to control of electric vehicles. A power battery model is built by the Partnership for a New Generation of Vehicles (PNGV) model to estimate the state of SOC. In order to make a high accurate estimate for SOC value, an information fusion algorithm based on unscented kalman filter (UKF) is introduced to design an observer. The test results show that the observer based information fusion and UKF are effective and accuracy, so it is may apply it the electric vehicle control and observation.

Joint Registration and State Estimation for Two-Station Passive Tracking

2013 ◽  
Vol 427-429 ◽  
pp. 675-679 ◽  
Author(s):  
Qiang Zhu ◽  
Jian Xun Li
Keyword(s):  
Computer Simulation ◽  
Kalman Filter ◽  
Expectation Maximization ◽  
Unscented Kalman Filter ◽  
System Model ◽  
Fusion Algorithm ◽  
Factors Affecting ◽  
Measurement Function ◽  
Passive Tracking ◽  
Locating System

Registration and nonlinearity are two crucial factors affecting the performance of the two-station passive locating system. In this paper, an online joint registration and data fusion algorithm is proposed to estimate the sensor bias and target state simultaneously using the angle-only measurements from the two ownship stations. The system model of the passive radar is firstly developed followed by the expectation-maximization (EM) approach dealing with the derivation of maximum likelihood (ML) function of the complete data. The unscented Kalman filter (UKF) is chosen to alleviate the influence caused by nonlinearity generated in the measurement function. Computer simulation shows that the proposed method is effective and reliable for this specific tracking scenario.

Sign in / Sign up

Export Citation Format

Share Document