Attitude Estimation By Separate-Bias Kalman Filter-Based Data Fusion

2004 ◽  
Vol 57 (2) ◽  
pp. 261-273 ◽  
Author(s):  
Peyman Setoodeh ◽  
Alireza Khayatian ◽  
Ebrahim Frajah
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Dynamic Modelling ◽  
Limited Range ◽  
Attitude Estimation ◽  
Fusion Algorithm ◽  
Experimental Implementation ◽  
Slow Drift ◽  
Error State ◽  
State Form

Attitude estimation systems often use two or more different sensors to increase reliability and accuracy. Although gyroscopes do not have problems like limited range, interference, and line of sight obscuration, they suffer from slow drift. On the other hand, inclinometers are drift-free but they are sensitive to transverse accelerations and have slow dynamics. This paper presents an extended Kalman filter (EKF)-based data fusion algorithm which utilizes the complementary noise profiles of these two types of sensors to extend their limits. To avoid complexities of dynamic modelling of the platform and its interaction with the environment, gyro modelling will be used to implement indirect (error state) form of the Kalman filter. The great advantage of this approach is its independence from the structure of the platform and its applicability to any system with a similar set of sensors. Separate bias formulation of the Kalman filter will be used to reduce the computational complexity of the algorithm. In addition, a systematic approach based on wavelet decomposition will be utilized to estimate noise covariances used in the Kalman filter formulation. This approach solves many of the convergence problems encountered in the implementation of EKF due to the choice of covariance matrices. Experimental implementation of the estimator shows the excellent performance of the filter.

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.

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.

Multisensor Tracking of Marine Targets - Decentralized Fusion of Kalman and Neural Filters

2011 ◽  
Vol 57 (1) ◽  
pp. 65-70 ◽  
Author(s):  
Andrzej Stateczny ◽  
Witold Kazimierski
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Weighted Average ◽  
General Regression Neural Network ◽  
Fusion Algorithm ◽  
Measurement Models ◽  
Radar Systems ◽  
Track Association ◽  
Decentralized Data Fusion ◽  
General Regression

Multisensor Tracking of Marine Targets - Decentralized Fusion of Kalman and Neural FiltersThis paper presents an algorithm of multisensor decentralized data fusion for radar tracking of maritime targets. The fusion is performed in the space of Kalman Filter and is done by finding weighted average of single state estimates provided be each of the sensors. The sensors use numerical or neural filters for tracking. The article presents two tracking methods - Kalman Filter and General Regression Neural Network, together with the fusion algorithm. The structural and measurement models of moving target are determined. Two approaches for data fusion are stated - centralized and decentralized - and the latter is thoroughly examined. Further, the discussion on main fusing process problems in complex radar systems is presented. This includes coordinates transformation, track association and measurements synchronization. The results of numerical experiment simulating tracking and fusion process are highlighted. The article is ended with a summary of the issues pointed out during the research.

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