Comparative Efficiency Analysis of Data Fusion Methods for Vehicle Trajectory Reconstruction

This article compares the efficiency of vehicle trajectory analysis methods based on data fusion from multiple cameras, monitoring the same area from different views under the condition having detection errors, which causes incorrectly localized and, in some cases, undetected vehicle during the movement. The experiment used the simulation of detection and localization of vehicle moving in straight, curved, zigzag and arbitrary trajectories, with localization errors and multi-level loss of data. By comparing Kalman-filter-based method and Linear-interpolation-based method for analyzing and reconstructing vehicle trajectory, the result shows that the data loss robustness of Kalman-filter-based method is higher than that of Linear-interpolation-based method, with data loss around 97% 97% and 90% for straight, curved and zigzag trajectories respectively. However, for arbitrary trajectory, the Linear-interpolation-based method is better than Kalman-filter-based method in all levels of data loss. In conclusion, Kalman-filter-based method is effective in the case of unchanged or slight transition of direction, while Linear-interpolation-based method is effective in the case of sudden transition of direction.

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A Review of Multisensor Fusion Methodologies for Aircraft Navigation Systems

Journal of Navigation ◽

10.1017/s0373463305003383 ◽

2005 ◽

Vol 58 (3) ◽

pp. 405-417 ◽

Cited By ~ 25

Author(s):

David J. Allerton ◽

Huamin Jia

Keyword(s):

Kalman Filter ◽

Data Fusion ◽

Navigation System ◽

Review Paper ◽

Fault Tolerant ◽

Navigation Systems ◽

System Architectures ◽

Advantages And Disadvantages ◽

Aircraft Navigation ◽

Fusion Methods

This paper reviews currently existing fault-tolerant navigation system architectures and data fusion methods used in the design and development of integrated aircraft navigation systems and also compares their advantages and disadvantages. Four fault-tolerant navigation system architectures are reviewed and the associated Kalman filter architectures and algorithms are discussed. These techniques have been used in most integrated aircraft navigation systems. The aim of this review paper is to provide a guide for navigation system designers to develop future aircraft multisensor navigation systems.

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A Novel Data Fusion Method Based on Signal's Reliability

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.321-324.561 ◽

2013 ◽

Vol 321-324 ◽

pp. 561-567 ◽

Cited By ~ 1

Author(s):

Ming Xiao ◽

Liang Pan ◽

Yan Long Bu ◽

Li Li Wan

Keyword(s):

Kalman Filter ◽

Data Fusion ◽

Optimal Estimation ◽

Minimum Variance ◽

Experimental Results ◽

Fusion Method ◽

One Dimensional ◽

The One ◽

Fusion Scheme ◽

Better Than

The traditional Kalman filter is able to obtain the optimal estimation of the estimated signals. However, it fails to consider their reliability. In real applications, the estimated signals may include outliers. Fortunately, we are able to know the reliability of the signals transcendentally. In this paper, we derive the one-dimensional data fusion formulas based on signals reliability which is according to minimum variance restriction. Furthermore, a corresponding data fusion scheme is proposed. Experimental results show the propose data fusion method performs much better than traditional methods.

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Multi sensor data fusion based approach for the calibration of airdata systems

The Aeronautical Journal ◽

10.1017/s0001924000005509 ◽

2011 ◽

Vol 115 (1164) ◽

pp. 113-122 ◽

Cited By ~ 4

Author(s):

M. Majeed ◽

I. N. Kar

Keyword(s):

Kalman Filter ◽

Data Fusion ◽

Test Data ◽

Flight Control ◽

Unscented Kalman Filter ◽

Flight Test ◽

Sensor Data ◽

Sensor Data Fusion ◽

Multi Sensor Data Fusion ◽

Fusion Methods

AbstractAccurate and reliable airdata systems are critical for aircraft flight control system. In this paper, both extended Kalman filter (EKF) and unscented Kalman filter (UKF) based various multi sensor data fusion methods are applied to dynamic manoeuvres with rapid variations in the aircraft motion to calibrate the angle-of-attack (AOA) and angle-of-sideslip (AOSS) and are compared. The main goal of the investigations reported is to obtain online accurate flow angles from the measured vane deflection and differential pressures from probes sensitive to flow angles even in the adverse effect of wind or turbulence. The proposed algorithms are applied to both simulated as well as flight test data. Investigations are initially made using simulated flight data that include external winds and turbulence effects. When performance of the sensor fusion methods based on both EKF and UKF are compared, UKF is found to be better. The same procedures are then applied to flight test data of a high performance fighter aircraft. The results are verified with results obtained using proven an offline method, namely, output error method (OEM) for flight-path reconstruction (FPR) using ESTIMA software package. The consistently good results obtained using sensor data fusion approaches proposed in this paper establish that these approaches are of great value for online implementations.

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Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion

IEEE Transactions on Aerospace and Electronic Systems ◽

10.1109/7.913685 ◽

2001 ◽

Vol 37 (1) ◽

pp. 273-279 ◽

Cited By ~ 328

Author(s):

Q. Gan ◽

C.J. Harris

Keyword(s):

Kalman Filter ◽

Data Fusion ◽

Multisensor Data Fusion ◽

Fusion Methods ◽

Multisensor Data

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Three State Estimation Fusion Methods Based on the Characteristic Function Filtering

Sensors ◽

10.3390/s21041440 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1440

Author(s):

Yiran Yuan ◽

Chenglin Wen ◽

Yiting Qiu ◽

Xiaohui Sun

Keyword(s):

Characteristic Function ◽

State Estimation ◽

Packet Loss ◽

Estimation Accuracy ◽

Measurement Systems ◽

Fusion Methods ◽

Estimation Fusion ◽

Parallel Filter ◽

Sequential Filter ◽

Better Than

There are three state estimation fusion methods for a class of strong nonlinear measurement systems, based on the characteristic function filter, namely the centralized filter, parallel filter, and sequential filter. Under ideal communication conditions, the centralized filter can obtain the best state estimation accuracy, and the parallel filter can simplify centralized calculation complexity and improve feasibility; in addition, the performance of the sequential filter is very close to that of the centralized filter and far better than that of the parallel filter. However, the sequential filter can tolerate non-ideal conditions, such as delay and packet loss, and the first two filters cannot operate normally online for delay and will be invalid for packet loss. The performance of the three designed fusion filters is illustrated by three typical cases, which are all better than that of the most popular Extended Kalman Filter (EKF) performance.

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