Cubature Kalman Filter Based Multi-sensor Data Fusion Algorithm for Maneuvering Target Tracking

2020 ◽  
pp. 577-584
Author(s):  
Zhi Guo ◽  
Chunyun Dong ◽  
Xu Yang ◽  
Hui Han ◽  
Qifeng Sun ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Target Tracking ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Maneuvering Target Tracking ◽  
Sensor Data Fusion ◽  
Maneuvering Target ◽  
Multi Sensor Data Fusion ◽  
Cubature Kalman Filter

An improved estimator using multiple sensor data fusion for radar maneuvering target tracking systems

2007 ◽  
Vol 30 (2) ◽  
pp. 203-210 ◽  
Author(s):  
Yi‐Nung Chung ◽  
Hsin‐Ta Chen ◽  
Pao‐Hua Chou ◽  
Maw‐Rong Yang
Keyword(s):  
Data Fusion ◽  
Target Tracking ◽  
Sensor Data ◽  
Tracking Systems ◽  
Maneuvering Target Tracking ◽  
Sensor Data Fusion ◽  
Maneuvering Target ◽  
Multiple Sensor

A Multi-sensor Data Fusion Algorithm Based on Improved Kalman Filter

2013 ◽  
pp. 219-229 ◽  
Author(s):  
Changchun Tang ◽  
Zhigang Ao ◽  
Kangyi Zhang ◽  
Youcheng Wang
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion

Multi-sensor Data Fusion Algorithm Based on Adaptive Trust Estimation and Neural Network

2020 ◽  
Author(s):  
Xuexin Zhao ◽  
Junhua Wu ◽  
Maoli Wang ◽  
Guangshun Li ◽  
Haili Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion

scholarly journals A Variational Bayesian Based Strong Tracking Interpolatory Cubature Kalman Filter for Maneuvering Target Tracking

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 52544-52560 ◽  
Author(s):  
Jian Wang ◽  
Tao Zhang ◽  
Xiang Xu ◽  
Yao Li
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Maneuvering Target Tracking ◽  
Variational Bayesian ◽  
Maneuvering Target ◽  
Cubature Kalman Filter

scholarly journals A Method Based on Multi-Sensor Data Fusion for UAV Safety Distance Diagnosis

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1467 ◽  
Author(s):  
Wenbin Zhang ◽  
Youhuan Ning ◽  
Chunguang Suo
Keyword(s):  
Mathematical Model ◽  
Data Fusion ◽  
Transmission Lines ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Sensor Data Fusion ◽  
Overhead Transmission Lines ◽  
Safety Distance ◽  
Multi Sensor Data Fusion ◽  
The Mathematical Model

With the increasing application of unmanned aerial vehicles (UAVs) to the inspection of high-voltage overhead transmission lines, the study of the safety distance between drones and wires has received extensive attention. The determination of the safety distance between the UAV and the transmission line is of great significance to improve the reliability of the inspection operation and ensure the safe and stable operation of the power grid and inspection equipment. Since there is no quantitative data support for the safety distance of overhead transmission lines in UAV patrol, it is impossible to provide accurate navigation information for UAV safe obstacle avoidance. This paper proposes a mathematical model based on a multi-sensor data fusion algorithm. The safety distance of the line drone is diagnosed. In these tasks, firstly, the physical model of the UAV in the complex electromagnetic field is established to determine the influence law of the UAV on the electric field distortion and analyze the maximum electric and magnetic field strength that the UAV can withstand. Then, based on the main factors affecting the UAV such as the maximum wind speed, inspection speed, positioning error, and the size of the drone, the adaptive weighted fusion algorithm is used to perform first-level data fusion on the homogeneous sensor data. Then, based on the improved evidence, the theory performs secondary fusion on the combined heterogeneous sensor data. According to the final processing result and the type of proposition set, we diagnose the current safety status of the drone to achieve an adaptive adjustment of the safety distance threshold. Lastly, actual measurement data is used to verify the mathematical model. The experimental results show that the mathematical model can accurately identify the safety status of the drone and adaptively adjust the safety distance according to the diagnosis result and surrounding environment information.

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