scholarly journals A Robust Multi-Sensor Data Fusion Clustering Algorithm Based on Density Peaks

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 238 ◽  
Author(s):  
Jiande Fan ◽  
Weixin Xie ◽  
Haocui Du
Keyword(s):  
Data Fusion ◽  
Clustering Algorithm ◽  
Sensor Data ◽  
Validity And Reliability ◽  
Sensor Data Fusion ◽  
Multiple Sensors ◽  
Density Peaks ◽  
Multi Sensor Data Fusion ◽  
Data Points ◽  
Sensor Clustering

In this paper, a novel multi-sensor clustering algorithm, based on the density peaks clustering (DPC) algorithm, is proposed to address the multi-sensor data fusion (MSDF) problem. The MSDF problem is raised in the multi-sensor target detection (MSTD) context and corresponds to clustering observations of multiple sensors, without prior information on clutter. During the clustering process, the data points from the same sensor cannot be grouped into the same cluster, which is called the cannot link (CL) constraint; the size of each cluster should be within a certain range; and overlapping clusters (if any) must be divided into multiple clusters to satisfy the CL constraint. The simulation results confirm the validity and reliability of the proposed algorithm.

Operator Use of Multi-Sensor Data Fusion for Airborne Picture Compilation

2020 ◽  
Author(s):  
Geoffrey Ho ◽  
Erin Kim ◽  
Shahzaib Khattak ◽  
Stephanie Penta ◽  
Tharmarasa Ratnasingham ◽  
...  
Keyword(s):  
Data Fusion ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion

Multisensor Degradation Data Fusion and Remaining Life Prediction

2017 ◽  
Author(s):  
Changxi Wang ◽  
E. A. Elsayed ◽  
Kang Li ◽  
Javier Cabrera
Keyword(s):  
Data Fusion ◽  
Prediction Accuracy ◽  
Life Prediction ◽  
Clustering Algorithm ◽  
Partial Information ◽  
Degradation Process ◽  
Sensor Data ◽  
Multiple Sensors ◽  
Degradation Data ◽  
Degradation Monitoring

Multiple sensors are commonly used for degradation monitoring. Since different sensors may be sensitive at different stages of the degradation process and each sensor data contain only partial information of the degraded unit, data fusion approaches that integrate degradation data from multiple sensors can effectively improve degradation modeling and life prediction accuracy. We present a non-parametric approach that assigns weights to each sensor based on dynamic clustering of the sensors observations. A case study that involves a fatigue-crack-growth dataset is implemented in order evaluate the prognostic performance of the unit. Results show that the fused path obtained with the proposed approach outperforms any individual sensor data and other paths obtained with an adaptive threshold clustering algorithm in terms of life prediction accuracy.

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