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

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 observatio...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-12, Vol.20 (1), p.238
Main Authors: Fan, Jiande, Xie, Weixin, Du, Haocui
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Clutter
Constraint modelling
data fusion
Data integration
Data points
Datasets
Multisensor fusion
Noise
Normal distribution
Sensors
target detection
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20010238