Monitoring Dynamically Changing Migratory Flocks Using an Algebraic Graph Theory-Based Clustering Algorithm

Migration flocks have different forms, including single individuals, formations, and irregular clusters. The shape of a flock can change swiftly over time. The real-time clustering of multiple groups with different characteristics is crucial for the monitoring of dynamically changing migratory flock...

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Vydáno v:Remote sensing (Basel, Switzerland) Ročník 16; číslo 7; s. 1215
Hlavní autoři: Jiang, Qi, Wang, Rui, Zhang, Wenyuan, Jiao, Longxiang, Li, Weidong, Wu, Chunfeng, Hu, Cheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.04.2024
Témata:
Algebra
algebraic graph theory
Algorithms
Behavior
Bird migration
Clustering
clustering algorithm
Comparative analysis
flocks
Graph theory
Graphs
group target
Identification and classification
mathematical theory
Matrix partitioning
migratory behavior
Migratory birds
Monitoring
Parameters
Radar
Radar data
radar data processing
Real time
Remote sensing
Similarity
Splitting
China
ISSN:2072-4292, 2072-4292
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Shrnutí:Migration flocks have different forms, including single individuals, formations, and irregular clusters. The shape of a flock can change swiftly over time. The real-time clustering of multiple groups with different characteristics is crucial for the monitoring of dynamically changing migratory flocks. Traditional clustering algorithms need to set various prior parameters, including the number of groups, the number of nearest neighbors, or the minimum number of individuals. However, flocks may display complex group behaviors (splitting, combination, etc.), which complicate the choice and adjustment of the parameters. This paper uses a real-time clustering-based method that utilizes concepts from the algebraic graph theory. The connected graph is used to describe the spatial relationship between the targets. The similarity matrix is calculated, and the problem of group clustering is equivalent to the extraction of the partitioned matrices within. This method needs only one prior parameter (the similarity distance) and is adaptive to the group’s splitting and combination. Two modifications are proposed to reduce the computation burden. First, the similarity distance can be broadened to reduce the exponent of the similarity matrix. Second, the omni-directional measurements are divided into multiple sectors to reduce the dimension of the similarity matrix. Finally, the effectiveness of the proposed method is verified using the experimental results using real radar data.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs16071215