A novel density-based clustering method for effective removal of spurious intersections in bearings-only localization

EURASIP Journal on Advances in Signal Processing

Research

Zhian Deng¹  Tianbao Zhang¹  Chunjie Zhang¹  Weijian Si¹  Zhiguo Wang¹ 

[1] College of Information and communication Engineering, Harbin Engineering University, Harbin, China;Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin, China;

关键词: Passive positioning;    Mahalanobis distance;    Density clustering;   

DOI  :  10.1186/s13634-023-00974-8

 received in 2022-08-04, accepted in 2023-01-11,  发布年份 2023

来源: Springer

【 摘 要 】

In bearings-only localization, clustering-based methods have been widely used to remove spurious intersections by fusing multiple bearing measurements from different observation stations. Existing clustering methods, including fuzzy C-mean (FCM) clustering and density-based spatial clustering of applications with noise (DBSCAN), must specify the number of clusters and the threshold for defining the neighborhood density, respectively, which are always unknown and difficult to estimate. Moreover, in dense radiation source scenes, existing clustering methods for removal of spurious intersections all deteriorate significantly. Therefore, we propose a novel density-based clustering method called K-M-DBSCAN, which combines the minimum K distance algorithm with Mahalanobis distance-based DBSCAN clustering. Firstly, K-M-DBSCAN uses minimum K distance algorithm for preprocessing to remove most of the spurious intersections and reduce the computational complexity of clustering. Mahalanobis distance-based DBSCAN is used for clustering and spurious intersections recognition. In order to adapt the large variations of sample density in clustering, we use Mahalanobis distance to define an explicit neighborhood of DBSCAN instead of traditional Euclidean distance. Simulation results show that the proposed K-M-DBSCAN performs better than FCM and DBSCAN in removing of spurious intersections.

【 授权许可】

CC BY   
© The Author(s) 2023

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