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

Abstract 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) and density-based spatial of applications with noise (DBSCAN), must specify the number clusters threshold for defining neighborhood density, respectively, which are always unknown difficult estimate. Moreover, in dense radiation source scenes, existing removal all deteriorate significantly. Therefore, we propose a novel method called K-M-DBSCAN, combines minimum K distance algorithm Mahalanobis distance-based DBSCAN clustering. Firstly, K-M-DBSCAN uses preprocessing most reduce computational complexity is recognition. order adapt large variations sample density clustering, use define an explicit instead traditional Euclidean distance. Simulation results show that proposed performs better than FCM removing intersections.