Aerial battlefield targets grouping based on DTW-DBSCAN algorithm

Targets grouping is the basis of second-level information fusion, which can effectively assist commanders to make decisions. Traditional aerial targets grouping algorithm only takes the radar acquisition data at the current time as the object and cannot update the clustering results automatically. A grouping method combining dynamic time warping(DTW) and the algorithm Density-Based Spatial Clustering of Applications with Noise(DBSCAN) is proposed. The DTW distance of each attribute historical time-series data is used to measure the similarity between targets. Besides, an improved DBSCAN algorithm is used for clustering. The simulation results show that the method has better grouping effect and can automatically cluster regularly.