Passive Location Discrete Point Trace Clustering Algorithm Based on Improved DBSCAN and ACO

Passive location scouting acquires target location information by passively receiving electromagnetic waves from the radiation source, which is more susceptible to the influence of the electromagnetic environment leading to the dispersion of the location point trace, and the acquisition of the location point feature information is relatively less. Therefore, to address the large positioning error and low feature quantity of passive location discrete traces, this paper proposes a passive location discrete point trace clustering algorithm based on improved Density Based Spatial Clustering of Applications with Noise (DBSCAN) and Ant Colony Optimization (ACO). In this algorithm, iterative blind DBSCAN (IB-DBSCAN) segments the multi-target discrete point traces to form two types of clusters, and the integration of clustered clusters is realized through cyclic pathfinding ACO (CP-ACO) algorithm processing to complete the clustering of passive location discrete point traces. Simulation experimental results demonstrate that the algorithm can effectively improve clustering accuracy in environments with large positioning error and high leakage rate, even in the face of multiple target passive location discrete traces with different motion characteristics.