Adaptive Fast Track Association for Small Samples in Distributed Multi‐Sensor Systems

The track association problem aims to determine which tracks belong to the same real target, supporting subsequent processes such as target state estimation, target tracking and situation assessment. Existing algorithms are primarily based on statistical mathematics, fuzzy mathematics, grey system theory and neural networks. However, they suffer from several limitations, including overly idealised modelling, manually set thresholds, two‐level traversal and poor association performance under few‐shot conditions. In the light of the above problems, we propose a K‐Point Input LSTM‐KNN‐DPC (KLKD) algorithm. Firstly, considering the rationality of feature selection, we introduce a similarity measurement method with an adaptive cutoff distance. Secondly, a method for cluster centre selection is presented. Finally, an association assignment strategy is provided. The proposed algorithm eliminates the need for timestamp alignment and exhaustive pairwise matching across different track sequences, thereby improving the efficiency of track association. Experimental results demonstrate that, in both typical manoeuvring and irregular manoeuvring scenarios, the KLKD algorithm achieves higher association accuracy and lower end‐to‐end latency compared to existing methods.