Deep Neural Fuzzy System-Based Track-to-Track Association Method for Heterogeneous Sensors1

This paper investigates the application of deep neural fuzzy network (DNFS) in track-to-track association (TTTA) under heterogeneous sensors, addressing the inaccuracy in associating tracks obtained from electro-optical (EO) sensors and passive radars. In complex association scenarios such as parallel, crossing, and converging–diverging tracks, existing methods based on Bayesian probability, fuzzy systems, and deep learning yield limited association accuracy. To overcome these drawbacks, we frame the geometric and kinematic features of trajectory segments as high-dimensional time series, leveraging the superior time-series modeling capabilities of DNFS. A convolutional feature extraction network is designed to capture the key features of track segments. Finally, the similarity of track segment features is evaluated, and the optimal assignment is derived. Experimental results demonstrate that the proposed method significantly improves TTTA accuracy in the aforementioned complex scenarios, offering a new and effective solution for heterogeneous-sensor track association in multi-target tracking systems.