In radar network systems, target tracks reported from different radars need to be associated and fused, and the track-to-track association (TTTA) effect is a key factor that directly affects the performance of the entire system. In order to solve the problem of the low accuracy of TTTA in network radar systems with asynchronous unequal rates, an asynchronous TTTA algorithm based on pseudo nearest neighbor distance is proposed. Firstly, the calculation method of pseudo nearest neighbor distance between the track point and the track data set is defined, then the correlation degree between the two track data sets is obtained by using grey theory, and then the Jonker-Volgenant algorithm is combined with the classical allocation method to judge the TTTA. The algorithm does not need time domain alignment and can effectively avoid the accumulation and propagation of estimation errors. The simulation results show that the algorithm has a high average correct association rate and is less affected by the radar sampling period ratio, startup time, and noise distribution, and the average correct association rate for different movement types of target tracks remains above 99%. Furthermore, compared with other algorithms, this algorithm maintains a stable low level of the number of false associations and the maximum false association rates, and has strong robustness and advantages.

Due to sensor characteristics, geographical environment, electromagnetic interference, electromagnetic silence, information countermeasures, and other reasons, there may be significant system errors in sensors in multi-sensor tracking systems, resulting in poor track-to-track association (TTTA) effect of the system. In order to solve the problem of TTTA under large system errors, this paper proposes an asynchronous anti-bias TTTA algorithm that utilizes the average distance between the nearest neighbor intervals between tracks. This algorithm proposes a systematic error interval processing method to track coordinates, and then defines the nearest neighbor interval average distance between interval coordinate datasets and interval coordinate points, and then uses grey theory to calculate the correlation degree between tracks. Finally, the Jonker–Volgenant algorithm is combined to use the canonical allocation method for TTTA judgment. The algorithm requires less prior information and does not require error registration. The simulation results show that the algorithm can ensure a high average correct association rate (over 98%) of asynchronous unequal rate tracks under large system errors, and achieve stable association, with good association and anti-bias performance. Compared with other algorithms, the algorithm maintains good performance for different target numbers and processing cycles, and has good superiority and robustness.