Abstract: This paper proposes a comprehensive and improved generalized grey relational analysis method. By employing clustering techniques, measured values ​​at each time step are selected from the original sequence and recombined to form a reference time series, effectively avoiding interference from outliers. Further modifications to the original time series are made through association order analysis and threshold comparison to improve fusion accuracy. Examples demonstrate that this method can further enhance the accuracy and reliability of target tracking. Keywords: target tracking; reference time series; gray association; track establishment [b][align=center]The Application of Gray Association Analysis in Multi-sensor Target Tracking LU Tao-rong, ZHU Lin-hu, Wang Guo-zheng[/align][/b] Abstract: This paper introduces a synthetically improved generalized gray association degree. It uses the cluster thought to choose measured values ​​in every moment from primitive time array to establish reference time array, which effectively evades interference of wild values. It modifies primitive time array by the analysis of association array and the comparison with critical value to raise fusion precision. The example proves that this approach is effective in target tracking. Key words: target tracking; reference time array; gray association; track establishment Data association is the process of establishing the relationship between a single sensor measurement and other previous measurement data to determine whether they have a common source. It arises from the uncertainty in the sensor observation process, mainly due to temporal asynchrony, different spatial resolutions, noise and interference producing false targets, making the association process necessary to establish the relationship between each measurement and a large number of possible data sets[6]. This paper focuses on the properties and definition of grey relational analysis, and comprehensively improves the original definition based on absolute and relative relational analysis. First, a suitable reference time series is determined through clustering. Then, weighted summaries based on similarity and proximity are used to improve the correlation accuracy. Finally, correlation order analysis is performed, and thresholds are compared. Sequences below the threshold are replaced with reference time series. This process is then repeated to establish the track, significantly improving tracking accuracy. Examples demonstrate the effectiveness of this method. 1. Problem Model The target state vector consists of characteristic parameters such as target position, target velocity, target heading, and target acceleration. For simplicity and ease of discussion, it is assumed that the track data sent to the fusion center has undergone preprocessing and time coordinate registration. The track measurements of a single target from m sensors at n time points are presented in sequence. Our goal is to eliminate time series with significant discrepancies caused by clutter, electronic interference, and other unstable factors, and to fuse the remaining densely clustered sequence points to determine the track, thus greatly improving tracking accuracy. For details, please click: Application of Grey Relational Analysis in Multi-Sensor Target Tracking