Research on a time scale algorithm based on multi-dimensional weighted average

Abstract

Abstract The widespread availability of high-performance clocks has motivated interest in time scale algorithms. There are various time scale algorithms in use today, ranging in application from scientific to commercial. The weighted average algorithm has been widely used to generate an ensemble time scale. It takes one weight for each clock according to the stability or predictability to generate the ensemble time scale. However, one weight cannot reflect the long-term, mid-term and short-term stability performance of the clock simultaneously. Therefore, it cannot improve the long-term, mid-term and short-term stability at the same time. To solve this problem, a multi-dimensional weighted average algorithm is proposed in this paper, which decomposes the clock difference into three dimensions and gets weights in three dimensions to give consideration to improve long-term, mid-term and short-term stability simultaneously. The final results of simulation and experiment demonstrate that the algorithm proposed in this paper improved Allan deviations of 3.94 × 10−15 on daily and 7.86 × 10−15 on monthly averaging times with respect to those obtained from AT1 and ALGOS, respectively.