天文学报
天文學報
천문학보
ACTA ASTRONOMICA SINICA
2015年
3期
264-277
,共14页
梁月吉%任超%杨秀发%庞光锋%蓝岚
樑月吉%任超%楊秀髮%龐光鋒%藍嵐
량월길%임초%양수발%방광봉%람람
天体测量学:时间%方法:数据分析
天體測量學:時間%方法:數據分析
천체측량학:시간%방법:수거분석
astrometry:time%methods:data analysis
根据星载原子钟钟差的特点,提出一种基于一次差的灰色GM(1,1)钟差预报方法。该方法首先对相邻历元的钟差作一次差,然后以一次差后的值建立灰色模型预报一次差的值,最后将预报的一次差还原即得到钟差预报值。以IGS (International GNSS Service)提供的采样率为5 min的精密钟差为实验数据,通过对不同长度的建模数据和不同预报步长进行对比分析。结果表明:该方法的预报精度较传统的灰色模型有了较大提高,特别是对于PRN01原子钟,其预报效果最好;采用一次差原理可有效改善和提高模型预报的精度和稳定性,应用于钟差较长时间预报是可行的,可靠性较强。
根據星載原子鐘鐘差的特點,提齣一種基于一次差的灰色GM(1,1)鐘差預報方法。該方法首先對相鄰歷元的鐘差作一次差,然後以一次差後的值建立灰色模型預報一次差的值,最後將預報的一次差還原即得到鐘差預報值。以IGS (International GNSS Service)提供的採樣率為5 min的精密鐘差為實驗數據,通過對不同長度的建模數據和不同預報步長進行對比分析。結果錶明:該方法的預報精度較傳統的灰色模型有瞭較大提高,特彆是對于PRN01原子鐘,其預報效果最好;採用一次差原理可有效改善和提高模型預報的精度和穩定性,應用于鐘差較長時間預報是可行的,可靠性較彊。
근거성재원자종종차적특점,제출일충기우일차차적회색GM(1,1)종차예보방법。해방법수선대상린역원적종차작일차차,연후이일차차후적치건립회색모형예보일차차적치,최후장예보적일차차환원즉득도종차예보치。이IGS (International GNSS Service)제공적채양솔위5 min적정밀종차위실험수거,통과대불동장도적건모수거화불동예보보장진행대비분석。결과표명:해방법적예보정도교전통적회색모형유료교대제고,특별시대우PRN01원자종,기예보효과최호;채용일차차원리가유효개선화제고모형예보적정도화은정성,응용우종차교장시간예보시가행적,가고성교강。
Considering the characteristics of satellite clock bias (SCB), a grey G-M(1,1) model based on the first difference method is proposed. Firstly, first difference for two SCB values of adjacent epoch is derived to obtain the corresponding first d-ifference sequences. Then, a grey model is made based on the sequences to predict first difference values of the following time series. Finally, the predicted sequences are recovered to the corresponding predicted SCB. The clock difference data provided by the IGS (International GNSS Service) experiment are used to the experiment, and the cases with different lengths of data model and prediction step lengths are compared and analyzed. The result shows that prediction accuracy of this method is higher than that of the traditional grey GM(1,1) model, especially for the PRN01 satellite clock, whose forecast effect is the best; With first difference, the model prediction accuracy and stability can be effectively improved and enhanced. This method is feasible and reliable in the application of the relatively long time SCB prediction.