本文選題：GEO + IGSO��； 參考：《空間科學(xué)學(xué)報》2016年01期

【摘要】：利用動力學(xué)模型得到的預(yù)報軌道精度隨時間推移衰減較快,針對這一問題,提出一種改進(jìn)地球同步衛(wèi)星軌道預(yù)報精度的新方法.將神經(jīng)網(wǎng)絡(luò)作為工具,結(jié)合軌道動力學(xué)特性建立一個訓(xùn)練樣本集,根據(jù)當(dāng)前時刻預(yù)報誤差特征在樣本集中搜索最佳訓(xùn)練樣本,利用訓(xùn)練得到的神經(jīng)網(wǎng)絡(luò)模型補(bǔ)償和改進(jìn)當(dāng)前時刻的預(yù)報軌道,達(dá)到提高預(yù)報精度的目的.基于實(shí)測數(shù)據(jù)的試驗(yàn)分析表明,不同衛(wèi)星在不同初始時刻下的改進(jìn)效果是不同的.預(yù)報4 d的軌道精度由43m提高至15m,預(yù)報8d的軌道精度由183m提高至80m.基于神經(jīng)網(wǎng)絡(luò)模型預(yù)報4d和8d的改進(jìn)成功率分別為78.33%和88.33%.
[Abstract]:The prediction orbit accuracy obtained from the dynamic model decreases rapidly with time. To solve this problem, a new method to improve the prediction accuracy of geostationary satellite orbit is proposed. The neural network is used as a tool to establish a training sample set in combination with orbital dynamic characteristics. According to the prediction error feature of the current time, the best training sample is searched in the sample set. The training neural network model is used to compensate and improve the prediction track at the present time to improve the prediction accuracy. The experimental analysis based on the measured data shows that the improvement effect of different satellites at different initial times is different. The orbit precision of 4 days is improved from 43m to 15m, and that of 8 days is improved from 183m to 80m. The improved success rates of 4 d and 8 d prediction based on neural network model were 78.33% and 88.33%, respectively.
【作者單位】： 北京市遙感信息研究所;北京衛(wèi)星導(dǎo)航中心;
【基金】：上海市空間導(dǎo)航與定位技術(shù)重點(diǎn)實(shí)驗(yàn)室基金項(xiàng)目資助(12DZ2273300)
【分類號】：V412.41

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