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基于張量分解的衛(wèi)星遙測(cè)缺失數(shù)據(jù)預(yù)測(cè)算法

馬友 賈樹(shù)澤 趙現(xiàn)綱 馮小虎 范存群 朱愛(ài)軍

馬友, 賈樹(shù)澤, 趙現(xiàn)綱, 馮小虎, 范存群, 朱愛(ài)軍. 基于張量分解的衛(wèi)星遙測(cè)缺失數(shù)據(jù)預(yù)測(cè)算法[J]. 電子與信息學(xué)報(bào), 2020, 42(2): 403-409. doi: 10.11999/JEIT180728
引用本文: 馬友, 賈樹(shù)澤, 趙現(xiàn)綱, 馮小虎, 范存群, 朱愛(ài)軍. 基于張量分解的衛(wèi)星遙測(cè)缺失數(shù)據(jù)預(yù)測(cè)算法[J]. 電子與信息學(xué)報(bào), 2020, 42(2): 403-409. doi: 10.11999/JEIT180728
You MA, Shuze JIA, Xiangang ZHAO, Xiaohu FENG, Cunqun FAN, Aijun ZHU. Missing Telemetry Data Prediction Algorithm via Tensor Factorization[J]. Journal of Electronics & Information Technology, 2020, 42(2): 403-409. doi: 10.11999/JEIT180728
Citation: You MA, Shuze JIA, Xiangang ZHAO, Xiaohu FENG, Cunqun FAN, Aijun ZHU. Missing Telemetry Data Prediction Algorithm via Tensor Factorization[J]. Journal of Electronics & Information Technology, 2020, 42(2): 403-409. doi: 10.11999/JEIT180728

基于張量分解的衛(wèi)星遙測(cè)缺失數(shù)據(jù)預(yù)測(cè)算法

doi: 10.11999/JEIT180728
基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61602126),國(guó)家863計(jì)劃項(xiàng)目(2011AA12A104)
詳細(xì)信息
    作者簡(jiǎn)介:

    馬友:男,1982年生,副研究員,主要研究方向?yàn)榉?wù)推薦與機(jī)器學(xué)習(xí)

    賈樹(shù)澤:男,1982年生,高級(jí)工程師,主要研究方向?yàn)樾l(wèi)星故障診斷

    趙現(xiàn)綱:男,1979年生,研究員,主要研究方向?yàn)樾l(wèi)星通訊技術(shù)

    馮小虎:男,1973年生,研究員,主要研究方向?yàn)楹教炱骶?xì)化管理

    范存群:男,1986年生,高級(jí)工程師,主要研究方向?yàn)樾l(wèi)星資料同化

    朱愛(ài)軍:男,1970年生,研究員,主要研究方向?yàn)樾l(wèi)星系統(tǒng)工程

    通訊作者:

    范存群 fancq@cma.gov.cn

  • 中圖分類號(hào): TN927; TP391

Missing Telemetry Data Prediction Algorithm via Tensor Factorization

Funds: The National Natural Science Foundation of China (61602126), The National 863 Plan Project (2011AA12A104)
  • 摘要:

    衛(wèi)星健康狀況監(jiān)測(cè)是衛(wèi)星安全保障的重要基礎(chǔ),而衛(wèi)星遙測(cè)數(shù)據(jù)又是衛(wèi)星健康狀況分析的唯一數(shù)據(jù)來(lái)源。因此,衛(wèi)星遙測(cè)缺失數(shù)據(jù)的準(zhǔn)確預(yù)測(cè)是衛(wèi)星健康分析的重要前瞻性手段。針對(duì)極軌衛(wèi)星多組成系統(tǒng)、多儀器載荷以及多監(jiān)測(cè)指標(biāo)形成的高維數(shù)據(jù)特點(diǎn),該文提出一種基于張量分解的衛(wèi)星遙測(cè)缺失數(shù)據(jù)預(yù)測(cè)算法(TFP),以解決當(dāng)前數(shù)據(jù)預(yù)測(cè)方法大多面向低維數(shù)據(jù)或只能針對(duì)特定維度的不足。所提算法將遙測(cè)數(shù)據(jù)中的系統(tǒng)、載荷、指標(biāo)以及時(shí)間等多維因素作為統(tǒng)一的整體進(jìn)行張量建模,以完整、準(zhǔn)確地表達(dá)數(shù)據(jù)的高維特征;其次,通過(guò)張量分解計(jì)算數(shù)據(jù)模型的成分特征,通過(guò)成分特征可對(duì)張量模型進(jìn)行準(zhǔn)確重構(gòu),并在重構(gòu)過(guò)程中對(duì)缺失數(shù)據(jù)進(jìn)行準(zhǔn)確預(yù)測(cè);最后,提出一種高效的優(yōu)化算法實(shí)現(xiàn)相關(guān)的張量計(jì)算,并對(duì)算法中最優(yōu)參數(shù)設(shè)置進(jìn)行嚴(yán)格的理論推導(dǎo)。實(shí)驗(yàn)結(jié)果表明,所提算法的預(yù)測(cè)準(zhǔn)確度優(yōu)于當(dāng)前大部分預(yù)測(cè)算法。

  • 圖  1  預(yù)測(cè)誤差在不同區(qū)間的分布

    圖  2  R取值對(duì)預(yù)測(cè)精度的影響

     算法1:TFP算法
     輸入:數(shù)據(jù)集$ {\cal X}\in {{\mathbb{R}}^{{{I}_{1}}\times {{I}_{2}}\times \cdots \times {{I}_{N}}}}$;
     輸出:訓(xùn)練后的成分矩陣$ {{ A}^{\left(j \right)}}$ (j=1 to N)
     隨機(jī)初始化成分矩陣$ {{ A}^{\left( j \right)}}$(j=1 to N)
     Repeat
      For each $ { A}_{{i_j}r}^{\left( j \right)}\left( {1 \le j \le N,1 \le {i_j} \le {I_j},1 \le r \le R} \right)$
       If $ g_{{i_j}r}^{\left( j \right)}{|_t} \cdot g_{{i_j}r}^{\left( j \right)}{|_{t - 1}} > 0$
        $ \delta _{ {i_j}r}^{\left( j \right)}{|_t} = {\rm{min} }\left( {\delta _{ {i_j}r}^{\left( j \right)}{|_{t - 1} } \cdot {\eta ^ + },{\rm{MaxSize}}} \right)$
        $ { A}_{{i_j}r}^{\left( j \right)}{|_{t + 1}} = { A}_{{i_j}r}^{\left( j \right)}{|_t} - {\rm{sign}}\left( {g_{{i_j}r}^{\left( j \right)}{|_t}} \right) \cdot \delta _{{i_j}r}^{\left( j \right)}{|_t}$
       Else If $ g_{{i_j}r}^{\left( j \right)} \cdot g_{{i_j}r}^{\left( j \right)}{\rm{'}} < 0$
        $ \delta _{ {i_j}r}^{\left( j \right)}{|_t} = {\rm{max} }\left( {\delta _{ {i_j}r}^{\left( j \right)}{|_{t - 1} } \cdot {\eta ^ - },{\rm {MinSize}}} \right)$
        If $ L{|_t} > L{|_{t - 1}}$
        $ { A}_{{i_j}r}^{\left( j \right)}{|_{t + 1}} = { A}_{{i_j}r}^{\left( j \right)}{|_t} + {\rm{sign}}\left( {g_{{i_j}r}^{\left( j \right)}{|_{t - 1}}} \right) \cdot \delta _{{i_j}r}^{\left( j \right)}{|_{t - 1}}$
         $ L{|_t} = 0$
        End If
       Else
        $ \delta _{{i_j}r}^{\left( j \right)}{|_t} = \delta _{{i_j}r}^{\left( j \right)}{|_{t - 1}}$
        $ { A}_{{i_j}r}^{\left( j \right)}{|_{t + 1}} = { A}_{{i_j}r}^{\left( j \right)}{|_t} - {\rm{sign}}\left( {g_{{i_j}r}^{\left( j \right)}{|_t}} \right) \cdot \delta _{{i_j}r}^{\left( j \right)}{|_t}$
       End If
      End For
     Until $ L \le \varepsilon $ or maximum iterations exhausted
    下載: 導(dǎo)出CSV

    表  1  TFP算法與其它5個(gè)方法的對(duì)比

    方法數(shù)據(jù)密度5%數(shù)據(jù)密度10%數(shù)據(jù)密度20%數(shù)據(jù)密度50%
    MAERMSEMAERMSEMAERMSEMAERMSE
    NMF0.61751.57890.60071.54850.59861.52330.48701.4847
    PMF0.56871.47920.49841.28420.44921.18550.40061.0820
    UPCC0.62041.40100.55131.31390.48751.23430.31141.0749
    IPCC0.68861.42780.59081.32450.44541.20940.28951.1724
    TA0.62391.40580.53601.30450.44961.20300.21061.0988
    TFP0.3815 0.9469 0.3073 0.7597 0.2270 0.5619 0.1235 0.3150
    下載: 導(dǎo)出CSV
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  • 收稿日期:  2018-07-19
  • 修回日期:  2019-04-20
  • 網(wǎng)絡(luò)出版日期:  2019-09-27
  • 刊出日期:  2020-02-19

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