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多傳感器分布式推廣卡爾曼濾波算法及其在雷達(dá)紅外目標(biāo)跟蹤中的應(yīng)用

崔寧周,  謝維信,  余雄南

崔寧周, 謝維信, 余雄南. 多傳感器分布式推廣卡爾曼濾波算法及其在雷達(dá)紅外目標(biāo)跟蹤中的應(yīng)用[J]. 電子與信息學(xué)報(bào), 1997, 19(3): 289-294.
引用本文: 崔寧周, 謝維信, 余雄南. 多傳感器分布式推廣卡爾曼濾波算法及其在雷達(dá)紅外目標(biāo)跟蹤中的應(yīng)用[J]. 電子與信息學(xué)報(bào), 1997, 19(3): 289-294.
Cui Ningzhou, Xie Weixin, Yu Xiongnan. MULTISENSOR DISTRIBUTED EXTENDED KALMAN FILTERING ALGORITHM AND ITS APPLICATION TO RADAR/IR TARGET TRACKING[J]. Journal of Electronics & Information Technology, 1997, 19(3): 289-294.
Citation: Cui Ningzhou, Xie Weixin, Yu Xiongnan. MULTISENSOR DISTRIBUTED EXTENDED KALMAN FILTERING ALGORITHM AND ITS APPLICATION TO RADAR/IR TARGET TRACKING[J]. Journal of Electronics & Information Technology, 1997, 19(3): 289-294.

多傳感器分布式推廣卡爾曼濾波算法及其在雷達(dá)紅外目標(biāo)跟蹤中的應(yīng)用

MULTISENSOR DISTRIBUTED EXTENDED KALMAN FILTERING ALGORITHM AND ITS APPLICATION TO RADAR/IR TARGET TRACKING

  • 摘要: 本文提出了一種用于非線性系統(tǒng)的多傳感器分布式推廣卡爾曼濾波算法,該算法中系統(tǒng)的動(dòng)態(tài)方程和傳感器的觀測(cè)方程分別圍繞全局估計(jì)和全局預(yù)測(cè)線性化,融合中心基于所有傳感器觀測(cè)的全局估計(jì)由各傳感器基于自身觀測(cè)的局部估計(jì)來(lái)重構(gòu)。算法分析說(shuō)明,全局估計(jì)的精度高、誤差小。最后介紹了文中算法在雷達(dá)和紅外兩種傳感器跟蹤機(jī)動(dòng)目標(biāo)中的應(yīng)用,仿真結(jié)果驗(yàn)證了該算法的有效性。
    Abstract: A multisensor distributed extended Kalman filtering algorithm is presented for nonlinear systems, in which the dynamic equations of the systems and the equations of sensor s measurements are linearized in the global estimates and global predictions respectively, and the suboptimal global estimates based on all available information can be reconstructed from the estimates computed by local sensors based solely on their own local information and transmitted to the data fusion center. An analysis of the properties of the algorithm presented here shows that the global estimate has higher precision than the local one and smaller linearization error than the existing method. Finally, an application of the algorithm to radar/IR, tracking of a maneuvering target is illustrated. Simulation results show the effectiveness of the algorithm.
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  • Luo R C, Michael G K. Multisensor integration and fusion in intelligence systems. IEEE Trans. on SMC, 1989, SMC-19(5): 901-931.[2]Liu P T, Bongiovanni P L. Combination of local estimation as applied to the tracking problem. 17th Asilomar Conf. on Circuits, Systems and Computer, California, USA: 1983, IEEE Computer Socit,y Press,378-382.[3]周一宇.分布估計(jì)及其在跟蹤系統(tǒng)中的應(yīng)用.系統(tǒng)工程與電子技術(shù),1993,15(6):58-70.[4]Blom H A P, Bar-Shalom Y. The interaction multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans. on AC, 1988, AC-33(8): 780-783.[5]Bar-Shalom Y, Chang K C. Tracking a maneuvering target using input estimation versus the inter-[6]acting multiple model algorithm. IEEE Trans.on AES, 1989, AES-25(2): 296-300.[7]Bar-Shalom Y. Multitarget Multisensor Tracking: Advanced Applications. Norwood MA: Artech House Inc., 1990, 29-35.[8]Houles A, Bar-Shalom Y. Multisensor tracking of a maneuvering target in clutter. IEEE Trans. on AES, 1989, AES-25(2): 176-189.[9]Raghavan V, Pattipati K R, Bar-Shalom Y. Efficient L-D fractionization algorithm for PDA, IMM[10]and IMMPDA filters. IEEE Trans. on AES, 1993, AES-29(4): 1297-1309.[11][意] A.費(fèi)利那著,匡永勝等譯.雷達(dá)數(shù)據(jù)處理(第一卷).北京:國(guó)防工業(yè)出版社,1988,101-103.[12]崔寧周.多傳感器數(shù)據(jù)融合一信號(hào)檢測(cè)與目標(biāo)跟蹤:[博士論文].西安:西安電子科技大學(xué),1995年5月.[13]Anderson B O O, Moore J B. Optimal Filtering. Englewood Cliffs, NJ: Prentice Hall, 1979,138-142.[14]Bar-Shalom Y, Fortmann T E. Tracking and Data Association. New York: Academic Press Inc., 1988,157-190.
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出版歷程
  • 收稿日期:  1995-01-11
  • 修回日期:  1996-04-16
  • 刊出日期:  1997-05-19

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