雜波中多傳感器數(shù)據(jù)融合改善目標(biāo)航跡丟失的理論分析
THEORETICAL ANALYSIS OF IMPROVEMENT OF TRACK LOSS IN CLUTTER WITH MULTISENSOR DATA FUSION
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摘要: 本文從理論上討論雜波環(huán)境中,多傳感器數(shù)據(jù)融合對目標(biāo)航跡丟失的改善。通過建立融合預(yù)測估計(jì)誤差的轉(zhuǎn)移概率密度函數(shù),分析了目標(biāo)航跡丟失的機(jī)理。在最近鄰關(guān)聯(lián)準(zhǔn)則下,計(jì)算了融合航跡維持和融合航跡起始時(shí)的跟蹤性能融合航跡平均丟失時(shí)間和融合航跡累積丟失概率與雜波密度的關(guān)系,并與單傳感器的情形作了比較。結(jié)果表明,多傳感器的航跡融合減小了目標(biāo)丟失的可能性,提高了跟蹤性能。這一結(jié)論對進(jìn)一步理解數(shù)據(jù)融合的作用具有重要的理論意義。Abstract: The paper analyses the improvement of track loss in clutter with multisensor data fusion. By adetemination of the transition probability desity function for the fusion prediction error, one can study the mechanism of track loss analytically. For nearest- neighbor association algorithm, we study the fusion tracking performance parameters,such as mean time to lose fusion track and the fraction of lost fusion track initiation,respectively. A comparison of the results obtained with the case of a single sensor is presented. These results show that the fusion tracks of multisensor reduce the possibility of track loss and improve the tracking performance. The analysis is of great importance for further understanding the action of data fusion.
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Bar-shalom,Y(Ed.). Multitarget-Multisensor Tracking: Advanced Applications. Norwood, MA:[2]Artech House, 1990, 187-198.[3]Bar-shalom,Y.,Tse.E, Tracking in a cluttered environment with probabilistic data association. Automatica, 1975, 11(9): 451-460.[4]周宏仁,等.機(jī)動目標(biāo)跟蹤.北京:國防工業(yè)出版社.1990, 253-265.[5]Rogers S. Diffusion analysis of track loss in clutter. IEEE Trans. on AES, 1991, AES-27(2): 380-387.[6]Singes R A, Sen R G. New results in optimizing surveillance system tracking and data corrlation performance in dense multitarget environments. IEEE Trans. on AC, 1973, AC-18(6): 571-581. -
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