采用不確定性推理的矩陣分析方法,建立了用于目標(biāo)識(shí)別的多傳感器數(shù)據(jù)融合的數(shù)學(xué)模型.
該模型綜合了來(lái)自多種不同傳感器的基于正態(tài)分布的檢測(cè)數(shù)據(jù),通過(guò)定義相關(guān)系數(shù)矩陣來(lái)獲取基本可信度分配值矩陣. 提出了一種多傳感器信息融合的新算法,該算法依靠可信度的積累,通過(guò)多級(jí)遞推融合可獲得目標(biāo)狀態(tài)基于全局信息的融合估計(jì)值. 實(shí)例分析表明:基于融合后的識(shí)別結(jié)果較各傳感器單獨(dú)決策的結(jié)果性能優(yōu)化,具有較強(qiáng)的容錯(cuò)性和有效性,并且不確定性值較融合前平均下降了74 % ,從而可精確快速地控制工業(yè)生產(chǎn).
關(guān)鍵詞: 數(shù)據(jù)融合;不確定性推理;目標(biāo)識(shí)別;矩陣分析
Abstract : Offered by matrix analysis of uncertainty reasoning method , a mathematical model of multisensor data fusion is estabilshed to resolve object recognition problem by analyzing comprehensively several different sensor acquisitions2based normal distribution , so that basic probability assignment is easily attained according to the correlative coefficient matrix. Following this a novel data fusion algorithm is proposed. Of this approach by the multiple levels accumulation of assignments recursively the fusion estimates based on global information are obtained.Application example shows the recognition result is optimal when compared with a single sensor , in improving the effectiveness and correctness with an average decline 74 percent of uncertainly value . There by the imprecision and quickness of industrial production will be improved.
Keywords : data fusion ; uncertainty reasoning ; object recognition ; matrix analysis