本文選題：機(jī)動目標(biāo)跟蹤 + 變結(jié)構(gòu)多模型方法　； 參考：《哈爾濱工程大學(xué)學(xué)報》2014年05期

【摘要】：為提高非線性復(fù)雜系統(tǒng)狀態(tài)估計的效率與精度,提出一種基于多因素模糊綜合評判的變結(jié)構(gòu)多模型方法(MFIE_MM)。MFIE_MM首先確定模型全集并提取各個模型的公共因素,進(jìn)而選擇模糊綜合鑒別函數(shù)構(gòu)建模糊評價集合;其次單因素模糊評判矩陣和多因素模糊評判準(zhǔn)則得到各個模型的相似度;最后選出當(dāng)前時刻最佳模型并以此模型為區(qū)域中心實時生成參與狀態(tài)估計的模型集合。仿真結(jié)果顯示,由MFIE_MM處理得到的位置變量估計誤差協(xié)方差從2.15 m降到2.05 m,單拍處理時間從0.002 7 s降到0.001 8 s。因此,MFIE_MM在顯著提高算法精度的同時有效降低了算法運行時間和模型平均誤差。
[Abstract]:In order to improve the efficiency and accuracy of state estimation for nonlinear complex systems, a variable structure multi-model method based on multi-factor fuzzy comprehensive evaluation is proposed. Firstly, MFIEMM. MFIEMM is used to determine the complete set of models and to extract the common factors of each model. Then the fuzzy comprehensive discriminant function is selected to construct the fuzzy evaluation set. Secondly, the similarity of each model is obtained by the single factor fuzzy evaluation matrix and the multi-factor fuzzy evaluation criterion. Finally, the optimal model of the current time is selected and the model is used as the center of the region to generate the model set of participating state estimation in real time. The simulation results show that the estimated error covariance of the position variables processed by MFIE_MM is reduced from 2.15m to 2.05m, and the time of single beat processing is reduced from 0.002 s to 0.001 8s. So MFIEMM can significantly improve the accuracy of the algorithm and reduce the running time of the algorithm and the average error of the model.
【作者單位】： 華北水利水電大學(xué)信息工程系;哈爾濱工程大學(xué)信息與通信工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項目(61240007) 博士后科研啟動基金資助項目(LBH-Q12122)
【分類號】：TN953

【參考文獻(xiàn)】

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本文編號：1963059