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1、摘要從20世紀(jì)70年代以后，人類在航天和航海領(lǐng)域開始運(yùn)用組合導(dǎo)航系統(tǒng)進(jìn)行導(dǎo)航。本文對(duì)慣性導(dǎo)航系統(tǒng)(INS)與全球定位系統(tǒng)(GPS)的組合導(dǎo)航系統(tǒng)進(jìn)行了研究。本文單獨(dú)對(duì)全球定位系統(tǒng)和慣性導(dǎo)航系統(tǒng)分別進(jìn)行了具體探討，詳細(xì)論述了它們各自的系統(tǒng)組成、定位原理、誤差模型和模型計(jì)算。Kalman濾波技術(shù)的發(fā)展，對(duì)組合導(dǎo)航系統(tǒng)的發(fā)展有重要的意義。標(biāo)準(zhǔn)Kalman濾波技術(shù)在實(shí)際運(yùn)用中對(duì)具體的運(yùn)動(dòng)模型要求較高，特別是噪聲協(xié)方差和模型參數(shù)不確切時(shí)，不能得到系統(tǒng)狀態(tài)的最優(yōu)估計(jì)值，甚至結(jié)果發(fā)散。本文論述了幾種典型的自適應(yīng)
2、濾波算法：Sage．Husa自適應(yīng)濾波算法、強(qiáng)跟蹤Kalman濾波算法、基于極大似然準(zhǔn)則的Kalman濾波算法以及其它改進(jìn)的自適應(yīng)濾波算法。并且在不同系統(tǒng)中對(duì)常規(guī)Kalman濾波、擴(kuò)展Kalman濾波(EK聊和文中討論的自適應(yīng)濾波進(jìn)行仿真，通過比較分析，對(duì)比了它們的優(yōu)缺點(diǎn)以及適用的范圍。設(shè)計(jì)了一種模糊推理系統(tǒng)，同時(shí)構(gòu)造了模糊規(guī)則庫(kù)，并將其與Kalman濾波算法相結(jié)合，在線修正系統(tǒng)量測(cè)噪聲協(xié)方差陣。仿真結(jié)果表明，該模糊Kalman濾波算法能很好地對(duì)系統(tǒng)狀態(tài)進(jìn)行最優(yōu)估計(jì)，同時(shí)能很好適應(yīng)系統(tǒng)噪聲的變化，
3、提高了導(dǎo)航系統(tǒng)的精度。關(guān)鍵詞：模糊推理系統(tǒng)卡爾曼濾波組合導(dǎo)航系統(tǒng)INS／GPSAbstractIn1970s，peoplebegintousingintegratednavigationsysteminspaceflightandseafaring．ThestudyontheintegratednavigationSystembasedonInertiaNavigationSystem(iNs)andGlobalPositioningSystem(GPS)isdoneinthepaper．TheG
4、PSandINSareseparatelydiscussedinthepaper．Andsystemiccomposition，orientationtheory,errormodelandmodelcalculationofGPSandINSarediscussed．ThedevelopmentoftheKalmanfilteringisofgreatsignificancefortheintegratednavigationsystem．Normalkalmanfilteringhaveahi
5、ghrequestintheapplication．，especiallyfortheinaccuratemodelparametersandnoisecovariance．Itworksworseanddonotgetoptimizedestimatedvaluesofsystemicstates，evendonotconverge．Manyappliedfilteralgorithmisoccurred，includingself-adaptivekalmanfilter．Someself-a
6、daptivealgorithms，Sage—Husaself-adaptivefilteringalgorithm，strongtrackingKalmanfilteringalgorithm，maximum—likelihoodkalmanfilteringandotherimprovedself-adaptivealgorithms，aleproposedinthispaper．Subsequently,simulationsofnormalKalmanfiltering,extension
7、Kalmanfilteringandself-filteringdiscussedinthepaperaledoneineverydifferentsystem．Theworkingfieldsofeachfilteringandcomparisonsoftheiradvantagesanddisadvantagesismadeinthepaper．WedesigntheFuzzyInferenceSystem，simultaneouslybuildsthestoreroomofthefuzzyr
8、ulesandthencombineitwiththeKalmanfilteringtomodifyonlinethecovariancematrixofsystemmeasurementnoise．SimulationsshowthisfilteringalgorithmcallgetabeRerresuRinsystemstatesoptimizationestimation．Simultaneouslyithasasuperiorityofadaptingsystemnois
