【摘要】：本文圍繞著降低動(dòng)態(tài)定位中算法復(fù)雜度、減少計(jì)算量、提升計(jì)算效率;解決動(dòng)態(tài)定位模型不匹配、使用單一模型誤差較大;解決動(dòng)態(tài)定位中狀態(tài)噪聲和量測(cè)噪聲為非高斯白噪聲的影響三類問題展開研究。主要內(nèi)容和創(chuàng)新點(diǎn)如下:1.基于衛(wèi)星導(dǎo)航的載體狀態(tài)方程為線性,且穩(wěn)健平方根容積卡爾曼(Square root Cubature Kalman Filtering—SCKF)在狀態(tài)更新時(shí)其容積點(diǎn)經(jīng)狀態(tài)轉(zhuǎn)移矩陣傳遞后的加權(quán)和為零,則可使用標(biāo)準(zhǔn)KF算法進(jìn)行狀態(tài)更新,量測(cè)更新過程仍采用SCKF;本文提出了簡(jiǎn)化型穩(wěn)健平方根容積卡爾曼算法(Simplified SCKF,簡(jiǎn)稱SSCKF)。該算法旨在解決動(dòng)態(tài)導(dǎo)航定位計(jì)算量大、效率低的問題。仿真及實(shí)測(cè)數(shù)據(jù)表明SSCKF與SCKF精度相當(dāng),而解算時(shí)間較SCKF算法降低25%左右,能有效地降低算法復(fù)雜度,提升算法效率。2.基于SSCKF,結(jié)合變維交互多模思想,本文提出了簡(jiǎn)化型穩(wěn)健平方根容積卡爾曼變維交互多模算法。該算法針對(duì)常規(guī)交互多模模型集覆蓋不全面及模型數(shù)目過多導(dǎo)致的模型競(jìng)爭(zhēng)等問題,將不同維數(shù)的模型交互,如勻速模型和勻加速模型,同時(shí)進(jìn)行并行濾波,并由二者的量測(cè)殘差計(jì)算出相應(yīng)的似然函數(shù),更新兩種模型濾波結(jié)果所占的權(quán)重,將最終加權(quán)和作為整個(gè)變維模型的結(jié)果輸出;下一時(shí)刻子模型的狀態(tài)輸入值不采用其自身上一時(shí)刻濾波結(jié)果,而是采用變維交互模型整體輸出結(jié)果乘以維數(shù)轉(zhuǎn)換矩陣得到的值,這樣就能保證各時(shí)刻的狀態(tài)輸入值的準(zhǔn)確性。3.針對(duì)動(dòng)態(tài)導(dǎo)航過程中,狀態(tài)噪聲和量測(cè)噪聲一般呈現(xiàn)非高斯白噪聲的特點(diǎn),本文提出了簡(jiǎn)化型SCKF高斯和算法。分析了動(dòng)態(tài)導(dǎo)航中偽距測(cè)量值噪聲的峭度值和相關(guān)系數(shù),得出其呈現(xiàn)非高斯特性的結(jié)論;若仍將實(shí)際運(yùn)動(dòng)中的非高斯噪聲強(qiáng)制當(dāng)成高斯白噪聲來處理,則會(huì)對(duì)濾波精度造成影響。采用多個(gè)高斯白噪聲作為子高斯項(xiàng),利用其加權(quán)和來近似表示非高斯白噪聲,同時(shí)限制各時(shí)刻子高斯項(xiàng)總數(shù)目以確保各時(shí)刻解算效率。跑車實(shí)測(cè)數(shù)據(jù)驗(yàn)證,該算法能夠有效抑制非高斯白噪聲的影響,提升算法的穩(wěn)定性和濾波精度。
[Abstract]:This paper focuses on reducing the complexity of the algorithm in dynamic location, reducing the computational complexity, improving the computational efficiency, solving the mismatch of the dynamic location model, and using a single model with large error. To solve the problem that the state noise and measurement noise are non-Gao Si white noise in dynamic positioning, three kinds of problems are studied. The main contents and innovations are as follows: 1. The carrier state equation based on satellite navigation is linear, and the weighted sum of the volume points transferred by the state transfer matrix is zero when the robust square root volume (Square root Cubature Kalman Filtering-SCKF) is updated. The standard KF algorithm can be used to update the state, and the SCKF; is still used in the measurement update process. In this paper, a simplified robust square-root volume Kalman algorithm (Simplified SCKF, for SSCKF). Is proposed. This algorithm aims to solve the problem of large amount of computation and low efficiency in dynamic navigation. The simulation and measured data show that the precision of SSCKF is equal to that of SCKF, and the solution time is about 25% lower than that of SCKF algorithm, which can effectively reduce the complexity of the algorithm and improve the efficiency of the algorithm. 2. Based on SSCKF, and variable dimensional interactive multimode theory, a simplified robust square root volume variable dimension interactive multimode algorithm is proposed. In order to solve the problem of model competition caused by incomplete coverage of conventional interactive multimode model set and excessive number of models, the model with different dimensions, such as uniform model and uniform acceleration model, is filtered in parallel at the same time. The corresponding likelihood function is calculated from the measurement residuals of the two models, the weight of the filtering results of the two models is updated, and the final weighted sum is taken as the output of the whole variable-dimensional model. The state input value of the next sub-model does not use its own filtering result at the last moment, but the global output of the variable dimensional interactive model is multiplied by the value obtained by the dimension conversion matrix. This ensures the accuracy of the state input values at each time. 3. In view of the fact that the state noise and the measurement noise generally present non-Gao Si white noise in the dynamic navigation process, this paper presents a simplified SCKF Gao Si and algorithm. The kurtosis and correlation coefficient of pseudo-range measurement noise in dynamic navigation are analyzed. If the non-Gao Si noise in the actual motion is still forced to be treated as Gao Si white noise, the filtering accuracy will be affected. Several Gao Si white noises are used as the subterms of Gao Si, and the weighted sum of them is used to approximate the non-Gao Si white noise, and at the same time to limit the total number of sub-Gao Si items at each time to ensure the efficiency of the solution at each time. The experimental data show that the algorithm can effectively suppress the influence of non-Gao Si white noise and improve the stability and filtering accuracy of the algorithm.
【學(xué)位授予單位】：國(guó)防科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN967.1;TN713

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張凱;單甘霖;;基于高斯和與SCKF的非線性非高斯濾波算法[J];儀器儀表學(xué)報(bào);2014年11期

2 李小民;杜占龍;鄭宗貴;張國(guó)榮;毛瓊;;基于STSCKF和改進(jìn)型χ~2檢驗(yàn)的模擬電路故障辨識(shí)[J];儀器儀表學(xué)報(bào);2014年10期

3 王樹磊;魏瑞軒;關(guān)旭寧;;容積法則輔助的交互式多模型濾波算法[J];控制與決策;2014年09期

4 張?chǎng)未?郭承軍;;均方根嵌入式容積卡爾曼濾波[J];控制理論與應(yīng)用;2013年09期

5 姜偉;呂澤均;藍(lán)瑤;;基于變維交互作用的IMM-CKF算法[J];計(jì)算機(jī)應(yīng)用與軟件;2013年05期

6 王碩;劉麗;;基于機(jī)動(dòng)轉(zhuǎn)彎目標(biāo)的自適應(yīng)交互式多模型算法[J];計(jì)算機(jī)仿真;2013年04期

7 孫楓;唐李軍;;Cubature卡爾曼濾波與Unscented卡爾曼濾波估計(jì)精度比較[J];控制與決策;2013年02期

8 孫楓;唐李軍;;Cubature卡爾曼濾波-卡爾曼濾波算法[J];控制與決策;2012年10期

9 李振華;寧磊;徐勝男;;基于均差濾波與高斯和的非線性非高斯系統(tǒng)濾波算法[J];控制與決策;2012年01期

10 孫楓;唐李軍;;Cubature粒子濾波[J];系統(tǒng)工程與電子技術(shù);2011年11期

相關(guān)博士學(xué)位論文 前3條

1 何可可;非線性非高斯條件下貝葉斯濾波若干問題研究[D];南京理工大學(xué);2012年

2 曹軼之;非高斯/非線性濾波算法研究及其在GPS動(dòng)態(tài)定位中的應(yīng)用[D];解放軍信息工程大學(xué);2012年

3 唐李軍;Cubature卡爾曼濾波及其在導(dǎo)航中的應(yīng)用研究[D];哈爾濱工程大學(xué);2012年

相關(guān)碩士學(xué)位論文 前3條

1 李家森;北斗/INS組合導(dǎo)航信息融合濾波算法研究[D];哈爾濱工程大學(xué);2013年

2 陳勇;信息融合技術(shù)在組合導(dǎo)航系統(tǒng)中的應(yīng)用研究[D];南京理工大學(xué);2007年

3 彭競(jìng);非線性濾波技術(shù)在衛(wèi)星導(dǎo)航系統(tǒng)中的應(yīng)用研究[D];國(guó)防科學(xué)技術(shù)大學(xué);2005年

，

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