【摘要】：本文圍繞著降低動態(tài)定位中算法復雜度、減少計算量、提升計算效率;解決動態(tài)定位模型不匹配、使用單一模型誤差較大;解決動態(tài)定位中狀態(tài)噪聲和量測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)新點如下:1.基于衛(wèi)星導航的載體狀態(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)更新時其容積點經(jīng)狀態(tài)轉(zhuǎn)移矩陣傳遞后的加權(quán)和為零,則可使用標準KF算法進行狀態(tài)更新,量測更新過程仍采用SCKF;本文提出了簡化型穩(wěn)健平方根容積卡爾曼算法(Simplified SCKF,簡稱SSCKF)。該算法旨在解決動態(tài)導航定位計算量大、效率低的問題。仿真及實測數(shù)據(jù)表明SSCKF與SCKF精度相當,而解算時間較SCKF算法降低25%左右,能有效地降低算法復雜度,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。該算法針對常規(guī)交互多模模型集覆蓋不全面及模型數(shù)目過多導致的模型競爭等問題,將不同維數(shù)的模型交互,如勻速模型和勻加速模型,同時進行并行濾波,并由二者的量測殘差計算出相應(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)和作為整個變維模型的結(jié)果輸出;下一時刻子模型的狀態(tài)輸入值不采用其自身上一時刻濾波結(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,這樣就能保證各時刻的狀態(tài)輸入值的準確性。3.針對動態(tài)導航過程中,狀態(tài)噪聲和量測噪聲一般呈現(xiàn)非高斯白噪聲的特點,本文提出了簡化型SCKF高斯和算法。分析了動態(tài)導航中偽距測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;若仍將實際運動中的非高斯噪聲強制當成高斯白噪聲來處理,則會對濾波精度造成影響。采用多個高斯白噪聲作為子高斯項,利用其加權(quán)和來近似表示非高斯白噪聲,同時限制各時刻子高斯項總數(shù)目以確保各時刻解算效率。跑車實測數(shù)據(jù)驗證,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.
【學位授予單位】：國防科學技術(shù)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN967.1;TN713

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