本文選題：GPS + EKF�。� 參考：《電子科技大學(xué)》2013年碩士論文

【摘要】：本文以全球定位系統(tǒng)（GPS，Global Positioning System）為研究背景，在利用GPS偽距測量值進(jìn)行絕對定位的解算基礎(chǔ)之上，為了提高解算的精度，，研究了當(dāng)前在GPS導(dǎo)航中常用的幾種運(yùn)動模型和卡爾曼濾波（KF，Kalman Filter）算法，詳細(xì)介紹了各種濾波算法的原理，實現(xiàn)過程，對比各自的性能優(yōu)劣，為實際運(yùn)用提供有效的理論指導(dǎo)。 首先本文研究分析了幾種常用的機(jī)動目標(biāo)方程，分為單模型和交互式多模型（IMM，Interacting Muliple Model）。重點研究了單模型中的辛格（Singer）模型，當(dāng)前統(tǒng)計（CS，Current Statistical）模型，分析對比了這兩種模型在不同的運(yùn)動狀態(tài)下的估計精度，為實際的工程應(yīng)用提供有效的依據(jù)。此外由于傳統(tǒng)的CS模型中，加速度方差的不合理取值，在不增加算法復(fù)雜度的情況下，提出了兩種簡單可行的改進(jìn)模型，通過加速度方差自適應(yīng)選取合適的值，實時地調(diào)整模型參數(shù)，使得模型更加地與實際相符。 由于卡爾曼濾波（KF，Kalman Filter）可以通過物體的運(yùn)動方程去將用戶相鄰時刻的運(yùn)動狀態(tài)信息聯(lián)系起來，使得解算結(jié)果更加的平滑。因此在GPS濾波算法這一部分，本文重點研究了目前在GPS中常用的，適用于非線性系統(tǒng)的擴(kuò)展卡爾曼濾波（EKF，Extend Kalman Filter）方法，改進(jìn)的無跡卡爾曼濾波（UKF，Unscented Kalman Filter）算法，以及用于保證誤差協(xié)方差矩陣非負(fù)定性和對稱性的平方根無跡卡爾曼濾波（SRUKF，Square Root Unscented Kalman Filter）算法，分析對比了各種算法的性能優(yōu)劣以及適用條件，為實際的工程應(yīng)用提供有效的依據(jù)。 最后本文在TI OMAP3530EVM上面，制作了QTE界面，完成以上算法在OMAP3530EVM上的開發(fā)，并且對仿真結(jié)果進(jìn)行驗證。為實際應(yīng)用中，運(yùn)動狀態(tài)方程以及濾波算法的選擇提供參考。
[Abstract]:In this paper, the GPS GPS Global Positioning system is used as the research background, on the basis of using the GPS pseudo-range measurement value to solve the absolute positioning, in order to improve the accuracy of the solution, Several motion models and Kalman filter algorithms commonly used in GPS navigation are studied in this paper. The principle and implementation process of various filtering algorithms are introduced in detail, and their performance is compared to provide effective theoretical guidance for practical application. In this paper, several commonly used maneuvering target equations are studied and analyzed, which can be divided into single model and interactive multiple model. The Singer model in single model and the current statistical model in current statistical model are studied. The estimation accuracy of these two models under different motion states is analyzed and compared, which provides an effective basis for practical engineering application. In addition, because of the unreasonable value of acceleration variance in the traditional CS model, two simple and feasible improved models are proposed without increasing the complexity of the algorithm, and the appropriate values are adaptively selected through the acceleration variance. The model parameters are adjusted in real time to make the model more consistent with the reality. Because the Kalman filter can connect the motion state information of the user at the adjacent time through the motion equation of the object, the result of the solution is smoother. Therefore, in the part of GPS filtering algorithm, this paper focuses on the extended Kalman filter (EKFO extend Kalman filter) method, an improved unscented Kalman filter algorithm, which is commonly used in GPS, which is suitable for nonlinear systems. And the square root unscented Kalman filter algorithm, which is used to guarantee the nonnegative definiteness and symmetry of error covariance matrix, is analyzed and compared, which provides an effective basis for practical engineering application. Finally, the QTE interface is made on TI OMAP3530 EVM, and the above algorithm is developed on OMAP3530 EVM, and the simulation results are verified. It provides a reference for the selection of motion state equations and filtering algorithms in practical applications.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：P228.4;TN713

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