衛(wèi)星遮擋交通環(huán)境下車輛融合定位策略研究
發(fā)布時(shí)間：2018-11-16 16:45
【摘要】：全球定位系統(tǒng)(GlobalPositioningSystem,GPS)和基于微機(jī)電系統(tǒng)(Micro-Electro-Mechanical System,MEMS)技術(shù)的慣性導(dǎo)航系統(tǒng)(Inertial Navigation System,INS)的組合為陸地車輛的準(zhǔn)確、可靠定位提供了一種低成本解決方案。但是在GPS中斷期間,MEMSINS/GPS組合系統(tǒng)的定位精度迅速降低。為了解決此問題,本文開展了衛(wèi)星遮擋交通環(huán)境下車輛融合定位策略研究,圍繞MEMS慣性測量單元(Inertial Measurement Unit,IMU)隨機(jī)誤差處理和 MEMSINS/GPS信息融合算法兩大關(guān)鍵技術(shù),針對(duì)其中的若干關(guān)鍵性問題展開深入探索。主要研究內(nèi)容及成果包括:(1)為了抑制MEMS IMU隨機(jī)誤差的高頻部分,本文設(shè)計(jì)了一種基于改進(jìn)小波濾波(Improved Wavelet Filter,IWF)的數(shù)據(jù)預(yù)處理算法。針對(duì)傳統(tǒng)小波濾波中分解層數(shù)確定以及閾值函數(shù)存在的不足,該算法采用頻譜分析結(jié)合實(shí)驗(yàn)評(píng)估的方法來選擇小波分解的最優(yōu)層數(shù),并設(shè)計(jì)了一種改進(jìn)的帶調(diào)節(jié)因子的閾值函數(shù)。實(shí)驗(yàn)結(jié)果表明,該方法在一定程度上克服了傳統(tǒng)閾值函數(shù)的缺點(diǎn),可以較好的抑制MEMS慣性數(shù)據(jù)隨機(jī)誤差的高頻部分。(2)針對(duì)MEMS IMU隨機(jī)誤差的低頻緩變部分,本文結(jié)合經(jīng)驗(yàn)?zāi)B(tài)分解(Empirical Mode Decomposition,EMD)和分形高斯噪聲(fractional Gaussian noise,fGn)的優(yōu)點(diǎn),設(shè)計(jì)了一種基于EMD區(qū)間閾值濾波的自適應(yīng)數(shù)據(jù)預(yù)處理算法。該算法首先利用fGn對(duì)慣性數(shù)據(jù)中的隨機(jī)誤差進(jìn)行建模,然后利用fGn在各層本征模函數(shù)(Intrinsic Mode Function,IMF)內(nèi)方差的關(guān)系,確定了 EMD濾波閾值選擇準(zhǔn)則;同時(shí),構(gòu)建了一種基于IMF區(qū)間的閾值處理方案,來消除濾波后信號(hào)的不連續(xù)性。實(shí)驗(yàn)結(jié)果表明,該預(yù)處理算法既可以有效的消除部分低頻緩變隨機(jī)誤差,又可以消除隨機(jī)誤差的高頻部分,與基于改進(jìn)小波濾波的預(yù)處理算法相比,濾波后慣性數(shù)據(jù)數(shù)據(jù)的精度得到進(jìn)一步提升。(3)提出了一種基于自回歸(Auto-Regressive,AR)模型輔助卡爾曼濾波(KalmanFilter,KF)的混合策略,來提高衛(wèi)星遮擋交通情況下車輛組合定位系統(tǒng)的精度和可靠性。該策略首先在系統(tǒng)結(jié)構(gòu)中引入了慣性數(shù)據(jù)預(yù)處理步驟,為后續(xù)信息融合提供高精度的慣性數(shù)據(jù);然后對(duì)傳統(tǒng)的INS誤差建模結(jié)構(gòu)進(jìn)行改進(jìn),設(shè)計(jì)了一種基于序列的INS誤差建模和預(yù)測結(jié)構(gòu);在此基礎(chǔ)上,設(shè)計(jì)了基于AR模型的前向估計(jì)器(AR model-based Forward Estimator,ARFE),并構(gòu)建基于ARFE/KF的混合策略對(duì)INS位置誤差建模并預(yù)測。實(shí)車實(shí)驗(yàn)結(jié)果表明,該方法可以有效地補(bǔ)償GPS中斷情況下INS位置誤差,并具有較好的泛化能力和實(shí)時(shí)性,大幅度提高了 GPS遮擋情況下的車輛定位精度。(4)為了進(jìn)一步提高車輛組合定位系統(tǒng)在較長GPS中斷情況下的性能,提出一種基于最小二乘向量機(jī)(Least Squares Support Vector Machine,LSSVM)的帶外部輸入非線性自回歸模型(Nonlinear Auto-Regressive with Exogenous inputs,NARX)輔助KF的INS誤差混合預(yù)測策略。該策略設(shè)計(jì)了一種帶有記憶功能和內(nèi)部反饋的INS誤差建模結(jié)構(gòu),兼顧了 INS誤差歷史發(fā)展趨勢和車輛運(yùn)動(dòng)狀態(tài)影響;構(gòu)建了 LSSVM-NARX/KF混合策略對(duì)INS誤差進(jìn)行建模,并在GPS中斷期間實(shí)現(xiàn)對(duì)INS誤差的預(yù)測和補(bǔ)償。實(shí)車實(shí)驗(yàn)表明,該方法對(duì)各種駕駛工況具有較好的適應(yīng)性,可以有效的抑制INS定位誤差的積累,能在GPS發(fā)生較長時(shí)間中斷的情況下為車輛提供更加準(zhǔn)確、可靠的定位信息。
[Abstract]:The combination of Global Positioning System (GPS) and inertial navigation system (INS) based on Micro-Electro-Mechanical System (MEMS) technology provides a low-cost solution for the accurate and reliable positioning of land vehicles. However, the positioning accuracy of the MEMSINS/ GPS combined system is rapidly reduced during the GPS interruption. In order to solve this problem, this paper has carried out the research of the vehicle fusion positioning strategy in the environment of the satellite-shielded traffic environment, the two key technologies of the random error processing of the MEMS inertial measurement unit (IMU) and the MEMSINS/ GPS information fusion algorithm, and the deep exploration of some of the key problems. The main research contents and achievements include: (1) In order to suppress the high-frequency part of the random error of the MEMS IMU, a data preprocessing algorithm based on improved wavelet filter (IWF) is designed. In the light of the limitation of the determination of the number of decomposition levels and the existence of the threshold function in the traditional wavelet filtering, the optimal number of layers of the wavelet decomposition is selected by the method of spectral analysis and experimental evaluation, and a modified threshold function with an adjustment factor is designed. The experimental results show that the method overcomes the shortcomings of the traditional threshold function to a certain extent, and can better restrain the high-frequency part of the random error of the MEMS inertial data. (2) Based on the advantages of empirical mode decomposition (EMD) and fractal Gaussian noise (fGn), an adaptive data preprocessing algorithm based on EMD interval threshold filtering is designed. The method firstly uses fGn to model the random error in the inertial data, then uses fGn to determine the relationship between the variance of the intrinsic mode function (IMF) of each layer, and determines the EMD filtering threshold selection criterion; and meanwhile, a threshold processing scheme based on the IMF interval is constructed, to eliminate the discontinuity of the filtered signal. The experimental results show that the pre-processing algorithm can effectively eliminate the random errors of some low-frequency and random errors, but also can eliminate the high-frequency part of the random error. Compared with the pre-processing algorithm based on the modified small-wave filtering, the accuracy of the post-filter inertial data data is further improved. (3) A hybrid strategy based on the self-regressive (AR) model-assisted Kalman filter (KF) is proposed to improve the accuracy and reliability of the vehicle-combined positioning system in the case of the satellite blocking traffic. The method comprises the following steps of: firstly, introducing an inertia data preprocessing step in a system structure to provide high-precision inertial data for subsequent information fusion; then, improving the traditional INS error modeling structure, and designing a sequence-based INS error modeling and prediction structure; and on the basis, An AR model-based forward estimator (ARFE) is designed and a mixed strategy based on ARFE/ KF is designed to model and predict the position error of the INS. The results of real-vehicle experiment show that the method can effectively compensate the position error of the INS in the case of GPS interruption, and has better generalization ability and real-time performance, and greatly improves the positioning accuracy of the vehicle under the condition of GPS shielding. (4) In order to further improve the performance of the vehicle-combined positioning system under the condition of longer GPS interruption, an INS-error mixed prediction strategy based on an external input non-linear auto-regressive model (NARX)-assisted KF based on the Least Squares Support Vector Machine (LSSVM) is proposed. In this paper, an INS error modeling structure with memory function and internal feedback is designed, and the development trend of INS error and the influence of vehicle motion state are taken into account; the INS error is modeled by the LSSVM-NARX/ KF mixing strategy, and the prediction and compensation of the INS error is realized during the GPS interruption. The real-vehicle experiment shows that the method has better adaptability to various driving conditions, can effectively inhibit the accumulation of the INS positioning error, and can provide more accurate and reliable positioning information for the vehicle under the condition that the GPS is interrupted for a long time.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：U463.67;U495
本文編號(hào)：2336059
[Abstract]:The combination of Global Positioning System (GPS) and inertial navigation system (INS) based on Micro-Electro-Mechanical System (MEMS) technology provides a low-cost solution for the accurate and reliable positioning of land vehicles. However, the positioning accuracy of the MEMSINS/ GPS combined system is rapidly reduced during the GPS interruption. In order to solve this problem, this paper has carried out the research of the vehicle fusion positioning strategy in the environment of the satellite-shielded traffic environment, the two key technologies of the random error processing of the MEMS inertial measurement unit (IMU) and the MEMSINS/ GPS information fusion algorithm, and the deep exploration of some of the key problems. The main research contents and achievements include: (1) In order to suppress the high-frequency part of the random error of the MEMS IMU, a data preprocessing algorithm based on improved wavelet filter (IWF) is designed. In the light of the limitation of the determination of the number of decomposition levels and the existence of the threshold function in the traditional wavelet filtering, the optimal number of layers of the wavelet decomposition is selected by the method of spectral analysis and experimental evaluation, and a modified threshold function with an adjustment factor is designed. The experimental results show that the method overcomes the shortcomings of the traditional threshold function to a certain extent, and can better restrain the high-frequency part of the random error of the MEMS inertial data. (2) Based on the advantages of empirical mode decomposition (EMD) and fractal Gaussian noise (fGn), an adaptive data preprocessing algorithm based on EMD interval threshold filtering is designed. The method firstly uses fGn to model the random error in the inertial data, then uses fGn to determine the relationship between the variance of the intrinsic mode function (IMF) of each layer, and determines the EMD filtering threshold selection criterion; and meanwhile, a threshold processing scheme based on the IMF interval is constructed, to eliminate the discontinuity of the filtered signal. The experimental results show that the pre-processing algorithm can effectively eliminate the random errors of some low-frequency and random errors, but also can eliminate the high-frequency part of the random error. Compared with the pre-processing algorithm based on the modified small-wave filtering, the accuracy of the post-filter inertial data data is further improved. (3) A hybrid strategy based on the self-regressive (AR) model-assisted Kalman filter (KF) is proposed to improve the accuracy and reliability of the vehicle-combined positioning system in the case of the satellite blocking traffic. The method comprises the following steps of: firstly, introducing an inertia data preprocessing step in a system structure to provide high-precision inertial data for subsequent information fusion; then, improving the traditional INS error modeling structure, and designing a sequence-based INS error modeling and prediction structure; and on the basis, An AR model-based forward estimator (ARFE) is designed and a mixed strategy based on ARFE/ KF is designed to model and predict the position error of the INS. The results of real-vehicle experiment show that the method can effectively compensate the position error of the INS in the case of GPS interruption, and has better generalization ability and real-time performance, and greatly improves the positioning accuracy of the vehicle under the condition of GPS shielding. (4) In order to further improve the performance of the vehicle-combined positioning system under the condition of longer GPS interruption, an INS-error mixed prediction strategy based on an external input non-linear auto-regressive model (NARX)-assisted KF based on the Least Squares Support Vector Machine (LSSVM) is proposed. In this paper, an INS error modeling structure with memory function and internal feedback is designed, and the development trend of INS error and the influence of vehicle motion state are taken into account; the INS error is modeled by the LSSVM-NARX/ KF mixing strategy, and the prediction and compensation of the INS error is realized during the GPS interruption. The real-vehicle experiment shows that the method has better adaptability to various driving conditions, can effectively inhibit the accumulation of the INS positioning error, and can provide more accurate and reliable positioning information for the vehicle under the condition that the GPS is interrupted for a long time.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：U463.67;U495
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