發(fā)布時(shí)間：2018-02-21 16:39

  本文關(guān)鍵詞： 導(dǎo)航定位 多徑殘留誤差 卡爾曼濾波 高度約束　出處：《東南大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：全球?qū)Ш叫l(wèi)星系統(tǒng)(Global Navigation Satellite System,GNSS)經(jīng)過三十余年不斷研究與發(fā)展,在軍事和民用的各個(gè)領(lǐng)域產(chǎn)生了深刻影響。城市環(huán)境中由于高樓林立產(chǎn)生的短多徑效應(yīng),成為GNSS觀測(cè)值中的一個(gè)重要誤差源,嚴(yán)重降低了定位精度。本文重點(diǎn)研究了卡爾曼濾波算法在抑制多徑誤差方面的應(yīng)用,在此基礎(chǔ)上研究復(fù)雜城市環(huán)境中的短多徑自適應(yīng)抑制方法。本文首先根據(jù)偽距定位原理建立球面定位模型,從理論上分析呈"矩形"分布和"震蕩"分布的偽距誤差對(duì)定位結(jié)果的影響,得出站心坐標(biāo)系下三個(gè)方向上的定位誤差也呈"矩形"和"震蕩"分布且高度定位誤差要大于東北平面水平定位誤差的結(jié)論,并通過衛(wèi)星信號(hào)模擬器仿真驗(yàn)證了該結(jié)論,并且,仿真結(jié)果表明高度定位誤差約為水平定位誤差的三倍。為解決高度定位誤差大的問題,本文提出了一種基于高度約束的自適應(yīng)卡爾曼濾波(Height Constrained Adaptive Kalman Filtering,HCAKF)算法,根據(jù)車載等低速接收機(jī)建立高度約束模型,通過構(gòu)造高度上的約束條件和上一歷元的接收機(jī)狀態(tài),利用最小均方誤差原理來約束當(dāng)前接收機(jī)的運(yùn)動(dòng)狀態(tài),從而使得定位結(jié)果更加準(zhǔn)確。為驗(yàn)證HCAKF算法對(duì)短多徑效應(yīng)的抑制性能,分別在靜態(tài)開放天空?qǐng)鼍昂椭本�運(yùn)動(dòng)場(chǎng)景下進(jìn)行仿真測(cè)試,結(jié)果表明:靜態(tài)開放天空?qǐng)鼍跋?HCAKF算法的高度定位誤差幾乎為零,水平定位誤差相對(duì)于標(biāo)準(zhǔn)擴(kuò)展卡爾曼濾波(Extended Kaman Filtering,EKF)算法減小了 25.36%,相對(duì)于AKF算法減小了 13.05%,總誤差相對(duì)于EKF算法減小了 44.37%,相對(duì)于自適應(yīng)卡爾曼濾波(Adaptive Kaman Filtering,AKF)算法減小了 39.86%;直線運(yùn)動(dòng)場(chǎng)景下,HCAKF算法的高度定位誤差相對(duì)于EKF算法減小了 67.12%,相對(duì)于AKF算法減小了 47.63%,總誤差相對(duì)于EKF算法減小了 63.50%,相對(duì)于AKF算法減小了 35.65%。為了對(duì)比本論文定位算法和市面上同類產(chǎn)品定位性能,建立了同信號(hào)源全球定位系統(tǒng)(Global Position System,GPS)/北斗二號(hào)(Beidou-2,BD2)衛(wèi)星導(dǎo)航系統(tǒng)雙模接收機(jī)驗(yàn)證平臺(tái)。基于該雙模接收機(jī)驗(yàn)證平臺(tái),在城市復(fù)雜環(huán)境中測(cè)試了 HCAKF算法的短多徑抑制性能。測(cè)試結(jié)果表明:在窗臺(tái)短多徑靜態(tài)環(huán)境中,HCAKF算法的高度定位誤差幾乎為零,水平定位誤差相對(duì)于對(duì)照組減小了 48.44%,總體定位誤差相對(duì)于對(duì)照組減小了 53.71%。在城市復(fù)雜動(dòng)態(tài)環(huán)境下,HCAKF算法的高度定位誤差相對(duì)于對(duì)照組減小了 69.24%,總體定位誤差相對(duì)于對(duì)照組減小了 50.32%。
[Abstract]:After more than 30 years' continuous research and development, the Global Navigation Satellite system (GNSS) has had a profound impact in military and civilian fields. It has become an important error source in GNSS observations, which seriously reduces the positioning accuracy. In this paper, the application of Kalman filtering algorithm to the suppression of multipath errors is mainly studied. On this basis, the short and multi-track self-adaptive suppression method in complex urban environment is studied. Firstly, a spherical localization model is established according to the pseudo-range positioning principle. The influence of pseudo-range error of "rectangular" distribution and "oscillation" distribution on the positioning results is theoretically analyzed. It is concluded that the positioning error in three directions is also "rectangular" and "oscillating" in the geostationary coordinate system, and the height positioning error is larger than the horizontal positioning error in the northeast plane, which is verified by the simulation of the satellite signal simulator. The simulation results show that the height location error is about three times that of the horizontal positioning error. In order to solve the problem of large height location error, a height Constrained Adaptive Kalman filtering algorithm based on height constraint is proposed in this paper. The height constraint model is established according to the vehicle equal low speed receiver. By constructing the height constraint condition and the receiver state of the previous epoch, the moving state of the current receiver is constrained by the principle of minimum mean square error. In order to verify the performance of the HCAKF algorithm to suppress the short multipath effect, the simulation tests are carried out in the static open sky scene and the linear motion scene, respectively. The results show that the altitude location error of HCAKF algorithm is almost zero in the static open sky scene. Compared with the standard extended Kaman filtering algorithm, the horizontal location error is reduced by 25.36, compared with the AKF algorithm by 13.05, the total error is reduced by 44.37 compared with the EKF algorithm, and the adaptive Kaman filtering algorithm is reduced by the adaptive Kalman filter algorithm. The height error of EKF algorithm is reduced 67.12 compared with that of EKF algorithm, the total error is reduced by 63.50 and 35.65 points relative to EKF algorithm, compared with AKF algorithm, the total error is reduced by 63.50 and 35.65 respectively, compared with that of EKF algorithm, the total error is reduced by 63.50 and 35.65, respectively, and that of EKF algorithm is reduced by 67.63 compared with AKF algorithm, and the total error is reduced by 63.50 compared with EKF algorithm. Comparing the localization algorithm of this paper with the localization performance of similar products on the market, A dual-mode receiver verification platform for Global Position system (GPS) / Beidou-2 (BD2) satellite navigation system with the same signal source is established. The short multipath suppression performance of HCAKF algorithm is tested in complex urban environment. The test results show that the height location error of HCAKF algorithm is almost zero in the short multipath static environment of window sill. Compared with the control group, the horizontal positioning error was reduced by 48.44 and the overall positioning error was reduced by 53.71.The height positioning error of HCAKF algorithm was reduced by 69.24and the total positioning error was decreased compared with the control group in the complex dynamic environment of the city. The difference was 50.32 in comparison with the control group.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN967.1

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