• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Altitude information fusion method and experiment for UAV①

    2017-06-27 08:09:22XuDongfu徐東甫PeiXinbiaoBaiYuePengChengWuZiyiXuZhijun
    High Technology Letters 2017年2期

    Xu Dongfu (徐東甫), Pei Xinbiao, Bai Yue, Peng Cheng, Wu Ziyi, Xu Zhijun

    (*Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, P.R.China) (**University of Chinese Academy of Sciences, Beijing 100039, P.R.China)

    Altitude information fusion method and experiment for UAV①

    Xu Dongfu (徐東甫)***, Pei Xinbiao***, Bai Yue②*, Peng Cheng*, Wu Ziyi***, Xu Zhijun*

    (*Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, P.R.China) (**University of Chinese Academy of Sciences, Beijing 100039, P.R.China)

    Altitude regulation is a fundamental problem in UAV (unmanned aerial vehicles) control to ensure hovering and autonomous navigation performance. However, data from altitude sensors may be unstable by interference. A digital-filter-based improved adaptive Kalman method is proposed to improve accuracy and reliability of the altitude measurement information.A unique sensor data fusion structure is designed to make different sensors switch automatically in different environment. Simulation and experimental results show that an improved Sage-Husa adaptive extended Kalman filter (SHAEKF) is adopted in altitude data fusion which means that altitude error is limited to 1.5m in high altitude and 1.2m near the ground. This method is proved feasible and effective through hovering flight test and three-dimensional track flight experiment.

    unmanned aerial vehicles (UAV), altitude information fusion, multi-sensor, adaptive Kalman filter

    0 Introduction

    In the last two decades, great efforts have been made for the research and development of multiple rotor UAVs driven by the significant progress of sensing and mechatronics technology[1-3]. This sort of aircrafts has shown its promising applications in both civic and military aspects. Compared with the traditional helicopters, they have the advantages of simplicity in mechanical construction, ease in modeling, control and maintenance[4-7].

    The accuracy and uniformity are very important for UAVs. However, due to the small structure and low cost, UAVs commonly use low cost sensors, such as MEM’s based acceleration, barometer and GPS (global positioning system). Suffered from severe drift and noise, the obtained estimation is unfeasible in practice. The attempt on this regard has been made by many researchers, by means of applying Kalman filter based techniques.

    Normally, Kalman filter is the acknowledged rule of technology for data fusion, which requires that measure noise and system noise are known[8]. However, when the Kalman filter is used in UAVs, measure noises and system noises are unknown and time-varied. The Kalman filter is no longer optimal and its convergence cannot be guaranteed.

    The SHAKF (Sage-Husa adaptive Kalman filter) can solve the above problems, which estimates system noises and measure noises in theory adaptively[9,10]. When estimating the altitude in UAVs, the SHAKF could not separate the system noises and the measure noises accurately, let alone the high accuracy estimation variance values. There is a constant error between estimations of the system noises and the measure noises[11]. The variance of the system noises is deviated, which result in wrong judgment of estimation, such as, increasing weight of barometer in the altitude estimation, decreasing precision of altitude and causing misconvergence. It cannot meet the need of UAVs autonomous flight.

    An improved Sage-Husa adaptive extended Kalman filtering (SHAEKF) is designed, aiming at the development of estimation for altitude. The improved SHAEKF uses the noise variance of MEMS accelerometer by real-time estimating the system noises variance of altitude[11], which not only reduces the estimation error of the system noises, but also guarantees the convergence. At the same time, united with the enhanced Kalman filter[12,13], the improved SHAEKF can inhibit the divergence of filter. Combined with the estimation structure based on the feature of different sensors, the sensor defects can be made up and accurate status information of the altitude can be obtained. At last, experiments were carried out to verify the feasibility and effectiveness of the method.

    1 Altitude system structure

    1.1 Brief introduction of UAV platform

    The UAV used in this paper, shown in Fig.1, is an electric-powered multi-rotor UAV, which is named Hex-rotor[14].

    Fig.1 UAV experimental platform

    The UAV’s structure is shown in Fig.2. Six equal-length, long light rods are placed evenly around the center of the UAV. On the tip of each rod there are two coaxial rotors with driving units. In clockwise direction the upper six rotors are numbered 1~6, while the lower six are numbered 7~12. Among them, rotor No.1, 3, 5, 8, 10, 12 rotate clockwise, while rotor No.2, 4, 6, 7, 9, 11 rotate counterclockwise. The angle between the rotor’s shaft and the body plane isγ(0<γ<90°),andtwoadjacentrotor’sshaftpointsoppositedirection.ThegeographiccoordinatesystemandthebodycoordinatesystemarealsoshowninFig.2.

    1.2 Altitude measurement unit

    Altitude measurement unit consists of MEMS-based accelerometers, barometer, GPS module and laser module.

    1.2.1 Accelerometer

    Fig.2 Diagram of Hex-Rotor aircraft structure

    (1)

    where:

    Positioninformationcanbesolvedbyintegrationofspeedva.

    (2)

    Fig.3 Altitude of MEMS ACC

    1.2.2 Barometric altimeter

    The air pressure decreases with the increasing of altitude. Based on this principle, altitude can be determined by measuring pressure with barometer. However, the decrease of altitude is not uniform. Under the standard atmospheric conditions, altimeter formula is expressed as

    (3)

    whereRisthegasconstant,gnistheaccelerationoffreefall.βistheverticalchangerateoftemperature,Ta,Paandhaaretheenvironment’satmospherictemperaturelowerlimit,atmosphericpressureandgeopotentialheightrespectively.Phistheatmosphericstaticpressuremeasuredundercurrentaltitude.Settingthebarometermeasuredaltitudeattake-offpositionasareference,andcomparingwithitscurrentvalue,thealtitudeofUAVwithrespecttothealtitudeofthetakingoffplanecanbeobtained,whichisthealtitudeinformationdesiredinnavigation.

    Inactualmeasurement,theactualatmosphericconditionscannotmeettherequiredstandard;therefore,measurementerrormayoccur.Thismeasurementerrorincreaseswithreducedaltitude.WhenUAVisinflight,barometeroutputaccuracyismainlyaffectedbythehigh-frequencynoiseandconstanterrors.Theerrorcanbeexpressedas

    hb=h0+εb+ωb

    (4)

    where,hbisthemeasuredvalueofthebarometer,h0istheidealaltitude.Constanterrorismainlyrelatedtotemperatureandpressure,andcanbecompensatedbyinitialcalibration.ThereadingsofaltimeterwhentheUAVishoveringareshowninFig.4,intheeffectoftherotor’sdownwash,winddisturbanceandbarometererror.Thereisalsoadriftsothisaltitudesensitivetooutsideinterferencecannotbeusedalone.

    Fig.4 Altitude of barometer

    1.2.3 GPS altitude measure

    The GPS module receives data from three or more satellites whose coordinates are already known, then calculates the coordinates of the measuring points. The altitude obtained from GPS positioning by calculation and conversion can be expressed as

    hg=h0+ωg

    (5)

    wherehgisthemeasuredGPSvalue,h0istheidealaltitude,ωgisthemeasuringnoise(whitenoise).

    Fig.5 Altitude of GPS

    Fig.5 is the GPS altitude of the UAV during hovering. The GPS module output is overall smooth with small volatile. However, the GPS output frequency is 1-10Hz, much smaller than the control frequency of the UAV which reaches 50Hz. So the GPS data cannot meet the requirements of the aircraft’s dynamic response. It can be also seen from Fig.5 that sometimes there is no output because the GPS module is blocked. Therefore, the GPS module cannot be used alone either.

    1.2.4 Laser ranging module

    The laser ranging module of the UAV uses 905nm, near-infrared laser with phase method. The measurement range is 0.1m~25m, while measurement accuracy reaches ±5mm at the frequency of 100Hz. The error of laser ranging module can be expressed as

    hl=h0+ωl

    (6)

    wherehgisthelasermeasuringvalue,h0istheidealaltitude,ωgisthemeasuringnoise(whitenoise).Fig.6isthelasermoduleheightoftheUAVduringhovering.

    AsseeninFig.6,thealtitudedataprovidedbylaserrangingmodulehashighprecisionwithtransition.Consideringitsmeasurementrangeof1m~25m,laserrangingmodulecanonlybeusedatlowaltitudes.

    Fig.6 Altitude of laser module

    2 Altitude data fusions

    As seen from the data above,the error of altitude obtained from accelerometer accumulates over time because of the two integration; The barometer output gave a much wide measuring range due to its sensing mechanism with larger white noise. The GPS module not only has rather big fluctuation and noise with a constant bias, but also has no output when blocked; The laser module reading is rather accurate detection but only in a limited range of 0 to 30m above ground level. Therefore, based on the characteristics of each sensor, a process of SHAEKF is designed as shown in Fig.7.

    Fig.7 Process of SHAEKF

    The process of SHAEKF is divided into two parts: data analysis module and improved SHAEKF which improves the accuracy of altitude in different environment.

    2.1 State and observation models of altitude

    UAVs vertical movement can be described by linear mathematical model:

    (7)

    It can be said by equation of state:

    (8)

    (9)

    Xk=ΦXk-1+Buk-1+ΓWk-1

    (10)

    where:

    (11)

    Xkis the state vector,Tsisthefusiontimecycle,Wk∈R1×1isthesystemnoise,itsvariancestatisticalpropertiesarecompletelydecidedbythestatisticalfeaturesofmodelinput,therefore,thesystemnoisedrivearray: Γ=B.

    The measurement equation of discrete time state model is

    Zk=HXk+Vk

    (12)

    where Zk∈R3×1is the measurement provided from the GPS module, the MEMS accelerometer, the laser module and the barometer. Vk∈R3×1is the measurement noise. H is the quantity measurement matrix.

    Above all, the discrete time linear state model (state equation and measurement equation) of UAVS integration system is

    (13)

    The laser module is in a limited range of 0 to 25m above ground level, the laser module is used below 25m and the barometer is used above 25m in the process of SHAEKF, as shown in Eq.(14):

    (14)

    2.2 Data analysis module

    There are many errors on the altitude status from different sensors before the Kalman filter. Fuzzy Kalman filter is used to calculate theoretical and actual variance of innovation. A data analysis is added to the process of data fusion to identify the outputs of sensors and determines weight coefficient.

    The actual variance of innovation is calculated byNsamplingdata:

    (15)

    Thefusionaltitudeistreatedaspredictivevalue,andtheoutputistreatedasmeasurementvalue.vkisthedifferencevaluebetweenpredictivevalueandmeasurementvalue,namelyinnovation.Nisdecidedbythecharacteristicsofeachsensor.Thetheoreticalvarianceofinnovationisdefinedas

    (16)

    UsingfuzzyKalmantomatchvariance,avariantisdefendedtoinspectthedifferenceofpredictivevalueandmeasurementvalue:

    (17)

    Iftheoutputsarestable,theratiooftheoreticalandtheactualvarianceαCxiscloseto1,otherwiseαCxwillbecomebiggerwhentheoutputsmalfunction.InspectingthechangeofαCx,thefuzzyruleisagain:

    (18)

    Measuredbyexperiment,β=[βg,βb,βm,βl]isusedtoadjustweightcoefficient,whichcanadaptivelyadjusttheweightofeachsensor,ensuringtheaccuracyofestimationinsituationofhighdynamicindifferentenvironmentofUAVs.

    2.3ImprovedSHAEKF

    TheimprovedSHAEKFisgivenbythefollowingrecursiveequations:

    ①Firststepofprediction:

    (19)

    ②Updateinnovation:

    (20)

    ③Updatethepredictionsquareerrormatrix:

    (21)

    ④Noiseofmeasurement:

    (22)

    ⑤ Filter convergence criterion:

    (23)

    If Eq.(23) is satisfied, the filter is in convergence and keep the Pk/k-1in ③; Otherwise, update Pk/k-1by strong Kalman filter:

    (24)

    ⑥ Update the filter gain:

    (25)

    State altitude estimation:

    (26)

    whereαistheweightcoefficient.

    ⑦Updatestateestimationsquareerrormatrix:

    (27)

    ⑧Systemnoiseestimation:

    The improved SHAEKF has advantages as follows:

    (1) Estimates system noise and measurement noise

    The top priority of the improved SHAEKF is separating the system noise and the measurement noise while their statistical properties are neither unknown. For altitude fusion system of UAVs, system noise is mainly derived from the integral of MEMS accelerometer error, noise parameters are relatively stable. Therefore, the variance of estimation system noise can be calculated in real-time by the altitude of the accelerometer. The improvement not only can solve the problem of big error in estimation of the system noise, but also keep the system stable.

    (2) Exponential fading factor in Eq.(28):

    (28)

    When the UAV is in a state of flight, system in the process of dynamic changes, noise parameter has a weak non-stationary. The fading memory factor is used to raise the innovation in state estimation.

    (3) Combined with the strong tracking Kalman filter, based on the filter convergence criterion, the improved SHAEKF is combined with the strong tracking Kalman filter. If the criterion is established, the filter convergence keeps updating Pk/k-1to calculate filter gain, otherwise , the actual error of filter exceeds expected value in theory and Pk/k-1is updated by the strong tracking Kalman. Meanwhile the weight of each sensor is adaptively adjusted according to the weight coefficient, ensuring the accuracy of estimation in situation of high dynamic in different environment.

    3 Simulation and experimental results

    To validate the estimation of altitude fusion method, simulation and actual experimental were presented. Since UAV usually flies in short time, experiments are carried within 15 minutes. And the low and high altitudes are distinguished by the range of the laser ranging module -25m.

    3.1 Simulation experiment

    The two simulations are as follows: one simulation is in the height of 15m in the case of sudden failure of the GPS, the other simulation is in the height of 35m, simulation of more than 25m in which the fusion algorithm could adjust the weights of the sensors. The output data of each sensor is shown in Fig.8 and Fig.9.

    Fig.8 Altitude data of sensors at 15m

    Fig.9 Altitude data of sensors at 35m

    Simulation results are shown in Fig.10 and Fig.11.

    Fig.10 Result of Information Fusion at 15m

    Fig.11 Result of information fusion at 35m

    As shown in Fig.10, the lines denote the altitude of improved algorithm and the altitude of Kalman filter improved before. When the GPS broke down, the accuracy of Kalman filter reduced while the improved algorithm keeps the accuracy of fusion altitude. In Fig.11 the output of laser ranging module is 0, the fusion effect of the improved algorithm is better than the improvement before.

    3.2 Multipoint hovering flight experiment

    In order to further verify the effectiveness of the altitude in different altitudes and different environments, this section presents experimental results from the multipoint hovering flight. The hovering altitudes are 10m, 22m and 37m. The real time altitudes with no wind recorded within 1000s are shown in Fig.12. Fig.13 is the altitude with winds grade 3. The attitude and altitude control is satisfactory in the sense that the UAV is stable around the desired altitude.

    Fig.12 Spot hovering test with no wind

    Fig.13 Spot hovering test with wind grade 3

    The error at low altitudeel,andtheerrorathighaltitudeehwithnowind:

    Theerroratlowaltitudeel,andtheerrorathighaltitudeehwiththewindgrade3:

    3.3 3-Dtrajectoriesexperiment

    Forfurtherverifyingthereliabilityandhighdynamicofthealtitude,experimentalresultsarepresentedfromtherotorcraftUAVsautonomousflightinJilin,China.Accordingtotherequirementsoftask,flyinginthegroundspeedof4m/s,theUAVtracksthetrajectoryofatriangleatthealtitudefrom5mto25m.TheActualflighttrajectoryisrecordedasFig.14.

    Fig.14 3D trajectory of the UAV

    As can be learned from Fig.14, the UAV can accomplish the trajectory with the altitude error less than 1.5m. The experiment shows that the fusion method could meet the need of high dynamic.

    4 Conclusions

    The paper presents a data fusion approach to the problem of low altitude accuracy in UAVs.

    Adopting a number of different altitude information, a unique data fusion structure is designed according to the characteristics of each sensor.

    Based on the use of rotor aircraft environment, an improved SHAKF-Kalman filter data fusion method is presented. The algorithm can adaptively adjust using the weight of each sensor for ensuring the accuracy of estimation in situation of high dynamic in UAVs.

    As mentioned above, the multi-sensor data fusion based on the improved SHAKF- Kalman filter is positive. The estimation of altitude reaches a precision of 1.5 meter. The algorithm is stable, and much more adaptable to engineering.

    Reference

    [ 1] Zoto V, Gao X G. Intermediate carriers for UAV swarms: problem of fleet composition.JournalofSystemsEngineering&Electronics, 2013, 24(1):101-107

    [ 2] Qian M S, Jiang B, Xu D Z, et al. Robust dynamics surface fault tolerant control design for attitude control systems of UAV.SystemsEngineering&Electronics, 2014, 36(9):1798-1803

    [ 3] Qu Y, Zhang Y. Cooperative localization against GPS signal loss in multiple UAVs flight.JournalofSystemsEngineeringandElectronics, 2011, 22(1): 103-112

    [ 4] Grzonka S, Grisetti G, Burgard W. A fully autonomous indoor quadrotor.IEEETransactionsonRobotics, 2012, 28(1):90-100

    [ 5] Sebesta K D, Boizot N. A real-time adaptive high-gain EKF, applied to a quadcopter inertial navigation system.IEEETransactionsonIndustrialElectronics, 2014, 61(1): 495-503

    [ 6] Wei G, Li J. Adaptive Kalman filtering for the integrated SINS/DVL system.JournalofComputationalInformationSystems, 2013, 16(9):6443-6450

    [ 7] Chingiz H, Halil E S. Robust adaptive kalman filter for estimation of UAV dynamics in the presence of sensor/actuator faults.AerospaceScienceandTechnology, 2013, 28(1): 376-383

    [ 8] Sage A P, Husa G W. Adaptive filtering with unknown prior statist. In: Proceedings of the Joint Automatic Control Conference, Tokyo, Japan, 1969. 760-769

    [ 9] Kownackl C. Optimization approach to adapt Kalman filters for the real-time application of accelerometer and gyroscope signals’ filtering.DigitalSignalProcessing, 2011, 21(1): 31-140

    [10] Zhang C Y. Approach to adaptive filtering algorithm.ChineseJournalofAeronautics, 1998, 19(75): 596-599 (In Chinese)

    [11] Rigatos G. Nonlinear Kalman filters and particle filters for integrated navigation of unmanned aerial vehicles.RoboticsandAutonomousSystems, 2012, 60(7): 978-995

    [12] Hajiyev C, Soken H E. Robust adaptive Kalman filter for estimation of UAV dynamics in the presence of sensor/actuator faults.AerospaceScienceandTechnology, 2013, 28(1): 376-383

    [13] Duan Z S, Han C A. A strong tracking adaptive state estimator and simulation.JournalofSystemSimulation, 2004, 16(5):1020-1023 (In Chinese)

    [14] Liu R H, Wang H. All attitude magnetic deviation compensation for digital magnetic compass.OpticsandPrecisionEngineering, 2011, 19(8): 1867-1873 (In Chinese)

    [15] Gao X X, Jiang R, Gao M M. Control scheme based on the inverse system method online learning BP neural network adaptive compensate. In: Proceedigns of the 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS), Xiamen, China, 2010. 874-878

    Xu Dongfu, male, born in 1987. He is a Ph.D candidate of the University of Chinese Academy of Sciences, and he received his B.S. degree in Jilin University in 2011. His main research direction is unmanned aerial vehicles integrated navigation and control.

    10.3772/j.issn.1006-6748.2017.02.007

    ①Supported by the National Natural Science Foundation of China (No. 61304017, 11372309), Key Technology Development Project of Jilin Province (No. 20150204074GX), the Project Development Plan of Science and Technology (No. 20150520111zh) and the Provincial Special Funds Project of Science and Technology Cooperation (No. 2014SYHZ0004).

    ②To whom correspondence should be addressed. E-mail: baiyueciomp@163.com

    on Mar. 4, 2016

    免费大片18禁| 看十八女毛片水多多多| 亚洲精品成人久久久久久| 国产亚洲午夜精品一区二区久久 | 欧美性猛交黑人性爽| 国产在视频线精品| av黄色大香蕉| 成人高潮视频无遮挡免费网站| 国产乱人视频| 丰满乱子伦码专区| 在线免费观看不下载黄p国产| 久久人妻av系列| 少妇高潮的动态图| 久久婷婷人人爽人人干人人爱| 综合色丁香网| 精品不卡国产一区二区三区| 久久久久久久久大av| 成人午夜高清在线视频| 老司机福利观看| 国产伦理片在线播放av一区| av播播在线观看一区| 国产精品三级大全| 日韩 亚洲 欧美在线| 亚洲av成人精品一区久久| av福利片在线观看| 久久婷婷人人爽人人干人人爱| 狠狠狠狠99中文字幕| 韩国高清视频一区二区三区| 波多野结衣巨乳人妻| 在线天堂最新版资源| 国产精品久久久久久精品电影| 国产黄色小视频在线观看| 国产视频内射| 国产探花极品一区二区| 少妇丰满av| 免费看美女性在线毛片视频| 欧美丝袜亚洲另类| 国产精品人妻久久久久久| av.在线天堂| 国产精品av视频在线免费观看| 免费观看性生交大片5| 日韩一区二区三区影片| 欧美+日韩+精品| 蜜臀久久99精品久久宅男| 色吧在线观看| 久久久久网色| 国产成人精品一,二区| .国产精品久久| 亚洲欧美成人综合另类久久久 | 成人亚洲欧美一区二区av| 日本色播在线视频| 女人十人毛片免费观看3o分钟| 超碰97精品在线观看| 国产激情偷乱视频一区二区| 两个人的视频大全免费| a级毛片免费高清观看在线播放| 99在线视频只有这里精品首页| 又黄又爽又刺激的免费视频.| 波野结衣二区三区在线| 亚洲av成人精品一二三区| 亚洲av一区综合| 麻豆乱淫一区二区| 麻豆成人av视频| 亚洲精品,欧美精品| 国产亚洲最大av| 国产黄色视频一区二区在线观看 | 日本免费a在线| 麻豆av噜噜一区二区三区| 看黄色毛片网站| 久久久亚洲精品成人影院| 国产成人福利小说| 国产乱来视频区| 日韩欧美在线乱码| 久久99蜜桃精品久久| 听说在线观看完整版免费高清| 三级毛片av免费| 亚洲av中文字字幕乱码综合| 久久精品人妻少妇| 一个人免费在线观看电影| 国产精品国产三级国产专区5o | 欧美成人a在线观看| 欧美成人免费av一区二区三区| 成人美女网站在线观看视频| 日日摸夜夜添夜夜添av毛片| 国产成人精品久久久久久| 亚洲成人精品中文字幕电影| 色吧在线观看| 久久久精品欧美日韩精品| 丰满少妇做爰视频| 精品无人区乱码1区二区| 久久久久久久久久久丰满| 大又大粗又爽又黄少妇毛片口| 亚洲无线观看免费| 你懂的网址亚洲精品在线观看 | 女人被狂操c到高潮| 国产亚洲av片在线观看秒播厂 | 欧美成人午夜免费资源| 天堂av国产一区二区熟女人妻| 女人久久www免费人成看片 | 国产成人aa在线观看| 欧美成人午夜免费资源| 日日摸夜夜添夜夜添av毛片| 亚洲欧美精品自产自拍| 黄片wwwwww| 听说在线观看完整版免费高清| 女人被狂操c到高潮| 国产精品国产高清国产av| 日韩精品有码人妻一区| 色5月婷婷丁香| 亚洲欧美精品自产自拍| 综合色丁香网| 久久精品国产鲁丝片午夜精品| 日本免费在线观看一区| 黑人高潮一二区| 中文乱码字字幕精品一区二区三区 | 色尼玛亚洲综合影院| 色网站视频免费| 免费不卡的大黄色大毛片视频在线观看 | 国产亚洲精品久久久com| 国产免费又黄又爽又色| 国产真实伦视频高清在线观看| 日日干狠狠操夜夜爽| 99热这里只有精品一区| 国语自产精品视频在线第100页| 亚洲国产精品专区欧美| 亚洲成人久久爱视频| 欧美变态另类bdsm刘玥| 色噜噜av男人的天堂激情| 天天躁日日操中文字幕| 国产综合懂色| 村上凉子中文字幕在线| 岛国在线免费视频观看| 中文字幕久久专区| 久久国产乱子免费精品| 成人无遮挡网站| 亚洲三级黄色毛片| 看非洲黑人一级黄片| 亚洲五月天丁香| 99热这里只有是精品50| 黄片无遮挡物在线观看| 一二三四中文在线观看免费高清| av在线老鸭窝| 日本与韩国留学比较| 最近中文字幕高清免费大全6| 亚洲国产精品国产精品| 波多野结衣高清无吗| 国产精品精品国产色婷婷| 欧美成人精品欧美一级黄| 网址你懂的国产日韩在线| 99久久中文字幕三级久久日本| 国产精华一区二区三区| 91狼人影院| 婷婷色综合大香蕉| 少妇裸体淫交视频免费看高清| 国产黄色视频一区二区在线观看 | 亚洲欧美成人综合另类久久久 | 男女那种视频在线观看| 看免费成人av毛片| 久久久午夜欧美精品| 欧美精品国产亚洲| 国模一区二区三区四区视频| 欧美成人精品欧美一级黄| 中文资源天堂在线| 精品人妻偷拍中文字幕| 国产一区亚洲一区在线观看| 亚洲高清免费不卡视频| 国产精品伦人一区二区| 亚洲三级黄色毛片| 亚洲欧美精品自产自拍| 欧美一区二区亚洲| 国产精品麻豆人妻色哟哟久久 | 综合色av麻豆| 久久精品国产鲁丝片午夜精品| 纵有疾风起免费观看全集完整版 | 日韩av不卡免费在线播放| 视频中文字幕在线观看| av在线老鸭窝| eeuss影院久久| 日本欧美国产在线视频| 少妇熟女aⅴ在线视频| 国产私拍福利视频在线观看| 啦啦啦啦在线视频资源| 熟女电影av网| 亚洲av熟女| 十八禁国产超污无遮挡网站| 精品人妻视频免费看| 国产在视频线精品| 亚洲国产精品合色在线| 国国产精品蜜臀av免费| 亚洲av电影不卡..在线观看| 日韩av不卡免费在线播放| 欧美zozozo另类| 久久久久久久亚洲中文字幕| 国产不卡一卡二| 久久久色成人| 国产探花极品一区二区| 亚洲国产欧洲综合997久久,| 女的被弄到高潮叫床怎么办| 22中文网久久字幕| 久久6这里有精品| 国产成人a区在线观看| 2021天堂中文幕一二区在线观| 性插视频无遮挡在线免费观看| 亚洲丝袜综合中文字幕| 精品久久久久久久久亚洲| 久久国内精品自在自线图片| 人妻制服诱惑在线中文字幕| 国国产精品蜜臀av免费| 亚洲国产精品久久男人天堂| 汤姆久久久久久久影院中文字幕 | 能在线免费观看的黄片| 伦理电影大哥的女人| 色哟哟·www| 中文字幕制服av| 精品一区二区三区人妻视频| av在线播放精品| 99热精品在线国产| 七月丁香在线播放| 久久久久久久国产电影| 波多野结衣高清无吗| 亚洲国产最新在线播放| av在线亚洲专区| 久久婷婷人人爽人人干人人爱| 免费黄色在线免费观看| 一个人看的www免费观看视频| 久久久精品94久久精品| 尤物成人国产欧美一区二区三区| 国产三级在线视频| 国产片特级美女逼逼视频| 熟妇人妻久久中文字幕3abv| 精品久久国产蜜桃| 国产亚洲av片在线观看秒播厂 | 我的女老师完整版在线观看| 精品久久久久久成人av| 深夜a级毛片| 国产老妇伦熟女老妇高清| 免费看光身美女| 亚洲av福利一区| 国产精品一二三区在线看| 小说图片视频综合网站| 日韩av在线大香蕉| 内射极品少妇av片p| 99在线人妻在线中文字幕| 久久精品国产99精品国产亚洲性色| 欧美三级亚洲精品| 一区二区三区免费毛片| 少妇高潮的动态图| 综合色av麻豆| 可以在线观看毛片的网站| 亚洲欧美日韩无卡精品| 最近2019中文字幕mv第一页| 啦啦啦韩国在线观看视频| 少妇被粗大猛烈的视频| 精品人妻熟女av久视频| 成人亚洲欧美一区二区av| 免费看美女性在线毛片视频| 国产又色又爽无遮挡免| 国产男人的电影天堂91| 又爽又黄a免费视频| 村上凉子中文字幕在线| 99热6这里只有精品| 国产老妇女一区| 欧美一区二区亚洲| 亚洲在线观看片| 黄片wwwwww| 中文字幕精品亚洲无线码一区| 一个人看的www免费观看视频| 嫩草影院精品99| 观看免费一级毛片| 国产精品av视频在线免费观看| 青春草国产在线视频| 九九在线视频观看精品| 国产免费视频播放在线视频 | 日产精品乱码卡一卡2卡三| 亚洲综合精品二区| h日本视频在线播放| 久久久精品94久久精品| 99热精品在线国产| 国产成人精品婷婷| 亚洲不卡免费看| 日产精品乱码卡一卡2卡三| 一本一本综合久久| 精品国产一区二区三区久久久樱花 | 黄片无遮挡物在线观看| 插阴视频在线观看视频| 免费无遮挡裸体视频| 狂野欧美激情性xxxx在线观看| 直男gayav资源| 亚洲国产精品sss在线观看| 狂野欧美白嫩少妇大欣赏| 精品不卡国产一区二区三区| 一二三四中文在线观看免费高清| 国产精品99久久久久久久久| 国产亚洲午夜精品一区二区久久 | 成年av动漫网址| 日本与韩国留学比较| 乱系列少妇在线播放| 色网站视频免费| 亚洲欧美日韩卡通动漫| 精品少妇黑人巨大在线播放 | 国内精品一区二区在线观看| 久久99热这里只频精品6学生 | 久久久久久久久久黄片| av黄色大香蕉| 美女国产视频在线观看| 亚洲人成网站在线播| 只有这里有精品99| 99热6这里只有精品| 国产极品精品免费视频能看的| 色播亚洲综合网| 国产免费男女视频| 校园人妻丝袜中文字幕| 91狼人影院| 一级毛片电影观看 | 日日撸夜夜添| 别揉我奶头 嗯啊视频| www日本黄色视频网| 中文字幕精品亚洲无线码一区| 我的老师免费观看完整版| av国产久精品久网站免费入址| 亚洲国产精品合色在线| 女人十人毛片免费观看3o分钟| 亚洲,欧美,日韩| 淫秽高清视频在线观看| 欧美变态另类bdsm刘玥| 男女边吃奶边做爰视频| 成人一区二区视频在线观看| 丰满人妻一区二区三区视频av| av线在线观看网站| www.色视频.com| 老司机福利观看| av播播在线观看一区| 免费在线观看成人毛片| 亚洲精品,欧美精品| 中文字幕精品亚洲无线码一区| 国产免费一级a男人的天堂| 最后的刺客免费高清国语| 亚洲av二区三区四区| 日本爱情动作片www.在线观看| 中国美白少妇内射xxxbb| 国产一区二区亚洲精品在线观看| 成人欧美大片| 国产一区二区在线av高清观看| 午夜久久久久精精品| 亚洲欧美成人综合另类久久久 | 国产成年人精品一区二区| 国产久久久一区二区三区| 久久国产乱子免费精品| 亚洲精品456在线播放app| 黄片无遮挡物在线观看| 亚洲一区高清亚洲精品| 免费观看a级毛片全部| 桃色一区二区三区在线观看| 亚洲乱码一区二区免费版| 热99re8久久精品国产| 国产女主播在线喷水免费视频网站 | 熟女人妻精品中文字幕| 男人舔奶头视频| 午夜激情福利司机影院| 国产三级在线视频| 啦啦啦啦在线视频资源| av国产免费在线观看| 久热久热在线精品观看| 日韩中字成人| 精品久久久久久成人av| 99热网站在线观看| 亚洲av成人精品一区久久| 日日撸夜夜添| 国产又黄又爽又无遮挡在线| 欧美精品国产亚洲| 看片在线看免费视频| 床上黄色一级片| 欧美97在线视频| 国产国拍精品亚洲av在线观看| 国产亚洲av片在线观看秒播厂 | 听说在线观看完整版免费高清| 在线播放无遮挡| 听说在线观看完整版免费高清| 亚洲欧美成人精品一区二区| 18禁在线播放成人免费| 人妻制服诱惑在线中文字幕| 亚洲五月天丁香| 老司机影院毛片| 一级毛片我不卡| 国产精品久久久久久久电影| 国产淫片久久久久久久久| 日韩欧美 国产精品| 国产老妇伦熟女老妇高清| 国产女主播在线喷水免费视频网站 | 三级毛片av免费| 国产三级中文精品| 久久久国产成人精品二区| 亚洲自拍偷在线| 99久国产av精品| 最新中文字幕久久久久| 在线播放国产精品三级| 亚洲精品乱久久久久久| 日韩视频在线欧美| 国产亚洲最大av| 国产伦理片在线播放av一区| 成人午夜高清在线视频| 小说图片视频综合网站| 国产精品嫩草影院av在线观看| 午夜福利在线观看免费完整高清在| 老司机影院成人| 我的老师免费观看完整版| 人妻少妇偷人精品九色| 只有这里有精品99| 国产精品伦人一区二区| 国内揄拍国产精品人妻在线| 久久人妻av系列| 国产成人精品婷婷| kizo精华| 日本爱情动作片www.在线观看| 国产毛片a区久久久久| 有码 亚洲区| 老女人水多毛片| 国产 一区精品| 国产乱来视频区| 日日啪夜夜撸| 秋霞在线观看毛片| 免费观看在线日韩| 亚洲精品国产av成人精品| 亚洲欧美日韩东京热| 18禁在线播放成人免费| 观看美女的网站| 欧美97在线视频| 国产伦精品一区二区三区视频9| 少妇熟女欧美另类| av.在线天堂| 一个人观看的视频www高清免费观看| 在线播放无遮挡| 亚洲熟妇中文字幕五十中出| 又爽又黄a免费视频| 欧美日韩精品成人综合77777| 精品欧美国产一区二区三| 国产精品一区二区在线观看99 | 免费播放大片免费观看视频在线观看 | 又爽又黄无遮挡网站| 久久久亚洲精品成人影院| 久久久色成人| 中文亚洲av片在线观看爽| 99久久无色码亚洲精品果冻| 久久精品国产亚洲av涩爱| 国产一区二区亚洲精品在线观看| 日韩视频在线欧美| 亚洲av电影在线观看一区二区三区 | 日本黄色片子视频| 午夜a级毛片| 丝袜喷水一区| 国产精品日韩av在线免费观看| 99视频精品全部免费 在线| 久久久久九九精品影院| 成人毛片a级毛片在线播放| 精品一区二区三区视频在线| 亚洲欧美精品专区久久| 久久精品国产鲁丝片午夜精品| 国产高清视频在线观看网站| 亚洲人成网站在线观看播放| 国产黄色小视频在线观看| 老司机影院毛片| 在现免费观看毛片| 日本与韩国留学比较| 内地一区二区视频在线| 黑人高潮一二区| 国产又色又爽无遮挡免| av在线播放精品| 国产熟女欧美一区二区| 午夜爱爱视频在线播放| 哪个播放器可以免费观看大片| 99久久成人亚洲精品观看| 国产一区亚洲一区在线观看| 国产又色又爽无遮挡免| 少妇猛男粗大的猛烈进出视频 | 老司机影院毛片| 精品一区二区三区视频在线| 亚洲欧美日韩无卡精品| 最近最新中文字幕大全电影3| 97在线视频观看| 国产精品乱码一区二三区的特点| 性色avwww在线观看| 日韩人妻高清精品专区| 亚洲av电影不卡..在线观看| 亚洲熟妇中文字幕五十中出| 特级一级黄色大片| 能在线免费看毛片的网站| 久久热精品热| 欧美另类亚洲清纯唯美| 亚洲av免费在线观看| 国产亚洲精品av在线| 最近中文字幕高清免费大全6| 熟女电影av网| a级毛色黄片| 久久婷婷人人爽人人干人人爱| 国产淫语在线视频| 深爱激情五月婷婷| 美女脱内裤让男人舔精品视频| 国产美女午夜福利| 午夜a级毛片| 啦啦啦韩国在线观看视频| 观看美女的网站| 夜夜看夜夜爽夜夜摸| 免费在线观看成人毛片| 亚洲国产精品专区欧美| 日韩欧美在线乱码| 亚洲高清免费不卡视频| 亚洲激情五月婷婷啪啪| 亚洲国产成人一精品久久久| 亚洲精品色激情综合| 亚洲性久久影院| 最后的刺客免费高清国语| 国产三级中文精品| 国产在线一区二区三区精 | 日本一本二区三区精品| 一级毛片久久久久久久久女| 亚洲电影在线观看av| 韩国高清视频一区二区三区| 免费电影在线观看免费观看| 日韩欧美在线乱码| 一边摸一边抽搐一进一小说| 中文字幕av成人在线电影| 亚洲人成网站在线播| 国产成人福利小说| 看十八女毛片水多多多| 亚洲人与动物交配视频| 亚洲怡红院男人天堂| 国产探花在线观看一区二区| 欧美性猛交黑人性爽| 日韩在线高清观看一区二区三区| eeuss影院久久| 97超视频在线观看视频| 国产在视频线在精品| 高清日韩中文字幕在线| 最近最新中文字幕大全电影3| 五月玫瑰六月丁香| 夜夜爽夜夜爽视频| a级毛片免费高清观看在线播放| av专区在线播放| 精品久久国产蜜桃| 亚洲三级黄色毛片| 久久久久久久午夜电影| 波多野结衣巨乳人妻| 嫩草影院精品99| 欧美人与善性xxx| 女人十人毛片免费观看3o分钟| 女人久久www免费人成看片 | 成人一区二区视频在线观看| 亚洲国产欧美人成| kizo精华| 69人妻影院| 黄色日韩在线| 春色校园在线视频观看| 亚洲电影在线观看av| av女优亚洲男人天堂| 男人舔奶头视频| 婷婷色av中文字幕| 久久精品夜夜夜夜夜久久蜜豆| 精品人妻一区二区三区麻豆| 免费电影在线观看免费观看| 高清视频免费观看一区二区 | 国产探花在线观看一区二区| 日韩av在线大香蕉| 日韩精品青青久久久久久| 天堂影院成人在线观看| 欧美性猛交╳xxx乱大交人| 欧美三级亚洲精品| 老司机福利观看| 国产精品日韩av在线免费观看| 国产高清不卡午夜福利| 麻豆av噜噜一区二区三区| 国产精品一区二区三区四区久久| 色综合色国产| 亚洲人与动物交配视频| 国产私拍福利视频在线观看| 久久精品国产亚洲av涩爱| 成人av在线播放网站| 精品久久久久久电影网 | 嫩草影院入口| 亚洲av中文av极速乱| 久久久a久久爽久久v久久| 国产黄片视频在线免费观看| 精品人妻偷拍中文字幕| 亚洲天堂国产精品一区在线| 精品免费久久久久久久清纯| 精品人妻偷拍中文字幕| videos熟女内射| 精品免费久久久久久久清纯| 天天躁夜夜躁狠狠久久av| 天堂影院成人在线观看| 日本爱情动作片www.在线观看| 22中文网久久字幕| 亚洲天堂国产精品一区在线| 69人妻影院| 亚洲精品乱码久久久久久按摩| 国产 一区 欧美 日韩| 一区二区三区四区激情视频| 欧美一区二区精品小视频在线| 99久久成人亚洲精品观看| 日韩成人av中文字幕在线观看| ponron亚洲| 国产亚洲av片在线观看秒播厂 | 国产片特级美女逼逼视频| 国产精品av视频在线免费观看| 色5月婷婷丁香| 成人av在线播放网站| 国产成人免费观看mmmm| 日韩,欧美,国产一区二区三区 | 国产精品av视频在线免费观看| 91狼人影院| 能在线免费观看的黄片| 日韩大片免费观看网站 | 18禁在线播放成人免费| 边亲边吃奶的免费视频| 天堂影院成人在线观看| 久久6这里有精品|