• <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 ?

    Direction-of-ArrivaI Method Based on Randomize-Then-Optimize Approach

    2023-01-13 01:56:38CaiYiTangShengPengZhiQinZhaoBoJiang

    Cai-Yi Tang | Sheng Peng | Zhi-Qin Zhao | Bo Jiang

    Abstract—The direction-of-arrival (DOA) estimation problem can be solved by the methods based on sparse Bayesian learning (SBL).To assure the accuracy,SBL needs massive amounts of snapshots which may lead to a huge computational workload.In order to reduce the snapshot number and computational complexity,a randomizethen-optimize (RTO) algorithm based DOA estimation method is proposed.The “l(fā)earning” process for updating hyperparameters in SBL can be avoided by using the optimization and Metropolis-Hastings process in the RTO algorithm.To apply the RTO algorithm for a Laplace prior,a prior transformation technique is induced.To demonstrate the effectiveness of the proposed method,several simulations are proceeded,which verifies that the proposed method has better accuracy with 1 snapshot and shorter processing time than conventional compressive sensing (CS) based DOA methods.

    1.Introduction

    The direction-of-arrival (DOA) estimation problem is an important research area in radar and antenna,which mainly concerns on recovering signals and getting impinging angles.In recent years,the compressive sensing (CS)[1]theory has been successfully applied in DOA estimation,aiming to reconstruct sparse signals.Several sparse signal construction algorithms have been developed,such as orthogonal matching pursuit(OMP) and basis pursuit (BP).OMP[2]is originated from matching pursuit (MP)[3].To reduce the computational burden of BP methods,[4] proposed a dimensionality reduction method,named asl1-SVD.Based on the CS theory,the sparse signal reconstruction method was extended to Bayesian compressive sensing (BCS)[5]which was formulated from a Bayesian perspective based on the sparse prior assumption of signal and noise.Under the frame of BCS,signal reconstruction was mainly achieved by sparse Bayesian learning (SBL)[6].SBL has obtained great developments in recent studies,such as root SBL[7],variational SBL[8],jointly SBL[9],and the grid evolution method[10].SBL has a multilayered assumption frame which is designed to iterate to“l(fā)earn” new information and update the hyperparameters.

    In the practical applications of DOA estimation problems,the snapshot is usually short or even single.However,the limited data results in poor accuracy of DOA estimation problems.To lower the demand of snapshots,most of the existed methods are developed to improve the traditional spatial spectrum estimation methods.The pseudo covariance matrix method[11]and the spatial smoothing technique[12]are widely used.The influence of snapshots on estimation accuracy still exists in sparse signal reconstruction methods.Usually for SBL based DOA methods,the accuracy of estimated DOA strongly depends on the number of snapshots.It causes poor performance if the number of snapshots is not large enough for retrieving DOA.This phenomenon also occurs in thel1-SVD method as it requires sufficient data for the singular value decomposition and dimensionality reduction.To sum up,the studies on single snapshot for sparse signal reconstruction are limited.

    Thus,aim to relax the requirement of snapshots and improve the performance of sparse signal reconstruction using 1 snapshot,this paper proposes a DOA estimation method based on the randomizethen-optimize (RTO)[13]approach.The RTO based method solves the Bayesian nonlinear inverse problem by using a process of optimization to generate the proposal samples and correcting these samples by the Metropolis-Hastings (MH) approach[14].This MH method is based on the Markov chain Monte-Carlo(MCMC)[15],which has been widely used to evaluate the posterior distribution in the Bayesian inverse problem.Different from traditional SBL,the “l(fā)earning” process in updating hyperparameters is no longer needed in this RTO based method,reducing the required number of snapshots.Also,an intrinsic shortcoming of SBL is that it consumes relative long time to “train” hyperparameters in the assumption frame.RTO has no more requirement on hyperparameters,so that the processing time is significantly reduced.Furthermore,the RTO approach[13]is based on a Gaussian prior,while a Laplace prior has better parametric sparsity for the DOA estimation problem[16].Therefore,a prior transformation method[17]is adopted.Simulation results show that good accuracy can be achieved even with 1 snapshot.

    2.Proposed Method

    2.1.ProbIem FormuIation

    Assume that there areKnarrowband far field signalssk(t),wherek=1,2,···,K,impinging on a linear array ofMsensors fromKdirectionsθk,wherek=1,2,···,K.In the DOA estimation problem,the time delays at different sensors can be represented by simple phase shifts,leading to an observation model of

    where y(t)=[y1(t),y2(t),…,yM(t)]T,θ=[θ1,θ2,…,θK]T,s(t)=[s1(t),s2(t),…,sK(t)]T,and e(t)=[e1(t),e2(t),…,eM(t)]T.ym(t) andem(t) (m=1,2,···,M) are the measurement and noise of themth sensor at timet,respectively.And A(θ)=[a(θ1),a(θ2),…,a(θK)] is the array manifold matrix,a(θk) is the steering vector of thekth source whose entryam(θK) contains the delay information of thekth source to themth sensor.To locate the direction of the sparse signal,the spatial domain is sampled by a grid,whereNdenotes the grid number and usuallyN>>M>K.The sampling is usually uniform because there is no prior information of sources.The measurement vector y(t) and array manifold matrix A () are known,but s (t) needs to be estimated.

    The model given in (1) can be regarded as a Bayesian inverse problem.The manifold matrix A(θ) is a nonlinear parameter-to-measurement mapping.Commonly,solving the Bayesian inverse problem mainly focuses on characterising the posterior density.For the traditional SBL method,the process of obtaining the posterior density requires the hyperparameters γ.According to [18],solving the Bayesian inverse problem leads to minimize the cost function of γ.The likelihood that SBL will converge to the global minimum of the cost function is increased along with increasing the snapshot number.This is important,because the maximally sparse representation is guaranteed due to globally minimizing SBL hyperparameters,and increasing the snapshot number improves the probability that these hyperparameters are found.Too few snapshots induce poor performance of SBL.

    From another perspective of BCS,the RTO method is freed from the dependency of the snapshot number.It solves the Bayesian inverse problem with limited snapshots.RTO produces samples by using repeated solutions of a randomly perturbed optimization problem from a proposal density,which can be used in the MH approach as a Metropolis independence proposal.

    2.2.RTO-MH AIgorithm

    Considering the Bayesian inverse problem with Gaussian measurement errors and the Gaussian prior,linear transformations are used to “whiten” the error model and the prior,so that the following model can be used to describe the inverse problem:

    where ε~N(0,Ii),θ~N(θ0,Ij)θ0is the prior mean,and Iiand Ijare identity matrices with sizeiand sizej,respectively.The model in (2) is only used to describe the Bayesian inverse problem.And y is the measurement vector,fis the forwarding function (also called as the “parameter-to-observable mapping”),and ε is the measurement error.

    By repeatedly optimizing a randomly perturbed cost function,RTO can obtain candidate samples.In order to obtain the unknown parameter θ,RTO requires the target distribution to have a specific form which is defined by (3).This distribution allows the RTO samples being used in the MH process.It is suitable for any form of the target distribution scenarios,because the model in (2) can be generally used in any inverse problem.The MH approach corrects these samples which contain the information of the spatial spectrum.Especially,it needs the target density (usually the posterior density of θ) to be in the form of

    where ||·|| denotes the norm.The right side in the symbol of the norm is defined as a vector-valued function of the parameter θ which has the form of

    We illustrate the steps of sampling from the posterior as (3) by RTO.

    1) Find a linearization point,denoted as,and fix it.Usually,this point is set to be the posterior mode.The following equation is used to obtain the posterior mode:

    2) Calculate the Jacobian ofF(θ) at the linearization point,denoted as JF().The orthonormal basis,denoted asfor the column space of JF(),is evaluated through a thin quadrature rectangle (QR)factorization approach of JF().

    3) Compute independent samples ξ according to ann-dimensional standard Gaussian distribution.The proposal points θpropare evaluated by tackling the following optimization problem:

    According to the previous study in [10],the proposal points are distributed in terms of the proposal density:

    where |·| denotes the absolute value of the matrix determinant.This proposal density is used in the MH approach as an independence proposal.For updating a pointθ(i-1)to the proposed point,the MH acceptance ratio is

    The process mentioned above is called as the RTO-MH algorithm which combines the RTO algorithm and the MCMC method[10].The framework of the MH algorithm can use the samples which are obtained by applying the RTO algorithm.This process produces the samples from a posterior based on an arbitrary measurement model.As described in (2),RTO-MH is proceeded with a Gaussian prior.However,in BCS based DOA estimation methods,a Laplace prior is preferred.From this consideration,a prior transformation technique is induced in RTO-MH in this paper.

    2.3.RTO-MH with Prior Transformation

    First,consider the single parameter with a Laplace prior of the model in (2):

    whereλis a hyperparameter used to describe the Laplace distribution and θ is a Laplace-distributed physical parameter.

    Then the posterior distribution is

    whereσobsis the error standard deviation.

    To satisfy the posterior form in (3),an invertible mapping functionT1Dwhich connects a Gaussian reference random variablevwith the Laplace-distributed physical parameter θ,such that θ=T1D(v) is constructed.The transformation equation can be written as

    whereφis the cumulative distribution function (CDF) of the standard Gaussian distribution,andLis CDF of the Laplace distribution.

    Based on the single parameter prior transformation,this technique is extended to the multiple parameters case.Assume the prior on θ is

    where (Dθ)iis theith element of the vector Dθ andD is an invertible matrix.Thus,the posterior can be derived:

    Random Gaussian distributed variables can be converted to each Laplace-distributed element of Dθ by means of the one-dimensional transformationT1Ddefined in (11).So Dθ=T(v),where

    whereviis the corresponding reference variable,resulting in the prior transformation:

    The Jacobian of the transformation is D-1JT,where JTis the Jacobian of T noted as

    Based on the transformation step as in (15),the posterior density of v can be derived as

    2.4.ImpIementation

    As the observation model stated in (1),the measurement vector is complex.However,RTO-MH with the prior transformation can only operate for real values.In order to apply the algorithm in DOA estimation,the observational model is transformed into the real form

    where tm=[Re{ym},Im{ym}]Tis the real measurement matrix,wm=[Re{si},Im{si}]Tis the real signal matrix,and nm=[Re{em},Im{em}]Tis the real noise matrix.Φ is the real manifold matrix which has the form:

    Equation (19) still satisfies the Bayesian inverse problem model as (2) for the corresponding parameters.Therefore,DOA can be retrieved by three steps: 1) Make the measurement matrix transformation from complex to real;2) input the real measurement matrix to RTO-MH with prior transformation;3) calculate the normalized power of the results and select the main lobes as DOA.

    Algorithm 1.

    RTO-MH with the prior transformation algorithm for DOA estimation is:

    1.Do an observation model data transformation from the complex value to the real value using (19).

    2.Use the prior transformation function as (11),so that v=T-1(θ) has a standard Gaussian distribution.

    5.Fori=1,2,…,nsamps

    Draw a standard Gaussian sampleξ(i)~N(0,In).

    Compute RTO samples as

    6.Fori=1,2,…,nsamps

    Sample v from a uniform distribution on [0,1].

    7.Fori=1,2,…,nsamps

    Defineθ(i)=T(v(i)),which are the corrected samples fromp(θ ∣y).

    End for

    8.The resultedθ(i)forms the spectrum of the spatial domain where the interested signals exist.Calculate the normalized power of the spectrum and select the main lobes as DOA.

    3.SimuIations

    Several simulations are conducted to verify the effectiveness of the proposed method.Two uncorrelated sources are from 30oand 60o,respectively.The signal sources are narrowband and impinge on a uniform linear array with 16 sensors.The spatial range of the interested signal is discretized by the means of a uniform grid sample in the range of [-90o,90o] with a grid interval of 1o.First,the performance of the SBL method and that of the proposed method are compared.In the simulations,1 snapshot is used for the proposed RTO based method and the SBL method.Also,the SBL method with 200 snapshots is performed for comparison.

    Fig.1.Spatial spectra of BCS and the proposed method.

    Fig.1 shows the simulation results of the proposed method with 1 snapshot and the SBL method with 1 and 200 snapshots,respectively.The vertical dotted lines indicate the true locations of DOA.For 1 snapshot,the SBL method can only retrieve DOA at 30o.And its side lobes are so high,implying the poor performance of SBL for 1 snapshot.When the number of snapshots increases to 200,the performance of SBL improves.Both the proposed method and the SBL method retrieve the signal at 30oexactly and have an 1odeviation at 60o.Though the main lobe at 60oof the proposed method is wider than that of the SBL method,the side lobes of the proposed method are much lower.

    To illustrate how the accuracy performs,the proposed method is compared with OMP,l1-SVD,and SBL in the root mean square error (RMSE).The signal-to-noise ratio (SNR) in the comparison ranges from -10 dB to 10 dB with an interval of 2 dB.Here 200 independent Monte-Carlo trials for each SNR are implemented.The SBL method is simulated for 1 snapshot and 200 snapshots,respectively.Thel1-SVD algorithm is simulated for 1 snapshot and 50 snapshots,respectively.RMSE is used to describe the estimation accuracy,defined by

    wherenis the number of Monte-Carlo simulations,is the estimated value for each simulation,andis the true value.

    Fig.2 shows that the proposed method has an obvious lower RMSE level than OMP,SBL with 1 snapshot,andl1-SVD with 1 snapshot algorithms.The OMP algorithm always keeps a high RMSE level which means its estimation accuracy is much worse than SBL andl1-SVD with many snapshots.For the SBL algorithm with 200 snapshots,it has almost the same accuracy with the proposed method.This indicates that the proposed method has an advantage over the snapshot number comparing with the SBL algorithm.Forl1-SVD with 50 snapshots,its accuracy exceeds all the mentioned methods,including the proposed method.However,RMSE can be regarded as an enlarged value for the true error.Thus,the proposed method almost has the same estimation accuracy asl1-SVD with 50 snapshots.

    Table 1 gives a comparison on the processing time of the above methods with SNR=10 dB.All experiments are carried out in MATLAB on a desktop with a 2.2-GHz central processing unit (CPU).It can be found that the proposed method costs the shortest time compared with SBL andl1-SVD.Although the OMP method has a similar time cost,the proposed method has better accuracy than OMP as shown in Fig.2.

    Fig.2.RMSE of OMP,l1-SVD,and the proposed method(RTO-MH) versus SNR.

    Table 1: Processing time comparison

    4.ConcIusions

    In this paper,RTO-MH with the prior transformation algorithm is proposed to improve the performance of the BCS method in DOA estimation.Compared with conventional BCS methods,such as SBL,whose accuracy highly depends on the snapshot number,the proposed method does not require many samples to update the hyperparameters.Simulation results demonstrate that the proposed method has better estimation accuracy than SBL,OMP,andl1-SVD,when the number of snapshots is 1,and the processing time is reduced effectively.Its computational burden is close to that of the OMP algorithm and lower than those ofl1-SVD and SBL methods.However,there exists a disadvantage that the proposed method shows limited resolution of 5obased on several experiments.In the future work,the proposed method will be extended to off-grid signals under the circumstance of wideband sources,and its resolution limit can be expected to be reduced to 1o.

    DiscIosures

    The authors declare no conflicts of interest.

    国产精品三级大全| 日韩欧美免费精品| 菩萨蛮人人尽说江南好唐韦庄 | 国产精品福利在线免费观看| 1000部很黄的大片| 国内精品一区二区在线观看| 成年版毛片免费区| 中文亚洲av片在线观看爽| 我的老师免费观看完整版| 久久久久久九九精品二区国产| 12—13女人毛片做爰片一| 成人av一区二区三区在线看| 亚洲第一区二区三区不卡| 国产视频一区二区在线看| 欧美一区二区精品小视频在线| 男人舔奶头视频| 久久精品久久久久久噜噜老黄 | 一a级毛片在线观看| 国产一区二区亚洲精品在线观看| 别揉我奶头~嗯~啊~动态视频| 老师上课跳d突然被开到最大视频| 久久久久国产精品人妻aⅴ院| 亚洲真实伦在线观看| 亚洲一级一片aⅴ在线观看| 久久韩国三级中文字幕| 久久久久久久久久久丰满| 两性午夜刺激爽爽歪歪视频在线观看| 人人妻,人人澡人人爽秒播| 亚洲熟妇中文字幕五十中出| 亚洲欧美清纯卡通| 免费人成在线观看视频色| 国产成人精品久久久久久| 亚洲av.av天堂| 天天躁日日操中文字幕| 色av中文字幕| 女同久久另类99精品国产91| 免费不卡的大黄色大毛片视频在线观看 | 最近中文字幕高清免费大全6| 非洲黑人性xxxx精品又粗又长| 国内精品久久久久精免费| av.在线天堂| 久久久久久伊人网av| 卡戴珊不雅视频在线播放| 一级av片app| 尾随美女入室| 国产三级在线视频| 狠狠狠狠99中文字幕| 美女高潮的动态| 赤兔流量卡办理| 国内少妇人妻偷人精品xxx网站| 波多野结衣高清作品| 午夜免费激情av| 国产乱人偷精品视频| 白带黄色成豆腐渣| 少妇丰满av| av在线播放精品| 免费看日本二区| 免费一级毛片在线播放高清视频| av天堂在线播放| 亚洲18禁久久av| 中国国产av一级| 一个人观看的视频www高清免费观看| 99在线视频只有这里精品首页| 男人狂女人下面高潮的视频| av视频在线观看入口| 一a级毛片在线观看| 有码 亚洲区| 午夜福利在线观看吧| 蜜桃亚洲精品一区二区三区| 精品久久久久久久人妻蜜臀av| 中文亚洲av片在线观看爽| 免费看a级黄色片| 国产欧美日韩精品亚洲av| 午夜老司机福利剧场| 少妇人妻精品综合一区二区 | 成人午夜高清在线视频| 99久国产av精品国产电影| 成人性生交大片免费视频hd| 老师上课跳d突然被开到最大视频| av在线亚洲专区| 亚洲欧美日韩东京热| 亚洲av五月六月丁香网| 日本-黄色视频高清免费观看| 国产爱豆传媒在线观看| 国产精品av视频在线免费观看| 五月玫瑰六月丁香| 欧美性猛交╳xxx乱大交人| 一级毛片我不卡| 美女 人体艺术 gogo| 99热这里只有精品一区| 成人欧美大片| 黄色一级大片看看| 悠悠久久av| 长腿黑丝高跟| 亚洲一区高清亚洲精品| 国产精品乱码一区二三区的特点| 一进一出好大好爽视频| 人妻少妇偷人精品九色| 国产伦在线观看视频一区| 亚洲乱码一区二区免费版| 超碰av人人做人人爽久久| 日本撒尿小便嘘嘘汇集6| 欧美+日韩+精品| 色在线成人网| 一夜夜www| 插阴视频在线观看视频| 国产黄色视频一区二区在线观看 | 老司机福利观看| 男女视频在线观看网站免费| 久久久精品94久久精品| 美女黄网站色视频| 国产精品无大码| 国产女主播在线喷水免费视频网站 | 国产高清视频在线观看网站| 欧美bdsm另类| av在线观看视频网站免费| 精品人妻偷拍中文字幕| 国产精品免费一区二区三区在线| 91久久精品国产一区二区成人| 乱系列少妇在线播放| 成人精品一区二区免费| 久久精品久久久久久噜噜老黄 | 69av精品久久久久久| 国产久久久一区二区三区| 欧美日韩一区二区视频在线观看视频在线 | 18禁黄网站禁片免费观看直播| 欧美在线一区亚洲| 国产又黄又爽又无遮挡在线| av在线蜜桃| 狂野欧美激情性xxxx在线观看| 最新在线观看一区二区三区| 黄色日韩在线| 久久久久久国产a免费观看| 亚洲成a人片在线一区二区| 一个人看视频在线观看www免费| 最新中文字幕久久久久| 99在线视频只有这里精品首页| 国产亚洲精品综合一区在线观看| 久久99热这里只有精品18| 给我免费播放毛片高清在线观看| 国产伦在线观看视频一区| 国产高清激情床上av| 午夜免费激情av| a级一级毛片免费在线观看| 欧美日韩在线观看h| 天天躁日日操中文字幕| 中文字幕av在线有码专区| 色在线成人网| 午夜福利在线观看吧| 亚洲第一电影网av| 在线观看av片永久免费下载| 欧美zozozo另类| 国产综合懂色| 午夜福利18| 一区二区三区免费毛片| 国产探花在线观看一区二区| 亚洲激情五月婷婷啪啪| 欧美xxxx黑人xx丫x性爽| 国产色婷婷99| 男人舔女人下体高潮全视频| 亚洲欧美中文字幕日韩二区| 欧美不卡视频在线免费观看| a级毛片免费高清观看在线播放| 久久99热6这里只有精品| 三级经典国产精品| 国产一区二区在线av高清观看| 久久99热6这里只有精品| 人人妻人人澡人人爽人人夜夜 | 别揉我奶头 嗯啊视频| 永久网站在线| 俄罗斯特黄特色一大片| 成人美女网站在线观看视频| 自拍偷自拍亚洲精品老妇| 少妇猛男粗大的猛烈进出视频 | 麻豆久久精品国产亚洲av| or卡值多少钱| 国产 一区 欧美 日韩| 日韩av不卡免费在线播放| 精品免费久久久久久久清纯| 国产高清三级在线| 国产午夜福利久久久久久| 国产女主播在线喷水免费视频网站 | 国产精品99久久久久久久久| 三级经典国产精品| 色哟哟哟哟哟哟| 波多野结衣高清无吗| 久久人人爽人人爽人人片va| 男女视频在线观看网站免费| 人妻久久中文字幕网| 18+在线观看网站| 国产免费一级a男人的天堂| 草草在线视频免费看| 岛国在线免费视频观看| 少妇熟女欧美另类| 国产欧美日韩精品一区二区| 亚洲精品一卡2卡三卡4卡5卡| 午夜福利在线在线| 久久久精品94久久精品| 久久久色成人| 日本一本二区三区精品| 亚洲成人av在线免费| 国产一区二区在线观看日韩| 超碰av人人做人人爽久久| 久99久视频精品免费| 最近最新中文字幕大全电影3| 色哟哟哟哟哟哟| 国产黄色小视频在线观看| 国产在视频线在精品| 久久久午夜欧美精品| 欧美成人一区二区免费高清观看| 91在线观看av| 亚州av有码| 又爽又黄无遮挡网站| 免费无遮挡裸体视频| 国产国拍精品亚洲av在线观看| 秋霞在线观看毛片| 国语自产精品视频在线第100页| АⅤ资源中文在线天堂| 精品日产1卡2卡| 身体一侧抽搐| 国产精品爽爽va在线观看网站| 蜜桃久久精品国产亚洲av| 一进一出抽搐gif免费好疼| 91久久精品国产一区二区成人| 可以在线观看毛片的网站| 少妇丰满av| 国产成人aa在线观看| 国产精品国产高清国产av| 精品久久国产蜜桃| 男女那种视频在线观看| 国产一区二区三区av在线 | 国产又黄又爽又无遮挡在线| 久久精品久久久久久噜噜老黄 | 欧美xxxx黑人xx丫x性爽| 日韩在线高清观看一区二区三区| 哪里可以看免费的av片| 色av中文字幕| 久久精品人妻少妇| 久久久色成人| 熟女电影av网| 亚洲aⅴ乱码一区二区在线播放| 啦啦啦韩国在线观看视频| 色在线成人网| 在线免费观看不下载黄p国产| 欧美日韩综合久久久久久| 嫩草影院精品99| 男女那种视频在线观看| 国产蜜桃级精品一区二区三区| 麻豆精品久久久久久蜜桃| 欧美三级亚洲精品| 欧美成人a在线观看| 97超碰精品成人国产| 国产精品国产三级国产av玫瑰| 欧美丝袜亚洲另类| 日本黄大片高清| 91在线精品国自产拍蜜月| 看片在线看免费视频| 久久婷婷人人爽人人干人人爱| 在线看三级毛片| 你懂的网址亚洲精品在线观看 | 床上黄色一级片| 欧美另类亚洲清纯唯美| 日本色播在线视频| 美女高潮的动态| 成人av一区二区三区在线看| 毛片女人毛片| 一个人看的www免费观看视频| 日韩强制内射视频| 99久久精品一区二区三区| 日韩制服骚丝袜av| 女人被狂操c到高潮| 国产女主播在线喷水免费视频网站 | 99热精品在线国产| 亚洲av免费在线观看| 中出人妻视频一区二区| 亚洲自拍偷在线| 午夜爱爱视频在线播放| 夜夜看夜夜爽夜夜摸| 日本a在线网址| 赤兔流量卡办理| 亚洲av免费在线观看| 午夜精品国产一区二区电影 | 国内揄拍国产精品人妻在线| 亚洲欧美精品综合久久99| 精品人妻偷拍中文字幕| 搡老岳熟女国产| 熟女人妻精品中文字幕| 亚洲中文字幕日韩| 插阴视频在线观看视频| 亚洲成人av在线免费| 少妇裸体淫交视频免费看高清| 99久久精品一区二区三区| 国产精品免费一区二区三区在线| 蜜桃久久精品国产亚洲av| 欧美+亚洲+日韩+国产| 久久综合国产亚洲精品| 九九爱精品视频在线观看| 久久人人精品亚洲av| 最近2019中文字幕mv第一页| 12—13女人毛片做爰片一| 性色avwww在线观看| 久久鲁丝午夜福利片| 淫秽高清视频在线观看| 精品国内亚洲2022精品成人| 深夜精品福利| 国产成人a区在线观看| 日韩精品有码人妻一区| 国产91av在线免费观看| 国产不卡一卡二| 性欧美人与动物交配| 日韩一本色道免费dvd| 国模一区二区三区四区视频| 老熟妇乱子伦视频在线观看| 卡戴珊不雅视频在线播放| 精品少妇黑人巨大在线播放 | 波多野结衣高清作品| 又爽又黄a免费视频| 色噜噜av男人的天堂激情| 免费看av在线观看网站| 亚洲真实伦在线观看| 最近的中文字幕免费完整| 国产一级毛片七仙女欲春2| 午夜老司机福利剧场| 亚洲色图av天堂| 亚洲av第一区精品v没综合| 亚洲精华国产精华液的使用体验 | 99在线视频只有这里精品首页| 国产亚洲精品久久久久久毛片| 色播亚洲综合网| 特级一级黄色大片| 91在线精品国自产拍蜜月| 国产成人一区二区在线| 美女xxoo啪啪120秒动态图| a级一级毛片免费在线观看| 国产精品国产三级国产av玫瑰| 国产av一区在线观看免费| 少妇丰满av| 精品午夜福利视频在线观看一区| eeuss影院久久| 女人被狂操c到高潮| 自拍偷自拍亚洲精品老妇| 秋霞在线观看毛片| 午夜影院日韩av| 在线看三级毛片| 波多野结衣高清无吗| 国产黄色视频一区二区在线观看 | 亚洲人成网站在线播放欧美日韩| 在线观看午夜福利视频| 国产精品久久久久久久电影| 日本欧美国产在线视频| 人妻夜夜爽99麻豆av| 亚洲欧美清纯卡通| 身体一侧抽搐| 人妻少妇偷人精品九色| 亚洲av中文av极速乱| 一进一出抽搐动态| 亚洲av中文av极速乱| 最近的中文字幕免费完整| 在线观看av片永久免费下载| 麻豆乱淫一区二区| 日韩欧美精品v在线| 亚洲一区高清亚洲精品| 欧美日韩精品成人综合77777| 中出人妻视频一区二区| 久久精品夜夜夜夜夜久久蜜豆| 校园春色视频在线观看| 久久精品夜夜夜夜夜久久蜜豆| 国产91av在线免费观看| 免费av毛片视频| 亚洲图色成人| 99久久中文字幕三级久久日本| 亚洲天堂国产精品一区在线| 国产一区亚洲一区在线观看| 国内精品久久久久精免费| 免费高清视频大片| 国产av麻豆久久久久久久| 亚洲18禁久久av| 国产色婷婷99| 一级毛片电影观看 | 在线免费观看不下载黄p国产| 久久精品人妻少妇| 麻豆av噜噜一区二区三区| av在线观看视频网站免费| 国产亚洲欧美98| 亚洲精品一区av在线观看| 国产69精品久久久久777片| 国产av在哪里看| 久久精品国产亚洲网站| av天堂在线播放| 欧美丝袜亚洲另类| 婷婷色综合大香蕉| 亚洲图色成人| 深夜精品福利| 成人精品一区二区免费| 偷拍熟女少妇极品色| 蜜桃久久精品国产亚洲av| 免费看a级黄色片| 一级毛片电影观看 | 国产精品久久久久久av不卡| 色尼玛亚洲综合影院| 亚洲精品日韩在线中文字幕 | 亚洲无线在线观看| 丝袜美腿在线中文| 精品久久久久久久人妻蜜臀av| 日日撸夜夜添| 欧美成人免费av一区二区三区| 国产免费一级a男人的天堂| 国产成人a∨麻豆精品| 亚洲国产日韩欧美精品在线观看| 国产高清视频在线观看网站| 九九爱精品视频在线观看| 国产男靠女视频免费网站| 亚洲四区av| 白带黄色成豆腐渣| 国产精品女同一区二区软件| 午夜精品一区二区三区免费看| 3wmmmm亚洲av在线观看| 精品无人区乱码1区二区| 国内精品美女久久久久久| 亚洲aⅴ乱码一区二区在线播放| 免费av观看视频| 亚洲18禁久久av| 国产精品永久免费网站| www日本黄色视频网| 精品日产1卡2卡| 男女做爰动态图高潮gif福利片| 国产综合懂色| 久久精品综合一区二区三区| 久久精品国产亚洲av涩爱 | 九九爱精品视频在线观看| 香蕉av资源在线| 淫妇啪啪啪对白视频| 国国产精品蜜臀av免费| 内射极品少妇av片p| 国产成人a∨麻豆精品| 高清午夜精品一区二区三区 | 天堂av国产一区二区熟女人妻| 成人特级av手机在线观看| 午夜视频国产福利| 深夜精品福利| 成年av动漫网址| 无遮挡黄片免费观看| 熟女人妻精品中文字幕| 日韩精品中文字幕看吧| 国产男人的电影天堂91| 在线播放国产精品三级| 免费在线观看影片大全网站| videossex国产| 国产精品99久久久久久久久| 久久久久久国产a免费观看| 中文字幕久久专区| 精品久久久久久久久亚洲| 一级av片app| 精品久久久久久久久久免费视频| 日韩一本色道免费dvd| 亚洲国产色片| 精品一区二区三区视频在线观看免费| 两个人视频免费观看高清| 伊人久久精品亚洲午夜| 亚洲七黄色美女视频| 一个人看视频在线观看www免费| 免费大片18禁| 看黄色毛片网站| 一级毛片久久久久久久久女| 在线a可以看的网站| 亚洲av免费高清在线观看| 97碰自拍视频| 精品久久久久久成人av| 久久人人爽人人爽人人片va| 欧美+日韩+精品| 美女内射精品一级片tv| ponron亚洲| 欧美中文日本在线观看视频| 精品一区二区免费观看| 日韩一区二区视频免费看| av在线天堂中文字幕| 免费无遮挡裸体视频| 男人和女人高潮做爰伦理| 成年女人看的毛片在线观看| 非洲黑人性xxxx精品又粗又长| 桃色一区二区三区在线观看| av国产免费在线观看| 亚洲天堂国产精品一区在线| 淫秽高清视频在线观看| 欧美激情久久久久久爽电影| 18禁裸乳无遮挡免费网站照片| 国产女主播在线喷水免费视频网站 | 国产伦在线观看视频一区| 亚洲婷婷狠狠爱综合网| 精品人妻偷拍中文字幕| 中文字幕免费在线视频6| 亚洲自偷自拍三级| 成人永久免费在线观看视频| 一区二区三区免费毛片| 欧美+亚洲+日韩+国产| 成人亚洲精品av一区二区| 日本精品一区二区三区蜜桃| 欧美日本视频| 97热精品久久久久久| 男女啪啪激烈高潮av片| 欧美另类亚洲清纯唯美| 少妇的逼好多水| 日本熟妇午夜| 午夜精品在线福利| 美女大奶头视频| 国产精品一区二区免费欧美| 嫩草影院精品99| 欧美最新免费一区二区三区| 国产成人a∨麻豆精品| 女同久久另类99精品国产91| 精品免费久久久久久久清纯| 久久九九热精品免费| 伦理电影大哥的女人| 乱人视频在线观看| 在线国产一区二区在线| 我的女老师完整版在线观看| 国产在视频线在精品| 国产高清激情床上av| 在线播放国产精品三级| 久久久久久久久久黄片| 午夜精品国产一区二区电影 | 日本黄色片子视频| 精品人妻视频免费看| 美女高潮的动态| 91av网一区二区| 亚洲人成网站在线播| avwww免费| 免费黄网站久久成人精品| 亚洲美女视频黄频| 精品人妻一区二区三区麻豆 | 国产片特级美女逼逼视频| 久久热精品热| aaaaa片日本免费| 午夜福利18| 成熟少妇高潮喷水视频| 菩萨蛮人人尽说江南好唐韦庄 | 男女做爰动态图高潮gif福利片| 国产综合懂色| 国产亚洲91精品色在线| 精品久久久久久久人妻蜜臀av| a级毛片a级免费在线| 国产精品不卡视频一区二区| av在线观看视频网站免费| 天美传媒精品一区二区| 欧美日韩国产亚洲二区| 舔av片在线| 国产伦一二天堂av在线观看| 国产精品女同一区二区软件| 熟妇人妻久久中文字幕3abv| 99国产精品一区二区蜜桃av| 婷婷精品国产亚洲av| 成人无遮挡网站| 国产极品精品免费视频能看的| 麻豆成人午夜福利视频| 人人妻,人人澡人人爽秒播| 一级av片app| 久久亚洲精品不卡| 国产在视频线在精品| 99在线人妻在线中文字幕| 国产在线男女| 日本免费a在线| 国产白丝娇喘喷水9色精品| 亚洲欧美清纯卡通| 精品人妻偷拍中文字幕| 午夜激情欧美在线| 午夜影院日韩av| 亚洲久久久久久中文字幕| 色吧在线观看| 国产欧美日韩精品一区二区| av在线天堂中文字幕| 亚洲av五月六月丁香网| 午夜福利视频1000在线观看| ponron亚洲| 国产单亲对白刺激| 搡女人真爽免费视频火全软件 | 又黄又爽又免费观看的视频| 成人无遮挡网站| 欧美一区二区精品小视频在线| 成年免费大片在线观看| 一区福利在线观看| 日本一本二区三区精品| 久久久精品94久久精品| 一个人看视频在线观看www免费| 欧美成人a在线观看| 国产一级毛片七仙女欲春2| 国产日本99.免费观看| 丰满人妻一区二区三区视频av| 精品少妇黑人巨大在线播放 | 午夜福利视频1000在线观看| 五月伊人婷婷丁香| 少妇的逼水好多| 日韩高清综合在线| 国产精品无大码| 日韩一区二区视频免费看| 国模一区二区三区四区视频| 亚洲成人精品中文字幕电影| 免费观看精品视频网站| 久久午夜福利片| 欧美日韩在线观看h| 可以在线观看毛片的网站| 在线观看午夜福利视频| 日本色播在线视频| 赤兔流量卡办理| 国产亚洲av嫩草精品影院| 最新中文字幕久久久久| 久久亚洲国产成人精品v| 最近在线观看免费完整版| 国产蜜桃级精品一区二区三区| 欧美激情国产日韩精品一区| 乱系列少妇在线播放| 国产精品,欧美在线| 日韩精品中文字幕看吧| a级毛色黄片| 亚洲内射少妇av| 插逼视频在线观看|