• <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 ?

    Gaussian-Student’s t mixture distribution PHD robust filtering algorithm based on variational Bayesian inference①

    2022-07-06 03:23:32HUZhentao胡振濤YANGLinlinHUYumeiYANGShibo
    High Technology Letters 2022年2期

    HU Zhentao(胡振濤), YANG Linlin,HU Yumei②, YANG Shibo

    (?School of Artificial Intelligence, Henan University, Zhengzhou 450046, P.R.China)(??School of Automation, Northwestern Polytechnical University, Xi’an 710029, P.R.China)

    Abstract Aiming at the problem of filtering precision degradation caused by the random outliers of process noise and measurement noise in multi-target tracking (MTT) system, a new Gaussian-Student’s t mixture distribution probability hypothesis density (PHD) robust filtering algorithm based on variational Bayesian inference (GST-vbPHD) is proposed. Firstly, since it can accurately describe the heavy-tailed characteristics of noise with outliers, Gaussian-Student’s t mixture distribution is employed to model process noise and measurement noise respectively. Then Bernoulli random variable is introduced to correct the likelihood distribution of the mixture probability, leading hierarchical Gaussian distribution constructed by the Gaussian-Student’s t mixture distribution suitable to model non-stationary noise. Finally, the approximate solutions including target weights,measurement noise covariance and state estimation error covariance are obtained according to variational Bayesian inference approach. The simulation results show that, in the heavy-tailed noise environment, the proposed algorithm leads to strong improvements over the traditional PHD filter and the Student’s t distribution PHD filter.

    Key words: multi-target tracking (MTT), variational Bayesian inference, Gaussian-Student’s t mixture distribution, heavy-tailed noise

    0 Introduction

    Multi-target tracking (MTT) technique based on point measurements is used to real-time estimate the number of targets, status, trajectory, and other attribute information with the processing of measurement information. The traditional implementation of MTT generally adopts the data association strategies, such as joint probabilistic data association (JPDA)[1], multihypothesis tracking ( MHT)[2], and probabilistic multi-hypothesis tracking (PMHT)[3]. However,these above methods cannot deal well with the time-varying characteristics of the target state, i.e. the time-varying number of targets makes it difficult to achieve an effective correlation between the state set and the measurement set of the target. Recently, since bypassing the complex data association,the MTT based on random finite set (RFS) theory and its improvements have attracted extensive attention[4]. Specifically, their complexity and track ability are better than those methods using data association strategy. A typical implementation mentioned above is the probability hypothesis density (PHD) filter which recursively solves the state posterior first-order statistical moments, thus gives the first engineering implementation of RFS[5]. The existing PHD filter implementation strategies mainly include sequential Monte Carlo PHD (SMC-PHD)[6]and Gaussian mixture PHD (GM-PHD)[7-8].

    In practical engineering applications, the noise outlier induced by electromagnetic interference, aging of the sensor, and uncertainty of the dynamic model will deteriorate PHD filter tracking accuracy. Besides,the outlier-containing noise usually exhibits heavytailed characteristics. However, traditional GM-PHD suffers poor robustness at heavy-tailed process noise and measurement noise existing[9]. Under the condition of Gaussian distribution, the SMC-PHD filter may partially relieve the above problem with high computational cost. Huber’s M-estimation theory can be used to improve the GM-PHD filter’s performance when outliers exist in the measurement model, but it cannot deal with outliers in process noisy. Moreover, since it is based on Gaussian distribution approximation, GMPHD filter may induce biased estimates on the state and number of targets, thus is unsuitable to handle non-Gaussian noise system model with noisy outliers[10]. Existing literatures show that heavy-tailed noise may not be efficiently tackled in Gaussian noise hypothetical scenario, so heavy-tailed noise modeling becomes the key to deal with multi-target tracking problem with noise outliers[11].

    Since Student’s t distribution exhibits heavier tail than Gaussian distribution and converges to Gaussian distribution as its freedom increasing, it may be suitable for modeling non-Gaussian noise with significant heavy-tailed. Assuming the measurement noise follows Student’s t distribution, Li et al.[12]proposed a robust PHD filter which used VB to update the posterior likelihood function, but the method is unsuitable for noisy outliers. Liu et al.[13]presented a robust Student’s t mixture PHD filter by recursively propagating the intensity as a mixture of Student’s t components in PHD filtering framework. In addition, to alleviate the unfavorable effects on filtering performance induced by heavytailed noise, Liu re-weighted on true measurement,outliers and clutter according to their value, and proposed M-estimation based dual-gating strategy to construct a Student’s t mixture distribution. With approximately regarding the process noise and measurement noise as the Student’s t distribution, Hong et al.[14]proposed a Student’s t mixture particle PHD (STMPPHD) filter. They argued that the intensity of the multi-target may be approximated by using a Student’s t mixture model,while Monte Carlo is utilized to calculate the Student’ s t function integral, leading to a closed Student’s t hybrid recursive framework. However, few literatures above focus on improving the filtering robustness by using variational Bayesian inference. Zhang et al.[15]designed a robust Student’s t based labeled multi-Bernoulli ( RSTLMB ) filter through modeling the Student’s t distribution with the state prediction probability density and the measurement likelihood function of individual targets. Moreover, a closed recursion filter is proposed to jointly estimate the target state and the parameters of the Student’s t distribution. Due to the random occurrence of the outliers in noise, RSTLMB hardly model nonstationarity of noise by using one single Student’s t distribution.

    Obviously, using fixed inverse scale matrix or Student’s t distribution can hardly model random noise with heavy-tailed outliers. To address the above problem, a new Gaussian-Student’s t mixture distribution PHD robust filtering algorithm based on variational Bayesian inference (GST-vbPHD) is proposed here.The main contributions are summarized as follows.

    (1) Random outliers existing in process noise and measurement noise are modeled as Gaussian-Student’s t mixture distribution, in addition, the parameters of the mixture distribution and kinematic state are integrated in the augmentation matrix.

    (2) Bernoulli random variables are introduced to transform the mixture distribution model of noise outliers into a hierarchical Gaussian form in which parameters including targets states and weights are updated by variational Bayesian inference.

    (3) In different experiment scenarios, two types of performance indicators, the optimal subpattern assignment (OSPA) distance and the accuracy of the target number estimation, are used to verify the feasibility and validity of the proposed algorithm. The experiment results demonstrate that the proposed algorithm outperforms the comparison methods on tracking accuracy.

    1 Gaussian mixed PHD filter

    whereSk∣k-1(x) andBk|k-1(x) denote the random finite sets of survival targets and spawned targets fromXk-1at timek,respectively.Γkis the random finite sets of birth targets at timek. Θ(x) andκkdenote respectively the observed random sets generated by targets and clutters at timek.

    The PHD filter estimates the states of targets and its number by iteratively propagating the posterior intensity, which is a first order statistic of the random finite set[7]. The linear Gaussian MTT system develops Gaussian mixture implementation in finding the analytic solution of the Bayesian integral, the process of which clearly demonstrates how the Gaussian components propagate analytically to the next moment. Assume that the prior intensity functionvk-1at timek- 1 obeys the Gaussian distribution

    2 GST-vbPHD filter

    2.1 Gaussian-Student’s t mixture distribution

    The outliers of the process noise and measurement noise may appear at different moments in practical engineering application, resulting in the non-stationarity characteristic of the non-Gaussian noise. The modeling of noise with outliers, by adopting the mixed probabilityηas a mixed Gaussian-Student’s t distribution, is as follows

    The auxiliary variableλis introduced to transform the mixed Gaussian-Student’s t distribution into the following hierarchical Gaussian form[16]

    2.2 GST-vbPHD filting

    2.2.1 Predict

    Combined with the model constructed by Eq.(9)and Eq.(10), the implementation of GST-vbPHD is derived for linear multi-target systems. According to Bayesian probability theory, a beta distribution is selected as the conjugate prior distribution of unknown mixing probabilityηk[16].The likelihood distribution of theηkis expressed as Bernoulli distribution, and Bernoulli componentεkis introduced to select Gaussian or Student’ s t distribution. It is well known that the Student’s t distribution can be expressed as the product of gamma distribution and Gaussian distribution after introducing auxiliary variablesλk.

    Suppose the augmented state?kof one single target, which contains one single target state and a set of parameters for constructing the distribution, can be represented as?k?(xk,ηk,εk,λk),whereηk,εkandλkrespectively refer to the mixed probability, Bernoulli random variables and auxiliary variables, and they are mutually independent ofxk.The components of the predicted intensity are the same as Eq.(1),so the mixed distribution model of joint probability density is expressed as

    Algorithm 1 The variational iteration process for each Gaussian Student’s t mixture components Inputs:m(j)k|k-1, P(j)k|k-1, w(j)k|k-1, Hk, Rk, zk, PD,k, r, e1, e2,ω1, ω2, N 1. Initialization:E(0)[γ] = 1,E(0)[logγ] = 0,E(0)[ε] = 1,E(0)[log(1 - σ)] = nψ(1 - e) - ψ(1)E(0)[logσ] = ψ(e) - ψ(1),2. m(j)(0)k = m(j)k∣k-1, P(j)(0)k = P(j)k∣k-1 3. for n = 0: N -1 do 4. Calculate ~P(j)(n)k|k-1 and ~R(j)(n)k using Eqs(48) and (49)5. Calculate m(j)(n+1)k and P(j)(n+1)k using Eqs(45) -(47)6. Calculate w(j)(n+1)k (z) using Eqs(43) -(44)7. Calculate A(n+1)k , B(n+1)k using Eqs(39) - (40) Update qn+1(ε1,k), qn+1(ε2,k) as Bernoulli distributions

    8. Calculate Prn+1(ε1,k = 1), Prn+1(ε1,k = 0) and Prn+1(ε2,k = 1),Prn+1(ε2,k = 0) using Eqs(33) -(36)9. Calculate En+1[ε1,k], En+1[ε2,k] using Eq.(50)Update qn+1(γ1,k), qn+1(γ2,k) as Gamma distributions 10. Calculate wn+1 1,k, hn+1 1,k and wn+12,k, hn+1 2,k using Eqs(29) -(32)11. Calculate En+1[γ1,k], En+1[γ2,k] and En+1[logγ1,k], En+1[logγ2,k] using Eqs(51) -(52)Update qn+1(σ1,k), qn+1(σ2,k) as Bate distributions 12. Calculate en+1 1,k, tn+1 1,k and en+12,k, tn+1 2,k using Eqs(25) -(28)13. Calculate En+1[logσ1,k], En+1[logσ2,k] and En+1[log(1 - σ1,k)], En+1[log(1 - σ2,k)] using Eqs(39) -(40)14. if m(j+1)(n+1)k - m(j+1)(n)k ≤ε then 15. Stop the iteration 16. end if 17. end for Outputs:m(j)k 、P(j)k and w(j)k

    3 Simulation results and analysis

    3.1 Scenario design

    To verify the tracking performance of the proposed algorithm, two simulation scenarios are designed in the 2-D plane, i.e. the scenario of the measurement noise with outliers and the scenario of outliers in both process noise and measurement noise. In addition, the different probabilities of generating outliers are compared in the two scenarios. For comparison, the tracking performance of the Gaussian mixture PHD filter (GMPHD), the robust Student’s t based PHD filter (RSTPHD) and the GST-vbPHD are employed.

    Assuming that there are four targets in the surveillance range,they are present at time [1 8 12 26](s)until time [20 25 25 40](s) in turn disappears,with uniform motion during the survival period. The real trajectories of all targets are plotted in Fig.1.

    Fig.1 The true trajectory of multiple targets

    3.2 Simulation parameters

    A total of 40 steps are running in the simulation process, and the simulation results are the average after 300 Monte Carlo (MC) trials. What is more, the objective survival probabilityPS,k= 0.99, detection probabilityPD,k= 0.98 and the clutter rateλ= 3.The state and measurement equations of the targets are modeled as the following form

    3.3 Results and analysis

    3.3.1 Scenario 1

    To observe the performance of MTT at measurement outliers existing, the measurement noise covariance is constructed with outlier according to Ref.[21].

    where,p1 is the probability of the measurement noise without outliers and is set to be in a range of 5 -30 s during the multi-target motion.

    Fig.2 shows the OSPA distance errors for the three filters with probabilityp1 = 0. 98. Due to the measurement outliers, it can be observed that the GMPHD has significantly inferior tracking performance to the other two filters. Specifically, when there are outliers in the measurement,because of the light-weight tail property of Gaussian distribution, the weight of Gaussian components tends to be a small value or even zero in some cases, which leads to a larger OSPA distance. In contrast, although RST-PHD takes into account the heavy-tailed feature of noise, it uses a fixed Student’s t distribution for modeling, which lacks robustness to randomly occurring outliers. The OSPA distance curve of GST-vbPHD is lower than that of GM-PHD and RSTPHD, which demonstrates that the tracking performance of GST-vbPHD surpasses the other two algorithms. Due to the random characteristic of the measurement noise outliers, the noise cannot always remain in a heavy-tailed or Gaussian distribution state. GSTvbPHD employs the model with mixture distribution to better estimate the target weights, which helps to track the target without loss. The results of three algorithms for estimating the number of targets are given in Fig.3,and it can be seen that GST-vbPHD significantly outperforms GM-PHD and RST-PHD.

    Fig.2 The OSPA distance

    Fig.3 The number of targets

    To further analyze the impact of the probabilityp1on the filter performance, the statistical analysis on the average OSPA distance is shown in Fig.4. When the probability of the measurement noise without outliers is 0.98,0.96,0.94,0.92 and 0.9 respectively, OSPA average distance all decrease with increasing of the probability of the measurement noise, and that is because the effect of measurement noise outliers on the system significantly weakens. GST-vbPHD has a lower OSPA average distance than GM-PHD and RST-PHD,and has better tracking accuracy under lighter-tailed measurements or even heavy-tailed measurements.

    Fig.4 Average OSPA with different p1

    3.3.2 Scenario 2

    To evaluate the performance of MTT at outlier both existing in process noise and measurement noise,a new experiment Scenario 2 is constructed. The measurement noise outliers can be generated according to Scenario 1, and the process noise covariance with outliers is shown as follows.

    wherep2is the probability of the process noise without outliers. Assume that the time period of outliers in the noise is the same as Scenario 1.

    For outliers of the process noise and the measurement noise with the same probability, Fig.5 shows the OSPA distance comparison of three filters. From Fig.5 and Fig.6, it can be found that GST-vbPHD shows a better tracking result for both the tracking accuracy and the estimation of target number. The process noise outliers may be induced by target maneuvers, while the GM-PHD filter cannot capture the target due to the light tail of the Gaussian distribution, and RST-PHD lacks adaptability to random outliers. The proposed algorithm utilizes the mixture distribution model of noise to correct state error covariance, effectively eliminates the adverse effects induced by process noise outliers.Overall, if there are outliers in both process noise and measurement noise, the GST-vbPHD can achieve reliable and effective performance in MTT.

    Fig.5 The OSPA distance

    Fig.6 The number of targets

    In order to deeply analyze the tracking performance of the filters under different probabilities of outliers, the additional experiments are executed. First,p2is fixed, whilep1is 0. 9, 0. 92, 0. 94, 0. 96, and 0.98 respectively. After that, the average OSPA distance is given in Fig.7. On the contrary, whenp1is fixed,p2changes and the corresponding simulation results are shown in Fig.8. The average OSPA distance of the three filters gradually decreases, which means that the poor tracking performance with the occurrence probability of outliers increases. In Fig.8, the average OSPA distance of the three filters decreases less obviously than that in Fig.7. The results can be attributed to the different multiples of setting outliers in the dynamic model, and the filter is more sensitive to the process noise. As shown in Fig.7 and Fig.8, the proposed algorithm achieves relatively stable tracking performance for different probabilities of outliers in process noise and measurement noise.

    Fig.7 Average OSPA with different p1

    Fig.8 Average OSPA with different p2

    The computation time of the RST-PHD and the GST-vbPHD in this paper is 1.4 s and 3.5 s respectively when both the variational approximation iterations are used. The proposed algorithm not only improves the accuracy of tracking and estimating the number of targets, but also increases the operation time. This is because we consider that both process noise and measurement noise may have outliers. Two sets of parameters are used to modify the measurement noise covariance and state error covariance respectively, and participate in the variational iteration. The contrast algorithm only considers the heavy tail characteristics of noise outliers, but ignores the associate the nonstationarity. It simply uses student t distribution to model the noise.Therefore, the proposed algorithm in this paper does not perform well on the evaluation index of operation time.

    4 Conclusions

    In this paper, a new Gaussian-Student’s t mixture distribution PHD robust filtering algorithm is proposed based on variational Bayesian inference, which models the one-step state prediction PDF and the measurement likelihood PDF as the hierarchical Gaussian forms. Concretely, the hierarchical Gaussian form is employed to correct state error covariance matrix and measurement noise covariance matrix, eliminating the adverse effects of process noise and measurement noise both with outliers on the tracking performance. In addition, the parameters in the mixed distribution term are iteratively optimized by variational inference to obtain the target posterior probability density. The simulation results show that the proposed algorithm can achieve competitive performance with the traditional Gaussian hybrid PHD filter and the Student’s t PHD filter on tracking accuracy in MTT. Future work will focus on how to construct a hierarchical Gaussian noise distribution for nonlinear systems to effectively solve the influence of noise outliers.

    亚洲av成人av| 深夜精品福利| 深爱激情五月婷婷| 夜夜夜夜夜久久久久| 99热精品在线国产| 12—13女人毛片做爰片一| 国产蜜桃级精品一区二区三区| 免费大片18禁| 成年女人毛片免费观看观看9| 人人妻人人看人人澡| 欧美日韩黄片免| 99久久中文字幕三级久久日本| 亚洲七黄色美女视频| 美女高潮的动态| 赤兔流量卡办理| 免费在线观看影片大全网站| 国产一区二区在线av高清观看| 春色校园在线视频观看| 国产色爽女视频免费观看| 黄色日韩在线| 精品久久久久久久久亚洲 | av在线亚洲专区| 大型黄色视频在线免费观看| 黄色视频,在线免费观看| 久99久视频精品免费| 日韩大尺度精品在线看网址| 99视频精品全部免费 在线| 精品国产三级普通话版| 亚洲aⅴ乱码一区二区在线播放| 99在线视频只有这里精品首页| 一进一出抽搐动态| 国产精品野战在线观看| 少妇高潮的动态图| 久久精品夜夜夜夜夜久久蜜豆| 一夜夜www| 啦啦啦啦在线视频资源| 欧美激情久久久久久爽电影| 深夜a级毛片| 美女xxoo啪啪120秒动态图| 99在线视频只有这里精品首页| 一级黄片播放器| 精品日产1卡2卡| 久久精品国产亚洲av天美| 尾随美女入室| 国产一区二区在线观看日韩| 嫩草影院新地址| 成人二区视频| 黄色日韩在线| 国产欧美日韩精品亚洲av| 亚洲四区av| 亚洲av成人精品一区久久| 国产精品亚洲一级av第二区| 天堂√8在线中文| 日韩亚洲欧美综合| 久久久久久久久中文| 又粗又爽又猛毛片免费看| 女生性感内裤真人,穿戴方法视频| 久久午夜亚洲精品久久| 色综合婷婷激情| 国产精品久久久久久亚洲av鲁大| 天美传媒精品一区二区| 亚洲精品乱码久久久v下载方式| 国产精品一区二区三区四区免费观看 | 少妇高潮的动态图| 国内毛片毛片毛片毛片毛片| 精品99又大又爽又粗少妇毛片 | 男人和女人高潮做爰伦理| 国产高清三级在线| 神马国产精品三级电影在线观看| 天美传媒精品一区二区| av黄色大香蕉| 三级国产精品欧美在线观看| or卡值多少钱| 色av中文字幕| 麻豆成人av在线观看| 99久久精品热视频| 精品一区二区三区视频在线| 少妇的逼水好多| 日韩欧美国产一区二区入口| 一级毛片久久久久久久久女| 欧洲精品卡2卡3卡4卡5卡区| 亚洲人成网站在线播放欧美日韩| 哪里可以看免费的av片| 韩国av在线不卡| 欧美日韩国产亚洲二区| 国产一区二区三区av在线 | 黄色视频,在线免费观看| 最新在线观看一区二区三区| 少妇熟女aⅴ在线视频| 一a级毛片在线观看| 少妇猛男粗大的猛烈进出视频 | 91麻豆av在线| 中文亚洲av片在线观看爽| 亚州av有码| 别揉我奶头~嗯~啊~动态视频| 美女被艹到高潮喷水动态| 久久热精品热| 日本爱情动作片www.在线观看 | 国产精品国产高清国产av| 热99在线观看视频| 美女cb高潮喷水在线观看| 成人美女网站在线观看视频| 99国产极品粉嫩在线观看| 国内毛片毛片毛片毛片毛片| 精品久久久久久久久久久久久| 在线天堂最新版资源| 十八禁网站免费在线| 国产人妻一区二区三区在| 少妇的逼水好多| 亚洲欧美日韩无卡精品| 亚洲欧美清纯卡通| 日韩一区二区视频免费看| 91午夜精品亚洲一区二区三区 | 看免费成人av毛片| 久久久久久九九精品二区国产| 999久久久精品免费观看国产| 午夜精品久久久久久毛片777| 亚洲四区av| 99热6这里只有精品| 高清在线国产一区| 欧美激情国产日韩精品一区| ponron亚洲| 99在线人妻在线中文字幕| 国产成人aa在线观看| 亚洲国产日韩欧美精品在线观看| 国产精品av视频在线免费观看| 性色avwww在线观看| 午夜精品一区二区三区免费看| 我的老师免费观看完整版| 色综合站精品国产| 精品99又大又爽又粗少妇毛片 | 一个人观看的视频www高清免费观看| 日韩欧美国产在线观看| 日韩精品青青久久久久久| 偷拍熟女少妇极品色| 人妻久久中文字幕网| 在线观看66精品国产| 亚洲av美国av| 欧美中文日本在线观看视频| 国产熟女欧美一区二区| 国内毛片毛片毛片毛片毛片| 中文在线观看免费www的网站| 国产精品久久久久久久电影| av在线天堂中文字幕| 成人亚洲精品av一区二区| 日韩中文字幕欧美一区二区| 欧美一区二区亚洲| 老女人水多毛片| 91久久精品电影网| 久久6这里有精品| 国产高清三级在线| 日本色播在线视频| 亚洲欧美日韩高清专用| 天天一区二区日本电影三级| 欧美精品国产亚洲| 成年女人毛片免费观看观看9| 小说图片视频综合网站| 深爱激情五月婷婷| 成人综合一区亚洲| 3wmmmm亚洲av在线观看| 亚洲成人久久爱视频| 欧美绝顶高潮抽搐喷水| 波多野结衣高清作品| 午夜激情福利司机影院| 国产精品久久久久久久电影| 日本a在线网址| 国产aⅴ精品一区二区三区波| 成人国产一区最新在线观看| 91久久精品国产一区二区成人| 男女那种视频在线观看| 美女免费视频网站| 久99久视频精品免费| 黄色配什么色好看| 久久久久久久午夜电影| 老师上课跳d突然被开到最大视频| 国产爱豆传媒在线观看| 久久6这里有精品| 免费观看精品视频网站| 精品国产三级普通话版| 久久久久性生活片| 动漫黄色视频在线观看| 美女xxoo啪啪120秒动态图| 国产av一区在线观看免费| 精品人妻偷拍中文字幕| 一区二区三区免费毛片| av在线亚洲专区| 高清日韩中文字幕在线| 欧美中文日本在线观看视频| 成人毛片a级毛片在线播放| 69人妻影院| 国产成人a区在线观看| 亚洲美女黄片视频| 国产精品一区www在线观看 | 少妇人妻精品综合一区二区 | 国内久久婷婷六月综合欲色啪| 99久久久亚洲精品蜜臀av| 在线观看美女被高潮喷水网站| 久久久久精品国产欧美久久久| 成人国产一区最新在线观看| 午夜福利视频1000在线观看| 欧美精品啪啪一区二区三区| 亚洲精品日韩av片在线观看| 久久九九热精品免费| 伦精品一区二区三区| 免费av毛片视频| 99热6这里只有精品| 亚洲精品一卡2卡三卡4卡5卡| 亚洲熟妇中文字幕五十中出| 亚洲不卡免费看| 国产爱豆传媒在线观看| 色播亚洲综合网| 国产精品野战在线观看| 日韩欧美三级三区| 欧美中文日本在线观看视频| 国内精品美女久久久久久| 国产黄色小视频在线观看| 中文在线观看免费www的网站| 午夜免费成人在线视频| 亚洲最大成人手机在线| 一个人看视频在线观看www免费| 国产成年人精品一区二区| 成年免费大片在线观看| 国产熟女欧美一区二区| 偷拍熟女少妇极品色| 亚洲人成网站高清观看| 美女免费视频网站| 99热这里只有是精品在线观看| 全区人妻精品视频| 日本免费a在线| 国产成人a区在线观看| 一级a爱片免费观看的视频| 在线免费十八禁| 黄色一级大片看看| 久久久精品欧美日韩精品| 九色国产91popny在线| 亚洲成人精品中文字幕电影| 51国产日韩欧美| 欧美最新免费一区二区三区| 亚洲七黄色美女视频| 99久久精品一区二区三区| 国内揄拍国产精品人妻在线| 日本黄色片子视频| 日韩一本色道免费dvd| 性插视频无遮挡在线免费观看| 精品久久久久久久人妻蜜臀av| 国产免费av片在线观看野外av| 亚洲成a人片在线一区二区| 动漫黄色视频在线观看| 97热精品久久久久久| 久久久久免费精品人妻一区二区| 欧美一区二区国产精品久久精品| 日韩大尺度精品在线看网址| 99热只有精品国产| 黄色丝袜av网址大全| 午夜影院日韩av| 久久午夜亚洲精品久久| 中文亚洲av片在线观看爽| а√天堂www在线а√下载| av专区在线播放| 高清日韩中文字幕在线| 日本在线视频免费播放| 国产男靠女视频免费网站| 日本欧美国产在线视频| 在线观看免费视频日本深夜| 性插视频无遮挡在线免费观看| 搡老熟女国产l中国老女人| 亚洲国产欧洲综合997久久,| 97超级碰碰碰精品色视频在线观看| 熟妇人妻久久中文字幕3abv| av天堂在线播放| 欧美日韩中文字幕国产精品一区二区三区| 久久久久国内视频| 乱码一卡2卡4卡精品| 欧美日韩黄片免| 成人特级av手机在线观看| 麻豆av噜噜一区二区三区| 不卡视频在线观看欧美| 丰满的人妻完整版| 午夜精品在线福利| 看片在线看免费视频| 男女啪啪激烈高潮av片| 欧美性猛交╳xxx乱大交人| 极品教师在线视频| 一级黄色大片毛片| 波多野结衣高清作品| aaaaa片日本免费| 国产精品一区二区三区四区免费观看 | 精品国产三级普通话版| 久久人人爽人人爽人人片va| 国产精品一区www在线观看 | 中文字幕熟女人妻在线| 国产美女午夜福利| 久久精品国产亚洲av天美| 欧美三级亚洲精品| 国产 一区精品| 制服丝袜大香蕉在线| 亚洲久久久久久中文字幕| 免费观看在线日韩| 综合色av麻豆| 色av中文字幕| 精品一区二区三区人妻视频| 少妇裸体淫交视频免费看高清| 欧美国产日韩亚洲一区| 一区福利在线观看| 蜜桃久久精品国产亚洲av| 亚洲美女黄片视频| 日本撒尿小便嘘嘘汇集6| 国产爱豆传媒在线观看| 简卡轻食公司| 国产女主播在线喷水免费视频网站 | 日本欧美国产在线视频| av在线亚洲专区| 丰满人妻一区二区三区视频av| 国产亚洲欧美98| 熟女电影av网| 久久精品国产自在天天线| 男人舔女人下体高潮全视频| 国产成人影院久久av| 欧美高清成人免费视频www| eeuss影院久久| 一本一本综合久久| 成人欧美大片| 午夜福利18| 俺也久久电影网| 色综合婷婷激情| 国产主播在线观看一区二区| 国产淫片久久久久久久久| 亚洲无线观看免费| 国产蜜桃级精品一区二区三区| 能在线免费观看的黄片| 国产精品乱码一区二三区的特点| 久久中文看片网| 精品无人区乱码1区二区| .国产精品久久| 变态另类丝袜制服| 午夜福利成人在线免费观看| av在线蜜桃| 制服丝袜大香蕉在线| 午夜精品一区二区三区免费看| 国产精品无大码| 99在线视频只有这里精品首页| 亚洲国产精品合色在线| 少妇的逼好多水| 91麻豆av在线| 国产不卡一卡二| 特大巨黑吊av在线直播| av国产免费在线观看| 久久99热6这里只有精品| 久久久午夜欧美精品| 国产精品美女特级片免费视频播放器| 人妻夜夜爽99麻豆av| 午夜福利视频1000在线观看| 国产一区二区三区视频了| 国产亚洲精品久久久com| 一级黄片播放器| 22中文网久久字幕| bbb黄色大片| 午夜福利高清视频| 欧美xxxx性猛交bbbb| 成人毛片a级毛片在线播放| av女优亚洲男人天堂| 99久久久亚洲精品蜜臀av| 麻豆成人午夜福利视频| 此物有八面人人有两片| 熟女人妻精品中文字幕| 神马国产精品三级电影在线观看| 欧美丝袜亚洲另类 | 午夜老司机福利剧场| 国产精品1区2区在线观看.| 欧美黑人巨大hd| 国产精品国产高清国产av| 欧美在线一区亚洲| 亚洲av二区三区四区| 色精品久久人妻99蜜桃| 亚洲电影在线观看av| 久久国内精品自在自线图片| 人妻夜夜爽99麻豆av| 国产真实乱freesex| ponron亚洲| 日韩亚洲欧美综合| 又粗又爽又猛毛片免费看| 少妇人妻精品综合一区二区 | 久久久久免费精品人妻一区二区| 校园人妻丝袜中文字幕| 日韩精品中文字幕看吧| 久久久久国内视频| 麻豆久久精品国产亚洲av| 日韩亚洲欧美综合| 男人舔奶头视频| 欧美色视频一区免费| 日韩中文字幕欧美一区二区| 国产高清有码在线观看视频| 在线国产一区二区在线| 波多野结衣高清作品| 直男gayav资源| 免费看a级黄色片| 亚洲四区av| 国产乱人伦免费视频| 极品教师在线免费播放| 国产精品一区二区免费欧美| 亚洲精品日韩av片在线观看| 午夜爱爱视频在线播放| 麻豆成人av在线观看| 天美传媒精品一区二区| 亚洲av中文字字幕乱码综合| 国产精品免费一区二区三区在线| 国产亚洲欧美98| 国产精品综合久久久久久久免费| 毛片一级片免费看久久久久 | 最新中文字幕久久久久| 夜夜夜夜夜久久久久| 成人毛片a级毛片在线播放| 一本久久中文字幕| 国产极品精品免费视频能看的| 麻豆成人午夜福利视频| av在线观看视频网站免费| 成人二区视频| 精品一区二区免费观看| 国产探花在线观看一区二区| 极品教师在线免费播放| 亚洲av五月六月丁香网| 久久久久九九精品影院| 国产精品综合久久久久久久免费| 日本黄色片子视频| 热99在线观看视频| 真实男女啪啪啪动态图| 日韩精品有码人妻一区| 啦啦啦啦在线视频资源| 又紧又爽又黄一区二区| 欧美激情国产日韩精品一区| 黄色女人牲交| 97超级碰碰碰精品色视频在线观看| 国产精品久久久久久久电影| 女人十人毛片免费观看3o分钟| 亚洲成av人片在线播放无| 五月玫瑰六月丁香| 91麻豆av在线| 色在线成人网| 国产成人av教育| 欧美三级亚洲精品| 午夜福利在线观看吧| 伊人久久精品亚洲午夜| 露出奶头的视频| 日韩一区二区视频免费看| 国产精品人妻久久久影院| 精品午夜福利视频在线观看一区| 男女啪啪激烈高潮av片| 在线免费十八禁| 国产午夜福利久久久久久| 日韩精品有码人妻一区| 亚洲久久久久久中文字幕| 精品乱码久久久久久99久播| 麻豆一二三区av精品| 欧美日韩乱码在线| 看十八女毛片水多多多| 国产欧美日韩一区二区精品| 国产午夜精品久久久久久一区二区三区 | 我要搜黄色片| 麻豆成人av在线观看| 久久久国产成人免费| 亚洲精品一区av在线观看| 一卡2卡三卡四卡精品乱码亚洲| 色吧在线观看| 真人做人爱边吃奶动态| 岛国在线免费视频观看| 全区人妻精品视频| x7x7x7水蜜桃| 搡女人真爽免费视频火全软件 | 欧美成人免费av一区二区三区| 少妇被粗大猛烈的视频| 极品教师在线视频| 国内揄拍国产精品人妻在线| 成人综合一区亚洲| 免费观看人在逋| 亚洲经典国产精华液单| 麻豆成人午夜福利视频| 成人三级黄色视频| 老女人水多毛片| 丰满乱子伦码专区| 婷婷亚洲欧美| 日本欧美国产在线视频| 国产av在哪里看| 欧美色欧美亚洲另类二区| 国产真实伦视频高清在线观看 | 国内揄拍国产精品人妻在线| 亚洲三级黄色毛片| 日本爱情动作片www.在线观看 | 国产精品一区二区三区四区久久| 韩国av一区二区三区四区| 91久久精品国产一区二区成人| 亚洲中文字幕日韩| 夜夜夜夜夜久久久久| 99久久精品热视频| 国产在线男女| 毛片一级片免费看久久久久 | 春色校园在线视频观看| av在线观看视频网站免费| 亚洲图色成人| 麻豆国产97在线/欧美| 天天一区二区日本电影三级| 亚洲av免费高清在线观看| 国产三级在线视频| 中文字幕人妻熟人妻熟丝袜美| 亚洲精品粉嫩美女一区| 成人国产综合亚洲| 欧美色视频一区免费| 亚洲欧美日韩无卡精品| 99热这里只有是精品50| 免费看美女性在线毛片视频| 国产精品久久久久久久电影| 日本黄色片子视频| 免费不卡的大黄色大毛片视频在线观看 | 成人国产综合亚洲| 国产淫片久久久久久久久| 国产 一区精品| 亚洲av成人精品一区久久| 一级毛片久久久久久久久女| 亚洲电影在线观看av| 日本黄色片子视频| 亚洲av美国av| 国产探花极品一区二区| 国产亚洲精品久久久com| 亚洲美女黄片视频| 99热这里只有是精品50| 校园人妻丝袜中文字幕| 身体一侧抽搐| 极品教师在线视频| 亚洲内射少妇av| 色综合色国产| 日韩 亚洲 欧美在线| avwww免费| 可以在线观看毛片的网站| 69av精品久久久久久| ponron亚洲| 免费黄网站久久成人精品| 99热只有精品国产| 亚洲成人精品中文字幕电影| 男女那种视频在线观看| 一夜夜www| 1000部很黄的大片| 中文字幕免费在线视频6| 亚洲精品粉嫩美女一区| 国产乱人伦免费视频| 热99re8久久精品国产| 99久久精品热视频| netflix在线观看网站| 性色avwww在线观看| 日本与韩国留学比较| 国产 一区 欧美 日韩| 日韩欧美免费精品| 色哟哟哟哟哟哟| 91麻豆av在线| 成年女人永久免费观看视频| 欧美最新免费一区二区三区| 在线看三级毛片| 国产精品国产三级国产av玫瑰| 国产精品亚洲美女久久久| 午夜精品在线福利| 国产三级在线视频| 淫妇啪啪啪对白视频| 岛国在线免费视频观看| 大型黄色视频在线免费观看| 精品久久久久久久久久久久久| 国产一区二区三区在线臀色熟女| 成人特级黄色片久久久久久久| 亚洲精品456在线播放app | 亚洲最大成人中文| 能在线免费观看的黄片| 久久99热这里只有精品18| 国产亚洲精品综合一区在线观看| 亚洲欧美清纯卡通| 18禁黄网站禁片免费观看直播| 日韩大尺度精品在线看网址| 免费搜索国产男女视频| av天堂在线播放| 国产成人a区在线观看| 女人十人毛片免费观看3o分钟| 国产乱人视频| 亚洲天堂国产精品一区在线| 男女之事视频高清在线观看| 麻豆久久精品国产亚洲av| 欧美高清性xxxxhd video| 舔av片在线| 永久网站在线| 内地一区二区视频在线| 国产精品自产拍在线观看55亚洲| 999久久久精品免费观看国产| 欧美日韩亚洲国产一区二区在线观看| 亚洲精品国产成人久久av| 一区二区三区激情视频| 午夜福利成人在线免费观看| a级毛片a级免费在线| 国产熟女欧美一区二区| 女的被弄到高潮叫床怎么办 | 国产乱人伦免费视频| 97超级碰碰碰精品色视频在线观看| 日本精品一区二区三区蜜桃| 白带黄色成豆腐渣| 美女cb高潮喷水在线观看| 97碰自拍视频| 嫁个100分男人电影在线观看| av在线老鸭窝| 在线免费观看的www视频| av专区在线播放| 午夜亚洲福利在线播放| 亚洲欧美日韩东京热| 国产亚洲av嫩草精品影院| 国产三级中文精品| 在线看三级毛片| 日韩精品有码人妻一区| av在线老鸭窝| 噜噜噜噜噜久久久久久91| 99久久九九国产精品国产免费| 日本欧美国产在线视频| 日本与韩国留学比较|