• <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 ?

    Cubature Kalman probability hypothesis density filter based on multi-sensor consistency fusion①

    2016-12-22 05:45:36HuZhentao胡振濤HuYumeiGuoZhenWuYewei
    High Technology Letters 2016年4期

    Hu Zhentao (胡振濤), Hu Yumei, Guo Zhen, Wu Yewei

    (*Institute of Image Processing and Pattern Recognition, Henan University, Kaifeng 475004, P.R.China)(**College of Automation, Northwestern Polytechnical University, Xi’an 710072, P.R.China)

    ?

    Cubature Kalman probability hypothesis density filter based on multi-sensor consistency fusion①

    Hu Zhentao (胡振濤)②*, Hu Yumei**, Guo Zhen*, Wu Yewei*

    (*Institute of Image Processing and Pattern Recognition, Henan University, Kaifeng 475004, P.R.China)(**College of Automation, Northwestern Polytechnical University, Xi’an 710072, P.R.China)

    The GM-PHD framework as recursion realization of PHD filter is extensively applied to multi-target tracking system. A new idea of improving the estimation precision of time-varying multi-target in non-linear system is proposed due to the advantage of computation efficiency in this paper. First, a novel cubature Kalman probability hypothesis density filter is designed for single sensor measurement system under the Gaussian mixture framework. Second, the consistency fusion strategy for multi-sensor measurement is proposed through constructing consistency matrix. Furthermore, to take the advantage of consistency fusion strategy, fused measurement is introduced in the update step of cubature Kalman probability hypothesis density filter to replace the single-sensor measurement. Then a cubature Kalman probability hypothesis density filter based on multi-sensor consistency fusion is proposed. Capabilily of the proposed algorithm is illustrated through simulation scenario of multi-sensor multi-target tracking.

    multi-target tracking, probability hypothesis density (PHD), cubature Kalman filter, consistency fusion

    0 Introduction

    Multi-target tracking techniques are always the hotspot research in target tracking field. The probability hypothesis density (PHD) filter as recursion that propagates the first-order statistical moment of random finite sets (RFS) of states, is an attractive approach to track unknown and time-varying targets in the presence of measurement uncertainty, clutter, noise, and detection uncertainty[1]. However, PHD filter contains multiple integrals with no closed forms in general. Due to its inherent computational hurdle, the application and popularization of PHD filter is limited. To solve this problem, some researches and work mainly focus on two categories. One of the effective implementations is sequential Monte Carlo PHD (SMC-PHD) filter[2,3]. In the non-linear and non-Gaussian system, the relationship between PHD filter and sequential Monte Carlo method is established through approximating PHD function by a group of random samples in state space, and leads the integral computation to be replaced by samples mean[4]. However, a large number of particles, needed to ensure filtering precision in the realization of SMC-PHD filter, lead to increase of computation cost, and extracting multi-target estimation is an additional cost. Moreover, the stochastic sampling mechanism often leads particle to degeneracy after a few iterations. The adverse effect caused by particle degeneracy is mitigated in a certain degree through re-sampling, but the re-sampling process results in the reduction of particle diversity. In addition, an estimated state is obtained through dividing the particle into different clusters in SMC-PHD filter, which leads to state estimation unreliable. The other one is Gaussian mixture PHD (GM-PHD) filter[5,6], for jointly estimating the time-varying number of targets and their states, closed-form recursions are given for propagating means, covariance, and weights of the constituent Gaussian component of posterior intensity, which meets three assumptions: ① Targets and sensor follow a linear and Gaussian model. ② The survival and detection probabilities are independent. ③ The intensities of birth and spawn RFSs are Gaussian mixture. In Ref.[7], Clark proved uniform convergence of the errors in GM-PHD filter. Aiming at the multi-detection from a same target, Tang derived a general multi-detection PHD update formulation, and established its recursion realization under the GM-PHD framework[8].

    However, with regard to the non-linear feature of multi-target system, assumption ① is extended to non-linear Gaussian model. Therefore, the non-linear filter such as extended Kalman filter (EKF) and unscented Kalman filter (UKF) are considered to unite the PHD filter under the framework of Gaussian mixture[9,10]. The implementation mechanism of EKF is to realize local linearization of state equation and observation equation. It only calculates the posterior mean and covariance accurately to the first order with all higher order moments truncated. If the nonlinearity of estimated system is very strong, usually EKF can not obtain good filtering result and even lead to the filtering divergence phenomenon[11,12]. While unscented Kalman filter (UKF)[13]and cubature Kalman filter (CKF)[14]are both typical implementation of deterministic sampling filter, UKF approaches nonlinear state posterior distribution by UT transformation strategy, and it has higher universality for non-linear system with Gaussian noise. But whether the parameters are selected reasonably or not in UKF, they may affect targets estimation precision directly. In addition, the problem that filtering variance is not positive definite may occur. However, in the implementation of CKF, a third-degree spherical-radial cubature rule is established to compute integrals numerically. The weights in CKF are positive to ensure that the filtering covariance is positive definite matrix, and it is verified that CKF is superior to UKF[15]. Therefore, CKF is adopted to realize PHD recursion under the framework of Gaussian mixture in this paper.

    The appropriate selection of filtering algorithm leads to the improvement of targets tracking precision. Measurement, obtained by sensor for providing latest information in the update step, is also an alternative vital factor to enhance estimation precision. The technique of information fusion based on multi-sensor measurement system[16,17]is a popular method to extend measurement range, improve information redundancy and credibility, through the synergy between sensors. Therefore, a consistency fusion strategy is proposed to process the multi-sensor measurement through constructing consistency matrix. On this basis, a cubature Kalman probability hypothesis density filter based on multi-sensor consistency fusion is proposed.

    The rest of the paper is organized as follows. In Section 1, the background information on PHD filter is presented. Section 2 proposes a cubature Kalman probability hypothesis density (CK-PHD) filter for single-sensor multi-target tracking under Gaussian mixture framework. Then, in Section 3, a consistency fusion strategy is established for fusing multi-sensor measurement through constructing consistency matrix. Furthermore, a new cubature Kalman probability hypothesis density filter based on multi-sensor consistency fusion (MC-CK-PHD) is proposed by introducing the fused measurement during update step in Section 4. The proposed algorithms are illustrated in Section 5 through a simulation example. Finally, conclusions are summarized in Section 6.

    1 PHD filter

    An optical Bayesian filter using RFS or point process for multi-target tracking is very computationally challenging, especially when the target number is large. To reduce complexity, Mahler devises PHD filter as an approximation of an optimal multi-target Bayesian filter. And it propagates the first-order statistical moment of the posterior multi-target state, i.e., the posterior density is propagated in PHD filter. Let the posterior density equal to Ik-1|k-1(xk-1|Z1:k-1) at time k. The recursion steps of PHD filter are as follows:

    ? Prediction steps:

    Lk|k-1(xk|Z1:k-1)=γk(xk) + [∫pS,k(xk-1)fk|k-1(xk|xk-1) +∫βk|k-1(xk|xk-1)] ×Lk-1|k-1(xk-1|Z1:k-1)dxk-1

    (1)

    where γk(xk) is the intensity of target appearing at time k, pS,k(xk-1) is the target survival probability, fk|k-1(xk|xk-1) is the single target Markov transition density, and βk|k-1(xk|xk-1) is the intensity of spawning of target from existing ones.

    ? Update steps:

    (2)

    where ψ(zk|Z1:k-1)=∫pD,kf(zk|xk)Lk|k-1(xk|Z1:k-1), pD,k(xk-1) denotes the detection probability, f(zk|xk) is the single target likelihood function, λkand ck(zk) are the false alarm(clutter) intensity and false alarm spatial density, respectively.

    The expected number of targets is given by

    Nk|k=∫Lk|k(xk|Z1:k)dxk

    (3)

    The PHD filter completely avoids the combinatorial computation arising from the unknown association of measurements with appropriate targets. However, the closed-form solutions of recursion in PHD filter cannot be achieved in general which results in that it is difficult for PHD filter to realize engineering application. And numerical integration suffers from the “curse of dimensionality”[5]. In Ref.[3], it is shown that Gaussian mixture probability hypothesis density (GM-PHD) filter provides a closed-form solution for multi-target tracking without measurement-to-track data association.

    2 Cubature Kalman probability hypothesis density filter

    In this section, combining CKF with PHD under Gaussian mixture framework, a cubature Kalman probability hypothesis density (CK-PHD) filter is proposed for jointly estimating time-varying number and position of targets.

    (4)

    L=2n denotes the number of cubature points, and n denotes the dimension of estimated system state, ξjis the jth cubature point.

    The GM-PHD filter propagates the multi-target posterior density through Gaussian mixture components, providing a closed-form solution under the three assumptions. The mathematical express of the three assumptions is given[18]:

    fk|k-1(xk|xk-1)=N(xk; fk-1xk-1, Qk-1)

    (5)

    gk(zk|xk)=N(zk;hkxk,Rk)

    (6)

    pS,k(xk)=pS,k

    (7)

    pD,k(xk)=pD,k

    (8)

    (9)

    (10)

    where, J and ω are the number and the weight of Gaussian mixture components, respectively.

    ? Prediction steps:

    The predicted intensity for time k is also a Gaussian mixture and is given by

    Lk|k-1(xk|Z1:k-1)=LS,k|k-1(xk|Z1:k-1) +Lβ,k|k-1(xk|Z1:k-1)+γk(xk)

    (11)

    (12)

    (13)

    (14)

    (15)

    (16)

    (17)

    State one-step prediction and its error covariance of the existing targets

    (18)

    (19)

    State one-step prediction and its error covariance of the spawned targets

    (20)

    (21)

    ? Update steps:

    (22)

    (23)

    (24)

    (25)

    (26)

    (27)

    (28)

    (29)

    (30)

    (31)

    (32)

    (33)

    (34)

    (35)

    (36)

    3 Consistency fusion strategy

    In the situation of multi-sensor measurement system, redundant and complementary information is extracted and utilized as much as possible to reduce the dependence of measurement noise statistics information. In this paper, the consistency distance and consistency matrix is built to characterize the mutual support degree between multi-sensor measurements. On this basis, the consistency fusion strategy for multi-sensor measurement is established through constructing consistency matrix. The elements in the matrix denote the mutual support degree. The measurement weights are allocated legitimately to utilize measurement effectively in fusion process.

    Considering the matrix of mutual support degree between multi-sensor measurements, the graphical representation of confidence distance is in Fig.1, and the equation is defined as

    (37)

    Fig.1 Consistency distance

    (38)

    (39)

    (40)

    where the consistency matrix Ψkand weight coefficient vector αkare expressed

    (41)

    (42)

    The numerical characteristic of the elements in Ψkshows: all diagonal elements are equal to 1, so Ψkis a positive definite symmetric matrix. The other elements in this matrix are positive and not greater than 1. According to Perron-Frobenius theorem: there is a maximum modulus eigenvalue λk>0. Only when all elements in eigenvector corresponding to eigenvalue λkare positive, λkβk=Ψkβk. Let αk=βk, combined with Eq.(40), then

    (43)

    (44)

    (45)

    The fused measurement noise variance is

    (46)

    Combining the above analysis, the pseudo-code of consistency fusion is given as follows:

    Algorithm1:Consistencyfusiongiventhemulti?sensormeasurement{zik|zik=h(xk)+vik,i=1,2,…,N}calculatetheconfidencedistancefori=1,…,N forj=1,…,N Rijk=(zik-zjk)Τ(zik-zjk)/(Rvik+Rvjk) endendcalculatetheconsistencydistancefori=1,…,N forj=1,…,N Θijk=1-Rijk/max(max(Rijk)) endendfindthemaximumeigenvalueandcorrespondingeigenvectorofconsistencydistance[β,λ]=eig(Ψk)m=max(max(λ))calculatetheweightωikofzik,andnormalizationfori=1,…,N αik=abs(β(i,m))endαik=αik/∑Ni=1αikmeasurementfusionfori=1,…,N ^z′k=∑Ni=1αikzikend

    4 Cubature Kalman probability hypothesis density filter based on multi-sensor consistency fusion

    A CK-PHD filter is extended to multi-sensor case. Assume that there are N sensors and that the measurement noises with the same covariance are irrelevant Gaussian white noise. Then consistency fusion strategy is designed to obtain fusion measurement. Based on the above work, a cubature Kalman probability hypothesis density filter based on multi-sensor consistency fusion is proposed. The key steps of MC-CK-PHD filter are given as follows:

    Algorithm2:CubatureKalmanprobabilityhypothesisdensi?tyfilterbasedonmulti?sensorconsistencyfusiongiven{ω(i)k-1,m(i)k-1,P(i)k-1}Jk-1i=1andthemeasurementsetZk.step1.predictionforbirthtargets i=0. forj=1,…,Jγ,k i:=i+1 ω(i)k|k-1=ω(j)γ|k,x(i)k|k-1=x(j)γ|k,P(i)k|k-1=P(j)γ|k. end forj=1,…,Jβ,k forl=1,…,Jk-1 i:=i+1 ω(i)k|k-1=ω(l)k-1ω(j)β,k ^x(i)k|k-1=f(j)β|k-1^x(l)β|k-1+v(j)β|k-1 P(i)k|k-1=Q(j)γ|k-1+f(j)β|k-1P(l)β|k-1f(j)β|k-1Τ end end forj=1,…,i setμ:=^x(j)k|k-10é?êêù?úú,C:=P(j)k|k?100Rké?êêù?úú usethethird?degreespherical?radialcubatureruletogenerateasetofcubaturepointswithmeanμ,covarianceC,andweightsdenotedby{y(l)k,μ(l)}Ll=1. z(l)k|k-1:=hk(x(l)k|k-1,ε(l)k),l=1,…,L. η(j)k|k-1=∑Ll=1μlz(l)k|k-1 P(j)zz,k=∑Ll=1μl(z(l)k|k-1-η(j)k|k-1)(z(l)k|k-1-η(j)k|k-1)Τ P(j)xz,k=∑Ll=1μl(z(l)k|k-1-^x(j)k|k-1)(z(l)k|k-1-η(j)k|k-1)Τ K(j)k=P(j)xz,k[P(j)zz,k]-1 P(j)k|k=P(j)k|k-1-K(j)k[P(j)xz,k]Τ endStep2.constructionofexistingtargetcomponents forj=1,…,i i:=i+1 ω(i)k|k-1=pS,kω(i)k-1|k-1 setμ:=^x(j)k|k-100é?êêêù?úúú,C:=P(j)k|k-1000Qk-1000Rké?êêêêù?úúúú usethethird?degreespherical?radialcubatureruletogenerateasetofcubaturepointswithmeanμ,covarianceC,andweightsdenotedby{y(l)k,μ(l)}Ll=1.

    5 Simulation results and analysis

    where, ω=0.025rad/s is the angular acceleration of targets, Τ=1 is the sampling period. pS,k=0.99, pD,k=0.98, U=5, Jmax=100, T_prun=10e-5.

    Table 1 The rest initial value of parameters in the algorithm

    Fig.2 Measurement and true trajectories

    (a) EK-PHD

    (b) UK-PHD

    (c) CK-PHD

    (d) MC-CK-PHD

    Fig.3 The target trajectories and their estimations of (a) EK-PHD, (b) UK-PHD, (c) CK-PHD and (d) MC-CK-PHD

    The proposed algorithm is compared with EK-PHD filter and UK-PHD filter presented in Ref.[5]. The results and analysis of simulation are given below.

    The measurement and the real trajectories of the targets are given in Fig.2. Note that square marks and circle marks denote the initial position and final position of targets, respectively.

    To verify the effectiveness of the proposed algorithm, Fig.3 gives the target trajectories and their estimations of (a) EK-PHD, (b) UK-PHD, (c) CK-PHD and (d) MC-CK-PHD. The figures illustrate that state estimation through MC-CK-PHD filter approximates real trajectories mostly.

    Fig.4 illustrates the comparison of the four algorithms estimation precisions of the number of targets. The plots demonstrate that both CK-PHD filter and MC-CK-PHD filter are superior to EK-PHD filter and UK-PHD filter for estimating the number of targets. Meanwhile, the MC-CK-PHD filter is more reliable than CK-PHD filter because consistency fusion strategy in MC-CK-PHD filter makes sure that fused measurement is more precise than single-sensor measurement does. For quantitative comparison, Table 2 gives the average estimation error of targets number through the four algorithms after 50 simulations. It is clear that the EK-PHD filter and UK-PHD filter have the average estimation error of 9.20 and 9.22 respectively, and the error of CK-PHD filter and MC-CK-PHD filter are 8.06 and 8.02, respectively. The results further suggest that the average estimation error of MC-CK-PHD filter is the lowest, namely, MC-CK-PHD filter outperforms others in targets number estimation.

    To verify the capability of proposed algorithm more clearly, Fig.5 gives the comparison of average OSPAs of EK-PHD filter, UK-PHD filter, CK-PHD filter and MC-CK-PHD filter after 50 Monte Carlo simulations. It shows that the average OSPA of CK-PHD filter is lower than EK-PHD’s and UK-PHD’s, and that the average OSPA of MC-CK-PHD filter is the smallest in all. Fig.5 also illustrates that both CK-PHD filter and MC-CK-PHD filter have the advantage of position estimation precision. Further, MC-CK-PHD filter is superior to CK-PHD filter. Table 3 gives the comparison of the total OSPAs of all step time.

    (a) EK-PHD

    (b) UK-PHD

    (c) CK-PHD

    (d) MC-CK-PHD

    Fig.4 Real number of targets and their estimation of (a) EK-PHD, (b) UK-PHD, (c) CK-PHD and (d) MC-CK-PHD

    Table 2 The comparison of average estimation error of the four algorithms for targets number

    Fig.5 The comparison of OSPAs

    AlgorithmsEK?PHDUK?PHDCK?PHDMC?CK?PHDOSPAsummation121.8744121.8126103.254171.4176

    6 Conclusions

    In this study, the multi-target tracking problem on estimation precision in linear is considered under PHD filter framework. Combined with the advantaged of CKF, CK-PHD filter is proposed based on single-sensor measurement system. And it is a generalized solution for estimating targets number and position. Furthermore, a consistency fusion strategy is established, and introduced into the CK-PHD filter. On this basis, the implementation denoted as MC-CK-PHD filter has been presented. Simulation results show that the CK-PHD filter and MC-CK-PHD filter outperform the published EK-PHD filter and UK-PHD filter in the scenario with time-varying number of multi-targets. Meanwhile, the MC-CK-PHD filter is superior to CK-PHD filter in targets number estimation and position estimation.

    [ 1] Mahler R. Multitarget Bayes filtering via first-order multitarget moments. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1152-1178

    [ 3] Ba-Ngu V, Sumeetpal S, Doucet A. Sequential Monte Carlo methods for multitarget filtering with random finite sets. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41 (4): 1224-1245

    [ 4] Baser E, Efe M. A novel auxiliary particle PHD filter. In: Proceedings of the 15th IEEE International Conference on Information Fusion, Singapore, 2012, 165-172

    [ 5] Ba-Ngu V, Ma W K. The Gaussian mixture probability hypothesis density filter. IEEE Transactions on Signal Processing, 2006, 54(11): 4091-4104

    [ 6] Pasha S A, Ba-Ngu V, Hoang D T, et al. A Gaussian mixture PHD filter for jump Markov system models. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(3): 919-936

    [ 7] Clark D, Ba-Ngu V. Convergence analysis of the Gaussian mixture PHD filter. IEEE Transactions on Signal Processing, 2007, 55(4): 1204-1212

    [ 8] Tang X, Chen X, McDonald M, et al. A multiple-detection probability hypothesis density filter. IEEE Transactions on Signal Processing, 2015, 63(8): 2007-2019

    [ 9] Melzi M, Ouldali A, Messaoudi Z. Multiple target tracking using the extended Kalman particle probability hypothesis density filter. In: Proceedings of the 18th European Signal Processing Conference, Aalborg, Denmark, 2010, 1821-1826

    [10] Kurian A P, Puthusserypady S. Performance analysis of nonliner predictive filer based on chaotic synchronization. IEEE Transactions on Circuits & Systems II: Express Briefs, 2006, 53(9): 886-890

    [11] Melzi M, Ouldali A, Messaoudi Z. The unscented Kalman particle PHD filter for joint multiple target tracking and classification. In: Proceedings of the 19th International Conference on Signal Processing, Barcelona, Spain, 2011, 1415-1419

    [12] Gustafsson F, Hendeby G. Some relations between extended and unscented Kalman filters. IEEE Transactions on Signal Processing, 2012, 60(2): 545-555

    [13] Julier S J, Uhlmann J K. Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 2004, 92(3):401-422

    [14] Arasaratnam I, Haykin S. Cubature Kalman filters. IEEE Transactions on Automatic Control, 2009, 54(6): 1254-1269

    [15] Wang H, Yu D. Jiang J. Comparison and error analysis of integral-free Kalman tracking filter algorithms. In: Proceedings of the 7th IEEE International Conference on Image and Signal Processing, Dalian, China, 2014, 783-787

    [16] Bar-Shalom Y, Li X R. Multitarget multisensor tracking: principles and techniques. IEEE Systems Magazine on Aerospace and Electronic, 1996, 16(4):93

    [17] Mahler R P S. Statistical Multisource Multitarget Information Fusion. USA: Artech House, 2007

    [18] Chakravorty R, Challa S. Multitarget tracking algorithm-joint IPDA and Gaussian mixture PHD filter. In: Proceedings of the 12th IEEE International Conference on Information Fusion, Seattle, USA, 2009, 316-323

    Hu Zhentao,born in 1979. He received his Ph.D degrees in Control Science and Engineering from Northwestern Polytechnical University in 2010. He also received his B.S. and M.S. degrees from Henan University in 2003 and 2006 respectively. Now, he is an assistant professor of college of computer and information engineering, Henan University. His research interests include complex system modeling and estimation, target tracking and particle filter, etc.

    10.3772/j.issn.1006-6748.2016.04.006

    ① Supported by the National Natural Science Foundation of China (No. 61300214), the Science and Technology Innovation Team Support Plan of Education Department of Henan Province (No. 13IRTSTHN021), the Post-doctoral Science Foundation of China (No. 2014M551999), and the Outstanding Young Cultivation Foundation of Henan University (No. 0000A40366).

    ② To whom correspondence should be addressed. E-mail: hym_henu@163.com Received on Oct. 12, 2015

    精品久久久久久久毛片微露脸| 日韩中文字幕欧美一区二区| 国产激情欧美一区二区| 色综合欧美亚洲国产小说| 一边摸一边抽搐一进一小说| 久久久久久久午夜电影 | 人妻久久中文字幕网| 校园春色视频在线观看| 国产精品电影一区二区三区| 韩国av一区二区三区四区| 亚洲色图av天堂| 12—13女人毛片做爰片一| 亚洲伊人色综图| 在线观看免费视频网站a站| 天堂√8在线中文| 免费女性裸体啪啪无遮挡网站| 精品福利永久在线观看| 亚洲欧美激情综合另类| 我的亚洲天堂| 又黄又粗又硬又大视频| 18禁观看日本| 88av欧美| 日本a在线网址| 亚洲色图 男人天堂 中文字幕| 很黄的视频免费| 国产片内射在线| 久久 成人 亚洲| 最近最新中文字幕大全电影3 | 精品午夜福利视频在线观看一区| 日韩一卡2卡3卡4卡2021年| 桃色一区二区三区在线观看| 国产99久久九九免费精品| 欧美 亚洲 国产 日韩一| 亚洲 欧美 日韩 在线 免费| 一区福利在线观看| 少妇裸体淫交视频免费看高清 | 叶爱在线成人免费视频播放| 国产精品偷伦视频观看了| 视频区图区小说| 国产精品永久免费网站| 亚洲av片天天在线观看| 又黄又粗又硬又大视频| 久久久久久免费高清国产稀缺| 成人三级黄色视频| 琪琪午夜伦伦电影理论片6080| 80岁老熟妇乱子伦牲交| 免费女性裸体啪啪无遮挡网站| 男人操女人黄网站| 色尼玛亚洲综合影院| 久久青草综合色| 高清欧美精品videossex| 动漫黄色视频在线观看| 亚洲国产看品久久| 亚洲精品美女久久久久99蜜臀| 国产精品 国内视频| 久久这里只有精品19| 夫妻午夜视频| 久久久久国产精品人妻aⅴ院| 少妇粗大呻吟视频| 1024香蕉在线观看| 国产成人精品久久二区二区91| 国产精品偷伦视频观看了| av网站在线播放免费| 亚洲精品久久午夜乱码| 不卡一级毛片| 久久精品国产清高在天天线| 久久精品91无色码中文字幕| 精品一区二区三区四区五区乱码| 国产片内射在线| 免费在线观看视频国产中文字幕亚洲| 嫩草影视91久久| 另类亚洲欧美激情| 免费搜索国产男女视频| 亚洲第一欧美日韩一区二区三区| 国产成人免费无遮挡视频| 国产精品乱码一区二三区的特点 | 一级片'在线观看视频| 精品一区二区三区av网在线观看| 国产精品一区二区在线不卡| 午夜91福利影院| 国产不卡一卡二| 亚洲欧美一区二区三区黑人| 日本三级黄在线观看| 九色亚洲精品在线播放| 黄片小视频在线播放| 亚洲五月婷婷丁香| 男男h啪啪无遮挡| 亚洲av成人不卡在线观看播放网| 黄频高清免费视频| 欧美大码av| 午夜两性在线视频| 色综合站精品国产| 中文字幕色久视频| 又紧又爽又黄一区二区| 亚洲av成人av| 国产成人精品无人区| 一区二区三区精品91| x7x7x7水蜜桃| 精品久久久久久成人av| 亚洲国产欧美网| 国产成+人综合+亚洲专区| 在线观看免费视频日本深夜| 身体一侧抽搐| 久久久久亚洲av毛片大全| 亚洲成人国产一区在线观看| 久久人妻熟女aⅴ| 国产欧美日韩综合在线一区二区| 母亲3免费完整高清在线观看| 亚洲成av片中文字幕在线观看| 久久天躁狠狠躁夜夜2o2o| 桃红色精品国产亚洲av| 成熟少妇高潮喷水视频| 香蕉久久夜色| 一进一出抽搐gif免费好疼 | 国产成人影院久久av| 男人操女人黄网站| 狂野欧美激情性xxxx| 在线观看舔阴道视频| 久久久久久免费高清国产稀缺| 一区福利在线观看| 久久久国产精品麻豆| 午夜福利在线观看吧| 免费不卡黄色视频| 最近最新中文字幕大全电影3 | 日韩欧美免费精品| 午夜精品在线福利| 欧美一级毛片孕妇| 国产精品电影一区二区三区| 一级a爱视频在线免费观看| 日韩中文字幕欧美一区二区| 免费在线观看视频国产中文字幕亚洲| 国产乱人伦免费视频| 在线观看舔阴道视频| 国产亚洲精品久久久久久毛片| 成人国产一区最新在线观看| 国产蜜桃级精品一区二区三区| 亚洲熟妇熟女久久| 满18在线观看网站| 欧美乱妇无乱码| 欧美丝袜亚洲另类 | 日韩 欧美 亚洲 中文字幕| 国产欧美日韩一区二区三| 欧美老熟妇乱子伦牲交| 十八禁网站免费在线| 91av网站免费观看| 两性夫妻黄色片| 性欧美人与动物交配| 黄色丝袜av网址大全| 国内久久婷婷六月综合欲色啪| av天堂久久9| 窝窝影院91人妻| 在线观看免费高清a一片| 成人永久免费在线观看视频| 日韩免费高清中文字幕av| 一本大道久久a久久精品| 手机成人av网站| 欧美日韩精品网址| 国产精品免费视频内射| 免费日韩欧美在线观看| 久久精品亚洲熟妇少妇任你| 亚洲va日本ⅴa欧美va伊人久久| 高清欧美精品videossex| 日韩免费高清中文字幕av| 黑人操中国人逼视频| 9191精品国产免费久久| 欧美不卡视频在线免费观看 | 成年女人毛片免费观看观看9| 国产av又大| 真人一进一出gif抽搐免费| 午夜福利在线观看吧| 夜夜爽天天搞| 这个男人来自地球电影免费观看| 欧美日韩视频精品一区| 大香蕉久久成人网| 无人区码免费观看不卡| 精品一区二区三区四区五区乱码| bbb黄色大片| 夜夜夜夜夜久久久久| 亚洲一区高清亚洲精品| 首页视频小说图片口味搜索| 久久久国产一区二区| 少妇的丰满在线观看| 亚洲人成电影免费在线| 99香蕉大伊视频| 99热国产这里只有精品6| 色精品久久人妻99蜜桃| a级毛片在线看网站| 久久香蕉激情| 99久久综合精品五月天人人| 正在播放国产对白刺激| 在线观看免费高清a一片| 亚洲午夜精品一区,二区,三区| 天天躁夜夜躁狠狠躁躁| 国产欧美日韩精品亚洲av| 国产成人精品久久二区二区91| 桃红色精品国产亚洲av| 欧美日韩福利视频一区二区| 久久伊人香网站| 午夜日韩欧美国产| 老司机亚洲免费影院| 老司机靠b影院| 精品一品国产午夜福利视频| 亚洲精品一二三| 五月开心婷婷网| 最近最新免费中文字幕在线| 精品久久久久久电影网| 国产精品一区二区在线不卡| 国产精品乱码一区二三区的特点 | 亚洲专区国产一区二区| 婷婷精品国产亚洲av在线| 欧美黑人精品巨大| 成在线人永久免费视频| 国产黄色免费在线视频| 在线av久久热| 国产精品永久免费网站| 亚洲av成人一区二区三| 午夜激情av网站| 自线自在国产av| 日本精品一区二区三区蜜桃| 国产精品久久久久久人妻精品电影| 99香蕉大伊视频| 黄色丝袜av网址大全| 日韩国内少妇激情av| 嫩草影视91久久| 深夜精品福利| 国产精品 国内视频| 欧美黄色淫秽网站| 亚洲在线自拍视频| 九色亚洲精品在线播放| 欧美+亚洲+日韩+国产| 国产男靠女视频免费网站| 亚洲美女黄片视频| 午夜a级毛片| 男人舔女人下体高潮全视频| 久久久国产精品麻豆| 国产成人影院久久av| 欧美中文日本在线观看视频| 国产精品电影一区二区三区| 在线观看www视频免费| 男女做爰动态图高潮gif福利片 | 亚洲欧美激情在线| 99re在线观看精品视频| 免费观看人在逋| 久99久视频精品免费| 久久午夜综合久久蜜桃| 午夜激情av网站| 午夜影院日韩av| 午夜福利免费观看在线| 午夜福利在线观看吧| 国内久久婷婷六月综合欲色啪| 亚洲午夜理论影院| 欧美一区二区精品小视频在线| 亚洲片人在线观看| 国产三级黄色录像| 一级片免费观看大全| 成人黄色视频免费在线看| 欧美另类亚洲清纯唯美| 色播在线永久视频| 久久精品91蜜桃| av视频免费观看在线观看| 嫩草影院精品99| 在线观看免费午夜福利视频| 丰满人妻熟妇乱又伦精品不卡| 久久精品亚洲熟妇少妇任你| 欧美日韩亚洲高清精品| 久久精品aⅴ一区二区三区四区| 满18在线观看网站| 免费不卡黄色视频| 精品国产乱子伦一区二区三区| 亚洲va日本ⅴa欧美va伊人久久| 波多野结衣高清无吗| 99在线人妻在线中文字幕| 免费看十八禁软件| 99精品在免费线老司机午夜| 亚洲中文字幕日韩| 精品国产一区二区三区四区第35| a级片在线免费高清观看视频| 国产成人精品久久二区二区91| 国产亚洲欧美98| 丁香六月欧美| 久久久久久免费高清国产稀缺| 午夜福利欧美成人| 国产有黄有色有爽视频| 国产极品粉嫩免费观看在线| 亚洲精品久久午夜乱码| 国产亚洲av高清不卡| 在线观看一区二区三区| 亚洲欧美一区二区三区黑人| 日本一区二区免费在线视频| 丰满迷人的少妇在线观看| 在线观看免费视频日本深夜| 男人舔女人下体高潮全视频| 国产有黄有色有爽视频| 免费在线观看黄色视频的| 91大片在线观看| 在线观看免费视频日本深夜| 日韩欧美一区二区三区在线观看| 99国产综合亚洲精品| 18禁黄网站禁片午夜丰满| 午夜成年电影在线免费观看| 亚洲在线自拍视频| 一边摸一边做爽爽视频免费| 国产精品国产av在线观看| 日韩av在线大香蕉| 日韩欧美在线二视频| 热99国产精品久久久久久7| 日本欧美视频一区| 51午夜福利影视在线观看| bbb黄色大片| 脱女人内裤的视频| 夜夜看夜夜爽夜夜摸 | 亚洲aⅴ乱码一区二区在线播放 | 国产激情久久老熟女| 国产熟女xx| 日韩精品青青久久久久久| 国产三级黄色录像| 日韩大码丰满熟妇| 天堂影院成人在线观看| 不卡一级毛片| 天堂√8在线中文| 久久精品亚洲av国产电影网| 正在播放国产对白刺激| 一级黄色大片毛片| a级片在线免费高清观看视频| 91国产中文字幕| 夜夜躁狠狠躁天天躁| 天堂俺去俺来也www色官网| 99热只有精品国产| 嫁个100分男人电影在线观看| 两个人看的免费小视频| 精品国产超薄肉色丝袜足j| 亚洲成人国产一区在线观看| 欧美乱妇无乱码| 可以在线观看毛片的网站| 亚洲 欧美 日韩 在线 免费| 亚洲精品一二三| 波多野结衣一区麻豆| 亚洲 欧美一区二区三区| 天天躁夜夜躁狠狠躁躁| 黑丝袜美女国产一区| 操美女的视频在线观看| 香蕉久久夜色| 巨乳人妻的诱惑在线观看| 免费女性裸体啪啪无遮挡网站| 亚洲第一欧美日韩一区二区三区| www日本在线高清视频| 日本欧美视频一区| 高清黄色对白视频在线免费看| 久久草成人影院| 国产精品98久久久久久宅男小说| 日本欧美视频一区| 国产伦人伦偷精品视频| 国产97色在线日韩免费| 日韩国内少妇激情av| 久久亚洲精品不卡| 国产精品久久视频播放| 两个人看的免费小视频| 少妇粗大呻吟视频| 中文字幕人妻丝袜一区二区| 久久久国产成人免费| 亚洲中文日韩欧美视频| 色在线成人网| 久久久久久久精品吃奶| 国产高清videossex| 国产精品亚洲一级av第二区| 亚洲激情在线av| 十八禁人妻一区二区| 黄色怎么调成土黄色| 少妇粗大呻吟视频| 亚洲狠狠婷婷综合久久图片| 久久久国产成人精品二区 | 亚洲av美国av| 午夜精品在线福利| 丁香六月欧美| 国产亚洲精品第一综合不卡| 国产一区二区激情短视频| 成人国语在线视频| 欧美精品啪啪一区二区三区| 免费少妇av软件| av超薄肉色丝袜交足视频| 精品一区二区三区av网在线观看| 亚洲全国av大片| 国产一区二区在线av高清观看| 国产极品粉嫩免费观看在线| 男男h啪啪无遮挡| 丁香欧美五月| 日韩人妻精品一区2区三区| 人妻久久中文字幕网| 午夜两性在线视频| 国产成人精品在线电影| 大码成人一级视频| 一本大道久久a久久精品| 最好的美女福利视频网| 亚洲国产精品一区二区三区在线| 亚洲精品国产色婷婷电影| 又紧又爽又黄一区二区| 深夜精品福利| 午夜老司机福利片| 欧美日韩瑟瑟在线播放| 亚洲视频免费观看视频| 欧美成狂野欧美在线观看| 麻豆一二三区av精品| 日本欧美视频一区| 别揉我奶头~嗯~啊~动态视频| 美女 人体艺术 gogo| 欧美乱码精品一区二区三区| 亚洲国产毛片av蜜桃av| 夜夜爽天天搞| 91大片在线观看| 人人妻,人人澡人人爽秒播| 亚洲精品中文字幕一二三四区| 久久人妻av系列| 天天躁夜夜躁狠狠躁躁| 欧美日韩亚洲综合一区二区三区_| 精品久久久久久成人av| 丰满饥渴人妻一区二区三| 久久香蕉国产精品| 国产亚洲欧美精品永久| 身体一侧抽搐| 极品教师在线免费播放| 美女扒开内裤让男人捅视频| 亚洲一区二区三区色噜噜 | 久久香蕉激情| 久久精品国产综合久久久| 丰满的人妻完整版| 国产国语露脸激情在线看| 色播在线永久视频| 国产精品国产av在线观看| 国产极品粉嫩免费观看在线| 超碰成人久久| 久热这里只有精品99| 免费在线观看黄色视频的| 午夜福利影视在线免费观看| 一区二区三区精品91| 中文字幕高清在线视频| 日韩欧美一区视频在线观看| 亚洲色图 男人天堂 中文字幕| 国产精品久久久久成人av| 日韩欧美一区二区三区在线观看| 中文字幕人妻熟女乱码| 欧美亚洲日本最大视频资源| 老司机午夜十八禁免费视频| 女人爽到高潮嗷嗷叫在线视频| av欧美777| 18禁黄网站禁片午夜丰满| 精品少妇一区二区三区视频日本电影| 亚洲熟妇中文字幕五十中出 | 欧美丝袜亚洲另类 | 黄色女人牲交| 精品久久久久久电影网| 日韩免费av在线播放| 亚洲国产欧美一区二区综合| 日韩大尺度精品在线看网址 | 国产欧美日韩一区二区三区在线| 一夜夜www| 日韩大尺度精品在线看网址 | 国产精品影院久久| 亚洲国产欧美一区二区综合| 窝窝影院91人妻| 精品人妻在线不人妻| 男人操女人黄网站| 又黄又粗又硬又大视频| a级片在线免费高清观看视频| e午夜精品久久久久久久| 国产精品98久久久久久宅男小说| 大香蕉久久成人网| 色婷婷av一区二区三区视频| av免费在线观看网站| 久久久久久久午夜电影 | av中文乱码字幕在线| 久久精品国产亚洲av高清一级| 黑人猛操日本美女一级片| 成人国产一区最新在线观看| 老鸭窝网址在线观看| 啦啦啦在线免费观看视频4| 国产欧美日韩一区二区三| av片东京热男人的天堂| 日本黄色视频三级网站网址| 亚洲精品中文字幕一二三四区| 国产激情欧美一区二区| 欧美黑人精品巨大| 精品一区二区三区四区五区乱码| 黑人巨大精品欧美一区二区mp4| 99精国产麻豆久久婷婷| 日韩欧美在线二视频| 亚洲精品在线观看二区| 亚洲五月婷婷丁香| 欧美精品啪啪一区二区三区| 日韩三级视频一区二区三区| 欧美另类亚洲清纯唯美| 久久久久亚洲av毛片大全| 嫁个100分男人电影在线观看| 无限看片的www在线观看| 十八禁网站免费在线| 亚洲avbb在线观看| 亚洲专区中文字幕在线| 男女之事视频高清在线观看| 久久人人爽av亚洲精品天堂| 亚洲自拍偷在线| 亚洲一卡2卡3卡4卡5卡精品中文| 少妇 在线观看| 黄频高清免费视频| 男男h啪啪无遮挡| 一区二区日韩欧美中文字幕| 性少妇av在线| 丝袜在线中文字幕| 日韩高清综合在线| 欧美激情久久久久久爽电影 | 婷婷六月久久综合丁香| 一级片免费观看大全| 99久久99久久久精品蜜桃| 在线免费观看的www视频| 国产激情久久老熟女| 新久久久久国产一级毛片| 国产欧美日韩一区二区精品| 九色亚洲精品在线播放| 亚洲欧美一区二区三区黑人| 久久精品亚洲精品国产色婷小说| 90打野战视频偷拍视频| 在线播放国产精品三级| 天天影视国产精品| 久久久精品国产亚洲av高清涩受| 亚洲中文字幕日韩| 交换朋友夫妻互换小说| 超碰成人久久| 亚洲性夜色夜夜综合| 高潮久久久久久久久久久不卡| 一级a爱片免费观看的视频| 三级毛片av免费| 9热在线视频观看99| 精品国产乱码久久久久久男人| 怎么达到女性高潮| 一区福利在线观看| 欧美黄色淫秽网站| 999久久久国产精品视频| 制服诱惑二区| 一边摸一边做爽爽视频免费| 久久久久久久精品吃奶| 久久精品亚洲精品国产色婷小说| 欧美激情极品国产一区二区三区| √禁漫天堂资源中文www| 亚洲国产精品sss在线观看 | 成熟少妇高潮喷水视频| 久久国产乱子伦精品免费另类| 操出白浆在线播放| 变态另类成人亚洲欧美熟女 | 亚洲av电影在线进入| 黑人欧美特级aaaaaa片| 婷婷六月久久综合丁香| 自拍欧美九色日韩亚洲蝌蚪91| 高清在线国产一区| 午夜福利欧美成人| 国产成人欧美在线观看| 999久久久国产精品视频| 国产熟女午夜一区二区三区| 亚洲欧美激情在线| 久久久久国内视频| 国产精品1区2区在线观看.| 亚洲一码二码三码区别大吗| 老司机深夜福利视频在线观看| 精品久久久久久久毛片微露脸| 国产激情欧美一区二区| 人人妻人人爽人人添夜夜欢视频| 校园春色视频在线观看| 午夜福利欧美成人| www国产在线视频色| 国产三级在线视频| av片东京热男人的天堂| 一进一出抽搐gif免费好疼 | 国产精品野战在线观看 | 在线观看日韩欧美| 老汉色∧v一级毛片| 欧美日韩福利视频一区二区| 欧美亚洲日本最大视频资源| 欧美成狂野欧美在线观看| 国产99白浆流出| 一区在线观看完整版| 久久久久久免费高清国产稀缺| 如日韩欧美国产精品一区二区三区| 亚洲欧美日韩另类电影网站| 视频在线观看一区二区三区| 亚洲欧美日韩另类电影网站| 亚洲一区二区三区欧美精品| 激情在线观看视频在线高清| 精品福利永久在线观看| 变态另类成人亚洲欧美熟女 | 欧美成人性av电影在线观看| 国产精品国产高清国产av| 大型av网站在线播放| 中文字幕高清在线视频| 国产精品爽爽va在线观看网站 | 神马国产精品三级电影在线观看 | 成人三级做爰电影| 在线观看一区二区三区| 午夜久久久在线观看| 久久精品国产亚洲av香蕉五月| 亚洲欧洲精品一区二区精品久久久| 亚洲成人国产一区在线观看| 国产日韩一区二区三区精品不卡| 黄片小视频在线播放| 亚洲欧美日韩高清在线视频| 亚洲熟妇熟女久久| 91精品国产国语对白视频| 成人免费观看视频高清| 91在线观看av| 精品国产乱码久久久久久男人| 免费日韩欧美在线观看| 电影成人av| 国产亚洲精品久久久久久毛片| 国产av又大| 久久欧美精品欧美久久欧美| 免费不卡黄色视频| 丁香欧美五月| 国内久久婷婷六月综合欲色啪| 欧美激情 高清一区二区三区|