In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. ...In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification.展开更多
In order to improve the video quality of transmission with data loss,a spatial and temporal error concealment method was proposed,which considered both the state information of the network and the perceptual weight of...In order to improve the video quality of transmission with data loss,a spatial and temporal error concealment method was proposed,which considered both the state information of the network and the perceptual weight of the video content.The proposed method dynamically changed the reliability weight of the neighboring macroblock,which was used to conceal the lost macroblocks according to the packet loss rate of the current channel state.The perceptual weight map was utilized as side information to do weighted pixel interpolation and side-match based motion compensation for spatial and temporal error concealment,respectively.And the perceptual weight of the neighboring macroblocks was adaptively modified according to the perceptual weight of the lost macroblocks.Compared with the method used in H.264 joint model,experiment results show that the proposed method performs well both in subjective video quality and objective video quality,and increases the average peak signal-to-noise ratio(PSNR) of the whole frame by about 0.4 dB when the video bitstreams are transmitted with packets loss.展开更多
基金The National High Technology Research and Development Program of China (863 Program) (No.2008AA01Z227)the Cultivatable Fund of the Key Scientific and Technical Innovation Project of Ministry of Education of China (No.706028)
文摘In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification.
基金Project(2006C11200) supported by the Science and Technology Project of Zhejiang Province of China
文摘In order to improve the video quality of transmission with data loss,a spatial and temporal error concealment method was proposed,which considered both the state information of the network and the perceptual weight of the video content.The proposed method dynamically changed the reliability weight of the neighboring macroblock,which was used to conceal the lost macroblocks according to the packet loss rate of the current channel state.The perceptual weight map was utilized as side information to do weighted pixel interpolation and side-match based motion compensation for spatial and temporal error concealment,respectively.And the perceptual weight of the neighboring macroblocks was adaptively modified according to the perceptual weight of the lost macroblocks.Compared with the method used in H.264 joint model,experiment results show that the proposed method performs well both in subjective video quality and objective video quality,and increases the average peak signal-to-noise ratio(PSNR) of the whole frame by about 0.4 dB when the video bitstreams are transmitted with packets loss.
