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
Detection of weak underwater signals is an area of general interest in marine engineering.A weak signal detection scheme was developed; it combined nonlinear dynamical reconstruction techniques, radial basis function ...Detection of weak underwater signals is an area of general interest in marine engineering.A weak signal detection scheme was developed; it combined nonlinear dynamical reconstruction techniques, radial basis function (RBF) neural networks and an extended Kalman filter (EKF).In this method chaos theory was used to model background noise.Noise was predicted by phase space reconstruction techniques and RBF neural networks in a synergistic manner.In the absence of a signal, prediction error stayed low and became relatively large when the input contained a signal.EKF was used to improve the convergence rate of the RBF neural network.Application of the scheme to different experimental data sets showed that the algorithm can detect signals hidden in strong noise even when the signal-to-noise ratio (SNR) is less than -40d B.展开更多
In order to investigate the feasibility of monitoring the fatigue cracks in turbine blades using acoustic emission (AE) technique, the AE characteristics of fatigue crack growth were studied in the laboratory. And the...In order to investigate the feasibility of monitoring the fatigue cracks in turbine blades using acoustic emission (AE) technique, the AE characteristics of fatigue crack growth were studied in the laboratory. And the characteristics were compared with those of background noise received from a real hydraulic turbine unit. It is found that the AE parameters such as the energy and duration can qualitatively describe the fatigue state of the blades. The correlations of crack propagation rates and acoustic emission count rates vs stress intensity factor (SIF) range are also obtained. At the same time, for the specimens of 20SiMn under the given testing conditions, it is noted that the rise time and duration of events emitted from the fatigue process are lower than those from the background noise; amplitude range is 49-74 dB, which is lower than that of the noise (90-99 dB); frequency range of main energy of crack signals is higher than 60 kHz while that in the noise is lower than 55 kHz. Thus, it is possible to extract the useful crack signals from the noise through appropriate signal processing methods and to represent the crack status of blade materials by AE parameters. As a result, it is feasible to monitor the safety of runners using AE technique.展开更多
The detection performance is evaluated for our proposed analog multiuser receiver in Ultra-WideBand (UWB) transmitted-reference system. In the presence of dense multipath and multi-access signals,the performance analy...The detection performance is evaluated for our proposed analog multiuser receiver in Ultra-WideBand (UWB) transmitted-reference system. In the presence of dense multipath and multi-access signals,the performance analysis is difficult due to the complicated waveform of received impulse. We develop an approach to analyze the steady-state Signal-to-Interference-plus-Noise (SINR) of the detector output. The multipath-spread impulse is fitted to an exponentially decaying profile in the analysis. A closed-form expression of steady-state SINR is further deduced for the proposed Least Minimum Square (LMS) detector. The analysis is validated by simulations in Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) channel respectively. Based on the theoretical results,the multipath delay spread is employed to determine the optimal width of the integration window of the detector.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Supported by China Postdoctoral Science Foundation No.20080441183
文摘Detection of weak underwater signals is an area of general interest in marine engineering.A weak signal detection scheme was developed; it combined nonlinear dynamical reconstruction techniques, radial basis function (RBF) neural networks and an extended Kalman filter (EKF).In this method chaos theory was used to model background noise.Noise was predicted by phase space reconstruction techniques and RBF neural networks in a synergistic manner.In the absence of a signal, prediction error stayed low and became relatively large when the input contained a signal.EKF was used to improve the convergence rate of the RBF neural network.Application of the scheme to different experimental data sets showed that the algorithm can detect signals hidden in strong noise even when the signal-to-noise ratio (SNR) is less than -40d B.
基金Project(50465002) supported by the National Natural Science Foundation of China
文摘In order to investigate the feasibility of monitoring the fatigue cracks in turbine blades using acoustic emission (AE) technique, the AE characteristics of fatigue crack growth were studied in the laboratory. And the characteristics were compared with those of background noise received from a real hydraulic turbine unit. It is found that the AE parameters such as the energy and duration can qualitatively describe the fatigue state of the blades. The correlations of crack propagation rates and acoustic emission count rates vs stress intensity factor (SIF) range are also obtained. At the same time, for the specimens of 20SiMn under the given testing conditions, it is noted that the rise time and duration of events emitted from the fatigue process are lower than those from the background noise; amplitude range is 49-74 dB, which is lower than that of the noise (90-99 dB); frequency range of main energy of crack signals is higher than 60 kHz while that in the noise is lower than 55 kHz. Thus, it is possible to extract the useful crack signals from the noise through appropriate signal processing methods and to represent the crack status of blade materials by AE parameters. As a result, it is feasible to monitor the safety of runners using AE technique.
基金Supported by the Guangxi Natural Science Foundation (No.0731025, No.0731026)the Established Project by Guangxi Education Department (No.200808LX117)
文摘The detection performance is evaluated for our proposed analog multiuser receiver in Ultra-WideBand (UWB) transmitted-reference system. In the presence of dense multipath and multi-access signals,the performance analysis is difficult due to the complicated waveform of received impulse. We develop an approach to analyze the steady-state Signal-to-Interference-plus-Noise (SINR) of the detector output. The multipath-spread impulse is fitted to an exponentially decaying profile in the analysis. A closed-form expression of steady-state SINR is further deduced for the proposed Least Minimum Square (LMS) detector. The analysis is validated by simulations in Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) channel respectively. Based on the theoretical results,the multipath delay spread is employed to determine the optimal width of the integration window of the detector.
