In order to overcome shortcomings of traditional BP neural network,such as low study efficiency, slow convergence speed,easily trapped into local optimal solution,we proposed an improved BP neural network model based ...In order to overcome shortcomings of traditional BP neural network,such as low study efficiency, slow convergence speed,easily trapped into local optimal solution,we proposed an improved BP neural network model based on adaptive particle swarm optimization( PSO) algorithm. This algorithm adjusted the inertia weight coefficients and learning factors adaptively and therefore could be used to optimize the weights in the BP network. After establishing the improved PSO-BP( IPSO-BP) model,it was applied to solve fault diagnosis of rolling bearing. Wavelet denoising was selected to reduce the noise of the original vibration signals,and based on these vibration signals a wide set of features were used as the inputs in the neural network models. We demonstrate the effectiveness of the proposed approach by comparing with the traditional BP,PSO-BP and linear PSO-BP( LPSO-BP) algorithms. The experimental results show that IPSO-BP network outperforms other algorithms with faster convergence speed,lower errors,higher diagnostic accuracy and learning ability.展开更多
Underwater wireless sensor networks(UWSNs)can provide a promising solution to underwater target tracking.Due to limited energy and bandwidth resources,only a small number of nodes are selected to track a target at eac...Underwater wireless sensor networks(UWSNs)can provide a promising solution to underwater target tracking.Due to limited energy and bandwidth resources,only a small number of nodes are selected to track a target at each interval.Because all measurements are fused together to provide information in a fusion center,fusion weights of all selected nodes may affect the performance of target tracking.As far as we know,almost all existing tracking schemes neglect this problem.We study a weighted fusion scheme for target tracking in UWSNs.First,because the mutual information(MI)between a node’s measurement and the target state can quantify target information provided by the node,it is calculated to determine proper fusion weights.Second,we design a novel multi-sensor weighted particle filter(MSWPF)using fusion weights determined by MI.Third,we present a local node selection scheme based on posterior Cramer-Rao lower bound(PCRLB)to improve tracking efficiency.Finally,simulation results are presented to verify the performance improvement of our scheme with proper fusion weights.展开更多
We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are dis...We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets.Since most clutter measurements do not participate in the update step, the computing time is reduced significantly.Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.展开更多
Noise statistics are essential for estimation performance. In practical situations, however, a priori information of noise statistics is often imperfect. Previous work on noise statistics identification in linear syst...Noise statistics are essential for estimation performance. In practical situations, however, a priori information of noise statistics is often imperfect. Previous work on noise statistics identification in linear systems still requires initial prior knowledge of the noise. A novel approach is presented in this paper to solve this paradox.First, we apply the H_∞ filter to obtain the system state estimates without the common assumptions about the noise in conventional adaptive filters. Then by applying state estimates obtained from the H_∞ filter, better estimates of the noise mean and covariance can be achieved, which can improve the performance of estimation. The proposed approach makes the best use of the system knowledge without a priori information with modest computation cost,which makes it possible to be applied online. Finally, numerical examples are presented to show the efficiency of this approach.展开更多
Underwater mobile sensor networks(UMSNs) with free-floating sensors are more suitable for understanding the immense underwater environment. Target tracking, whose performance depends on sensor localization accuracy, i...Underwater mobile sensor networks(UMSNs) with free-floating sensors are more suitable for understanding the immense underwater environment. Target tracking, whose performance depends on sensor localization accuracy, is one of the broad applications of UMSNs. However, in UMSNs, sensors move with environmental forces,so their positions change continuously, which poses a challenge on the accuracy of sensor localization and target tracking. We propose a high-accuracy localization with mobility prediction(HLMP) algorithm to acquire relatively accurate sensor location estimates. The HLMP algorithm exploits sensor mobility characteristics and the multistep Levinson-Durbin algorithm to predict future positions. Furthermore, we present a simultaneous localization and target tracking(SLAT) algorithm to update sensor locations based on measurements during the process of target tracking. Simulation results demonstrate that the HLMP algorithm can improve localization accuracy significantly with low energy consumption and that the SLAT algorithm can further decrease the sensor localization error. In addition, results prove that a better localization accuracy will synchronously improve the target tracking performance.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.61174115 and 51104044)
文摘In order to overcome shortcomings of traditional BP neural network,such as low study efficiency, slow convergence speed,easily trapped into local optimal solution,we proposed an improved BP neural network model based on adaptive particle swarm optimization( PSO) algorithm. This algorithm adjusted the inertia weight coefficients and learning factors adaptively and therefore could be used to optimize the weights in the BP network. After establishing the improved PSO-BP( IPSO-BP) model,it was applied to solve fault diagnosis of rolling bearing. Wavelet denoising was selected to reduce the noise of the original vibration signals,and based on these vibration signals a wide set of features were used as the inputs in the neural network models. We demonstrate the effectiveness of the proposed approach by comparing with the traditional BP,PSO-BP and linear PSO-BP( LPSO-BP) algorithms. The experimental results show that IPSO-BP network outperforms other algorithms with faster convergence speed,lower errors,higher diagnostic accuracy and learning ability.
基金Project supported by the National Natural Science Foundation of China(Nos.61531015,61673345,and 61374021)the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization(Nos.U1609204 and U1709203)
文摘Underwater wireless sensor networks(UWSNs)can provide a promising solution to underwater target tracking.Due to limited energy and bandwidth resources,only a small number of nodes are selected to track a target at each interval.Because all measurements are fused together to provide information in a fusion center,fusion weights of all selected nodes may affect the performance of target tracking.As far as we know,almost all existing tracking schemes neglect this problem.We study a weighted fusion scheme for target tracking in UWSNs.First,because the mutual information(MI)between a node’s measurement and the target state can quantify target information provided by the node,it is calculated to determine proper fusion weights.Second,we design a novel multi-sensor weighted particle filter(MSWPF)using fusion weights determined by MI.Third,we present a local node selection scheme based on posterior Cramer-Rao lower bound(PCRLB)to improve tracking efficiency.Finally,simulation results are presented to verify the performance improvement of our scheme with proper fusion weights.
基金supported by the National Natural Science Foundation of China(Nos.61374021,61222310,and 61328302)the Zhejiang Provincial Natural Science Foundation of China(No.LZ14F030003)+3 种基金the Specialized Research Fund for the Doctoral Program of Higher Education of China(Nos.20130101110109 and 20120101110115)the Zhejiang Provincial Science and Technology Planning Projects of China(No.2012C21044)the Marine Interdisciplinary Research Guiding Funds for Zhejiang University(No.2012HY009B)the Fundamental Research Funds for the Central Universities,China(No.2014XZZX003-12)
基金Project supported by the National Natural Science Foundationof China(Nos.61174142,61222310,and 61374021)the Specialized Research Fund for the Doctoral Program of Higher Education of China(Nos.20120101110115 and 20130101110109)+3 种基金theZhejiang Provincial Science and Technology Planning Projects ofChina(No.2012C21044)the Marine Interdisciplinary ResearchGuiding Funds for Zhejiang University(No.2012HY009B)theFundamental Research Funds for the Central Universities(No.2014XZZX003-12)the Aeronautical Science Foundation ofChina(No.20132076002)
文摘We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets.Since most clutter measurements do not participate in the update step, the computing time is reduced significantly.Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.
基金Project supported by the National Natural Science Foundation of China(Nos.61374021 and 61531015)the Zhejiang Provincial Natural Science Foundation of China(Nos.LZ14F030002.LZ14F030003,and LY15F030007)+2 种基金the Specialized Research Fund for the Doctoral Program of Higher Education of China(Nos.20130101110109 and 20120101110115)the Open Fund for the Aircraft Marine Measurement and Control Joint Laboratory,China(No.FOM2015OF009)the Aerospace Science Foundation of China(Nos.20132076002 and 2015ZC76006)
文摘Noise statistics are essential for estimation performance. In practical situations, however, a priori information of noise statistics is often imperfect. Previous work on noise statistics identification in linear systems still requires initial prior knowledge of the noise. A novel approach is presented in this paper to solve this paradox.First, we apply the H_∞ filter to obtain the system state estimates without the common assumptions about the noise in conventional adaptive filters. Then by applying state estimates obtained from the H_∞ filter, better estimates of the noise mean and covariance can be achieved, which can improve the performance of estimation. The proposed approach makes the best use of the system knowledge without a priori information with modest computation cost,which makes it possible to be applied online. Finally, numerical examples are presented to show the efficiency of this approach.
基金supported by the National Natural Science Foundation of China(Nos.61222310,61174142,and 61374021)the Zhejiang Provincial Natural Science Foundation of China(No.LZ14F030002)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education of China(Nos.20120101110115 and 20130101110109)the Fundamental Research Funds for the Central Universities,China(No.2014XZZX003-12)
基金Project supported by the Basic Public Research Program of Zhejiang Province,China(No.LGF18F030001)the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT1800414)
基金Project supported by the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization(No.U1609204)the National Natural Science Foundation of China(Nos.61531015 and 61673345)the Key Research and Development Program of Zhejiang Province,China(No.2018C03030)
文摘Underwater mobile sensor networks(UMSNs) with free-floating sensors are more suitable for understanding the immense underwater environment. Target tracking, whose performance depends on sensor localization accuracy, is one of the broad applications of UMSNs. However, in UMSNs, sensors move with environmental forces,so their positions change continuously, which poses a challenge on the accuracy of sensor localization and target tracking. We propose a high-accuracy localization with mobility prediction(HLMP) algorithm to acquire relatively accurate sensor location estimates. The HLMP algorithm exploits sensor mobility characteristics and the multistep Levinson-Durbin algorithm to predict future positions. Furthermore, we present a simultaneous localization and target tracking(SLAT) algorithm to update sensor locations based on measurements during the process of target tracking. Simulation results demonstrate that the HLMP algorithm can improve localization accuracy significantly with low energy consumption and that the SLAT algorithm can further decrease the sensor localization error. In addition, results prove that a better localization accuracy will synchronously improve the target tracking performance.