According to the basic requirements of underground mine personnel position systems and the working characteristics of active RFID tags,we studied the cause of concurrent collision of RFID tags and leak reading probabi...According to the basic requirements of underground mine personnel position systems and the working characteristics of active RFID tags,we studied the cause of concurrent collision of RFID tags and leak reading probability,by means of theoretical analysis and computation.The result shows that the probability of wireless collision increases linearly with an increase in the number of tags.The probability of collision and leak reading can be reduced by extending the working period of the duty cycle and using a backoff algorithm.In a practical application,a working schedule for available labels has been designed according to the requirement of the project.展开更多
Researchers face many class prediction challenges stemming from a small size of training data vis-a-vis a large number of unlabeled samples to be predicted. Transductive learning is proposed to utilize information abo...Researchers face many class prediction challenges stemming from a small size of training data vis-a-vis a large number of unlabeled samples to be predicted. Transductive learning is proposed to utilize information about unlabeled data to estimate labels of the unlabeled data for this condition. This work presents a new transductive learning method called two-way Markov random walk(TMRW) algorithm. The algorithm uses information about labeled and unlabeled data to predict the labels of the unlabeled data by taking random walks between the labeled and unlabeled data where data points are viewed as nodes of a graph. The labeled points correlate to unlabeled points and vice versa according to a transition probability matrix. We can get the predicted labels of unlabeled samples by combining the results of the two-way walks. Finally, ensemble learning is combined with transductive learning, and Adboost.MH is taken as the study framework to improve the performance of TMRW, which is the basic learner. Experiments show that this algorithm can predict labels of unlabeled data well.展开更多
Cross-eye jamming is an electronic attack technique that induces an angular error in the monopulse radar by artificially creating a false target and deceiving the radar into detecting and tracking it.Presently,there i...Cross-eye jamming is an electronic attack technique that induces an angular error in the monopulse radar by artificially creating a false target and deceiving the radar into detecting and tracking it.Presently,there is no effective anti-jamming method to counteract cross-eye jamming.In our study,through detailed analysis of the jamming mechanism,a multi-target model for a cross-eye jamming scenario is established within a random finite set framework.A novel anti-jamming method based on multitarget tracking using probability hypothesis density filters is subsequently developed by combining the characteristic differences between target and jamming with the releasing process of jamming.The characteristic differences between target and jamming and the releasing process of jamming are used to optimize particle partitioning.Particle identity labels that represent the properties of target and jamming are introduced into the detection and tracking processes.The release of cross-eye jamming is detected by estimating the number of targets in the beam,and the distinction between true targets and false jamming is realized through correlation and transmission between labels and estimated states.Thus,accurate tracking of the true targets is achieved under severe jamming conditions.Simulation results showed that the proposed method achieves a minimum delay in detection of cross-eye jamming and an accurate estimation of the target state.展开更多
基金supported by the Fund of Coal Gas Sensing Technology and Early Warning Systems-Based Theory and Key Technology Research (No.50534050)
文摘According to the basic requirements of underground mine personnel position systems and the working characteristics of active RFID tags,we studied the cause of concurrent collision of RFID tags and leak reading probability,by means of theoretical analysis and computation.The result shows that the probability of wireless collision increases linearly with an increase in the number of tags.The probability of collision and leak reading can be reduced by extending the working period of the duty cycle and using a backoff algorithm.In a practical application,a working schedule for available labels has been designed according to the requirement of the project.
基金Project(61232001) supported by National Natural Science Foundation of ChinaProject supported by the Construct Program of the Key Discipline in Hunan Province,China
文摘Researchers face many class prediction challenges stemming from a small size of training data vis-a-vis a large number of unlabeled samples to be predicted. Transductive learning is proposed to utilize information about unlabeled data to estimate labels of the unlabeled data for this condition. This work presents a new transductive learning method called two-way Markov random walk(TMRW) algorithm. The algorithm uses information about labeled and unlabeled data to predict the labels of the unlabeled data by taking random walks between the labeled and unlabeled data where data points are viewed as nodes of a graph. The labeled points correlate to unlabeled points and vice versa according to a transition probability matrix. We can get the predicted labels of unlabeled samples by combining the results of the two-way walks. Finally, ensemble learning is combined with transductive learning, and Adboost.MH is taken as the study framework to improve the performance of TMRW, which is the basic learner. Experiments show that this algorithm can predict labels of unlabeled data well.
基金Project supported by the National Natural Science Foundation of China(No.61401475)
文摘Cross-eye jamming is an electronic attack technique that induces an angular error in the monopulse radar by artificially creating a false target and deceiving the radar into detecting and tracking it.Presently,there is no effective anti-jamming method to counteract cross-eye jamming.In our study,through detailed analysis of the jamming mechanism,a multi-target model for a cross-eye jamming scenario is established within a random finite set framework.A novel anti-jamming method based on multitarget tracking using probability hypothesis density filters is subsequently developed by combining the characteristic differences between target and jamming with the releasing process of jamming.The characteristic differences between target and jamming and the releasing process of jamming are used to optimize particle partitioning.Particle identity labels that represent the properties of target and jamming are introduced into the detection and tracking processes.The release of cross-eye jamming is detected by estimating the number of targets in the beam,and the distinction between true targets and false jamming is realized through correlation and transmission between labels and estimated states.Thus,accurate tracking of the true targets is achieved under severe jamming conditions.Simulation results showed that the proposed method achieves a minimum delay in detection of cross-eye jamming and an accurate estimation of the target state.
