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Quantized innovations Kalman filter: stability and modification with scaling quantization 被引量:3
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作者 Jian XU jian-xun li Sheng XU 《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2012年第2期118-130,共13页
The stability of quantized innovations Kalman filtering (QIKF) is analyzed. In the analysis, the correlation between quantization errors and measurement noises is considered. By taking the quantization errors as a ran... The stability of quantized innovations Kalman filtering (QIKF) is analyzed. In the analysis, the correlation between quantization errors and measurement noises is considered. By taking the quantization errors as a random perturbation in the observation system, the QIKF for the original system is equivalent to a Kalman-like filtering for the equivalent state-observation system. Thus, the estimate error covariance matrix of QIKF can be more exactly analyzed. The boundedness of the estimate error covariance matrix of QIKF is obtained under some weak conditions. The design of the number of quantized levels is discussed to guarantee the stability of QIKF. To overcome the instability and divergence of QIKF when the number of quantization levels is small, we propose a Kalman filter using scaling quantized innovations. Numerical simulations show the validity of the theorems and algorithms. 展开更多
关键词 Kalman filtering Quantized innovation STABILITY Scaling quantization Wireless sensor network
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A multiple maneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order 被引量:1
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作者 Shi-cang ZHANG jian-xun li +1 位作者 liang-bin WU Chang-hai SHI 《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2013年第6期417-424,共8页
We describe the design of a multiple maneuvering targets tracking algorithm under the framework of Gaussian mixture probability hypothesis density(PHD) filter.First,a variation of the generalized pseudo-Bayesian estim... We describe the design of a multiple maneuvering targets tracking algorithm under the framework of Gaussian mixture probability hypothesis density(PHD) filter.First,a variation of the generalized pseudo-Bayesian estimator of first order(VGPB1) is designed to adapt to the Gaussian mixture PHD filter for jump Markov system models(JMS-PHD).The probability of each kinematic model,which is used in the JMS-PHD filter,is updated with VGPB1.The weighted sum of state,associated covariance,and weights for Gaussian components are then calculated.Pruning and merging techniques are also adopted in this algorithm to increase efficiency.Performance of the proposed algorithm is compared with that of the JMS-PHD filter.Monte-Carlo simulation results demonstrate that the optimal subpattern assignment(OSPA) distances of the proposed algorithm are lower than those of the JMS-PHD filter for maneuvering targets tracking. 展开更多
关键词 Gaussian mixture PHD filter Jump Markov system Generalized pseudo-Bayesian estimator of first order(GPB1) Multi-target tracking
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Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder 被引量:9
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作者 Zong-feng QI Qiao-qiao liU +1 位作者 Jun WANG jian-xun li 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2017年第12期1991-2000,共10页
The nodes number of the hidden layer in a deep learning network is quite difficult to determine with traditional methods. To solve this problem, an improved Kullback-Leibler divergence sparse autoencoder (KL-SAE) is... The nodes number of the hidden layer in a deep learning network is quite difficult to determine with traditional methods. To solve this problem, an improved Kullback-Leibler divergence sparse autoencoder (KL-SAE) is proposed in this paper, which can be applied to battle damage assessment (BDA). This method can select automatically the hidden layer feature which contributes most to data reconstruction, and abandon the hidden layer feature which contributes least. Therefore, the structure of the network can be modified. In addition, the method can select automatically hidden layer feature without loss of the network prediction accuracy and increase the computation speed. Experiments on University ofCalifomia-Irvine (UCI) data sets and BDA for battle damage data demonstrate that the method outperforms other reference data-driven methods. The following results can be found from this paper. First, the improved KL-SAE regression network can guarantee the prediction accuracy and increase the speed of training networks and prediction. Second, the proposed network can select automatically hidden layer effective feature and modify the structure of the network by optimizing the nodes number of the hidden layer. 展开更多
关键词 Battle damage assessment Improved Kullback-Leibler divergence sparse autoencoder Structural optimization Feature selection
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