Beijing, China
Beijing, China

Time filter

Source Type

Lu C.-G.,PLA Air Force Aviation University | Feng X.-X.,PLA Air Force Aviation University | Zhang D.,Chinese Aviation Museum | Zhang D.,Academy of Armored force Engineering
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | Year: 2012

To solve the nonlinear problem in pure bearing tracking, a range parametrization root-mean-square cubature Kalman filter is proposed to eliminate the effect of the unobservable range and the error introduced by arithmetic operations performed on the finite word-length computer. On the basis of the initial range expressed by several parameterized models, a root-mean-square cubature Kalman filter is run corresponding to every interval, and the probability is computed according to the Bayesian rule. All the weighting outputs of the filters are summed as the final estimation. Simulation results show the root-mean-square cubature Kalman filter can obtain better accuracy and robustness although the complexity of the algorithm is a little too high.


Lu C.,PLA Air Force Aviation University | Feng X.,PLA Air Force Aviation University | Lei Y.,PLA Air Force Aviation University | Kong Y.,PLA Air Force Aviation University | Zhang D.,Chinese Aviation Museum
2011 3rd International Workshop on Intelligent Systems and Applications, ISA 2011 - Proceedings | Year: 2011

A novel improved particle filter ,cubature particle filter, is proposed for the estimation of nonlinear non-Gaussian system. Each particle is estimated by means of cubature kalman filter. The importance density function gets closer to the real posterior after taking the current observation into consideration on the basis of state transition. Both theoretical analysis and simulation experiment show that the cubature particle filter performs much better than the other parallel filters. © 2011 IEEE.


Lu C.-G.,Airforce Engineering University | Lu C.-G.,Chinese People's Liberation Army | Feng X.-X.,Airforce Engineering University | Kong Y.-B.,Airforce Engineering University | And 2 more authors.
Zidonghua Xuebao/Acta Automatica Sinica | Year: 2014

After summarizing and analyzing the multi-target data association algorithms based on the S-D assignment for multi-passive-sensor system, it is pointed out that the association algorithms above have ignored both the error introduced by the maximum likelihood estimation and the relativity between the measurements and the pseudo ones. Then, a decorrelation-based data association model is built and the unscented transform is proposed to compute the mutual covariance between measurements and the pseudo ones. Meanwhile, a new concept, the discrimination of answers, is defined to evaluate the association cost forming methods. Lastly, results of simulation have shown that the uncorrelated cost function can reflect the association probability more accurately and the proposed algorithm can achieve better performance at the cost of more computing time. Copyright © 2014 Acta Automatica Sinica. All rights reserved.


Lu C.-G.,PLA Air Force Aviation University | Feng X.-X.,PLA Air Force Aviation University | Kong Y.-B.,PLA Air Force Aviation University | Zhang D.,Chinese Aviation Museum | Zhang D.,Academy of Armored force Engineering
Kongzhi yu Juece/Control and Decision | Year: 2013

The traditional multi-dimension assignment data association algorithm for the multi-passive-sensor data association algorithm has ignored the errors introduced by location localization estimation. Therefore, a data association algorithm is proposed based on information divergence. The differentia between the probability density function of pseudo measurements and the most posterior probability density function works as the association cost. The Kullback-Leibler divergence and symmetric Kullback-Leibler divergence are used respectively to quantify the differentia above. Finally, simulation results show that the proposed algorithm can achieve better performance and its association cost reflects the association probability more accurately.

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