For a netted radar system to counteract the deceptionelectronic countermeasure （ECM） signals, an effective electroniccounter countermeasure （ECCM） approach is proposed. The proposedapproach is realized based on th...For a netted radar system to counteract the deceptionelectronic countermeasure （ECM） signals, an effective electroniccounter countermeasure （ECCM） approach is proposed. The proposedapproach is realized based on the new signaling strategyfor the temporal pulse diversity, which makes use of transmittingpulses at each pulse repetition interval （PRI） with specific transmissionpulse block, and then following proper processing andinformation fusion. The existence of the deceptive ECM signal isconfirmed by one station, while the other stations in the nettedradar with same parameters applied the pulse diversity skillfully.Meanwhile, this method ensured that, pulse diversity can be appliedin netted radar. The performance assessment shows that theproposed solutions are effective in presence of ECM signals. Thisalgorithm has been demonstrated by simulations. The presentedsimulation results are in excellent consensus with theoretical predictions.展开更多
A novel neural network based on iterated unscented Kalman filter （IUKF） algorithm is established to model and com- pensate for the fiber optic gyro （FOG） bias drift caused by temperature. In the network, FOG tempe...A novel neural network based on iterated unscented Kalman filter （IUKF） algorithm is established to model and com- pensate for the fiber optic gyro （FOG） bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter （UKF）. The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measure- ment noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and mea- surement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise （-20-20℃） and drop （70-20℃） tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the back- propagation （BP） and UKF network models.展开更多
基金supported by the National Defense Pre-Research Foundation of China(9140A07030713DZ02101)
文摘For a netted radar system to counteract the deceptionelectronic countermeasure （ECM） signals, an effective electroniccounter countermeasure （ECCM） approach is proposed. The proposedapproach is realized based on the new signaling strategyfor the temporal pulse diversity, which makes use of transmittingpulses at each pulse repetition interval （PRI） with specific transmissionpulse block, and then following proper processing andinformation fusion. The existence of the deceptive ECM signal isconfirmed by one station, while the other stations in the nettedradar with same parameters applied the pulse diversity skillfully.Meanwhile, this method ensured that, pulse diversity can be appliedin netted radar. The performance assessment shows that theproposed solutions are effective in presence of ECM signals. Thisalgorithm has been demonstrated by simulations. The presentedsimulation results are in excellent consensus with theoretical predictions.
基金supported by the National Natural Science Foundation of China(6110418440904018)+3 种基金the National Key Scientific Instrument and Equipment Development Project(2011YQ12004502)the Research Foundation of General Armament Department(201300000008)the Doctor Innovation Fund of Naval University of Engineering(HGBSCXJJ2011008)the Youth Natural Science Foundation of Naval University of Engineering(HGDQNJJ12028)
文摘A novel neural network based on iterated unscented Kalman filter （IUKF） algorithm is established to model and com- pensate for the fiber optic gyro （FOG） bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter （UKF）. The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measure- ment noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and mea- surement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise （-20-20℃） and drop （70-20℃） tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the back- propagation （BP） and UKF network models.
