In order to study the problem of intelligent information processing in new types of imaging fuze, the method of extracting the invariance features of target images is adopted, and radial basis function neural network ...In order to study the problem of intelligent information processing in new types of imaging fuze, the method of extracting the invariance features of target images is adopted, and radial basis function neural network is used to recognize targets. Owing to its ability of parallel processing, its robustness and generalization, the method can realize the recognition of the conditions of missile-target encounters, and meet the requirements of real-time recognition in the imaging fuze. It is shown that based on artificial neural network target recognition and burst point control are feasible.展开更多
Generally,Doppler fuze can only estimate actuation delay-time with a limited precision. As an improvement,imaging fuze can estimate actuation delay-time more precisely with the available two-dimensional image of the t...Generally,Doppler fuze can only estimate actuation delay-time with a limited precision. As an improvement,imaging fuze can estimate actuation delay-time more precisely with the available two-dimensional image of the target. In this paper,imprecision of actuation delay-time estimation with Doppler fuze is first analyzed theoretically in brief. Secondly,feasibility analysis and theoretical model of imaging fuze are described,in which a criterion is established for the actuation delay-time based on the image,and then an image based gray-value weighted least square（ GWLS） algorithm is presented to calculate actuation delay-time of the imaging fuze. Finally,a simulation model of missiletarget near-field encounter is established. Simulation results indicate that actuation delay-time of the imaging fuze is estimated more precisely than by the Doppler fuze.展开更多
文摘In order to study the problem of intelligent information processing in new types of imaging fuze, the method of extracting the invariance features of target images is adopted, and radial basis function neural network is used to recognize targets. Owing to its ability of parallel processing, its robustness and generalization, the method can realize the recognition of the conditions of missile-target encounters, and meet the requirements of real-time recognition in the imaging fuze. It is shown that based on artificial neural network target recognition and burst point control are feasible.
基金Supported by the Ministerial Level Advanced Research Foundation of China(9140A05030213HT25012)
文摘Generally,Doppler fuze can only estimate actuation delay-time with a limited precision. As an improvement,imaging fuze can estimate actuation delay-time more precisely with the available two-dimensional image of the target. In this paper,imprecision of actuation delay-time estimation with Doppler fuze is first analyzed theoretically in brief. Secondly,feasibility analysis and theoretical model of imaging fuze are described,in which a criterion is established for the actuation delay-time based on the image,and then an image based gray-value weighted least square（ GWLS） algorithm is presented to calculate actuation delay-time of the imaging fuze. Finally,a simulation model of missiletarget near-field encounter is established. Simulation results indicate that actuation delay-time of the imaging fuze is estimated more precisely than by the Doppler fuze.
