In order to cooperate with algorithm debugging and internal field testing of an imaging system, a front-end simulator was designed to provide excitation signals for a multi aperture imaging system. Visual simulation technology was utilized to simulate the functionality of the front-end of the imaging system. The dual channel FC cards was employed to simulate the communication between the front-end and ICP. The communication efficiency between front-end and ICP, multi-channel image alignment algorithm, object detection algorithm, and error response of the system were tested by the simulator in the hardware in loop mode.
In the process of guiding the target device, the pointing and guidance accuracy of the on-board electro-optical reconnaissance system is affected by the relative installation position of the equipment, as it can not realize the coaxial installation with the guided device. According to the structural characteristics of the equipment, the mathematical model of target orientation function from the image plane coordinate system of the electro-optical reconnaissance system to the equipment base coordinate system is constructed, the error sources affecting the target pointing accuracy are analyzed, and the target pointing error model is established. The Monte Carlo method is used to analyze the relationship between the pointing error and its error sources. The probability density distribution of each error source is established. The target pointing accuracy performance under the influence of different errors is analyzed. And the sensitive factors affecting the pointing accuracy are figured out, which provides a certain theoretical guidance for the error allocation in the accuracy design stage.
An artificial intelligent decision-making system based on Deep Q Network is developed according to the characteristic of the optoelectronic countermeasures for defense. In view of the high complexity of the input state variables of the system, simulation method is used to sift the state variables so as to reduce the input dimension of the network. In addition, simulation method is used to generate enough samples for the network training. Aiming at the adaptability evaluation of the system, evolutionary evaluation index is designed and simulation method is used to evaluate the adaptability of online learning ability of the system.
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