Image Processing And Research Of Satellite-Borne Laser Warning

In the process of scientific and technological development,laser technology is being used more and more widely,and the development of laser weapons is also maturing,while the laser warning system can capture and identify enemy laser signals and issue warnings,playing an important role in the modern battlefield.With the background of grating diffraction satellite-borne laser warning system,the following research has been conducted.Firstly,the principle of grating diffraction type laser warning system is analyzed,and based on the principle and related formulae,the main research objective is established as the extraction of spot center coordinates,and the introduction of the laser warning system is carried out.Next,FPGA is selected as the main control chip,and the driving and control methods of the detector,data transmission chip,data cache chip and the flow of image acquisition and transmission are analyzed.According to the imaging characteristics,common image pre-processing methods are analyzed,including filtering,blind element elimination,etc.The design of FPGA-based BP neural network image compression part is analyzed;two FPGA-based spot target identification and center extraction methods are introduced: the first one uses the spot area segmentation method and calculates the spot center in the segmented area to improve the design;the second one is the FPGA-based adaptive region growth method,which can automatically find and merge the spot regions and calculate the coordinate values of each center.Based on SOPC system,the calculation idea of the inverse tangent in the laser angle calculation formula is designed in combination with Nios II processor,and the effect of software and hardware interaction and the calculation effect of CORDIC algorithm in Nios II processor are verified.The effect of spot center extraction is verified by simulation,and the deviation of center coordinate extraction is small and the speed of calculation is fast.Finally,the factors that may produce errors in the validation results of this paper are analyzed,including the experimental conditions,program design and environmental influences,and the aspects that can be improved and optimized subsequently are explained.