Coating-Stealth Armored Targets Millimeter-wave Radiation Brightness Temperature Inversion And Image Reconstruction

Difference between microwave and infrared characteristics, millimeter-wave makes up for lack of microwave system and infrared systems, much of the attention of the domestic and foreign experts and scholars. In recent years, research of millimeter-wave technology has become the new hot spot, and gradually has been widely applied in the national defense, transportation, medical, industrial control, security management and other areas.Millimeter-wave Near-sensing technology is use of the target’s millimeter-wave radiation characteristics to detect and identify targets. In the millimeter-wave radiation detection, radiometer detects the antenna mouth surface temperature of the target instead of the measured target the radiation brightness temperature. To make precise measurements of the radiometric brightness temperature of coating-stealth armored target, we must solve the first kind of Fredholm integral equation.This paper studies the content and innovation points as follow:1. The millimeter-wave detection experiments were done according to a stealth coating armored targets and not stealth armored targets. Through the establishment of corresponding antenna temperature model of stealth coating armored targets, using BFGS algorithm was applied to recovery of the millimeter-wave radiation target’s brightness temperatures from the measurement of the temperature data antenna. According to the performance of the bright temperature, a method of the calculated stealth material reflectivity and emissivity were presented.2. Using approximation control method to study the first kind of morbid Fredholm integral equation and using the conjugate gradient method to obtain the inversion of the not stealth armored target’s radiation brightness temperatures. Using BP neural network gives the learning curve of error and verified the effectiveness of the inversion method.3. Millimeter-wave passive imaging although has the ability to work in all weather. But compared with infrared and visible light imaging, one of most importantdrawback in millimeter-wave passive imaging is the lower resolution, cannot fully reflect the scene and the target’s detail. It is most important to improve millimeter-wave image resolution for better detecting and identifying the target. This paper, we use wavelet domain regularization method. Conduct millimeter-wave degraded image wavelet domain local noise variance estimation, the latter with adaptive regularization method for reconstruction of high-resolution millimeter-wave images. The experimental results show that this method significantly noise cancellation, sharpen the image, while preserving image detail.