The MMW Passive Detecting System Based On Virtual Prototyping Technology

Virtual prototyping technology is a computer-aided engineering technology developed in the 1990s with the evolution of information and computer technology. It is a new research method of product design, which uses the digitization design of computer simulation model to replace the design of real physical prototype.As its potential on military application, millimeter-wave (MMW) passive detection has became a worldwide technology competed by all the countries. In this paper, virtual prototyping technology is introduced into MMW passive detecting system, which not only reducing the research cost, but also cutting down the research time. Virtual prototyping technology will certainly promote the development of MMW passive detecting system.Regarding the every components of virtual MMW passive detecting system, this paper conducts the research mainly on the areas as below.(1) Major components of MMW radiometer and noise/target signal are modeling to form virtual components, which established the foundation of virtual MMW passive detecting system.(2) The db5 wavelet coefficient characteristic of video-frequency direct current (DC) signal in MMW radiometer is researched, and wavelet threshold denoising method is taken on video-frequency DC signal. Moreover, the method is improved based on the defects of wavelet denoising above. Module of video-frequency DC signal of MMW radiometer is explored based on these algorithms.(3) The discrete cosine transform (DCT) is utilized to analysis the video-frequency alternating current (AC) signal of MMW radiometer. Based on the DCT coefficient characteristics of target signal and Gaussian white noise, video-frequency AC signal of MMW radiometer compensation and denoising algorithm is proposed with support vector machine (SVM), least square support vector machine (LSSVM) and relative vector machine (RVM). In view of the thought of wavelet threshold denoising, video-frequency AC signal is denoised in DCT domain, which improved the denoising effect of AC MMW radiometer. Compensation and denoising module of video-frequency AC signal of MMW radiometer is explored based on above algorithms.(4) The selection of membership function in fuzzy support vector machine (FSVM) is studied. Considering the relationship of samples with the minimal sphere surrounding by sample set, an improved membership function is researched and signal recognition is realized based on FSVM. After three output signal of three-beam array detecting system are integrated into one signal, fractional Fourier transform (FrFT) is used in feature extraction of integrated output signal and FSVM is utilized in target identification. Target recognition of MMW radiometer module is explored based on the algorithms.(5) Main components composed of the virtual MMW radiometer system, and the influence to output signal is researched whether there is a low-noise amplifier or not. The feasibility of main components modeling is verified, which established the foundation of application of MMW radiometer. At last, virtual MMW passive detecting system is constituted by virtual MMW radiometer, compensation and denoising module of target video-frequency DC signal and target identification module.