Infrared point source target recognition is the target recognition technology based on non-imaging time-domain signal characteristics of infrared detectors for small size or long distance targets. It has wide application prospects in many fields, such as industry and national defense. However, infrared point source targets recognition has become a recognized problem, because this kind of targets has less information and more interference factors. Therefore, the research on infrared point source target recognition has important theoretical and practical value. This paper proposed a mathematical construction method of infrared point source target signal .And through the modeling of sensor response and the verification of hardware-in-the-loop simulation experiment, a set of time-domain signal data for infrared point source target recognition method is generated. Then, by extracting several time–domain features, a nearest neighbor classification method based on feature weighting is proposed by combining the idea of mathematical model matching. At the same time, the influence of the uncertainty of the model itself on the recognition effect is considered. The result shows, using the sample data of point source target generated by simulation, the best recognition time of proposed methods is approximately 6-8 seconds, and the recognition accuracy rate is 64.6%-79.2%.
KEYWORDS: Thermometry, Wavelets, Neural networks, Temperature metrology, Neurons, Signal to noise ratio, Interference (communication), Target recognition, Signal processing, Data modeling
In the field of target recognition, target detection and tracking can be achieved by measuring the temperature of it. At present, most of the temperature measurement technologies are used for surface targets. Dim small targets are often faced with several problems during temperature measurement, such as the low filling rate of field, unknown emissivity and serious noise interference. Referring to the current issues about dim small targets temperature measurement, this paper built a new model for dual-waveband thermometry of them based on the wavelet analysis theory and neural network theory, obtaining the dual-waveband thermometry results of the dim small targets which are very close to the theoretical temperature. What’s more, the model validation is carried out by using the measured data of dim small targets. Analysis results show that the new model is more suitable to measure the dim small targets temperature of the radiation intensity signal-to-noise ratio within the scope of 3-8, laying the theoretical foundation and technical foundation for the recognition of dim small targets.
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