Fuzzy recognition of missile tail flame spectrum
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摘要: 分析导弹尾焰可知，影响其光谱变化的主要因素包括发动机、燃料等，故根据尾焰光谱可以达到导弹型号识别的目的。为保证识别效率，用特征光谱代表尾焰特征，可大大缩减数据量。首先根据光谱差模型计算出各波长处的辐射差，通过设定阈值将辐射差分段，高于阈值的波段即为所选的特征波段。改变积分步长和阈值可获得几组不同数据。分别采用光谱角匹配算法（Spectral Angle Matching algorithm，SAM）和模糊算法对不同精度和不同特征波段的数据进行处理，得到识别结果。以识别结果的正确率和待识别样本与各型光谱的相似度距离为衡量标准，模糊算法识别效果可与SAM相当，但其在算法复杂度方面优于SAM。Abstract: The analysis of the missile plume shows that the main factors influencing its spectral change include the engine, fuel, etc., so the purpose of missile model recognition can be achieved according to the tail flame spectrum. In order to ensure the efficiency of recognition, the characteristic spectrum was used to represent the plume characteristics, which can greatly reduce the data volume. Firstly, the radiation difference of each wavelength was calculated according to the spectral difference model. By setting the threshold value, the radiation difference segment was obtained, and the band of higher than the threshold was the selected characteristic band. Changing the number of steps and thresholds can obtain several sets of different data. The Spectral Angle Matching algorithm (SAM) and fuzzy algorithm were used to deal with the data of different precision and different feature bands. The accuracy of the recognition results and the similarity distance between the samples and the different spectra were measured, and the fuzzy algorithm can identify with SAM, but it was better than SAM in algorithm complexity.
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Key words:
- spectrum identification /
- missile stern flame /
- fuzzy algorithm /
- SAM
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