Improvement of the Recognition Probability about Camouflage Target Based on BP Neural Network

Using static Michelson interferometer to get the spectrum information of measurement targets for spectrum identification, under the condition that the interference length is constant, the system can be optimized by BP neural network algorithm for the mixed spectral separation process. Thereby it can realize improving the recognition probability of camouflage target. Collecting the spectrum information in field of view (FOV) by the interferometer and linear array CCD detector, composing the set of mixed spectrum data, with known absorption spectrum of the material as a hidden layer of rules, it used BP neural network to separate the mixed spectrum data. Experiment with different distances, different combinations of mixed background spectrum as the initial data, using steel target (size; 1. 5 m X 1. 5 m) made of four kinds, the recognition probability of non-camouflage target is about 90% by BP neural network algorithm or the traditional algorithm, while the recognition probability of camouflage target is 75. 5% with BP, better than 31. 7% with the traditional, so it can effectively improve the recognition probability of camouflage target.