1 November 2016 Optimal interference code based on machine learning
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Proceedings Volume 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control; 101572I (2016) https://doi.org/10.1117/12.2246913
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China
Abstract
In this paper, we analyze the characteristics of pseudo-random code, by the case of m sequence. Depending on the description of coding theory, we introduce the jamming methods. We simulate the interference effect or probability model by the means of MATLAB to consolidate. In accordance with the length of decoding time the adversary spends, we find out the optimal formula and optimal coefficients based on machine learning, then we get the new optimal interference code. First, when it comes to the phase of recognition, this study judges the effect of interference by the way of simulating the length of time over the decoding period of laser seeker. Then, we use laser active deception jamming simulate interference process in the tracking phase in the next block. In this study we choose the method of laser active deception jamming. In order to improve the performance of the interference, this paper simulates the model by MATLAB software. We find out the least number of pulse intervals which must be received, then we can make the conclusion that the precise interval number of the laser pointer for m sequence encoding. In order to find the shortest space, we make the choice of the greatest common divisor method. Then, combining with the coding regularity that has been found before, we restore pulse interval of pseudo-random code, which has been already received. Finally, we can control the time period of laser interference, get the optimal interference code, and also increase the probability of interference as well.
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Ye Qian, Ye Qian, Qian Chen, Qian Chen, Xiaobo Hu, Xiaobo Hu, Ercong Cao, Ercong Cao, Weixian Qian, Weixian Qian, Guohua Gu, Guohua Gu, } "Optimal interference code based on machine learning", Proc. SPIE 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control, 101572I (1 November 2016); doi: 10.1117/12.2246913; https://doi.org/10.1117/12.2246913
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