基于改进欧几里德算法的(n,1,m)卷积码识别

针对信息截获领域中(n,1,m)卷积码识别算法的应用范围受限和所需数据量大的问题,提出基于改进欧几里德算法的识别方法。该方法利用剩余定理改进经典欧几里德算法,使其可求n个多项式的最高公因式,识别(n,1,m)卷积码只要求码字多项式最高幂次大于记忆长度m,即截获的码序列长度大于其约束长度。Matlab仿真表明:在只需少量数据情况下,可识别所有(n,1,m)卷积码的生成多项式。

Recgonition of(n,1,m)Convolutional Code Based on Improved Euclidean Algorithm

A recognition method based on the improved Euclidean algorithm was proposed in order to solve the applied limitation and requiring mass data of(n,1,m) convolutional codes recognizing methods in information interception.This method improved the classical Euclidean algorithm to calculate the largest common polynomial of n polynomials by residual theory and recognizd(n,1,m) convolutional codes only on the condition that the largest power of code polynomials was larger than the memory length m,the length of code sequence was larger than the constraint length.The simulations on Matlab showed that this method could recognize the generator polynomials of all(n,1,m) convolutional codes with fewwer data.