基于最大似然检测的(n,1,m)卷积码盲识别方法
doi: 10.3724/SP.J.1146.2012.01578
Blind Recognition of (n,1,m) Convolutional Code Based on Maximum Likelihood Detection
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摘要: 该文针对非合作信号处理中的(n,1,m)卷积码的盲识别问题，提出一种基于最大似然检测的识别方法。该方法将接收码序列按不同长度进行分段，构建卷积码校验多项式系数的线性方程组，利用最大似然检测方法估计线性方程组的解，并利用解向量与卷积码生成多项式的校验关系，构建生成多项式系数的线性方程组，最终求解得到卷积码的生成多项式。仿真实验验证了算法的有效性，并对算法的误码适应能力进行了理论和仿真分析，仿真结果表明该方法能够在较高误码率条件下实现(n,1,m)卷积码的检测与识别。Abstract: Considering recognition of (n,1,m) convolutional code in non-cooperative signal processing, an algorithm based on maximum likelihood detection is proposed. Firstly, the linear equations of check polynomial coefficients are constructed with different lengths, and the equations are solved based on maximum likelihood detection. Then the equations of generator polynomial coefficients are constructed according to the relationship between check polynomial and generator polynomial. The generator polynomial is deduced by the equations at last. Validity of the algorithm is verified by the simulation results. Case studies are presented to illustrate that the method can recognize the (n,1,m) convolutional code in a high noisy environment.
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