基于概率逼近的本原BCH码编码参数的盲识别方法

针对本原BCH码编码参数的盲识别问题，该文提出了一种基于概率逼近的盲识别方法。首先，利用Gauss分布和Poisson分布逼近随机码字的根概率特性，确定了搜索BCH码长的门限；然后，通过分析本原域元素的检错能力及同构对域的影响，应用临近域对的方法确定编码域，提高了其识别能力；最后，给出识别生成多项式时的共轭根系表，从而减少了计算量。仿真结果表明，在较高的误码率下，该方法能快速地识别出BCH码编码所采用的编码参数。

Blind Identification of Primitive BCH Codes Parameters Based on Probability Approximation

To solve the issues of blind identification of primitive BCH codes encoding parameters, a novel identification algorithm with probability approximation is presented. Frist, by taking advantage of the approximation of random code words’ root probability character which uses Gaussian distribution and Poisson distribution, the thresholds for searching code length are structured. Second, though analyzing the checking ability of the primitive element and the impact of isomorphism on searching, the coding filed is determined by using the method of nearby fields pair which improves the performace of identification. Finally, the calculation is reduced by creating and using the conjugate roots table in the recognition of generator polynomial. Simulation results show that, the proposed algorithm achieves a significant improvement in identification probability even if in high BER situation.