CAJ | 학술논문

　　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。
　　해문침대비합작신호처리중적(n,1,m)권적마적맹식별문제，제출일충기우최대사연검측적식별방법。해방법장접수마서렬안불동장도진행분단，구건권적마교험다항식계수적선성방정조，이용최대사연검측방법고계선성방정조적해，병이용해향량여권적마생성다항식적교험관계，구건생성다항식계수적선성방정조，최종구해득도권적마적생성다항식。방진실험험증료산법적유효성，병대산법적오마괄응능력진행료이론화방진분석，방진결과표명해방법능구재교고오마솔조건하실현(n,1,m)권적마적검측여식별。
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.