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针对现有的卷积码识别方法存在容错性不高,需已知编码参数且计算量大的问题,提出一种高容错的(2,1,m)卷积码快速盲识别方法。首先建立以(2,1,m)卷积码校验序列为解向量的含错校验方程组;然后基于校验方程系数的结构特性,循环利用校验序列的已知元素值递推估计其未知元素,在有误码条件下,进一步利用多个校验方程联合判决未知元素估计值,实现误码条件下校验序列的快速估计;最后基于卷积码自由距离特性及恒虚警准则检验校验序列估计值的正确性,并相应地识别出(2,1,m)卷积码生成多项式矩阵。在编码参数未知的情况下,根据校验序列估计值的检测结果,快速识别编码参数。仿真实验表明:该方法具有较高的容错性和较低的计算复杂度,无需先验已知编码参数;当误码率为0.08时,识别正确率能达到80%以上,此时矩阵分析识别法已无法正确识别卷积码,与矩阵分析识别法相比,该方法的识别正确率提高了80%以上。
In order to solve the existing convolution code recognition methods, which have low fault-tolerance and high computational complexity, we need to know the coding parameters and propose a fast fault-tolerant (2,1, m) convolutional code blind recognition method. Firstly, the error-checking equations with (2, 1, m) convolutional code sequences as the solution vectors are established. Based on the structural characteristics of the calibration equation coefficients, the recursive estimates of the known element values of the check sequence are reused The unknown element, under the condition of error code, further uses multiple check equations to jointly estimate the unknown element estimation value to realize the fast estimation of the check sequence under the error condition. Finally, based on the free distance characteristic of convolutional code and the constant false alarm criterion Verifies the correctness of the check sequence estimates, and accordingly identifies (2, 1, m) convolutional code generator polynomial matrices. In the case of unknown coding parameters, the coding parameters are quickly identified based on the detection results of the check sequence estimates. Simulation results show that the proposed method has higher fault tolerance and lower computational complexity, without prior knowledge of coding parameters. When the bit error rate is 0.08, the recognition accuracy can reach over 80%. At this time, the matrix analysis identifies The method has been unable to correctly identify convolutional codes. Compared with the matrix analysis and recognition method, the recognition accuracy of this method has been improved by more than 80%.