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中文摘要：
针对现有卷积码分组交织盲识别存在运算数据量大、识别正确率不高、难以满足非合作情况下盲识别需求的问题,提出了基于矩阵秩统计的卷积码分组交织盲识别方法。该方法首先根据不同卷积码分组交织矩阵中同行数据具有相同线性约束的性质,采用遍历处理求周期性相关列的方法,识别出交织块的行数和列数;然后通过对不同交织块中的同行组成的多段连续卷积码数据求解校验矩阵,利用校验矩阵验证方式获取交织数据的起始点,完成对分组交织的盲识别。仿真实验表明,该方法在0.01的误码率条件下识别正确率在85%以上,验证了该算法的有效性和可靠性。
英文摘要：
In order to avoid failures of blind parameter identification for connolutional code and packet interleaver,a blind identification approach based on matrix rank statistic was proposed in this paper.Since the same row data of different packet interleaving matrix had a linear constraint relation,therefore,the matrix of column difference constructed by the output data of interleaver with the sampled period equaling to the row of the interleaving matrix had the linear relationship column,and the relationship was periodical.By using exhaustively search method,the row and column of the interleaving matrix was found.After that,the checked vector of convolutional code was calculated from the matrix which was composed from the any same row of different interleaving packets,and the start point of the interleaved data was found by checking vector.Simulation showed that the correct percentage of recognition was 85% higher.
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