基于可信度累积的伪随机序列多项式估计算法

针对高阶误码条件下伪随机序列多项式估计算法效率不高、容错性能差等问题进行研究,提出一种基于可信度累积的多项式估计算法。该算法结合了基于二元序列迭代的BM算法和改进型Chase算法,利用序列软信息,在BM算法外部构建序列及多项式可信度集合,通过可信度累积实现估计。然后,利用本原多项式的二元域性质,通过缩小累积多项式规模,提高估计精度。仿真结果表明,该算法性能不受制于多项式抽头个数,在误码率为18%的条件下能够完成17阶m序列多项式估计。

Polynomial estimation method for PN sequence based on reliability accumulation

The issues of insufficient efficiency and poor error-tolerance of current high-order polynomial estimation methods under error conditions were studied.By combining the iterative berlekamp-massey(BM) algorithm in GF(2) and the improved Chase algorithm,a novel algorithm based on reliability accumulation was proposed.The sequence soft in-formation was mapped to sequence reliability as the input information of BM algorithm,and the corresponding output re-liability of estimated polynomial was accumulated as the rule of correct polynomial.In order to improve the estimation precision,the candidate reliability-accumulated polynomial set were reduced by the characteristics of primitive polyno-mial in GF(2).The simulation results show that the proposed method is irrelevant with the tapped number of generator polynomial,and has a good estimation performance to estimate 17-order polynomials when the sequence BER is 18%.