基于相关熵的扩频周期估计方法

针对低信噪比下直接序列扩频信号扩频周期估计难题,利用相关熵可度量随机过程局部相似性的特点,提出了一种基于相关熵的估计新方法。首先分析了相关熵隐含的周期性,理论上推导了证明周期性存在的系列表达式,据此分析出可由接收信号的相关熵峰值间隔估计扩频周期。此外,研究了信息码的存在对扩频周期估计性能产生的影响,并针对此问题,在相关熵法的基础上提出了一种延时相乘相关熵分析法。仿真结果表明,与常用的基于功率谱的倒谱法和二次谱法,以及基于自相关函数的波动相关法相比,该文所提出的基于相关熵的新方法有更好的扩频周期估计性能。

PN sequence period estimation method based on correntropy

The estimation of the pseudo-noise (PN) sequence period of a direct sequence spread spectrum (DS/SS) signal is addressed in this paper. According to the fact that correntropy is a local similarity measure of a random variable, two novel methods are proposed. The implicit periodicity of the correntropy is analyzed, and a series of formulas are derived to prove the periodicity. On the basis of that, the period of PN sequence can be estimated from the peak interval of the correntropy. Moreover, an improved delay-multiplied correntropy algorithm is proposed in view of the influence that the information code has on the PN sequence. Simulation results show that, compared with the cepstrum method, the spectrum reprocessing method based on power spectrum, and the fluctuation correlation method based on autocorrelation, the estimation performance of the proposed methods based on the correntropy is much better.