基于MCC-PAST算法的直扩信号PN码序列盲估计

针对在脉冲噪声环境下的直接序列扩频信号的伪随机(PN)码盲估计问题,提出了一种利用最大熵准则投影逼近子空间(MCC-PAST)算法和滑窗技术相结合的PN码盲估计方法。该方法利用最大熵准则,实现了在脉冲噪声背景下对扩频信号的特征进行稳定快速的追踪。通过对迭代过程中的特征子空间进行梯度估计,实现了可变遗忘因子(VFF)的自适应更新,使得权值的收敛精度和收敛速度得到了同时的优化。算法数据存储量小,复杂度低,易于硬件实现和实时数据处理。仿真结果表明,在脉冲噪声环境下该方法对PN序列估计的正确率和稳定性优于传统方法。

Blind Estimation of PN Code Sequence of DSSS Based on MCC-PAST Algorithm

Aiming at the problem of pseudo-random (PN) code blind estimation for direct sequence spread spectrum signals in impulse noise environment, a PN code combining maximum entropy criterion projection approximation subspace (MCC-PAST) algorithm and sliding window technology is proposed. Blind estimation method. The method utilizes the maximum entropy criterion to achieve stable and fast tracking of the characteristics of the spread spectrum signal in the background of impulse noise. Through the gradient estimation of the feature subspace in the iterative process, the adaptive update of the variable forgetting factor (VFF) is realized, and the convergence precision and convergence speed of the weight are optimized simultaneously. The algorithm has small data storage and low complexity, and is easy to implement in hardware and real-time data processing. The simulation results show that the accuracy and stability of the proposed PN sequence estimation is better than the traditional method in the impulse noise environment.