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Blind Estimation of PN Sequence Based on FLO Joint M Estimation for Short-Code DSSS Signals

机译:基于FLO联合M估计的短码DSSS信号PN序列盲估计

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When using a singular value decomposition (SVD) algorithm to estimate the pseudo code sequence of shortcode direct sequence spread spectrum (DSSS) signals directly under impulse noise, the pseudo code information extracted by the algorithm will be seriously interfered, and the estimation performance will deteriorate obviously. In this paper, proposed is a pseudo code sequence blind estimation algorithm based on fractional low order(FLO) joint M estimation. Under the condition of known pseudo code rate and pseudo code period, the received signal is segmented by the size of double PN period, and the fractional low order matrix of the received signal is constructed by using this algorithm in order to reduce the noise component, and then the matrix is decomposed by the SVD algorithm. By taking the summation and subtraction operation between the absolute value of the principal component and its complement sets to estimate the position of the out-of-step point of the pseudo code. Finally, the blind estimation of a pseudo code sequence is realized. Simulation results show that the proposed algorithm can greatly improve the performance of pseudo code sequence blind estimation in an impulse noise channel. When the signal-to-noise ratio (SNR) is about -5 db, the accuracy of the]pseudo code estimation can be kept above 90%.
机译:直接在脉冲噪声下使用奇异值分解(SVD)算法估计短码直接序列扩频(DSSS)信号的伪码序列时,该算法提取的伪码信息会受到严重干扰,估计性能会下降明显地。本文提出了一种基于分数低阶联合M估计的伪码序列盲估计算法。在已知伪码率和伪码周期的条件下,将接收信号按双PN周期的大小进行分割,并使用该算法构造接收信号的分数低阶矩阵,以降低噪声分量;然后用SVD算法分解矩阵。通过对主成分的绝对值及其补集进行求和和减法运算,可以估算伪代码失步点的位置。最后,实现了伪码序列的盲估计。仿真结果表明,该算法可以在脉冲噪声信道中极大地提高伪码序列盲估计的性能。当信噪比(SNR)约为-5 db时,伪代码估计的精度可以保持在90%以上。

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