水下跳频通信信号时域参数盲检测算法仿真

Peng XIE,Rui LIANG,Hong-mei ZHANG,Xiao-feng ZHANG

Computer Simulation（2020）

Cited 0|Views7

Abstract

针对传统跳频信号时域检测算法需要提前预知一个或者多个信号参数,不能直接进行盲检测,且检测过程较为繁琐的问题,提出水下跳频通信信号时域参数盲检测算法.首先分析跳频系统工作原理,突出跳频通信系统具有抗干扰性强、截获概率低等诸多优势;再结合跳频信号的时变性与非平稳性构建数学模型,将跳频信号转换为分段稳定的随机时间序列;其次对信号做滤波处理,消除噪声干扰,并利用时频反变换获取时域信号;最后在广义矩形时域分布算法基础上,提取信号时域脊线,计算能量之和,实现水下跳频通信信号时域周期、频率以及跳变时刻盲检测.实验结果表明,本文算法在进行水下跳频通信信号时域参数盲检测时,检测性能优于其它算法,且检测准确率较高.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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