摘要
目前对于无线电弱信号检测的算法有线性和非线性算法两类,但是其都存在检测精度不高的问题,本文针对上述问题提出了一种迭代叠加抗噪的无线电弱信号检测模型。首先对含噪无线电信号进行时域叠加后取平均,以抵消部分噪声,然后对上述去噪信号进行傅里叶变换,并利用分组的方式进行频域叠加操作,最后用前部分数据对未知的后续数据进行线性预测,以提高弱信号检测的精度。仿真实验结果表明,本文提出的改进算法能有效的提取含噪无线电信号中的弱信号,且相比较随机共振法而言,具有较高的弱信号检测精度。
At present,there are two kinds of algorithms for the detection of weak signals in wireless networks,such as linear and non-linear algorithms,but all of them have low detection accuracy. In this paper,we propose an iterative overlay anti-noise radio weak signal detection model. Firstly,the timedomain superposition of the noisy radio signals is averaged to counteract part of the noise,then the abovementioned de-noised signals are Fourier transformed,and the method of grouping is used to perform the frequency domain superposition. Finally,Follow-up data linear prediction to improve the accuracy of weak signal detection. Simulation results show that the improved algorithm proposed in this paper can effectively extract weak signals from noisy radio signals,and has higher detection accuracy of weak signals than stochastic resonance method.
出处
《科技通报》
2018年第2期168-171,共4页
Bulletin of Science and Technology
关键词
弱信号提取
迭代叠加
线性预测
时域叠加
频域叠加
weak signal extraction
iterative superposition
linear prediction
time domain superposition
frequency domain superposition
