改进的最优小波基选取方法与跳频信号检测研究
|
摘要
跳频通信因其良好的抗干扰性和低截获性,在军事通信中备受青睐。因其在军事通信中的重要作用,研究跳频信号的检测方法显得尤为紧迫。基于小波分解与希尔伯特-黄变换的跳频信号检测方法,有效地解决了跳频信号检测过程中时间分辨率和频率分辨率不能同时兼顾的问题,提高了跳频信号检测的精度。在此基础上对最优去噪小波基的选取问题进行了深入研究。通过改进信噪比与信噪比增益这个小波去噪质量评价指标,提出了改进的最优小波基选取方法,有效地解决了非合作通信中信号真值未知情况下最优小波基的选取问题。同时利用这一方法成功实现了跳频信号的检测,为检测跳频信号提供了新思路。
Frequency hopping communication is popular in military communication due to its good anti-jamming and low interception. Because of its important role in military communication,it is urgent to study the detection methods of frequency-hopping signals. The new algorithm W-H solves the problem that the time-frequency analysis cannot take the time resolution and frequency resolution into account at the same time effectively,and it has been proved to improve the detection precision. In this paper,the selection of the optimal de-noising wavelet base is studied based on W-H. By means of SNR and SNR gain,a new algorithm was proposed to select the optimal de-noising wavelet base when the original signal is unknown in non-cooperative communication. Meanwhile,the detection of the frequency hopping signal is realized successfully by the algorithm,it provides a new way for frequency hopping signal detection.
Frequency hopping communication is popular in military communication due to its good anti-jamming and low interception. Because of its important role in military communication,it is urgent to study the detection methods of frequency-hopping signals. The new algorithm W-H solves the problem that the time-frequency analysis cannot take the time resolution and frequency resolution into account at the same time effectively,and it has been proved to improve the detection precision. In this paper,the selection of the optimal de-noising wavelet base is studied based on W-H. By means of SNR and SNR gain,a new algorithm was proposed to select the optimal de-noising wavelet base when the original signal is unknown in non-cooperative communication. Meanwhile,the detection of the frequency hopping signal is realized successfully by the algorithm,it provides a new way for frequency hopping signal detection.
引文
[1]张浩楠,刘秀琦,杜庚辰.通信对抗中的现代信号处理技术应用研究[J].电子技术与软件工程,2016,5(3):46.
[2]齐扬阳,于淼,关志强.基于小波降噪和盲源分离的跳频通信抗干扰方法研究[J].南京邮电大学学报(自然科学版),2015,35(1):72-78.
[3]宁晓鹏.基于时频分析的跳频信号盲分离算法研究[D].哈尔滨:哈尔滨工业大学,2016.
[4]ZHENG Y,CHEN X H,ZHU R.Frequency hopping signal detection based on wavelet decomposition and HilbertHuang transform[J].Modern Physics Letters B,2017,31:1740078-1740084.
[5]佘鹏果.基于信号匹配和最优分解层的小波去噪方法研究[D].扬州:扬州大学,2014.
[6]朱建军,章浙涛,匡翠林,等.一种可靠的小波去噪质量评价指标[J].武汉大学学报(信息科学版),2015,40(5):688-694.
[7]陶珂.小波去噪质量评价方法研究[D].长沙:中南大学,2012.
[8]涂斌斌,谷丽华,许会.步态识别的小波去噪质量评价方法[J].沈阳工业大学学报,2017,39(1):61-66.
[9]王锴,刘志国,刘刚,等.基于自适应层数分解的小波变换滤除冲击信号[J].湖南大学学报(自然科学版),2015,61(10):65-71.
[10]唐鹏,郭宝平.改进型阈值函数寻优法的小波去噪分析[J].信号处理,2017,33(1):102-110.
[11]陶珂,朱建军.多指标融合的小波去噪最佳分解尺度选择方法[J].测绘学报,2012,41(5):749-755.
[12]李海,刘新龙,蒋婷,等.基于随机矩阵理论和最小描述长度的机载前视阵雷达杂波自由度估计[J].电子与信息学报,2016,38(12):3224-3229.
[2]齐扬阳,于淼,关志强.基于小波降噪和盲源分离的跳频通信抗干扰方法研究[J].南京邮电大学学报(自然科学版),2015,35(1):72-78.
[3]宁晓鹏.基于时频分析的跳频信号盲分离算法研究[D].哈尔滨:哈尔滨工业大学,2016.
[4]ZHENG Y,CHEN X H,ZHU R.Frequency hopping signal detection based on wavelet decomposition and HilbertHuang transform[J].Modern Physics Letters B,2017,31:1740078-1740084.
[5]佘鹏果.基于信号匹配和最优分解层的小波去噪方法研究[D].扬州:扬州大学,2014.
[6]朱建军,章浙涛,匡翠林,等.一种可靠的小波去噪质量评价指标[J].武汉大学学报(信息科学版),2015,40(5):688-694.
[7]陶珂.小波去噪质量评价方法研究[D].长沙:中南大学,2012.
[8]涂斌斌,谷丽华,许会.步态识别的小波去噪质量评价方法[J].沈阳工业大学学报,2017,39(1):61-66.
[9]王锴,刘志国,刘刚,等.基于自适应层数分解的小波变换滤除冲击信号[J].湖南大学学报(自然科学版),2015,61(10):65-71.
[10]唐鹏,郭宝平.改进型阈值函数寻优法的小波去噪分析[J].信号处理,2017,33(1):102-110.
[11]陶珂,朱建军.多指标融合的小波去噪最佳分解尺度选择方法[J].测绘学报,2012,41(5):749-755.
[12]李海,刘新龙,蒋婷,等.基于随机矩阵理论和最小描述长度的机载前视阵雷达杂波自由度估计[J].电子与信息学报,2016,38(12):3224-3229.