摘要
针对雷达回波信号能量弱、信噪比低、难以获取高质量中频信号的频域特征,从而导致测量误差较大的问题,提出了同步挤压小波变换(SST)结合中值滤波的信号时频分析方法,实现强随机噪声压制。首先,构造24G连续调频雷达回波信号的仿真模型,通过添加瑞利杂波和高斯随机噪声,对比短时傅里叶变换(STFT)、连续小波变换(CWT)、SST时频分析图,提出瞬时频率识别精度作为评价指标I;其次,利用STFT、CWT、SST算法对实际距离为6m的回波信号进行时频谱分析。研究结果表明:SST能更好地突出瞬时频率特性,并且可将随机噪声压缩为低能量短线条形。基于该分布特点,SST结合中值滤波能很好地抑制噪声,I值相对于SST、STFT、CWT分别降低了0.21、0.55、0.71,瞬时频率识别精度更高,同时逆变换重构信号的波峰波谷值得到较好的保持,且在不同距离下探测精度误差也最小。
Due to the weak energy and low signal-to-noise ratio of radar echo signals,it is difficult to obtain the frequency domain characteristics of high quality medium frequency signals,which often leads to large measurement error.To address this issure,a signal time-frequency analysis method combining synchrosqueezing wavelet transform(SST)with median filtering is proposed to achieve strong random noise suppression.Firstly,a simulation model of 24 G frequency modulated continuous wave(FMCW)radar echo signal is constructed.By adding Rayleigh clutter and Gaussian random noise,the frequency analysis diagrams from short-time Fourier transform(STFT),continue wavelet transform(CWT),and SST are compared,and the instantaneous frequency identification accuracy is proposed as an evaluation index I.Secondly,STFT,CWT and SST algorithms are used to analyze the time spectrum of the echo signals with an actual distance of 6 meters.The research results indicate that SST can better highlight the instantaneous frequency characteristics and compress the random noise into low-energy short lines and strips.Based on the distribution characteristics,SST combined with median filtering can effectively suppress the noise,with I value decreasing by 0.21,0.55 and 0.71 compared with SST,STFT and CWT,respectively.Compared with the traditional SST,STFT and CWT,the instantaneous frequency recognition accuracy of the proposed method is highest,while the peak and valley values of the reconstructed signals through inverse transform can be well maintained,at the same time,and the detection accuracy error at different distances is also the smallest.
作者
李丛
徐华
戴聪聪
冯阳
邓权
周志明
LI Cong;XU Hua;DAI Congcong;FENG Yang;DENG Quan;ZHOU Zhiming(Shenzhen Hongdian Technology Co.,Ltd.,Perception Product Center,Shenzhen 518100,China)
出处
《大气与环境光学学报》
CAS
CSCD
2023年第6期503-515,共13页
Journal of Atmospheric and Environmental Optics
基金
深圳市战略新兴产业发展专项资金(GCZX2015083116590400)
广州市科技计划项目(201707020020)。
关键词
毫米波雷达
同步挤压小波变换
中值滤波
时频分析
识别精度
随机噪声
millimeter wave radar
synchrosqueezing wavelet transform
median filtering
time frequency analysis
identification accuracy
random noise
