低信噪比信号的检测与参数估计方法研究

【作者】 谢艾彤；

【导师】 吕幼新；

【作者基本信息】 电子科技大学 ， 信号与信息处理， 2007， 硕士

【摘要】 超宽带雷达是一种新体制雷达,它的信号带宽很宽,具有高距离分辨率,在雷达探测、精确定位、目标成像、目标识别技术中得到广泛应用。如何在低信噪比下检测宽带信号,并进行参数估计,具有重要的意义。本文针对雷达信号中出现最多的信号形式—线性调频信号,在低信噪比的条件下的检测和参数估计进行了分析与仿真和研究。本文的主要工作如下:1、超宽带LFM是非平稳信号,传统的傅里叶分析方和理论已经不能完全表述信号的性质。利用典型的时频分析方法,包括短时傅里叶变换(STFT)、Wigner-Ville分布(WVD)以及在此基础上的Hough-Wigner分布对信号进行了分析。并提出了一种修正的Hough-Wigner方法,实现对强信号和弱信号的一次检测。2、经验模态分解(EMD: Empirical mode decomposition)方法能把时间序列信号分解成有限数目的本征模态函数(IMF: Intrinsic mode function)。本文利用EMD方法把线性调频信号从混合信号中分解出来,完成对线性调频信号的检测。针对EMD自身的端点效应,提出了改进办法,获得了更好的分解效果。3、信噪比对信号的检测有很大的影响,在低信噪比下要完成对信号的检测,具有一定的难度,而在高信噪比的条件下,则很容易。本文利用小波变换对线性调频信号进行消噪,以此来提高信噪比。针对线性调频信号自身特点,提出了在频域进行消噪的新方法,获得了很好的消噪效果。4、通过相位差分法、时频分析等不同的方法对信号的瞬时频率进行估计,并将各种方法进行分析比较。根据解线调方法对线性调频信号进行参数估计的运算量少但精度不高,而最大似然方法估算精度高但运算量大的特点,提出了将两种方法相结合的参数估算方法,获得了很好的效果。更多还原

【Abstract】 Ultra-wideband radar is a new type of radar system, and it has high range resolution because of its wide bandwidth. Thus, it has been used for target identification, precise position, target imaging and discrimination. How to detect the UVB radar signal and estimate the signal parameters in low signal to noise ratio condition is very important. The main concerned signals are Linear Frequency Modulated (LFM) signal.The main work can be summarized as:1. Ultra-wideband LFM is non-stationary signals and the conventional Fourier analysis can’t describe their character completely. We make use of the typical time-frequency analysis which includes STFT, WVD and Hough_Wigner to analyze the LFM. After that, we proposed a mending Hough_Wigner to detect the strong signals and weak signal at the same time.2. Time series signals can be decomposed to IMFs(Intrinsic mode function) by EMD(Empirical mode decomposition). This paper makes use of EMD algorithm to decompose the mix signals and get the LFM signals. We proposed a new method to eliminate the end effect and got a better decomposition.3. Signal to noise ratio has strong affection for the signal detection. We can detect the signal in high signal to noise ratio easily but difficultly in low signal to noise ratio. This paper made use of wavelet transform to denoise signals for increase the signal to noise ratio. We proposed a new denosing method which denoise signals in frequency time in stead of time domain and got a better result.4. We had a comparison between the phase difference method and time-frequency presentation which can be used for estimating the instantaneous frequency. The dechirp method for estimating the parameter had less computation and lower precision ; the maximum likelihood method had more computation and higher precision ;as a result ,we can estimate the instantaneous frequency by making use of the two method’s advantages.更多还原