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为提高在低信噪比与先验信息不足条件下对线性调频(LFM)信号识别能力,借鉴信息论中的熵权法改进WHT(Wigner-Hough Transform),提出了一种基于切片熵权的WHTE(Wigner-Hough Transform based on Entropy)算法。推导出LFM信号的WHT与对应特性,将WHT变换域内极半径和角度切片的熵值来转换为权重因子,进而对每个切片进行加权处理,采用双层权重以弱化噪声与干扰项的影响,并推导出LFM信号与高斯白噪声在WHT维度内不同假设条件下的概率密度分布函数,构建了对于LFM信号WHT后恒虚警检测的完备流程。通过理论分析与公式推导论证了算法的可行性,并与WHT、分数阶傅里叶变换与周期WHT算法的仿真对比,验证了算法的有效性,凸显WHTE算法能够在强噪声背景下与没有先验支撑时实现对LFM信号的良好检测。
In order to improve the recognition ability of Linear Frequency Modulation (LFM) signal under the conditions of low signal to noise ratio and prior information, and improve the WHT (Wigner-Hough Transform) by using entropy method in information theory, a WHTE based on slice entropy is proposed (Wigner-Hough Transform based on Entropy) algorithm. The WHT and corresponding characteristics of LFM signal are deduced. The entropy of the polar radius and angle slice in the WHT transform domain is converted into a weighting factor, and then each slice is weighted, and the weight of two layers is used to weaken the influence of noise and interference term. The probability distribution function of LFM signal and Gaussian white noise under different assumptions in the WHT dimension is derived, and a complete flow of CFAR detection for the LFM signal is constructed. The feasibility of the algorithm is demonstrated through theoretical analysis and formula deduction. Compared with the WHT, fractional Fourier transform and periodic WHT algorithm, the validity of the algorithm is verified. It is shown that the WHTE algorithm can be used in a strong noisy environment with no prior Achieve good LFM signal detection while supporting.