基于分形特性改进的EMD目标检测算法

为克服原有检测算法在目标和海杂波混叠时检测性能下降的问题，该文提出一种基于分形特性改进的经验模态分解(EMD)目标检测算法。该算法对原始信号经经验模态分解后得到的固有模态函数进行数据重构，再采用快速傅里叶变换获得去噪后的海杂波单元和目标单元的频谱，计算两者的单一Hurst指数，并将其输入非参量检测器中进行目标检测。研究表明，虽然目标和海杂波在频谱中难以区分，但两者在无标度区间内的单一Hurst指数存在差异，因此所提检测算法相比于原有频域检测算法性能更优。

Improved EMD Target Detection Method Based on Mono Fractal Characteristics

In order to overcome the detection performance degradation of the existing detection method when the target and sea clutter is hard to distinguish, an improved target detection method based on mono fractal characteristics is proposed. Firstly, for getting the Intrinsic Mode Function (IMF) after reconstruction, the original signal is decomposed by using Empirical Mode Decomposition (EMD), then the spectrum of target bin and sea clutter bin after denoising is gained by using Fast Fourier Transform (FFT), Mono-Hurst exponents are calculated and the target is detected by nonparametric detector. The results show that, although target and sea clutter is hard to distinguish from frequency spectrum, but their Mono-Hurst exponents is different in scale-invariant interval, compared with original detection method in frequency domain, the proposed method can achieve good detection performance.