基于局部Hilbert边际谱隶属度的微弱目标检测算法

文章采用Hilbert-Huang变换处理海杂波数据，获得海杂波的Hilbert边际谱，并分析了海杂波的Hilbert边际谱特点及其目标的影响。分析发现:无目标时，海杂波信号的Hilbert边际谱的谱宽较宽，低频成份较弱；而当目标出现时，其谱宽明显变窄，低频成份明显增强。为了描述目标对海杂波Hilbert边际谱的这种影响，将隶属度引入Hilbert边际谱，并采用目标出现频率处的海杂波局部Hilbert边际谱计算隶属度，通过分析发现，目标出现时，该隶属度明显减小，在此基础上，提出了基于局部Hilbert边际谱隶的微弱目标检测算法。仿真结果表明，对于实测和仿真目标数据，该算法都具备较好的检测性能，其检测性能明显优于基于盒维数的微弱目标检测算法、频域检测方法和多脉冲CA-CFAR检测算法的检测性能，对海杂波中的慢起伏目标具备较强的检测能力。 

Weak Target Detection Based on the Membership Degree of Partial Hilbert Marginal Spectrum

The sea clutter data is processed by the Hilbert-Huang transformation to obtain the Hilbert marginal spectrum of the sea clutter, and the characteristic of its spectrum and the effect of the target on the spectrum are analyzed. It is found that the spectrum of the sea clutter is wider and the low frequency components are weaker when no target is hit, while it is narrower and the low frequency components are stronger when the target is hit. To describe the effect of the target on the spectrum, the membership degree is introduced, which is calculated by the partial Hilbert marginal spectrum where the target is hit, and it is found that the membership degree is smaller obviously when the target is hit, so the method based on the membership degree of the partial Hilbert marginal spectrum is proposed to detect the weak target. Shown as the simulation results of the real and simulating target data, the slowly fluctuate target in the sea clutter is detected effectively by the method whose detection performance is better than the method directly using the box dimension, the method to detect the target in the frequency domain and the multi-pulses CA-CFAR method.