Spectral Norm Feature Detection Method in FRFT Domain of Targets in Sea Clutter
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摘要: 对于海上机动目标，采用分数阶傅里叶变换(FRFT)可以很好地解决其回波多普勒谱能量扩散的问题，为了使机动目标回波能量做最佳化的相参积累，需要反复搜索变换阶数，然而由于海上目标机动状态的随机性和时变性，难以搜索得到最佳变换阶数。针对这一问题，该文利用矩阵理论中的奇异值分解实现各变换阶数条件下FRFT谱的特征提取，设计特征检测统计量，提出基于分数阶域奇异值的海杂波抑制与目标检测方法，在增加利用了机动目标在FRFT域形状信息的同时避免了最佳变换阶数搜索。在高斯白噪声仿真数据评估条件下，所提方法在信杂比为–2.5 dB时可以达到60%的检测概率；经过实测数据验证，方法可以在信杂比为4.7 dB的条件下，稳定完成目标检测，具有较好的检测性能，且易于工程化实现。Abstract: In order to optimize the coherent accumulation, repeated searches are required. However, due to the randomness and time variability, it is difficult to search for the optimal transformation order. In order to solve this problem, singular value decomposition in matrix theory is used to realize the feature extraction of FRFT spectrum under the condition of each transformation order, designs feature detection, and proposes sea clutter suppression and target detection based on singular value in the FRFT domain. The method avoids the search for the optimal transformation order while increasing the use of the shape information of the maneuvering target in the FRFT domain. Under the condition of Gaussian white noise simulation data evaluation, the proposed method can achieve a detection probability of 60% when the SNR is –2.5 dB; Verified by the measured data, the method can be stably completed under the condition that the SNR is 4.7 dB Target detection has good detection performance and is easy to implement in engineering.
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Key words:
- Target detection /
- Sea clutter /
- Singular value decomposition
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表 1 试验平均飞行参数表
UTC Vel_E(m/s) Vel_N(m/s) Vel_U(m/s) Lon(°) Lat(°) Height(m) 0200-0800 –29.1 0.06 –5.30 121.52 37.62 4432 
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