雷达动目标短时稀疏分数阶表示域探测方法

机动目标检测和精细化运动状态估计始终是雷达信号处理的热点和难点问题,基于时频分布的动目标检测方法难以同时获得高时频分辨率,且参数估计精度受搜索步长的限制。稀疏时频分布技术结合了经典时频分析技术和高分辨稀疏域信号处理的优势,是传统变换域处理技术的扩展。构建了短时稀疏分数阶表示域信号处理框架,并在此基础上,提出了两种雷达机动目标检测和估计方法,即短时稀疏分数阶变换和短时稀疏分数阶模糊函数,实现了时变信号的时间稀疏变换域高分辨表示。对海雷达目标探测试验验证表明,所提方法适用于复杂背景下雷达机动目标的探测,并能获得目标运动状态的精细估计。

Radar detection for moving target in short-time sparse fractional representative domain

Moving target detection and refined estimation for motion status are hot and difficult problems in radar signal processing. Traditional moving target detection via time-frequency distribution (TFD) cannot obtain high time and frequency resolution at the same time. Also, the estimation accuracy of motion parameters is limited by searching steps. Sparse TFD (STFD) technique combines the merits of classical TFD and high-resolution signal processing in sparse domain. The STFD is an extension of traditional transform-based methods. The concept of short-time sparse fractional representative domain is proposed. Two radar moving target detection and estimation methods are proposed accordingly, named as short-time sparse fractional Fourier transform and short-time sparse fractional ambiguity function, which achieve the high resolution representation for time-varying signals in time and sparse transform domain. It is verified using real marine radar datasets that the proposed method can improve the moving target detection performance in complex environment, and can obtain the refined estimation for motion status.