In the real application scenario, the time information when a moving target enters/leaves the radar coverage is often unknown, which would lead to severe performance loss for target refocusing, parameter estimation and imaging. We address the refocusing and motion parameters estimation problem for a moving target with unknown entry/departure time, involving range cell migration (RCM) and Doppler frequency migration (DFM) within the coherent refocusing period. A computationally efficient refocusing method based on extended generalized Radon Fourier transform (EGRFT) and window Fractional Fourier transform (WFRFT), i.e., EGRFT-WFRFT, is proposed. By employing the four-dimensional searching in the parameter space, the proposed EGRFT-WFRFT method could extract and match the target signal very well, resulting in good refocusing output, where the target's entry time, radial acceleration, velocity and initial range could be estimated. Then the radar echoes corresponding to the “entry time” are extracted. After quadratic phase term compensation, WFRFT is performed on the extracted echoes to obtain the estimation of target's departure time. Numerical experiments and real radar dataset processing are carried out to evaluate the performance of the proposed method. The results show that EGRFT-WFRFT could achieve superior focusing performance.

中文翻译：

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未知时间信息的雷达动目标计算高效重聚焦估计方法

在实际应用场景中，运动目标进入/离开雷达覆盖范围的时间信息往往是未知的，这将导致目标重聚焦、参数估计和成像的严重性能损失。我们解决了进入/离开时间未知的运动目标的重聚焦和运动参数估计问题，涉及相干重聚焦周期内的距离单元迁移 (RCM) 和多普勒频率迁移 (DFM)。提出了一种基于扩展广义Radon傅里叶变换(EGRFT)和窗分数傅里叶变换(WFRFT)的计算高效的重聚焦方法，即EGRFT-WFRFT。通过在参数空间中采用四维搜索，所提出的 EGRFT-WFRFT 方法可以很好地提取和匹配目标信号，从而产生良好的重聚焦输出，其中可以估计目标的进入时间、径向加速度、速度和初始范围。然后提取“进入时间”对应的雷达回波。经过二次相位项补偿后，对提取的回波进行WFRFT，得到目标离开时间的估计。数值实验和真实的雷达数据集处理被用来评估所提出方法的性能。结果表明，EGRFT-WFRFT 可以实现优越的聚焦性能。数值实验和真实的雷达数据集处理被用来评估所提出方法的性能。结果表明，EGRFT-WFRFT 可以实现优越的聚焦性能。数值实验和真实的雷达数据集处理被用来评估所提出方法的性能。结果表明，EGRFT-WFRFT 可以实现优越的聚焦性能。