基于移不变二维混合变换的机场雷达噪声抑制

机场跑道异物(foreign object debris, FOD)检测对飞行器的安全起降有着非常重要的意义,而机场跑道异物检测的一个关键环节是很好地抑制机场雷达图像的噪声,因此提出一种基于距离时间维的移不变混合变换以抑制机场雷达图像的噪声。首先,在雷达成像时进行离散傅里叶变换(discrete Fourier transform, DFT)和维纳滤波滤除距离维上的噪声。然后,在雷达成像时进行超分析离散小波变换(hyperanalytic wavelet transform, HWT)自适应滤波去除时间维上的噪声。与传统的成像后去噪算法相比,本文的算法可以有效地去除机场雷达图像噪声,显著地改善图像的视觉效果。最重要的是该算法具有很强的实时性,可以很好地应用到工程实践中。

Airport runway radar image de-noising based on 2-D shift-invariance hybrid transform

Foreign object debris (FOD) detection in airport runway is very important to airplanes′ safety, and the airport runway radar image noise suppressing plays a vital role in foreign object detection. Therefore, an airport runway radar image de-noising method based shift invariant hybrid transform is proposed in the range-time dimension. Firstly, the radar image noise in the range dimension is removed by the Wiener filter in discrete Fourier transform(DFT)domain. Secondly, the radar image noise in the time dimension is removed by the adaptive threshold in hyperanalytic wavelet transform(HWT)domain.Compared with traditional de-noising methods after imaging, the proposed mothod can remove the runway radar image noise effectively and improve the visual effect of images significantly, and most importantly, it can run in real time and be suitable for engineering practice.