Near field radar employing UWB (Ultra Wideband) signals with its high range resolution provides various sensing applications. It enables a robotic or security sensor that can identify a human body even in invisible situations. As one of the most efficient radar algorithms, the RPM (Range Points Migration) is proposed. This achieves fast and accurate estimating shapes of surfaces, even for complex-shaped targets by eliminating the difficulty of connecting range points. However, in the case of a complicated target surface whose variation scale is less than wavelength, it still suffers from image distortion caused by multiple interference signals mixed together by different waveforms. As a substantial solution, this paper proposes a novel range extraction algorithm by extending the Capon, known as FDI (Frequency Domain Interferometry). This algorithm combines reference signal optimization with the original Capon method to enhance the accuracy and resolution for an observed range into which a deformed waveform model is introduced. The result obtained from numerical simulation proves that super-resolution UWB radar imaging is accomplished by the proposed method, even for an extremely complex-shaped targets including edges.