Penetrating detection and recognition of behind wall indoor human activities has drawn great attentions from social security and emergency service department in recent years since intelligent surveillance aforehand could avail the proper decision making before operations being carried out. However, due to the influence of the wall effects, the obtained micro-Doppler signatures would be severely degenerated by strong near zero-frequency DC components, which would inevitably smear the detailed characteristic features of different behind wall motions in time-frequency (TF) map and further hinder the motion recognition and classification. In this paper, an ultra-wideband (UWB) radar system is first employed to probe through the opaque wall to detect the behind wall motions, which often span a certain number of range bin cells. By employing such a system, a high resolution range map can be obtained, in which the embedded rich range information is expected to be fully exploited to improve the subsequent recognition and classification performance. Secondly, a high-pass filter is applied to remove the effect of the wall in the raw range map. Then, with the aim of enhancing the characteristic features of different behind wall motions in TF maps, a novel range-max enhancement strategy is proposed to extract the most significant micro-Doppler feature of each TF cell along all range bins for a specific motion. Lastly, the effectiveness of the proposed micro-Doppler signature enhancement strategy is investigated by means of onsite experiments and comparative classification. Both the feature enhanced TF maps and classification results show that the proposed approach outperforms other state-of-art Short-Time Fourier Transform (STFT) based TF feature extraction methods.