  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.
