Ultra-wideband (UWB) impulse radar is widely used for through-wall human detection due to its high-range resolution and high penetration capability. UWB impulse radar can detect human targets in non-line-of-sight (NLOS) conditions, mainly based on the chest motion caused by human respiration. The automatic detection and extraction of multiple stationary human targets is still a challenge. Missed alarms often exist if the detection method is based on the energy of the human target. This is mainly because factors such as the range of the target, the intensity of the respiratory movement, and the shadow effect will make a difference between the energy scattered by targets. Weak targets are easily masked by strong targets and thus cannot be detected. Therefore, in this paper, a multiple stationary human targets detection method based on convolutional neural network (CNN) in through-wall UWB impulse radar is proposed. After performing the signal-to-clutter-and-noise ratio (SCNR) enhancement method on the raw radar data, the range-slow-time matrix is fed into a six-layer CNN. Benefiting from the powerful feature extraction capability of CNN, the target point of interest (TPOI) can be extracted from the data matrix. The clustering algorithm is used to simplify the TPOIs to achieve accurate detection of multiple targets behind the wall. The effectiveness of the proposed method is verified by the experimental data.

Ultra-wideband (UWB) impulse radar is widely used for through-wall human respiration detection due to its high range resolution and high penetration capability. UWB impulse radar emits very narrow time pulses, which can directly obtain the impulse response of the target. However, the time interval between successive pulses emitted is not ideally fixed because of timing jitter. This results in the impulse response position of the same target not being fixed, but it is related to slow-time. The clutter scattered by the stationary target becomes non-stationary clutter, which affects the accurate extraction of the human respiration signal. In this paper, we propose a method for reducing timing jitter’s impact in through-wall human detection by UWB impulse radar. After the received signal is processed by the Fast Fourier transform (FFT) in slow-time, we model the range-frequency matrix in the frequency domain as a superposition of the low-rank representation of jitter-induced clutter data and the sparse representation of human respiratory data. By only extracting the sparse component, the impact of timing jitter in human respiration detection can be reduced. Both numerical simulated data and experimental data demonstrate that our proposed method can effectively remove non-stationary clutter induced by timing jitter and improve the accuracy of the human target signal extraction.