A Multifrequency Radar System for Detecting Humans and Characterizing Human Activities for Short-Range Through-Wall and Long-Range Foliage Penetration Applications

Abstract

A multifrequency radar system for detecting humans and classifying their activities at short and long ranges is described. The shortrange radar system operates within the S-Band frequency range for through-wall applications at distances of up to 3 m. It utilizes two separate waveforms which are selected via switching: a wide-band noise waveform or a continuous single tone. The long-range radar system operating in the W-Band millimeter-wave frequency range performs at distances of up to about 100 m in free space and up to about 30 m through light foliage. It employs a composite multimodal signal consisting of two waveforms, a wide-band noise waveform and an embedded single tone, which are summed and transmitted simultaneously. Matched filtering of the received and transmitted noise signals is performed to detect targets with high-range resolution, whereas the received single tone signal is used for the Doppler analysis. Doppler measurements are used to distinguish between different human movements and gestures using the characteristic micro-Doppler signals. Our measurements establish the ability of this system to detect and range humans and distinguish between different human movements at different ranges.

Time-Frequency Spectral Signature of Limb Movements and Height Estimation Using Micro-Doppler Millimeter-Wave Radar

“…Several works exploiting micro-Doppler signatures for target classification and human identification have been published [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. In this paper, we focused on the possibility of characterizing object movement and differences between targets.…”

Through the Wall, Recognize Moving Targets Based on Micro-Doppler Signatures

“…In this context, the round-trip return time τ R for a target located at a range of R is given by [8];…”

“…Analysis of the micro-Doppler signatures in the joint time-frequency domain can provide useful information for target detection, classification, and recognition [8], [26]. Therefore, the micro-Doppler signatures from the scene are calculated by applying a time-frequency transforms, such as the Short-Time Fourier Transform (STFT) with a Gaussian window, to the scattered fields.…”

“…The experimental work for detection and recognition of the human movements behind the wall have been done with the specific parameters as in Table 1, these parameters are; The human body model has been based on the model which is presented in [7], [8], [9] as shown in Figure 2. The sampling rate is directly proportional to the maximum speed observed.…”

Classification of Doppler radar reflections as preprocessing for breathing rate monitoring

“…A comprehensive set of features was extracted in [10] and feature selection was used to find the most informative and distinguishing features among classes of human activities. Besides studying time and frequency features, researchers have applied other methods of feature extraction using wavelet packets [31], entropy analysis [32] and empirical mode decomposition [33,34] to monitor and classify human activities.…”