Detection and Classification of Finer-Grained Human Activities Based on Stepped-Frequency Continuous-Wave Through-Wall Radar

Abstract

The through-wall detection and classification of human activities are critical for anti-terrorism, security, and disaster rescue operations. An effective through-wall detection and classification technology is proposed for finer-grained human activities such as piaffe, picking up an object, waving, jumping, standing with random micro-shakes, and breathing while sitting. A stepped-frequency continuous wave (SFCW) bio-radar sensor is first used to conduct through-wall detection of finer-grained human activities; Then, a comprehensive range accumulation time-frequency transform (CRATFR) based on inverse weight coefficients is proposed, which aims to strengthen the micro-Doppler features of finer activity signals. Finally, in combination with the effective eigenvalues extracted from the CRATFR spectrum, an optimal self-adaption support vector machine (OS-SVM) based on prior human position information is introduced to classify different finer-grained activities. At a fixed position (3 m) behind a wall, the classification accuracies of six activities performed by eight individuals were 98.78% and 93.23%, respectively, for the two scenarios defined in this paper. In the position-changing experiment, an average classification accuracy of 86.67% was obtained for five finer-grained activities (excluding breathing) of eight individuals within 6 m behind the wall for the most practical scenario, a significant improvement over the 79% accuracy of the current method.

MHHT-Based Method for Analysis of Micro-Doppler Signatures for Human Finer-Grained Activity Using Through-Wall SFCW Radar

“…Moreover, we employ the comprehensive T-F analysis method [7] based on STFT (0.42 s Hanning window), which is considered as the most commonly used and stable method, as the reference method.…”

“…The analog-to-digital conversion (ADC) accuracy is >12 bit, and the sampling rate is 250 Hz. The detailed working principle and formula derivation are presented in our previous paper [7]. During the latter processing, we used only one channel signal derived from one of two receiving antennas, which has a better performance.…”

“…The channel with the greatest energy will be located at the center of the motion scope along the range direction, referred to as d 0 , because most of the direct waves and the noise are removed after the preprocessing, and the strongest motion will result in the greatest energy. According to characteristic analysis of finer-grained human motion [7,36], the energy of the human motion radar signal will decay gradually from the center to both sides. By traversing from the center to both sides, taking two channels with energy values close to E 0 as the valuable channel boundaries, recorded as d c and d f , the number of valuable channel scopes is M = d f − d c + 1.…”

“…Owing to these shortcomings, although these T-F transform methods have made some contribution to the analysis and feature extraction of extremely sketchy and general MDS of human motion, the T-F resolution, detail resolution, and anti-interference capability are very poor; hence, more detailed MDS of different limbs and torsos cannot be separated and displayed clearly in the 2D T-F spectrum [5,28]. In addition, as our previous manuscript [7] and other studies [5,13] demonstrated, the MDS resulting from the common methods will attenuate sharply or become too weak and blurred under the through-wall or remote detection conditions. Consequently, the MDS of various components of motion will be intertwined or concealed in ineluctably strong noise and clutters; consequently, the corresponding instantaneous MDS features related to different target components will be difficult to extract and distinguish with the conventional method.…”

“…Remote sensing for the recognition and classification of various human activities using radar has attracted great attention from researchers [1][2][3][4][5][6][7][8] since Victor Chen introduced micro-motion in radar observation [9][10][11], especially for finer-grained human activities (e.g., waving, jumping, picking up an object, standing with random micro-shaking, etc.). It has a critical and promising applicability in many fields, such as anti-terrorism, post-disaster search and rescue, border control, and patient monitoring in hospitals.…”