Classification of micro‐Doppler signatures of human motions using log‐Gabor filters

Abstract

In recent years, Doppler radar has been used as a sensing modality for human gait recognition, due to its ability to operate in adverse weather and penetrate opaque obstacles. Doppler radar captures not only the speed of the target, but also the micro-motions of its moving parts. These micro-motions induce frequency modulations that can be used to characterise the target movements. However, a major challenge in Doppler signal processing is to extract discriminative features from the radar returns for target classification. This study presents a feature extraction method for classification of human motions from the micro-Doppler radar signal. The proposed method applies the log-Gabor filters at multiple spatial frequencies and orientations on a joint time-frequency representation. To achieve invariance to the target speed, features are extracted from local patches along the torso Doppler shift. Then, the (2D)2PCA (two-directional two-dimensional principal component analysis) method is applied to create a compact feature vector. Experimental results based on real radar data obtained from multiple human subjects demonstrate the effectiveness of the proposed approach in classifying arm motions.Abstract-In recent years, Doppler radar has been used as a sensing modality for human gait recognition, due to its ability to operate in adverse weather and penetrate opaque obstacles. Doppler radar captures not only the speed of the target, but also the micro-motions of its moving parts. These micro-motions induce frequency modulations that can be used to characterize the target movements. However, a major challenge in Doppler signal processing is to extract discriminative features from the radar returns for target classification. This study presents a feature extraction method for classification of human motions from the micro-Doppler radar signal. The proposed method applies the log-Gabor filters at multiple spatial frequencies and orientations on a joint time-frequency representation. To achieve invariance to the target speed, features are extracted from local patches along the torso Doppler shift. Then, the (2D) 2 PCA method is applied to create a compact feature vector. Experimental results based on real radar data obtained from multiple human subjects demonstrate the effectiveness of the proposed approach in classifying arm motions.Index Terms-Human micro-Doppler signature classification, feature extraction, S-method, Log-Gabor filters. ∞ −∞

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.…”

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

“…What is more outstanding, even the time delay of the MDS changes caused by moving left and right arms could be observed. In the aspect of adaptability, the proposed method is applicable to various non-stationary movements shown in Figure 7, not just to some stationary and regular actions, such as walking with or without arm swing [20][21][22]. More importantly, compared with the weak performance of the conventional T-F analysis method that the MDS will be inundated by the clutter and noise easily [5], as shown in Figure 8, the proposed method could carry out the above two advantages even under a through-wall and remote detection environment.…”

Template free Micro Doppler Signature Classification for Wheeled and Tracked Vehicles

“…Radar is a kind of microwave sensor which can exploit signal processing techniques for target detecting, imaging and classification [1][2][3][4][5][6][7] . When the microwave pulse transmit by the radar bounced from the moving targets, the carrier frequency of the signal will be shifted, which is known as the Doppler effect.…”

“…Different kinds of targets may have different kinds of micro-motions, which makes micro-Doppler signature classification a promising means for identifying radar targets. The early studies are mainly focused on template-based classification of many kinds of targets, such as aircraft 1,[16][17] , human aquatic activity [3][4][5][6][7]18 , missile defence [19][20][21] and ground vehicles 10,[22][23][24] . Chen analysed the micro-Doppler modulation with a mathematical model in detail and introduced the micro-Doppler effect in radar [8][9] .…”