Real-time capable micro-Doppler signature decomposition of walking human limbs

Unique micro-Doppler signature (m-D) of a human body motion can be analyzed as the superposition of different body parts m-D signatures. Extraction of human limbs m-D signatures in real-time can be used to detect, classify and track human motion especially for safety application. In this paper, two methods are combined to simulate m-D signatures of a walking human. Furthermore, a novel limbs m-D signature time independent decomposition feasibility study is presented based on features as m-D signatures and range profiles also known as micro-Range (m-R). Walking human body parts can be divided into four classes (base, arms, legs, feet) and a decision tree classifier is used. Validation is done and the classifier is able to decompose m-D signatures of limbs from a walking human signature on real-time basis.