Real-Time Capable Micro-Doppler Signature Decomposition of Walking Human Limbs

Unique micro-Doppler signature (\(\boldsymbol{\mu}\)-D) of a human body motion can be analyzed as the superposition of different body parts \(\boldsymbol{\mu}\)-D signatures. Extraction of human limbs \(\boldsymbol{\mu}\)-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 \(\boldsymbol{\mu}\)-D signatures of a walking human. Furthermore, a novel limbs \(\mu\)-D signature time independent decomposition feasibility study is presented based on features as \(\mu\)-D signatures and range profiles also known as micro-Range (\(\mu\)-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 \(\mu\)-D signatures of limbs from a walking human signature on real-time basis.