  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.
