'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
The work presented in this paper aims to distinguish
between armed or unarmed personnel using multi-static radar
data and advanced Doppler processing. We propose two modified
Deep Convolutional Neural Networks (DCNN) termed SCDopNet
and MC-DopNet for mono-static and multi-static micro-
Doppler signature (μ-DS) classification. Differentiating armed
and unarmed walking personnel is challenging due to the effect
of aspect angle and channel diversity in real-world scenarios.
In addition, DCNN easily overfits the relatively small-scale μ-DS
dataset. To address these problems, the work carried out in this
paper makes three key contributions: first, two effective schemes
including data augmentation operation and a regularization
term are proposed to train SC-DopNet from scratch. Next,
a factor analysis of the SC-DopNet are conducted based on
various operating parameters in both the processing and radar
operations. Thirdly, to solve the problem of aspect angle diversity
for μ-DS classification, we design MC-DopNet for multi-static μ-
DS which is embedded with two new fusion schemes termed
as Greedy Importance Reweighting (GIR) and `21-Norm. These
two schemes are based on two different strategies and have been
evaluated experimentally: GIR uses a “win by sacrificing worst
case” whilst `21-Norm adopts a “win by sacrificing best case”
approach. The SC-DopNet outperforms the non-deep methods
by 12.5% in average and the proposed MC-DopNet with two
fusion methods outperforms the conventional binary voting by
1.2% in average. Note that we also argue and discuss how to
utilize the statistics of SC-DopNet results to infer the selection
of fusion strategies for MC-DopNet under different experimental
scenarios
