TY  - JOUR
N2  - Micro-Doppler signatures are extremely valuable in
the classification of a wide range of targets. This work
investigates the effects of jamming on micro-Doppler
classification performance and explores a potential deep topology
enabling low bandwidth data fusion between nodes in a
multistatic radar network. The topology is based on an array of
three independent deep neural networks (DNNs) functioning
cooperatively to achieve joint classification. In addition to this, a
further DNN is trained to detect the presence of jamming and
from this it attempts to remedy the degradation effects in the
data fusion process. This is applied to real experimental data
gathered with the multistatic radar system NetRAD, of a human
operating with seven combinations of holding a rifle-like object
and a heavy backpack which is slung on their shoulders. The
resilience of the proposed network is tested by applying synthetic
jamming signals into specific radar nodes and observing the
networks? ability to respond to these undesired effects. The
results of this are compared with a traditional voting system
topology, serving as a convenient baseline for this work.
ID  - discovery10078510
UR  - https://doi.org/10.1109/JSEN.2019.2909685
JF  - IEEE Sensors Journal
A1  - Patel, JS
A1  - Fioranelli, F
A1  - Ritchie, M
A1  - Griffiths, HD
KW  - Radar; Multistatic Radar; Human Micro
Doppler; Radar Classification; Fusion; DNN; Synthetic Jamming
TI  - Fusion of Deep Representations in Multistatic Radar Networks to Counteract the Presence of Synthetic Jamming
AV  - public
Y1  - 2019/08/01/
VL  - 19
SP  - 6362
EP  - 6370
IS  - 15
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ER  -