Page 168 - ITU Journal Future and evolving technologies – Volume 2 (2021), Issue 2

ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 2

ent targets. The LDA is able to correctly classify most of [6] Liu Xuanzun. China’s meter wave anti‑stealth radar
the targets except ixed‑wing UAVs and small ixed‑wing capable of guiding missiles to destroy stealth air‑
planes. The LDA misclassi ies ixed‑wing UAVs and small craft: senior designer, May 23, 2019. Accessed on:
ixed‑wing planes as ighter jets, as many of the features Aug. 2, 2020. URL: https://www.globaltimes.cn/
are similar. Similar to LDA, the RF classi ier misclassi ies content/1151216.shtml.
ixed‑wing UAVs and small ixed‑wing planes. However, [7] M. A. El Diwiny, E. Hassanen, A. M. El‑Sayed, and G.
the total number of misclassi ications is smaller for the RF Abouelmagd. “Proposed surface to air anti stealth
classi ier compared to the LDA classi ier. Overall perfor‑ technology for homeland security”. In: Proc. IEEE
mance of the models proves the viability of our proposed
Int. Conf. on Eng. & Technol. (ICET). Cairo, Egypt,
approach.
Apr. 2014.
[8] W. H. Sha, Q. X. Jiang, and Y. Zhou. “Anti‑stealth inte‑
8. CONCLUSIONS AND FUTURE WORK
grated detection model of network radar counter‑
In this work, a novel technique called laser mesh for de‑ measure system”. In: Proc. IEEE Int. Conf. on Com‑
tection, classi ication, localization, and tracking of aerial mun. Software and Networks (ICCSN). Guangzhou,
targets as an alternative to radars is provided. Mesh of China, May 2017, pp. 498–504.
laser beams are proposed to detect, classify, and local‑ [9] Konstantinos C Zikidis. “Early Warning Against
ize aerial targets. To create the mesh, at least two air‑ Stealth Aircraft, Missiles and Unmanned Aerial Ve‑
borne platforms are required. Any aerial object crossing hicles”. In: Surveillance in Action, Springer. pp. 195–
the mesh will block the path of the laser beams and, sub‑ 216, 2018.
sequently, will be detected and localized in space. Using
our laser mesh setup, we can obtain the 3D shape, veloc‑ [10] Rebecca Grant. “The radar game: Understanding
ity, pitch and drift angles, and a maximum altitude of a tar‑ stealth and aircraft survivability”. In: (IRIS Indepen‑
get. ML models for classi ication are used assuming Gaus‑ dent Research, 1998).
sian distributed features of 3D shape, maximum velocity, [11] T. Vu and O. Aycard. “Laser‑based detection and
and pitch and drift angles, and a maximum altitude of 11 tracking moving objects using data‑driven Markov
different classes. Simulations proved the viability of the chain Monte Carlo”. In: Proc. IEEE Int. Conf. on
proposed approach. Future work includes carrying out Robotics and Automation. Kobe, Japan, May 2009,
the real‑world implementation of the proposed approach. pp. 3800–3806.
[12] Jinshi Cui, Hongbin Zha, Huijing Zhao, and Ryosuke
ACKNOWLEDGEMENT Shibasaki. “Laser‑based detection and tracking of
multiple people in crowds”. In: Computer Vision and
This work has been supported by NASA under the Federal
ImageUnderstanding106.2‑3(2007),pp.300–312.
Award ID number NNX17AJ94A.
[13] A. Mendes, L. C. Bento, and U. Nunes. “Multi‑target
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