TY - GEN
T1 - Target detection algorithm with information geometry under cooperative position
AU - Liu, Jun
AU - Zhang, Yi
AU - Tang, Chengkai
AU - Liu, Jiaqi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - Due to the display of the advantages and advantages of 'stealth' weapons and equipment in modern warfare, the trend of invisibility and miniaturization of new weapons and equipment is becoming more and more obvious. Therefore, exploring and researching the detection methods for these weapons and equipments has a strong military application value. Based on electromagnetic field theory and information geometry theory, this paper obtains electromagnetic wave excitation potential in electromagnetic space, and then acquires multi-point electromagnetic wave state potential changes through passive receivers in space, and constructs Riemann geometric statistics by using electromagnetic space situation big data. Manifolds, perceptions and discoveries may enter targets in the electromagnetic space region and their positional orientation. Finally, the electromagnetic potential data of the cyberspace is used to quickly find and identify the target, and the rapid discovery and effective identification of the low, slow, low and fast targets are realized. Finally, the performance of the algorithm is analyzed by experimental simulation from the aspects of target scattering intensity, time base and position reference.
AB - Due to the display of the advantages and advantages of 'stealth' weapons and equipment in modern warfare, the trend of invisibility and miniaturization of new weapons and equipment is becoming more and more obvious. Therefore, exploring and researching the detection methods for these weapons and equipments has a strong military application value. Based on electromagnetic field theory and information geometry theory, this paper obtains electromagnetic wave excitation potential in electromagnetic space, and then acquires multi-point electromagnetic wave state potential changes through passive receivers in space, and constructs Riemann geometric statistics by using electromagnetic space situation big data. Manifolds, perceptions and discoveries may enter targets in the electromagnetic space region and their positional orientation. Finally, the electromagnetic potential data of the cyberspace is used to quickly find and identify the target, and the rapid discovery and effective identification of the low, slow, low and fast targets are realized. Finally, the performance of the algorithm is analyzed by experimental simulation from the aspects of target scattering intensity, time base and position reference.
KW - Cooperative position
KW - Information geometry
KW - Statistical manifold
KW - Target detection
UR - http://www.scopus.com/inward/record.url?scp=85078901992&partnerID=8YFLogxK
U2 - 10.1109/ICSPCC46631.2019.8960893
DO - 10.1109/ICSPCC46631.2019.8960893
M3 - 会议稿件
AN - SCOPUS:85078901992
T3 - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
BT - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
Y2 - 20 September 2019 through 22 September 2019
ER -