TY - GEN
T1 - On the underwater target detection with decision fusion in UASN
AU - Leng, Bing
AU - Shen, Xiaohong
AU - Yan, Yongsheng
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - Data fusion in multi-sensor networks can significantly improve the perception gain of targets. We re-investigated the detection fusion system by considering the local decision rule and fusion rule as a whole. Based on the characteristics of underwater target detection in underwater acoustic sensor network (UASN), we construct a fusion detection system with parallel topology. In such a system, the energy detector is employed in each sensor while the Chair-Varshney and Counting decision fusion strategies are taken into account, respectively. In addition, the decision statistics in each sensor and the fusion statistics in the fusion center are used to determine the detection threshold in each sensor and the fusion center by Monte Carlo simulation. The results show that the performance of fusion system performs much better than single sensor detection system. When the detection distance is larger, Chair-Varshney fusion statistics and Counting fusion statistics have the comparable detection performance.
AB - Data fusion in multi-sensor networks can significantly improve the perception gain of targets. We re-investigated the detection fusion system by considering the local decision rule and fusion rule as a whole. Based on the characteristics of underwater target detection in underwater acoustic sensor network (UASN), we construct a fusion detection system with parallel topology. In such a system, the energy detector is employed in each sensor while the Chair-Varshney and Counting decision fusion strategies are taken into account, respectively. In addition, the decision statistics in each sensor and the fusion statistics in the fusion center are used to determine the detection threshold in each sensor and the fusion center by Monte Carlo simulation. The results show that the performance of fusion system performs much better than single sensor detection system. When the detection distance is larger, Chair-Varshney fusion statistics and Counting fusion statistics have the comparable detection performance.
KW - Data fusion
KW - Monte Carlo
KW - Underwater acoustic sensor network
KW - Underwater target detection
UR - http://www.scopus.com/inward/record.url?scp=85078897619&partnerID=8YFLogxK
U2 - 10.1109/ICSPCC46631.2019.8960837
DO - 10.1109/ICSPCC46631.2019.8960837
M3 - 会议稿件
AN - SCOPUS:85078897619
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 -