Target Detection in Underwater Sensor Networks by Fusion of Active and Passive Measurements

Underwater target detection plays an important role in the ocean exploration. However, the strong noise and weak communication properties of underwater environment make it challenging to achieve the detection task. This paper is concerned with a non-cooperative target detection issue by fusion of ac...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on network science and engineering Vol. 10; no. 4; pp. 1 - 15
Main Authors Yan, Jing, Zhang, Zeqian, Yang, Xian, Luo, Xiaoyuan, Guan, Xinping
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Active measurement
Algorithms
Chi-square test
Communication
Detection
Fusion
Measurement units
Noise measurement
Object detection
Particle measurements
Passive measurement
Sea measurements
Sensors
Signal to noise ratio
Target detection
Time measurement
Topology
Underwater communication
Underwater detectors
Underwater sensor networks (USNs)
Online AccessGet full text

Cover

More Information
Summary:Underwater target detection plays an important role in the ocean exploration. However, the strong noise and weak communication properties of underwater environment make it challenging to achieve the detection task. This paper is concerned with a non-cooperative target detection issue by fusion of active and passive measurements in underwater sensor networks (USNs). The detection procedure is mainly divided into two phases, i.e., local decision and external fusion. For the first phase, the active and passive measurements are both employed to develop a Chi-square test based local decision rule, where the membership functions are conducted to bias the weight of each measurement in accordance with signal reliability. Based on this, we formulate the communication topology of USNs as a two-layer multi-group (2LMG) structure, such that a hybrid Bayesian fusion algorithm is designed in the second phase to fuse the detection decisions from different sensor nodes. It is worth mentioning that, our solution can integrate the active and passive measurements by local decision, and more importantly, it can improve the detection accuracy and reduce the communication load through the 2LMG-based fusion algorithm. Finally, simulation results are performed to validate the effectiveness of our solution in this paper.
ISSN:2327-4697
2334-329X
DOI:10.1109/TNSE.2023.3246098