|Table of Contents|

Citation:
 Zhenhua Xu,Jianguo Huang,Hai Huang and Qunfei Zhang.Research on the Strategy of Underwater United Detection Fusion and Communication Using Multi-sensor[J].Journal of Marine Science and Application,2011,(3):358-363.[doi:10.1007/s11804-011-1080-3]
Click and Copy

Research on the Strategy of Underwater United Detection Fusion and Communication Using Multi-sensor

Info

Title:
Research on the Strategy of Underwater United Detection Fusion and Communication Using Multi-sensor
Author(s):
Zhenhua Xu Jianguo Huang Hai Huang and Qunfei Zhang
Affilations:
Author(s):
Zhenhua Xu Jianguo Huang Hai Huang and Qunfei Zhang
College of Marine Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Keywords:
detection fusion likelihood ratio test (LRT) Neyman-Pearson (NP) low signal to noise ratio
分类号:
-
DOI:
10.1007/s11804-011-1080-3
Abstract:
In order to solve the distributed detection fusion problem of underwater target detection, when the signal to noise ratio (SNR) of the acoustic channel is low, a new strategy for united detection fusion and communication using multiple sensors was proposed. The performance of detection fusion was studied and compared based on the Neyman-Pearson principle when the binary phase shift keying (BPSK) and on-off keying (OOK) modes were used by the local sensors. The comparative simulation and analysis between the optimal likelihood ratio test and the proposed strategy was completed, and both the theoretical analysis and simulation indicate that using the proposed new strategy could improve the detection performance effectively. In theory, the proposed strategy of united detection fusion and communication is of great significance to the establishment of an underwater target detection system.

References:

Ahmadi HR, Vosoughi A (2009). On the effect of channel estimation error upon the performance of distributed detection systems. Proc. IEEE Asilomar Conf. Signals, Syst., Comput., Pacific Grove, 1709 -1712.
Aldosari S, Moura JMF (2005). Saddlepoint approximation for sensor network optimization. Proc. of ICASSP’05, Philadelphia, 741-744.
Alhakeem S, Varshney PK (1996). Decentralized bayesian detection with feedback. IEEE Trans. on Systems, Man and Cybernetics, 26(4), 503-513.
Blum RS (1996). Locally optimum distributed detection of dependent random signals based on ranks. IEEE Trans. on Information Theory, 42(3), 990-994.
Chen B, Jiang R, Varshney PK (2004). Channel aware decision fusion in wireless sensor networks. IEEE Trans. Signal Processing, 52(12), 3454-3458.
Fabeck G, Mathar R (2009). Chernoff information based optimization of sensor networks for distributed detection. Proceedings: IEEE ISSPIT 2009, Ajman, 606-611.
Kay SM (1993). Fundamentals of statistical signal processing I: estimation theory. Prentice-Hall, Englewood Cliffs, 65-78.
Liu K, Sayeed AM (2007). Type-based decentralized detection in wireless sensor networks. IEEE Trans. Signal Processing, 55(5), 1899-1910.
Masazade E, Rajagopalan R, Varshney PK, Sendur GK, Keskinoz M (2008). Evaluation of local decision thresholds for distributed detection in wireless sensor networks using multiobjective optimization. Proc. IEEE Asilomr Conf. Signals, Syst., Comput., Pacific Grove, 232-236.
Mergen G, Naware V, Tong L (2007). Asymptotic detection performance of type based multiple access over multiaccess fading channels. IEEE Trans. Signal Processing, 55(3), 1081-1092.
Mirjalily G, Luo ZQ, Davision TN, Bosse E (2003). Blind adaptive decision fusion for distributed detection. IEEE Trans. Aerospace and Electronic Systems, 39(1), 34-52.
Niu R, Chen B, Varshney PK (2006). Fusion of decisions transmitted over Rayleigh fading channels in wireless sensor networks. IEEE Trans. Signal Processing, 54(3), 1018-1027.
Niu R, Varshney PK (2006). Performance evaluation of decision fusion in wireless sensor networks. Proceedings of the 40th Annual Conference On Information Sciences and Systems, Princeton, 69-74.
Quan Z, Ma WK, Cui S, Sayed AH (2009).Optimal linear fusion for distributed spectrum sensing via semidefinite programming. Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), Taipei, 3629-3632.
Quan Z, Cui S, Sayed AH (2008). Optimal linear cooperation for spectrum sensing in cognitive radio networks. IEEE J. Sel. Topics Signal Process, 2(1), 28-40.
Varaee H, Mirjalily G, Tadaion AA (2009). Performance analysis of a general tree structure for target detection in wireless sensor networks. Proc. ICFCC 2009, Kuala Lumpar, Malaysia, 232-236.

Memo

Memo:
Supported by the National Natural Science Foundation of China under Grant No.60972152
Last Update: 2011-09-15