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New method for detection fusion of MAC based on DCM and NP rule 被引量：1

New method for detection fusion of MAC based on DCM and NP rule
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摘要 The problem of distributed detection fusion using multiple sensors for remote underwater target detection is studied. Considering that multiple access channel （MAC） schemes are able to offer high efficiency in bandwidth usage and consume less energy than the parallel access channel （PAC）, the MAC scheme is introduced into the underwater target detection field. The model of underwater distributed detection fusion based on MAC schemes is established. A new method for detection fusion of MAC based on deflection coefficient maximization （DCM） and Neyman-Pearson （NP） rule is proposed. Under the power constraint of local sensors, this paper uses the DCM theory to derive the optimal weight coefficients and offsets. The closed-form expressions of detection probability and false alarm probability for fusion systems are obtained. The optimal detection performance of fusion systems is analyzed and deeply researched. Both the theory analysis and simulation experiments indicate that the proposed method could improve the detection performance and decrease the error probability effectively under power constraints of local sensors and low signal to noise ratio. The problem of distributed detection fusion using multiple sensors for remote underwater target detection is studied. Considering that multiple access channel （MAC） schemes are able to offer high efficiency in bandwidth usage and consume less energy than the parallel access channel （PAC）, the MAC scheme is introduced into the underwater target detection field. The model of underwater distributed detection fusion based on MAC schemes is established. A new method for detection fusion of MAC based on deflection coefficient maximization （DCM） and Neyman-Pearson （NP） rule is proposed. Under the power constraint of local sensors, this paper uses the DCM theory to derive the optimal weight coefficients and offsets. The closed-form expressions of detection probability and false alarm probability for fusion systems are obtained. The optimal detection performance of fusion systems is analyzed and deeply researched. Both the theory analysis and simulation experiments indicate that the proposed method could improve the detection performance and decrease the error probability effectively under power constraints of local sensors and low signal to noise ratio.
作者 Zhenhua Xu Jianguo Huang Qunfei Zhang
机构地区 College of Marine Engineering
出处 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第4期612-617,共6页 系统工程与电子技术（英文版）
基金 supported by the National Natural Science Foundation of China (60972152) Northwestern Polytechnical University Foun dations for Fundamental Research (JC201027 JC20100223)
关键词 deflection coefficient maximization （DCM） multipleaccess channel （MAC） Neyman-Pearson （NP） rule detection fusion target detection. deflection coefficient maximization （DCM）, multipleaccess channel （MAC）, Neyman-Pearson （NP） rule, detection fusion, target detection.
分类号 TN95 [电子电信—信号与信息处理] TN915 [电子电信—通信与信息系统]
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参考文献14

  • 1G. Fabeck, R. Mathar. Chernoff information based optimization of sensor networks for distributed detection. IEEE International Symposium on Signal Processing and Information Technology, 2009:606-611.
  • 2Z. Quan, w. K. Ma, S. Cui, et al. Optimal linear fusion for distributed spectrum sensing via semidefinite programming. Proc. of IEEE International Conference Acoustics, Speech, and Signal Processing, 2009, 4: 3629-3632.
  • 3H. Varaee, G. Mirjalily, A. A. Tadaion. Performance analysis of a general tree structure for target detection in wireless sensor networks. IEEE International Symposium on Signal Processing and Information Technology, 2009, 4: 232-236.
  • 4E. Masazade, R. Rajagopalan, R K. Varshney, et al. Evaluation of local decision thresholds for distributed detection in wireless sensor networks using multiobjective optimization. Proc. of the 42nd Asilomar Conference on Signals, Systems and Computers, 2008.
  • 5K. Liu, A. M. Sayeed. Type-based decentralized detection in wireless sensor networks. IEEE Trans. on Signal Processing, 2007, 55(5): 1899-1910.
  • 6ELi, J. S. Evans, Optimal strategies for distributed detection over rnultiaccess channels. IEEE International Conference on Acoustics, Speech and Signal Processing, 2008: 2417-2420.
  • 7M. K. Banavar, A. D. Smith, C. Tepedelenlioglu, et al. Distributed detection over fading MACS with multiple antennas at the fusion center. Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2010: 2894-2897.
  • 8G. Mergen, V. Naware, L. Tong. Asymptotic detection on performance of type-based multiple access over multiaccess fading channels. IEEE Trans. on Signal Processing, 2007, 55(3): 1081-1092.
  • 9W. Li, H. Dai. Distributed detection in wireless sensor networks using a multiple access channels. IEEE Trans. on Signal Processing, 2007, 55(3): 822-833.
  • 10F. Li, J. S. Evans. Design of distributed detection schemes for multiaccess channels. Australian Communications Theory Workshop, 2008: 51-57.

同被引文献21

  • 1PARK J,KIM E,KIM K. Large-signal Robustness of the Chair-varshney Fusion Rule under Generalized Gaussian Noises[J]. IEEE Sensors Journal, 2010,10 (9) :1438.
  • 2CHANG Q,CHENG B,CHEN, et al. Detection Per- formance Limits for Distributed Sensor Networks in the Presence of Nonideal Channels[J]. IEEE Transac tion on Wireless Communications, 2006,5 ( 11 ) : 3034.
  • 3KIN Y,CHEN B, SUTER B. Robust Binary Quantiz- ers for Distributed Detection[J]. IEEE Transaction on Wireless Communications,2007,6(6) : 2172.
  • 4CHEN B, JIANG R, KASETKASEM T, et al. Chan- nel Aware Decision Fusion in Wireless Sensor Net- works[J]. IEEE Transaction on Signal Processing, 2004,52(12) :3454.
  • 5LIU K, SAYEED A M. Type-based Decentralized De- tection in Wireless Sensor Networks[J]. IEEE Trans- action on Signal Processing, 2007,55 (5) : 1899.
  • 6LIN Y, CHEN B, TONG, L. Distributed Detection Over Multiple Access Channels[C]//IEEE Interna tional Conference on Acoustics, Speech and Signal Processing, Honolulu, Hawaii, USA, 2007,3 : 541.
  • 7LI F, JAMIE S. Decision Fusion over Noncoherent Fading Multi-access Channels]-JJ. IEEE Transaction on Signal Processing, 2011,59 (9) : 4367.
  • 8CHAMBERLAND J F, VEERAVALLI V V . As- ymptotic Results for Decentralized Detection in Pow- erconstrained Wireless Sensor Networks [J]. IEEE Journal on Selected Areas in Communications, 2004, 22(6):1007.
  • 9JAYWEERA S K. Large Sensor System Performance of Decentralized Detection in Noisy, Bandlimited Channels[C]//IEEE 61st Vehicular Technology Con- ference (VTC) 2005, Stockholm, Sweden, 2005, 2: 1096.
  • 10ZHANG X, POOR H V,CHIANG M. Optimal Power Allocation for Distributed Detection over MIMO Chan- nels in Wireless Sensor Networks[J]. IEEE Transac- tion on Signal Processing,2008,56(9) : 4124.

引证文献1

Journal of Systems Engineering and Electronics
2012年 第4期

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