Semantic Information Fusion for Coordinated Signal Processing in Mobile Sensor Networks

Distributed cognition of dynamic processes is commonly observed in mobile groups of animates like schools of fish, hunting lions, or in human teams for sports or military maneuvers. This paper presents methods for dynamic distributed cognition using an ad hoc mobile network of microsensors to detect, identify and track targets in noisy environments. We develop off-line algorithms for aggregating the most appropriate knowledge abstractions into semantic information, which is then used for on-line fusion of relevant attributes observed by local clusters in the sensor network. Local analysis of time series of sensor data yields aggregated semantic information, which is exchanged across nodes for higher level distributed cognition. This eliminates the need for exchanging high volumes of signal data and, thus reduces bandwidth and energy requirements for battery powered microsensors.

[1]  Edward H. Adelson,et al.  MECHANISMS FOR MOTION PERCEPTION , 1991 .

[2]  Arnab K. Shaw,et al.  Improved automatic target recognition using singular value decomposition , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[3]  P. Vaidyanathan.  Multirate Systems And Filter Banks , 1992 .

[4]  I. Jolliffe.  Principal Component Analysis , 2002 .

[5]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[6]  Nils Sandell,et al.  Detection with Distributed Sensors , 1980, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[8]  Gene H. Golub,et al.  Matrix computations , 1983 .

[9]  Richard A. Harshman,et al.  Indexing by Latent Semantic Analysis , 1990, J. Am. Soc. Inf. Sci..

[10]  T. Landauer,et al.  A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. , 1997 .

[11]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[12]  Thomas P. Krauss,et al.  Signal processing toolbox for use with MATLAB : ユーザーズガイド , 1994 .

[13]  B. Picinbono,et al.  A new approach of decentralized detection , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[14]  Martin Vetterli,et al.  Atomic signal models based on recursive filter banks , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).