Design Considerations for a Heterogeneous Network of Bearings-only Sensors using Sensor Management

This paper presents a design characterization of heterogeneous sensor networks with the goal of geolocation accuracy. It is assumed that the network exploits sensor management to conserve node power usage. We focus on bearings-only sensor networks consisting of acoustic and imaging modalities. Each available node modality is a bearings-only sensor of varying capability. The optimal mixture of modalities is discussed under the constraint of the overall network cost. Finally, simulations verify the theory and demonstrate design choices.

[1]  Ruzena Bajcsy,et al.  The Sensor Selection Problem for Bounded Uncertainty Sensing Models , 2005, IEEE Transactions on Automation Science and Engineering.

[2]  Gregory J. Pottie,et al.  Instrumenting the world with wireless sensor networks , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[3]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[4]  Lance M. Kaplan,et al.  On exploiting propagation delays for passive target localization using bearings-only measurements , 2005, J. Frankl. Inst..

[5]  A. Artes-Rodriguez,et al.  Target location estimation in sensor networks using range information , 2004, Processing Workshop Proceedings, 2004 Sensor Array and Multichannel Signal.

[6]  Feng Zhao,et al.  Information-driven dynamic sensor collaboration , 2002, IEEE Signal Process. Mag..

[7]  Péter Molnár,et al.  Maximum likelihood methods for bearings-only target localization , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[8]  Feng Zhao,et al.  Collaborative In-Network Processing for Target Tracking , 2003, EURASIP J. Adv. Signal Process..

[9]  L.M. Kaplan,et al.  Global node selection for localization in a distributed sensor network , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Y. Oshman,et al.  Optimization of observer trajectories for bearings-only target localization , 1999 .

[11]  C. Pilotto,et al.  Performance Characterization of Random Proximity Sensor Networks , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[12]  Jerry M. Mendel,et al.  Lessons in digital estimation theory , 1986 .

[13]  Gilbert Strang,et al.  Introduction to applied mathematics , 1988 .

[14]  L.M. Kaplan,et al.  Local node selection for localization in a distributed sensor network , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[15]  R. G. Stansfield,et al.  Statistical theory of d.f. fixing , 1947 .

[16]  Feng Zhao,et al.  Scalable Information-Driven Sensor Querying and Routing for Ad Hoc Heterogeneous Sensor Networks , 2002, Int. J. High Perform. Comput. Appl..

[17]  Rudolf Mathar,et al.  On the distribution of cumulated interference power in Rayleigh fading channels , 1995, Wirel. Networks.

[18]  Sheldon M. Ross.  Introduction to Probability Models. , 1995 .

[19]  Sathish Chandran,et al.  Advances in Direction-of-Arrival Estimation , 2005 .

[20]  Mani Srivastava,et al.  Energy-aware wireless microsensor networks , 2002, IEEE Signal Process. Mag..

[21]  Alfonso Farina,et al.  Algorithms for the selection of the active sensors in distributed tracking: comparison between Frisbee and GNS methods , 2006, 2006 9th International Conference on Information Fusion.

[22]  Tien Pham,et al.  Simulation of detection and beamforming with acoustical ground sensors , 2002, SPIE Defense + Commercial Sensing.

[23]  Alfonso Farina,et al.  Target tracking with bearings - Only measurements , 1999, Signal Process..

[24]  Ivan Kadar.  Optimum geometry selection for sensor fusion , 1998, Defense, Security, and Sensing.

[25]  Ivan Kadar,et al.  Self-organizing cooperative sensor network for remote surveillance: target tracking while optimizing the geometry between bearing-reporting sensors and the target , 2001, SPIE Defense + Commercial Sensing.