Entropy-based sensor selection heuristic for target localization

We propose an entropy-based sensor selection heuristic for localization. Given 1) a prior probability distribution of the target location, and 2) the locations and the sensing models of a set of candidate sensors for selection, the heuristic selects an informative sensor such that the fusion of the selected sensor observation with the prior target location distribution would yield on average the greatest or nearly the greatest reduction in the entropy of the target location distribution. The heuristic greedily selects one sensor in each step without retrieving any actual sensor observations. The heuristic is also computationally much simpler than the mutual-information-based approaches. The effectiveness of the heuristic is evaluated using localization simulations in which Gaussian sensing models are assumed for simplicity. The heuristic is more effective when the optimal candidate sensor is more informative.

[1]  Kenneth J. Hintz,et al.  A measure of the information gain attributable to cueing , 1991, IEEE Trans. Syst. Man Cybern..

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

[3]  Deborah Estrin,et al.  Coherent acoustic array processing and localization on wireless sensor networks , 2003, Proc. IEEE.

[4]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[5]  John W. Fisher,et al.  Maximum Mutual Information Principle for Dynamic Sensor Query Problems , 2003, IPSN.

[6]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[7]  Mani B. Srivastava,et al.  On the Error Characteristics of Multihop Node Localization in Ad-Hoc Sensor Networks , 2003, IPSN.

[8]  Hugh Durrant-Whyte,et al.  Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach , 1995 .

[9]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[10]  Feng Zhao,et al.  Distributed Group Management for Track Initiation and Maintenance in Target Localization Applications , 2003, IPSN.

[11]  G. Asada,et al.  Wireless integrated network sensors: Low power systems on a chip , 1998, Proceedings of the 24th European Solid-State Circuits Conference.

[12]  Ralph E. Hudson,et al.  Source localization and time delay estimation using constrained least-squares and best-path smoothing , 1999, Optics & Photonics.

[13]  Kung Yao,et al.  Maximum-likelihood source localization and unknown sensor location estimation for wideband signals in the near-field , 2002, IEEE Trans. Signal Process..