Sensor Fusion: From Dependence Analysis via Matroid Bases to Online Synthesis

  • Asaf Cohen
  • Shlomi Dolev
  • Guy Leshem
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7111)


Consider the two related problems of sensor selection and sensor fusion. In the first, given a set of sensors, one wishes to identify a subset of the sensors, which while small in size, captures the essence of the data gathered by the sensors. In the second, one wishes to construct a fused sensor, which utilizes the data from the sensors (possibly after discarding dependent ones) in order to create a single sensor which is more reliable than each of the individual ones.

In this work, we rigorously define the dependence among sensors in terms of joint empirical measures and incremental parsing. We show that these measures adhere to a polymatroid structure, which in turn facilitates the application of efficient algorithms for sensor selection. We suggest both a random and a greedy algorithm for sensor selection. Given an independent set, we then turn to the fusion problem, and suggest a novel variant of the exponential weighting algorithm. In the suggested algorithm, one competes against an augmented set of sensors, which allows it to converge to the best fused sensor in a family of sensors, without having any prior data on the sensors’ performance.


Sensor Fusion Entropy Estimate Sensor Selection Cumulative Loss Minimum Cycle Base 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Asaf Cohen
    • 1
  • Shlomi Dolev
    • 2
  • Guy Leshem
    • 2
    • 3
  1. 1.Department of Communication System EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael
  2. 2.Department of Computer ScienceBen-Gurion University of the NegevBeer-ShevaIsrael
  3. 3.Department of Computer ScienceAshkelon Academic CollageIsrael

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