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Analytic hierarchy process for multi-sensor data fusion based on belief function theory

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  • Frikha, Ahmed
  • Moalla, Hela

Abstract

Multi-sensor data fusion is an evolving technology whereby data from multiple sensor inputs are processed and combined. The data derived from multiple sensors can, however, be uncertain, imperfect, and conflicting. The present study is undertaken to help contribute to the continuous search for viable approaches to overcome the problems associated with data conflict and imperfection. Sensor readings, represented by belief functions, have to be fused according to their corresponding weights. Previous studies have often estimated the weights of sensor readings based on a single criterion. Mono-criteria approaches for the assessment of sensor reading weights are, however, often unreliable and inadequate for the reflection of reality. Accordingly, this work opts for the use of a multi-criteria decision aid. A modified Analytical Hierarchy Process (AHP) that incorporates several criteria is proposed to determine the weights of a sensor reading set. The approach relies on the automation of pairwise comparisons to eliminate subjectivity and reduce inconsistency. It assesses the weight of each sensor reading, and fuses the weighed readings obtained using a modified average combination rule. The efficiency of this approach is evaluated in a target recognition context. Several tests, sensitivity analysis, and comparisons with other approaches available in the literature are described.

Suggested Citation

  • Frikha, Ahmed & Moalla, Hela, 2015. "Analytic hierarchy process for multi-sensor data fusion based on belief function theory," European Journal of Operational Research, Elsevier, vol. 241(1), pages 133-147.
  • Handle: RePEc:eee:ejores:v:241:y:2015:i:1:p:133-147
    DOI: 10.1016/j.ejor.2014.08.024
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    References listed on IDEAS

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    7. Macharis, Cathy & Springael, Johan & De Brucker, Klaas & Verbeke, Alain, 2004. "PROMETHEE and AHP: The design of operational synergies in multicriteria analysis.: Strengthening PROMETHEE with ideas of AHP," European Journal of Operational Research, Elsevier, vol. 153(2), pages 307-317, March.
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    Cited by:

    1. Bowen Qin & Fuyuan Xiao, 2019. "An improved method to determine basic probability assignment with interval number and its application in classification," International Journal of Distributed Sensor Networks, , vol. 15(1), pages 15501477188, January.

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