Quantum Mechanical Approach to Modeling Reliability of Sensor Reports

Dempster-Shafer (D-S) evidence theory is widely applied in multi-sensor data fusion. However, lots of uncertainty and interference exist in practical situations, especially on the battlefield. It is still an open issue to model the reliability of sensor reports. Many existing methods are proposed based on the relationship among collected data. In this letter, we proposed a quantum mechanical approach to evaluate the reliability of sensor reports, which is based on the properties of a sensor itself. The proposed method is used to modify the combining of evidences. A numerical example is used to show the effectiveness and efficiency of our method.

[1]  Chunhe Xie,et al.  Sensor Data Fusion with Z-Numbers and Its Application in Fault Diagnosis , 2016, Sensors.

[2]  Xinyang Deng,et al.  Evidence Combination From an Evolutionary Game Theory Perspective , 2015, IEEE Transactions on Cybernetics.

[3]  Catherine K. Murphy Combining belief functions when evidence conflicts , 2000, Decis. Support Syst..

[4]  Xianguo Wu,et al.  Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach , 2017 .

[5]  Krikor B. Ozanyan,et al.  Tomography defined as sensor fusion , 2015, 2015 IEEE SENSORS.

[6]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[7]  Qilian Liang,et al.  Multistep Information Fusion for Target Detection Using UWB Radar Sensor Network , 2015, IEEE Sensors Journal.

[8]  Weiru Liu,et al.  Analyzing the degree of conflict among belief functions , 2006, Artif. Intell..

[9]  Uwe Mönks,et al.  Information Fusion of Conflicting Input Data , 2016, Sensors.

[10]  Chunhe Xie,et al.  Failure mode and effects analysis based on a novel fuzzy evidential method , 2017, Appl. Soft Comput..

[11]  Yong Deng,et al.  Evaluating Sensor Reliability in Classification Problems Based on Evidence Theory , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Tahmina Zebin,et al.  Inertial sensing for gait analysis and the scope for sensor fusion , 2015, 2015 IEEE SENSORS.

[13]  Arkady Bolotin Quantum mechanical approach to fuzzy logic modelling , 2001 .

[14]  Wen Jiang,et al.  A modified combination rule in generalized evidence theory , 2017, Applied Intelligence.

[15]  David Declercq,et al.  Using the conflict in Dempster-Shafer evidence theory as a rejection criterion in classifier output combination for 3D human action recognition , 2016, Image Vis. Comput..

[16]  Yafei Song,et al.  Combination of unreliable evidence sources in intuitionistic fuzzy MCDM framework , 2016, Knowl. Based Syst..

[17]  Xiaohong Yuan,et al.  Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory , 2007, Inf. Fusion.

[18]  Jarosław Pykacz Quantum Physics, Fuzzy Sets and Logic , 2015 .

[19]  Jamal Abd Ali,et al.  A Novel Quantum-Behaved Lightning Search Algorithm Approach to Improve the Fuzzy Logic Speed Controller for an Induction Motor Drive , 2015 .

[20]  Xinyang Deng,et al.  Zero-Sum Matrix Game with Payoffs of Dempster-Shafer Belief Structures and Its Applications on Sensors , 2017, Sensors.

[21]  Fuyuan Xiao,et al.  An improved distance-based total uncertainty measure in belief function theory , 2017, Applied Intelligence.

[22]  A. Dallil,et al.  Sensor Fusion and Target Tracking Using Evidential Data Association , 2013, IEEE Sensors Journal.

[23]  Ronald R. Yager,et al.  Soft likelihood functions in combining evidence , 2017, Inf. Fusion.

[24]  Yuxin Zhao,et al.  A novel combination method for conflicting evidence based on inconsistent measurements , 2016, Inf. Sci..

[25]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[26]  Éloi Bossé,et al.  A new distance between two bodies of evidence , 2001, Inf. Fusion.

[27]  François Dubois,et al.  Eigenlogic: A Quantum View for Multiple-Valued and Fuzzy Systems , 2016, QI.

[28]  Arturo de la Escalera,et al.  Sensor Fusion Methodology for Vehicle Detection , 2017, IEEE Intelligent Transportation Systems Magazine.

[29]  Chulantha Kulasekere,et al.  Dempster–Shafer Information Filtering Framework: Temporal and Spatio-Temporal Evidence Filtering , 2015, IEEE Sensors Journal.