Evaluating Sensor Reliability in Classification Problems Based on Evidence Theory

Abstract

This paper presents a new framework for sensor reliability evaluation in classification problems based on evidence theory (or the Dempster-Shafer theory of belief functions). The evaluation is treated as a two-stage training process. First, the authors assess the static reliability from a training set by comparing the sensor classification readings with the actual values of data, which are both represented by belief functions. Information content contained in the actual values of each target is extracted to determine its influence on the evaluation. Next, considering the ability of the sensor to understand a dynamic working environment, the dynamic reliability is evaluated by measuring the degree of consensus among a group of sensors. Finally, the authors discuss why and how to combine these two kinds of reliabilities. A significant improvement using the authors' method is observed in numerical simulations as compared with the recently proposed method.

A belief-based approach to spectrum occupancy mapping

“…The reliability r is computed using (12), with a maximum distance D s = 10 km and β = 0.5; the justification for these parameters is discussed in Section 6. The resulting belief functions bel{A} and bel{B} are shown in Figure 2c,d.…”

“…The function m is known as the basic belief assignment, given http://jwcn.eurasipjournals.com/content/2013/1/112 by the mapping of the power set m : 2 →[ 0, 1] (see for example [12]), as defined by…”

“…It was used to evaluate the reliability of different types of sensors, with application to the problem of identifying vehicle types, in [12]. Related to the topic of spectrum access, a DS approach was used in [13] to identify the presence of a licensed user using the output of cyclostationarity-based detectors, assuming that the signal-to-noise ratios (SNRs) of the licensed user signal at the sensors were known.…”

An Enhanced Approach for a Multiagent Distributed Context Using Tree Topology and Transferable Belief Model

“…Evidence combination is shown to improve significantly sensor coverage by utilizing collaboration about sensors [2]. A new framework for sensor reliability evaluation is presented in a classification problem based on evidence theory in [3]. Also a method for evaluating the reliability of sensor in a classification problem is been proposed based on the transferable belief model [4].…”

A Model of Federated Evidence Fusion for Real-Time Traffic State Estimation

“…In Reference (Guo et al, 2006), reliability was divided into static reliability and dynamic reliability. Herein, we only consider the static one.…”