Conflict Redistribution Rule Research Articles

An Improved Radar Emitter Recognition Method Based on Dezert‐Smarandache Theory

The parameters of radar emitter are fast changing in the current complicated electromagnetic environment, and the radar emitter recognition rate which used single sensor method cannot satisfied. To solve the problem, an improved radar emitter recognition method based on Dezert-Smarandache theory is proposed, which can improve proportional conflict redistribution rule to solve fuzzy and conflicting information from radar emitter. Some examples are given to show the validity of the improved method.

A PCR-BIMM filter For Maneuvering Target Tracking

In this paper we show how to correct and improve the Belief Interacting Multiple Model filter (BIMM) proposed in 2009 by Nassreddine et al.for tracking maneuvering targets. Our improved algorithm,called PCR-BIMM is based on results developed in DSmT (Dezert-Smarandache Theory) framework and concerns two main steps of BIMM: 1) the update of the basic belief assignment of modes which is done by the Proportional Conflict Redistribution Rule no. 5 rather than Smets’ rule (conjunctive rule); 2) the global target state estimation which is obtained from the DSmP probabilistic transformation rather than the commonly used Pignistic transformation. Monte-Carlo simulation results are presented to show the performances of this PCR-BIMM filter with respect to classical IMM and BIMM filters obtained on a very simple maneuvering target tracking scenario.

Performance evaluation of fuzzy-based fusion rules for tracking applications

The objective of this paper is to present and to evaluate the performance of particular fusion rules based on fuzzy T-Conorm/T-Norm operators for two tracking applications: 1) tracking object’s type changes, supporting the process of objects’ identification (e.g., fighter against cargo, friendly aircraft against hostile ones), which, consequently is essential for improving the quality of generalised data association for targets’ tracking; 2) alarms’ identification and prioritisation in terms of degree of danger relating to a set of a priori defined, out of the ordinary dangerous directions. The aim is to present and demonstrate the ability of these rules to assure coherent and stable way for identification and to improve decision-making process in a temporal way. A comparison with performance of Dezert-Smarandache Theory-based Proportional Conflict Redistribution rule no. 5 and Dempster’s rule is also provided.

On the Validity of Dempster's Fusion Rule and its Interpretation as a Generalization of Bayesian Fusion Rule

The purpose of this paper is to focus on and discuss both: (i) the validity of the Dempster's rule of combination and foundations of Dempster–Shafer theory and (ii) the real compatibility (or not) of the Dempster's rule with the Bayes fusion rule. We analyze and explain, on the basis of a generic example, the inconsistent behavior of the Dempster's rule of combination, introduced by Shafer in his mathematical theory of evidence, as a valid method to combine sources of information. We identify the cause and the effect of the dictatorial power behavior of this rule and of its impossibility to manage the conflicts between the sources no matter of their level. For comparison purpose, we present the respective solutions obtained by the more efficient proportional conflict redistribution rule number 5 proposed originally in Dezert–Smarandache theory framework. The inherent contradiction of Dempster–Shafer theory foundations is identified and proved. Then, a deep analysis of the compatibility of the Dempster's fusion rule with the Bayes fusion rule (from a fusion standpoint) is made on the basis of proposed new interesting formulation of the Bayes rule. We prove that the Dempster's rule does not behave as the Bayes fusion rule in general, because both methods deal very differently with the prior information when it is really informative (not uniform). Only in the very particular case where the basic belief assignments to combine are Bayesian and when the prior information is uniform (or vacuous), the Dempster's rule remains consistent with the Bayes fusion rule. It is proved, that in more general cases, the Dempster's rule is incompatible with the Bayes rule and it is not a generalization of the Bayes fusion rule.

Intelligent fire-detection model using statistical color models data fusion with Dezert–Smarandache method

The aim of this article is to present a novel and efficient method for early fire detection in video sequences. Fire detection in video sequences is the need of the hour for surveillance applications and de-noising processes used in target detection in defense applications. The proposed fire-detection model fuses evidences from statistical models using Dezert–Smarandache Theory (DSmT) of plausible and paradoxical reasoning for evidence fusion. The conflicting masses are redistributed using the proportional conflict redistribution (PCR5) rule of combination for calculation of final belief of evidence. The results of the proposed model are compared with some of the earlier methods by taking large data sets, which contain both fire and fire-like regions created under different environmental conditions. Results show that color models data fusion using DSmT resolve the conflict in earlier models and increase belief level i.e. high fire-detection rate and low false-alarm rate.

A Threat Sorting Approach Based on the Fuzzy Beliefs

To overcome the limitations of the traditional threat assessment algorithms, a new threat sorting approach based on the fuzzy belief is proposed in this paper. In the new approach, the threat degrees described by quantitative number, interval number or linguistic label are all converted into the triangular fuzzy beliefs. The proportional conflict redistribution rule is used to combine the threat degrees of the same kind of assessment factors, the decision discounting rule is used to combine the threat degrees of different kinds of assessment factors, and the centroid method is utilized to make threat sorting. The new approach can not only deal with uncertain threat degrees, but also fuse the quantitative and qualitative threat degrees. The performance of the approach is analyzed in theory in this paper and the experimental results have certified its validity too.

SVM-DSmT COMBINATION FOR THE SIMULTANEOUS VERIFICATION OF OFF-LINE AND ON-LINE HANDWRITTEN SIGNATURES

A combination handwritten signature verification system is proposed for managing conflicts provided from each individual off-line and on-line support vector machine (SVM), respectively. Basically, the system is divided into three parts: (i) Off-line verification system, (ii) on-line verification system and (iii) combination module using belief function theory. The proposed framework allows combining the normalized SVM outputs and uses an estimation technique based on the dissonant model of Appriou to compute the belief assignments. Combination is performed using belief models such as Dempster-Shafer (DS) rule and proportional conflict redistribution (PCR) rule followed by the likelihood ratio-based decision making. Experiments are conducted on the well-known NISDCC signature collection using false rejection and false acceptance criteria. The obtained results show that the proposed combination framework using Dezert-Smarandache (DSm) theory yields the best verification accuracy even when individual off-line and on-line classifications provide conflicting results.

UNIFORM AND PARTIALLY UNIFORM REDISTRIBUTION RULES

This paper introduces two new fusion rules for combining quantitative basic belief assignments. These rules although very simple have not been proposed in literature so far and could serve as useful alternatives because of their low computation cost with respect to the recent advanced Proportional Conflict Redistribution rules developed in the DSmT framework.

Mixture surrogate models based on Dempster-Shafer theory for global optimization problems

Recent research in algorithms for solving global optimization problems using response surface methodology has shown that it is in general not possible to use one surrogate model for solving different kinds of problems. In this paper the approach of applying Dempster-Shafer theory to surrogate model selection and their combination is described. Various conflict redistribution rules have been examined with respect to their influence on the results. Furthermore, the implications of the surrogate model type, i.e. using combined, single or a hybrid of both, have been studied. The suggested algorithms were applied to several well-known global optimization test problems. The results indicate that the used approach leads for all problems to a thorough exploration of the variable domain, i.e. the vicinities of global optima could be detected, and that the global minima could in most cases be approximated with high accuracy.

An approximate reasoning method in Dezert-Smarandache Theory

With the increment of focal elements number in discernment framework, the computation amount in Dezert-Smarandache Theory (DSmT) will exponentially go up. This has been the bottleneck problem to block the wide application and development of DSmT. Aiming at this difficulty, in this paper, a kind of fast approximate reasoning method in hierarchical DSmT is proposed. Presently, this method is only fit for the case that there are only singletons with assignment in hyper-power set. These singletons in hyper-power set are forced to group through bintree or tri-tree technologies. At the same time, the assignments of singletons in those different groups corresponding to each source are added up respectively, in order to realize the mapping from the refined hyper-power set to the coarsened one. And then, two sources with the coarsened hyper-power set are combined together according to classical DSm Combination rule (DSmC) and Proportional Conflict Redistribution rule No. 5 (PCR5). The fused results in coarsened framework will be saved as the connecting weights between father and children nodes. And then, all assignments of singletons in different groups will be normalized respectively. Tree depth is set, in order to decide the iterative times in hierarchical system. Finally, by comparing new method with old one from different views, the superiority of new one over old one is testified well.

A FUSION MACHINE BASED ON DSMT AND PCR5 FOR ROBOT'S MAP RECONSTRUCTION

Characteristics of uncertainty, imprecision, and even imperfection are presented from knowledge acquisition in map reconstruction using sonar sensors fixed on autonomous mobile robot. In order to improve the precision of the fusion and performances of map reconstruction, we propose in this paper a new fusion machine based on Dezert-Smarandache Theory (DSmT) coupled with the fifth Proportional Conflict Redistribution rule (PCR5) for dealing with uncertain and conflicting evidences provided by homogeneous or heterogeneous sources of information. We propose a belief model for sonar grid map and show how to construct efficiently generalized basic belief assignment functions for sensors onboard. A Pioneer II mobile robot with 16 sonar range finders serves as the experiment platform. In our experiment, the robot evolves in a real environment with some obstacles and the environment map is rebuilt online with our self-developing software platform. In this study, we also compare our new approach with other ones based on probability theory, fuzzy theory, Dempster-Shafer Theory (DST) and gray system theory. Our results show an improvement of the performances in precision of map reconstruction of mobile robot with respect to those obtained from aforementioned classical approaches.

A New Class of Fusion Rules Based on T-Conorm and T-Norm Fuzzy Operators

A new combination rule based on specified fuzzy T-Conorm/T-Norm operators is proposed and analyzed in this article - the TCN Rule of Combination. The rule does not belong to the general Weighted Operator Class. The advan- tages of the new rule could be defined as: very easy to implement, satisfying the impact of neutrality of Vacuous Belief Assignment, commutative, convergent to idempotence, reflecting majority opinion, and assuring an adequate data process- ing in case of total conflict. Several numerical examples and comparisons with the new advanced Proportional Conflict Redistribution Rules proposed recently by Florentin Smarandache and Jean Dezert within their theory of plausible and paradoxical reasoning are presented.