Proportional Conflict Redistribution Rule Research Articles

Fusion of Identification Information from ESM Sensors and Radars Using Dezert–Smarandache Theory Rules

This paper presents a method of fusion of identification (attribute) information provided by two types of sensors: combined primary and secondary (IFF) surveillance radars and ESMs (electronic support measures). In the first section, the basic taxonomy of attribute identification is adopted in accordance with the standards of STANAG 1241 ed. 5 and STANAG 1241 ed. 6 (draft). These standards provide the following basic values of the attribute identifications: FRIEND; HOSTILE; NEUTRAL; UNKNOWN; and additional values, namely ASSUMED FRIEND and SUSPECT. The basis of theoretical considerations is Dezert–Smarandache theory (DSmT) of inference. This paper presents and uses in practice six information-fusion rules proposed by DSmT, i.e., the proportional conflict redistribution rules (PCR1, PCR2, PCR3, PCR4, PCR5, and PCR6), for combining identification information from different ESM sensors and radars. This paper demonstrates the rules of determining attribute information by an ESM sensor equipped with the database of radar emitters. It is proposed that each signal vector sent by the ESM sensor contains an extension specifying a randomized identification declaration (hypothesis)—a basic belief assignment (BBA). This paper also presents a model for determining the basic belief assignment for a combined primary and secondary radar. Results of the PCR rules of sensor information combining for different scenarios of a radio electronic situation (deterministic and Monte Carlo) are presented in the final part of this paper. They confirm the legitimacy of the use of Dezert–Smarandache theory in information fusion for primary radars, secondary radars, and ESM sensors.

Fusing Landsat-8, Sentinel-1, and Sentinel-2 Data for River Water Mapping Using Multidimensional Weighted Fusion Method

River water extent is critical for understanding river discharge or its hydrological conditions. Although numerous methods have been proposed to map river water from either optical or synthetic aperture radar (SAR) remotely sensed images, uncertainties still exist broadly. In this study, we developed an image fusion method that integrates Landsat-8, Sentinel-1 and Sentinel-2 images simultaneously for river water mapping with two major steps. Firstly, a posterior probability support vector machine model was adopted to generate water probability maps from each individual image; and second, a Multi-dimensional Weighted Fusion Method (MDWFM) was developed to fuse these probability maps. Four reaches with different characteristics were selected as case study sites. High resolution aerial images were acquired and used as the reference to evaluate our results. We found the fusion process not only improves the quality of river water mapping, but also excludes the cloud interference. The fused river water maps become more reliable after the conflicts from difference images being solved by the proposed MDWFM method that contains a proportional conflict redistribution rule. The weighted root mean square difference was reduced to 0.066, and the Area Under the ROC curve reached up to 0.984. The Critical Success Index, Kappa Coefficient, and F-measure reached up to 0.810, 0.836 and 0.895, respectively. These stable and accurate river extent mapping results obtained through fusing multiple images with high spatial resolution (10 m) and short revisit interval (0.4~4.4 days) are of great significance for enriching the data and methodology of hydrological studies.

An ECR-PCR rule for fusion of evidences defined on a non-exclusive framework of discernment

In the research of uncertain information processing, Dempster-Shafer Theory (DST) provides a framework for dealing with uncertain information, where evidence is defined on a Frame of Discernment (FOD) consisting of mutually exclusive elements. However, the requirement of exclusiveness on FOD sometimes is not satisfied, as shown in Dezert-Smarandache Theory(DSmT), a derivative of DST. In DSmT, the non-exclusiveness is expressed by propositions’ intersection and the fusion of evidence is realized through a Proportional Conflict Redistribution (PCR) rule. In order to handle non-exclusive FODs, a new framework called D Number Theory (DNT) has been proposed recently, which quantifies the non-exclusive degree between propositions different from DSmT. In previous studies, an Exclusive Conflict Redistribution (ECR) rule has been designed in DNT to implement the fusion of evidence defined on a non-exclusive FOD, but there are some deficiencies in the ECR rule. In this paper, a new rule called ECR-PCR rule is proposed by combining the ECR and PCR rules to better implement the fusion of evidence defined on a non-exclusive FOD. Within the proposed rule, the definition of conflict utilizes the idea of ECR’s exclusive conflict, and the disposal of conflict is following the idea of PCR’s proportional redistribution. Properties of the ECR-PCR rule are presented. The effectiveness of the proposed new rule is verified through numerical examples and applications, in comparison with other fusion methods.

A Study on Human Learning Ability during Classification of Motion and Colour Visual Cues and Their Combination

Abstract The paper presents a study on the human learning process during the classification of stimuli, defined by motion and color visual cues and their combination. Because the classification dimension and the features that define each category are uncertain, we model the learning curves using Bayesian inference and more precisely the Normalized Conjunctive Consensus rule, and also on the base of the more efficient probabilistic Proportional Conflict Redistribution rule No 5 (pPCR5) defined within Dezert-Smarandache Theory (DSmT) of plausible and paradoxical reasoning. Our goal is to study how these rules succeed to model consistently both: human individual and group behaviour during the learning of the associations between the stimuli and the responses in categorization tasks varying by the amount of relevant stimulus information. The effect of age on this process is also evaluated.

A new approach to the application of conflict redistribution rule in Satellite Image Classification

Nowadays solution of different scientific problems using satellite images, generally includes a classification procedure. Classification is one of the most important procedures used in remote sensing, because it involves a lot of mathematical operations and data preprocessing. The processing of information and combining of conflicting data is a very difficult problem in classification tasks. Nowadays many classification methods are applied in remote sensing. Classification of conflicting data has been a key problem, both from a theoretical and practical point of view. But a lot of known classification methods can not deal with highly conflicted data and uncertainty. The main purpose of this article is to apply proportional conflict redistribution rule (PRC5) for satellite image classification in conditions of uncertainty, when conflicting sources of evidence give incomplete and vague information. This rule can process conflicting data and combine conflicting bodies of evidence (spectral bands). Proportional conflict redistribution rule can redistribute the partial conflicting mass proportionally on non-empty sets involved in the conflict. It was noticed, that this rule can provide a construction of aggregated estimate under conflict. It calculates all partial conflicting masses separately. It was also shown, that proportional conflict redistribution rule is the most mathematically exact redistribution of conflicting mass to non-empty set. But this rule consists of difficult calculation procedures. The more hypotheses and more masses are involved in the fusion, the more difficult is to implement proportional conflict redistribution rule, therefore special computer software should be used. It was considered an example of practical use of the proposed conflict redistribution rule. It also was noticed, that this new approach to the application of conflict redistribution rule in satellite image classification can be applied for analysis of satellite images, solving practical and ecological tasks, assessment of agricultural lands, classification of forests, in searching for oil and gas.

SYNTESIS OF GROUP DECISIONS IN THE PROBLEM OF ANALYSIS OF THE TECHNICAL CONDITION OF MILITARY-CIVILIAN OBJECTS

Context. Quite often, experts are involved in the process of diagnosis and monitoring the technical condition of buildings and structures, and in this case, situations might arise when expert data is generated under some specific types of uncertainty, and their possible combinations. This, in turn, necessitates the development of new approaches aimed at solving the problems of structuring and analytical processing of inaccurate, uncertain, fuzzy expert knowledge. Objective. The methodology for choosing the category of technical condition of construction objects, including buildings and structures, and ranking the corresponding construction objects within the given category of the technical condition according to their degree of danger (expected damage in the event of an emergency) has been proposed in this paper. The proposed approach is based on the expert assessment methods and the mathematical apparatus of the evidence theory, which allows operating correctly with data generated under uncertainty, incompleteness, and inaccuracy. In order to improve the quality of combination results, it is proposed to use one of the proportional conflict redistribution rules and determine the optimal evidence combination order based on metrics in evidence theory. Results. The paper proposes a methodology for the synthesis of group solutions for assessing the technical condition of civil, industrial and military-technical construction objects, and determining objects that primarily need maintenance or overhaul under complex forms of uncertainty and multi-alternatives. Application of the proposed methodology will allow rational distribution of available resources when planning preventive measures and carrying out repair work (overhaul, reconstruction, etc.) to increase the efficiency of their trouble-free operation. Conclusions. The methodology proposed in this study constitutes the theoretical basis for the design of decision support systems for monitoring the technical condition of residential and/or non-residential real estate (buildings, structures) for various purposes.

DSmT-Based Group DEMATEL Method with Reaching Consensus

The decision-making trial and evaluation laboratory (DEMATEL) method employs expert assessments expressed by crisp values to construct a group initial direct-relation (IDR) matrix. However, it tends to be a low-precision expression, especially in complex practical problems. Although significant efforts have been made to improve the DEMATEL method, these improvements tend to neglect individual characteristics and group consensus, resulting in unconvincing decision results. This study provides a Dezert–Smarandache theory-based group DEMATEL method with reaching consensus. In order to reasonably determine the group IDR matrix, basic belief assignment function is employed to extract expert assessments and the proportional conflict redistribution rule no.5 of DSmT is employed to make fusion to derive the temporary group IDR matrix. Moreover, the consensus measures at both expert level and pair-factors level are calculated to determine whether the acceptable consensus level has been reached or not. If the required consensus level is not reached, a feedback mechanism will be activated to help experts reach a consensus. A consensus group IDR matrix for the group DEMATEL can be obtained with the help of feedback mechanism, based on which an algorithm is summarized for the proposed method to identify major factors in a complex system. Finally, numerical comparison and discussion are introduced to verify the effectiveness and applicability of the proposed method and algorithm.

Secure multimodal biometric system based on diffused graphs and optimal score fusion

Biometric authentication systems offer potential advantages over traditional knowledge-based methods. However, most of the biometric systems which are extensively used lack template security and robustness. In order to address these issues, in this study, the authors have proposed a multimodal biometric system based on the combination of multiple modalities and optimal score level fusion. In addition, key features are introduced for each modality for generating cancellable biometric features. Features from individual traits are combined with corresponding key features to provide feature transformation. A robust template is generated by diffusion of individual transformed matrices using graph-based random walk cross-diffusion. In addition, individual classifier score is optimally fused using proposed multistage score level fusion model. Optimal belief masses for individual classifier are determined using cuckoo search optimisation. Wherein, optimal classifier beliefs are fused using DSmT based proportional conflict redistribution (PCR-6) rules. Experimental results demonstrate that optimal score fusion applied on cross-diffused features produce better results than existing state-of-the-art multimodal fusion schemes. On average of the outcome, equal error rate and accuracy achieved using the proposed method on four chimeric benchmarked datasets, are 2.32 and 98.316%.

Enhance the Performance of Back Propagation Algorithm Using Proportional Conflict Redistribution Rule for the Diagnosis of Hypertension

Enhance the Performance of Back Propagation Algorithm Using Proportional Conflict Redistribution Rule for the Diagnosis of Hypertension

Robust multimodal biometric system based on optimal score level fusion model

Multimodal biometric systems fuse information from multiple modalities to overcome limitations of individual classifiers. Score level fusion of multiple classifiers can effectively combine information from different modalities. However, most of the multimodal biometric systems are impaired by conflicting classifier scores under dynamic environment, which results in degradation in system's robustness and reliability. To address this, we propose a multimodal biometric system based on an optimal score level fusion model. The key idea of this work is to optimally integrate three complementary biometric traits namely iris, finger vein and fingerprint. For this, individual classifier performance is optimized using evolutionary Backtracking Search Optimization Algorithm (BSA). In addition, conflicting beliefs from individual classifiers are resolved using proportional conflict redistribution rules (PCR-6) to obtain a concurrent solution. The system exhibits optimal behaviour under dynamic environment through boosting or suppression of concurrent classifiers and resolving conflicts among discordant classifiers. The proposed biometric system is evaluated over chimeric multimodal datasets created from benchmark images. On an average, we achieve an accuracy of 98.43% and an EER of 1.57%. The proposed biometric system not only outperforms state-of-the-art techniques but also shows directions towards development of an expert multimodal biometric system.

Online Object Tracking via Novel Adaptive Multicue Based Particle Filter Framework for Video Surveillance

Multicue based object tracking frameworks have been extensively explored due to their numerous applications in the field of computer vision. However, the online adaptive fusion of multicue under scale and illumination variations, partial or full occlusion, background clutters and object deformation remains an open challenge problem. In order to address this, we propose an online visual tracking algorithm using adaptive integration of multicue in a particle filter framework. The particle level fusion process is modelled as Shafer’s model with a power set defined over two focal elements. Partial conflicting masses and conjunctive consensus among three cues are estimated for each evaluated particle. Partial conflicts among cues are redistributed using Dezert-Smarandache Theory (DSmT) based proportional conflict redistribution rules (PCR-6). Additionally, context sensitive transductive cues reliabilities are used for discounting the particle likelihoods for quick adaptation of tracker. In the proposed model, automatic boosting of good particles and suppression of low performing particles not only improves resampling process but also enhances tracker accuracy. Experimental validation over benchmarked video sequences reveals that the proposed multicue tracking framework outperforms state-of-the-art trackers under various dynamic environmental challenges.

A New PCR Combination Rule for Dynamic Frame Fusion

Dynamic frame fusion which is based on hybrid DSm model is an important problem in information fusion. But the traditional combination rules are mainly under fixed discernment frame (Shafer model and free DSm model) responding to static model. A new method for dynamic proportional conflict redistribution rules (dynamic PCR rules) based on hybrid DSm model is proposed for the shortness of classical dynamic PCR rules. In the new dynamic PCR rule, combination involved with empty set is defined as one kind to obtain more reasonable results. For the redistribution weight, the conjunction Basic belief assignment (BBA) and conflict redistribution BBA are both taken into account to raise the fusion precision. The effectiveness of revised dynamic PCR rule is studied and simulated in both aspect of fusion accuracy and calculation.

A fast combination method in DSmT and its application to recommender system.

In many applications involving epistemic uncertainties usually modeled by belief functions, it is often necessary to approximate general (non-Bayesian) basic belief assignments (BBAs) to subjective probabilities (called Bayesian BBAs). This necessity occurs if one needs to embed the fusion result in a system based on the probabilistic framework and Bayesian inference (e.g. tracking systems), or if one needs to make a decision in the decision making problems. In this paper, we present a new fast combination method, called modified rigid coarsening (MRC), to obtain the final Bayesian BBAs based on hierarchical decomposition (coarsening) of the frame of discernment. Regarding this method, focal elements with probabilities are coarsened efficiently to reduce computational complexity in the process of combination by using disagreement vector and a simple dichotomous approach. In order to prove the practicality of our approach, this new approach is applied to combine users’ soft preferences in recommender systems (RSs). Additionally, in order to make a comprehensive performance comparison, the proportional conflict redistribution rule #6 (PCR6) is regarded as a baseline in a range of experiments. According to the results of experiments, MRC is more effective in accuracy of recommendations compared to original Rigid Coarsening (RC) method and comparable in computational time.

A Novel Decision Support for Composite Sketch Matching using Fusion of Probabilistic Neural Network and Dictionary Matching

Traditional pencil sketches drawn by skilled pencil sketch artists are a product of illustration based on exaggeration; there is always some amount of discrepancy between the description of eye-witnesses and depiction of the offender by the sketch artist. To overcome this difficulty, law enforcement agencies worldwide have started using composite sketches (sketches created using computer). Composite sketches have obviated the need of a skilled sketch artist. Composite sketches can be easily drawn by eyewitnesses using face design system software (SketchCop FACETTE) in a very short time period without any prior specialized software training. Matching composite sketches with photos available in database are still a challenging task. In this paper, a novel technique is proposed to match composite sketches with photos available in database. The key contribution of the proposed system in this paper is an efficient and novel methodology developed to match composite sketches with photos available in databases which could be used by law enforcement agencies. The photos taken from law enforcement agencies databases are passed to face detection module. On the detected faces and composite sketches, feature extraction and classification are performed using Multi-resolution uniform Local Binary Pattern (LBP) and Probabilistic Neural Network (PNN) to generate score 1. Similarly, on detected faces and composite sketches, feature extraction and classification are again performed using Dictionary Matching (DM) to generate score 2. The generated scores are collected using Dempster-Shafer (DS) theory and Proportional Conflict Redistribution rule no. 5 (PCR5). In this study, the authors have performed pilot testing of their technique and results of their analysis are presented to the readers. Keywords: Local binary pattern, probabilistic neural network, dictionary matching, SketchCop FACETTE, dempster-shafer, Proportional Conflict Redistribution.

ADAPTIVE MULTIMODAL BIOMETRIC FUSION ALGORITHM USING PARTICLE SWARM OPTIMIZATION AND BELIEF FUNCTIONS

In this paper, an adaptive multimodal biometric fusion algorithm is proposed. It is based on belief functions and Particle Swarm Optimization (PSO). The fusion is performed at the score level using belief functions such as Dempster Shafer, Yager, Proportional Conflict Redistribution and Dezert-Smarandache hybrid rules. A hybrid PSO is employed to select the best belief function and estimate its parameters. Several experiments have been conducted on BANCA dataset and a comparison between the well established methods has been performed. The preliminary results provide adequate motivation towards future research in the application of optimization techniques in the belief functions.

Analysis and Improvement for Proportional Conflict Redistribution Rules

Ab stract—The Proportional Conflict Redistribution (PCR) rules based on Dezert-Smarandache theory (DSmT) is a useful method for dealing with uncertainty problems. It is more efficient in combining conflicting evidence. Therefore, it has been successfully applied in identity identification. However, there exist shortcomings in PCR rule. So in this paper we propose a new improved rule which is based on new Proportional Conflict Redistribution. The six PCR rules (PCR1-PCR6) and improved PCR rule are analyzed and compared through numerical examples, and the results show that the improved rule is effective.

Human Integration of Motion and Texture Information in Visual Slant Estimation

The present research is aimed to: (i) characterize the ability of human visual system to define the objects’ slant on the base of combination of visual stimulus characteristics, that in general are uncertain and even conflicting. (ii) evaluate the influence of human age on visual cues assessment and processing; (iii) estimate the process of human visual cue integration based on the well known Normalized Conjunctive Consensus and Averaging fusion rules, as well on the base of more efficient probabilistic Proportional Conflict Redistribution rule no.5 defined within Dezert-Smarandache Theory for plausible and paradoxical reasoning.

Intelligent Alarm Classification Based on DSmT

In this paper the critical issue of alarms’ classification and prioritization (in terms of degree of danger) is considered and realized on the base of Proportional Conflict Redistribution rule no.5, defined in Dezert-Smarandache Theory of plausible and paradoxical reasoning. The results obtained show the strong ability of this rule to take care in a coherent and stable way for the evolution of all possible degrees of danger, relating to a set of a priori defined, out of the ordinary dangerous directions.

Multistep Information Fusion for Target Detection Using UWB Radar Sensor Network

In this paper, we propose a multistep information fusion scheme for target detection through foliage and wall, using ultrawideband (UWB) radar sensor networks. We apply an information theory to detect target with poor signal quality in dynamic forest-environment. This method is motivated by the fact that echoes from the stationary target that is obscured by foliage has strong random characteristics. This is resolved by three steps of information fusion. For the first step of information fusion, we use Kullback–Leibler divergence-based weighting and generated a modified histogram. In the second step, we use entropy- and mutual information-based information fusion. Finally, we use three different fusion methods: 1) Dempster and Shafer theory of evidence; 2) proportional conflict redistribution rule 5; and 3) Bayesian network for decision fusion. Results show that when echoes are in poor quality, accurate detection can be achieved by applying our method. To demonstrate that our algorithm could be applied to other scenarios, we apply it to sense-through-wall human detection using different UWB radars, and simulation results show that our approach works well.

Robust object tracking based upon adaptive multi-cue integration for video surveillance

It was well argued in literature that integrating multi-cue increases accuracy and robustness of visual tracking. Although, multi-cues object tracking using singlemodal or multimodal was explored by some of researchers, it still remains an open challenge to fuse multi-cue from different modularity under dynamic environment conditions. The aim of this research paper is to introduce a novel multi-cue object tracking framework using particle filter. In particle filter framework, our approach integrates cues while evaluating each particle instead of primitive approach of deciding cues performance in current frame based upon either a few present particles or previous state particles. First, we model our multi-cue tracking framework using Shafer's model and multi-cue data is combined using Conjunctive combination rules. The partial and total conflict among cues at particle level is redistributed efficiently using Proportional Conflict Redistribution (PCR-5) rules. In proposed model, automatic suppression/boosting of particles along with online conflict resolving facilitate resampling process for efficiently handling of particle degeneracy. Most importantly, compared to other state-of-art trackers, our proposed algorithm can handle more efficiently various dynamic environments conditions such as partial or full occlusion, illumination changes, weather, and visibility. In this manuscript, we demonstrate our proposed adaptive multi-cue fusion model on challenging benchmark video and thermal sequences and compare tracking results of our tracker with state-of- art trackers.