Paradox Elimination in Dempster–Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion

Khan, Nazmuzzaman; Anwar, Sohel

doi:10.3390/s19214810

Cited by 31 publications

(18 citation statements)

References 30 publications

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“…Common approaches include knowledge distillation [206], where a compact student network is trained to mimic a larger network, e.g., by guiding the network to produce similar activations for similar inputs, and advanced network models, such as Operational Neural Networks [207], where the linear operators of CNNs are replaced by various (non-)linear operations, which allows to produce complex outputs which much fewer parameters. [208], [209]. In gray, we highlight the stage in which the fusion happens.…”

Section: Fast and Computationally Light Methodsmentioning

confidence: 99%

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

et al. 2020

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Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims' conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area.

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Section: Fast and Computationally Light Methodsmentioning

confidence: 99%

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

et al. 2020

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“…Proposed Entropy: m1=2.195,m2=2.27. A separate detailed work on this novel entropy function and its properties can be found in the literature [20].…”

Section: Dempster–shafer Evidence-based Combination Rulementioning

confidence: 99%

“…At the end, modified evidence is fused using original DS sensor fusion equation. The proposed algorithm can also be applied to space-domain sensor fusion with some minor modification [20]. As Figure 1 suggests, in space-domain, multiple physical sensors are used to collect data.…”

Section: Proposed Algorithm For Time-domain Data Fusionmentioning

confidence: 99%

Time-Domain Data Fusion Using Weighted Evidence and Dempster–Shafer Combination Rule: Application in Object Classification

Khan

Anwar

2019

Sensors

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To apply data fusion in time-domain based on Dempster–Shafer (DS) combination rule, an 8-step algorithm with novel entropy function is proposed. The 8-step algorithm is applied to time-domain to achieve the sequential combination of time-domain data. Simulation results showed that this method is successful in capturing the changes (dynamic behavior) in time-domain object classification. This method also showed better anti-disturbing ability and transition property compared to other methods available in the literature. As an example, a convolution neural network (CNN) is trained to classify three different types of weeds. Precision and recall from confusion matrix of the CNN are used to update basic probability assignment (BPA) which captures the classification uncertainty. Real data of classified weeds from a single sensor is used test time-domain data fusion. The proposed method is successful in filtering noise (reduce sudden changes—smoother curves) and fusing conflicting information from the video feed. Performance of the algorithm can be adjusted between robustness and fast-response using a tuning parameter which is number of time-steps(ts).

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“…The second type of algorithm focuses on preprocessing the original evidence. Algorithms of this type include Murphy [52], Han et al [53], Zhang et al [54], Yuan et al [55], Xiao [15], Radim and Prakash [56], Ye et al [57], Khan and Anwar [58], and Ma and An and Jiang et al [59,60]. Murphy [52] averaged multiple sets of evidence without considering the correlation between the sets of evidence.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the weighted mass function, they designed rational mass assignment of conflicting probability instead of directly employing the DS combination rule. Khan and Anwar [58] proposed a novel entropy function based on Shannon entropy, which was better at capturing uncertainties compared to Shannon and Deng entropies. Meanwhile, an 8-step algorithm had been developed which can eliminate the inherent paradoxes of a classical DS theory.…”

Section: Introductionmentioning

confidence: 99%

Multisensor Fusion Method Based on the Belief Entropy and DS Evidence Theory

Fan

Guo

et al. 2020

Journal of Sensors

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The Dempster–Shafer evidence theory has been widely applied in multisensor information fusion. Nevertheless, illogical results may occur when fusing highly conflicting evidence. To solve this problem, a new method of the grouping of evidence is proposed in this paper. This method uses a combination of the belief entropy and the degree of conflict of the evidence as the judgment rule and divides the entire body of evidence into two separate groups. For the grouped evidence, both the credibility weighted factor based on the belief entropy function and the support weighted factor based on the Jousselme distance function are taken into consideration. The two determined weighted factors are integrated to adjust the evidence before applying the DS combination rule. Numerical examples are provided to demonstrate the theoretical feasibility and rationality of the proposed method. The fusion results indicate that the proposed method is more accurate than the compared algorithms in handling the paradoxes. A decision-making case analysis of the biological system is performed to validate the practical applicability of the proposed method. The results confirm that the proposed method has the highest belief degree of the target concentration (50.98%) and has superior accuracy compared to other related methods.

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Paradox Elimination in Dempster–Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion

Cited by 31 publications

References 30 publications

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

Time-Domain Data Fusion Using Weighted Evidence and Dempster–Shafer Combination Rule: Application in Object Classification

Multisensor Fusion Method Based on the Belief Entropy and DS Evidence Theory

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