J. Adv. Inf. Fusion最新文献

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Robust Bayesianism: Relation to Evidence Theory 鲁棒贝叶斯理论:与证据理论的关系

J. Adv. Inf. Fusion Pub Date : 2014-12-01 DOI: 10.5281/ZENODO.23232

S. Arnborg

{"title":"Robust Bayesianism: Relation to Evidence Theory","authors":"S. Arnborg","doi":"10.5281/ZENODO.23232","DOIUrl":"https://doi.org/10.5281/ZENODO.23232","url":null,"abstract":"We are interested in understanding the relationship between Bayesian inference and evidence theory. The concept of a set of probability distributions is central both in robust Bayesian analysis and in some versions of Dempster-Shafer’s evidence theory. We interpret imprecise probabilities as imprecise posteriors obtainable from imprecise likelihoods and priors, both of which are convex sets that can be considered as evidence and represented with, e.g., DS-structures. Likelihoods and prior are in Bayesian analysis combined with a place’s parallel composition. The natural and simple robust combination operator makes all pairwise combinations of elements from the two sets representing prior and likelihood. Our proposed combination operator is unique, and it has interesting normative and factual properties. We compare its behavior with other proposed fusion rules, and earlier efforts to reconcile Bayesian analysis and evidence theory. The behavior of the robust rule is consistent with the behavior of Fixsen/Mahler’s modified Dempster’s (MDS) rule, but not with Dempster’s rule. The Bayesian framework is liberal in allowing all significant uncertainty concepts to be modeled and taken care of and is therefore a viable, but probably not the only, unifying structure that can be economically taught and in which alternative solutions can be modeled, compared and explained.","PeriodicalId":303238,"journal":{"name":"J. Adv. Inf. Fusion","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114921864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 32

Decision-Level Fusion Performance Improvement From Enhanced HRR Radar Clutter Suppression 基于增强HRR雷达杂波抑制的决策级融合性能改进

J. Adv. Inf. Fusion Pub Date : 2011-12-05 DOI: 10.5281/ZENODO.22868

B. Kahler, Erik Blasch

{"title":"Decision-Level Fusion Performance Improvement From Enhanced HRR Radar Clutter Suppression","authors":"B. Kahler, Erik Blasch","doi":"10.5281/ZENODO.22868","DOIUrl":"https://doi.org/10.5281/ZENODO.22868","url":null,"abstract":"Airborne radar tracking in moving ground vehicle scenarios is impacted by sensor, target, and environmental dynamics. Moving targets can be characterized by 1-D High Range Resolution (HRR) Radar profiles with sufficient Signal-to-Noise Ratio (SNR). The amplitude feature information for each range bin of the HRR profile is used to discern one target from another to help maintain track or to identify a vehicle. Typical radar clutter suppression algorithms developed for processing moving ground target data not only remove the surrounding clutter, but a portion of the target signature. Enhanced clutter suppression can be achieved using a Multi-channel Signal Subspace (MSS) algorithm, which preserves target features. In this paper, we (1) exploit extra feature information from enhanced clutter suppression for Automatic Target Recognition (ATR), (2) present a Decision-Level Fusion (DLF) gain comparison using Displaced Phase Center Antenna (DPCA) and MSS clutter suppressed HRR data; and (3) develop a confusion-matrix identity fusion result for Simultaneous Tracking and Identification (STID). The results show that more channels for MSS increase identification over DPCA, result in a slightly noisier clutter suppressed image, and preserve more target features after clutter cancellation. The paper contributions include extending a two-channel MSS clutter cancellation technique to three channels, verifying the MSS is superior to the DPCA technique for target identification, and a comparison of these techniques in a novel multi-look confusion matrix decision-level fusion process.","PeriodicalId":303238,"journal":{"name":"J. Adv. Inf. Fusion","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133003594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 32

Issues and Challenges in Situation Assessment (Level 2 Fusion) 态势评估中的问题与挑战(2级融合)

J. Adv. Inf. Fusion Pub Date : 2006-12-01 DOI: 10.21236/ada520878

Erik Blasch, I. Kadar, J. Salerno, M. Kokar, S. Das, G. Powell, D. Corkill, E. Ruspini

{"title":"Issues and Challenges in Situation Assessment (Level 2 Fusion)","authors":"Erik Blasch, I. Kadar, J. Salerno, M. Kokar, S. Das, G. Powell, D. Corkill, E. Ruspini","doi":"10.21236/ada520878","DOIUrl":"https://doi.org/10.21236/ada520878","url":null,"abstract":"Abstract : Situation assessment (SA) involves deriving relations among entities, e.g., the aggregation of object states (i.e., classification and location). While SA has been recognized in the information fusion and human factors literature, there still exist open questions regarding knowledge representation and reasoning methods to afford SA. For instance, while lots of data is collected over a region of interest, how does this information get presented to an attention constrained user? The information overload can deteriorate cognitive reasoning so a pragmatic solution to knowledge representation is needed for effective and efficient situation understanding. In this paper, we present issues associated with Level 2 Information Fusion (Situation Assessment) including: (1) user perception and perceptual reasoning representation, (2) knowledge discovery process models, (3) procedural versus logical reasoning about relationships, (4) userfusion interaction through performance metrics, and (5) syntactic and semantic representations. While a definitive conclusion is not the aim of the paper, many critical issues are proposed in order to characterize future successful strategies for knowledge representation, presentation, and reasoning for situation assessment.","PeriodicalId":303238,"journal":{"name":"J. Adv. Inf. Fusion","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115169991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 142