Particle Filter Based Recursive Data fusion with Sensor Indexing for Large Core Neutron Flux Estimation

Tamboli, Prakash Kumar; Duttagupta, Siddhartha P.; Roy, Kallol

doi:10.1109/tns.2017.2695663

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…Nonlinear filtering algorithms based on Monte Carlo simulation, such as Particle Filters (PF) [6,7,8] and Box Particle Filters (BPF) [9,10,11], have also been used in multi-sensor target tracking and data fusion. The PF algorithm can approximate the posterior probability density function of a state by extracting random particles.…”

Section: Introductionmentioning

confidence: 99%

Research into a Multi-Variate Surveillance Data Fusion Processing Algorithm

Mao

Yang

2019

Sensors

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Targeted information sources include radar and ADS (Automatic Dependent Surveillance) for civil ATM (Air Traffic Management) systems, and the new navigation system based on satellites has the capability of global coverage. In order to solve the surveillance problem in mid-and-high altitude airspace and approaching airspace, this paper proposes a filter-based covariance matrix weighting method, measurement variance weighting method, and measurement-first weighted fusion method weighting integration algorithm to improve the efficiency of data integration calculation under fixed accuracy. Besides this, this paper focuses on the technology of the integration of a multi-radar surveillance system and automated related surveillance system in the ATM system and analyzes the constructional method of a multigeneration surveillance data integration system, as well as establishing the targeted model of sensors and the target track and designing the logical structure of multi-radar and ADS data integration.

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