Data Fusion With Inverse Covariance Intersection for Prior Covariance Estimation of the Particle Flow Filter

Kang, Chang Ho; Kim, Sun Young; Song, Ju Yeon

doi:10.1109/access.2020.3041928

Cited by 7 publications

(5 citation statements)

References 49 publications

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“…Hypothesis Two H O2 : Students taught Technical drawing using a Web-based intelligent tutoring system, and those taught using the traditional teaching technique in terms of their mean interest scores. "Analysis of Covariance (ANCOVA) Summary for the Test of Significant Difference in Interests Mean Scores between Students Taught Technical Drawing Using a Web-Based Intelligent Tutoring System and Those Taught Using a Conventional Teaching Method [27], [28] " 4 Shows, The F-calculated value for the influence of instructions on interest, is shown in Table 4, along with a comparison of students taught technical drawing using a web-based intelligent tutoring system versus those taught using a traditional teaching method. The calculated F value for the groups is 1698.93, and the level of significance for F is.00, which is less than.05.…”

Section: Hypothesis One H O1mentioning

confidence: 99%

Web-Based Intelligent Tutoring System On Students Achievement And Interest In Technical Drawing In Niger State

Hassan

Usman²,

Bala³

et al. 2023

JISTR

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The study examines how a web-based innovative coaching system affects students' performance and interest in technical drawing in Niger, Nigeria. The hypotheses created in answer to the two study questions are evaluated using the A.05 significance threshold. The investigation was conducted utilizing a pre-post non-equivalent control group quasi-experimental study design. The study's participants comprised 428 National Technical College (NTC) II, students. The sample size for this study was 180 students, of whom 68 were in the experimental group, and 112 were in the control group. The experimental group employed Simple Random Sampling (SRS) and intentional sampling techniques. The instruments used to gather data are the Technical Drawing Achievement Test (TDAT) and the Technical Drawing Interest Inventory (TDII). The reliability coefficient for TDAT is 0.77, whereas the reliability coefficient for TDII is 0.79 using the Pearson Product Moment Correlation coefficient. The mean was used to answer research questions, and ANCOVA was used to evaluate the hypotheses. The study's findings demonstrated that web-based intelligent tutoring solutions are more successful in raising student achievement and piquing their interest in technical drawing than traditional teaching techniques. It was suggested that professors, particularly those teaching technical drawing, employ a web-based intelligent tutoring system for technical college students to enhance students' academic performance and pique their interest in technical drawing.

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Section: Hypothesis One H O1mentioning

confidence: 99%

Web-Based Intelligent Tutoring System On Students Achievement And Interest In Technical Drawing In Niger State

Hassan

Usman²,

Bala³

et al. 2023

JISTR

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“…The weights and the cubature points of the DICI-GFCKF algorithm are calculated according to the generalized cubature rule, and the algorithm steps are as follows [32], [33]:…”

Section: A Generalized Cubature Rulementioning

confidence: 99%

Electric Vehicle State Parameter Estimation Based on DICI-GFCKF

et al. 2022

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To improve the estimation accuracy of the state parameters of distributed electric vehicles, a double inverse covariance intersection generalized fifth-order cubature Kalman filter (DICI-GFCKF) estimation algorithm is proposed. Based on the fifth-order cubature Kalman filter algorithm, the generalized cubature rule is used to directly obtain the weight and cubature point of the algorithm. Then, the inverse covariance intersection (ICI) data fusion algorithm is introduced and combined with the generalized fifthorder CKF, and the double inverse covariance intersection-generalized fifth-order cubature Kalman filter is derived. The algorithm is applied to estimate the state parameters of distributed electric vehicles. Finally, the simulation and the vehicle experiment show that the algorithm not only improves the estimation accuracy and stability but also reduces the influence of the system model nonlinearity on the algorithm, and has good effectiveness and robustness.INDEX TERMS Electric vehicles, state parameter estimation, generalized cubature rule, ICI data fusion, fifth-order CKF.

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“…To obtain a fusion result that guarantees consistency and less conservative than CI, inverse covariance intersection (ICI) was proposed [32]. It was proved that the covariance of the ICI is smaller than CI [32], [33]. As CI has been extended to GCI, both the EI and ICI are also extended to a generalized ellipsoidal intersection [34] and nonlinear inverse covariance intersection (NICI) [35] to fuse arbitrary pdfs, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…The thing to note (25-28) is, by changing covariance Recently, ICI is proposed to obtain fusion results, which guarantee consistency and less conservative than CI [32]. It is proved that the covariance of the ICI is smaller than CI [32], [33]. The ICI formula can be written as [32], , …”

mentioning

confidence: 99%

Distributed GM-CPHD Filter Based on Generalized Inverse Covariance Intersection

Park

2021

IEEE Access

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In this paper, we propose a distributed Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filter based on generalized inverse covariance intersection that fuses multiple node information effectively for multi-target tracking applications. Covariance intersection (CI) is a well-known fusion method that produces a conservative estimate of the joint covariance regardless of the actual correlation between the different nodes. Inverse covariance intersection (ICI) is the updated version to obtain fusion results that guarantee consistency and less conservative than CI. However, the ICI is not extended to multi-sensor multi-target tracking system yet. Since the ICI formula can be re-structured as naïve fusion with covariance inflation in Gaussian pdf, this method was applied to the GM-CPHD with generalization. The formula for random finite set (RFS) fusion was derived in the same way as the conventional generalized covariance intersection (GCI) based fusion. The simulation results for multi-target tracking show that the proposed algorithm has smaller optimal sub-pattern assignment (OSPA) errors than naïve fusion and the GCI-based fusions.

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Data Fusion With Inverse Covariance Intersection for Prior Covariance Estimation of the Particle Flow Filter

Cited by 7 publications

References 49 publications

Web-Based Intelligent Tutoring System On Students Achievement And Interest In Technical Drawing In Niger State

Web-Based Intelligent Tutoring System On Students Achievement And Interest In Technical Drawing In Niger State

Electric Vehicle State Parameter Estimation Based on DICI-GFCKF

Distributed GM-CPHD Filter Based on Generalized Inverse Covariance Intersection

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