Optimal Estimation of Gasoline LP-EGR via Unscented Kalman Filtering with Mixed Physics-based/Data-driven Components Modeling

Kim, Kwangmin; Kim, Jin-Sung; Kwon, Oheun; Oh, Se-Kyu; Kim, Yong-Wha; Lee, Dongjun

doi:10.1016/j.ifacol.2020.12.112

Cited by 2 publications

(2 citation statements)

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“…Noise can lead to degradation and blurring of the image quality and seriously affect the important characteristics of the image, which causes increased difficulty in the analysis and understanding of the image, especially in the image acquisition and input process. Therefore, the role of noise suppression is particularly prominent [21]. The specific steps are shown in Fig.…”

Section: B Establishment Of a Kalman Filter-based Signal Processingmentioning

confidence: 99%

“…In Equation (19), the eigenvalues of   20), ( , ) R x y is the image function of the image block m R ; n is the number of its pixels; ( , ) xy is the value of the pixels, expressed in coordinates, and ()  is the Kronecker function; R has the following relationship with the corresponding pixels of its image block I of the same size, as shown in Equation (21). 21), ( , ) R x y is the image function of the image block R and the image block I .…”

Section: Time Update (Forecast)mentioning

confidence: 99%

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Kalman Filter-based Signal Processing for Robot Target Tracking

Gong¹

2023

IJACSA

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In the field of computer vision, the signal tracking of moving objects is a highly representative problem. Therefore, how to accurately and quickly track the target unit has become the focus of the research. Based on this, a Cam Shift algorithm improved by Kalman filtering algorithm is introduced to realize fast tracking of moving targets. This method uses the prediction function of the Kalman filter to predict the moving target of the next frame, transforms the global search problem into a local search problem, and improves the real-time performance. The experimental results show that, in the case of complete occlusion, the trajectory of the unimproved algorithm will deviate compared with the actual trajectory of the improved trajectory tracking curve, but the improved algorithm has no trajectory deviation. The error of the improved algorithm is about 4%, while the maximum error of the unimproved algorithm is about 90%. The improved algorithm reached the expected target accuracy after 110 and 78 trainings in X and Y coordinates, respectively, while the CamShift algorithm without Kalman filtering still failed to reach the expected error after 200 trainings in X and Y coordinates. This indicates that the performance of the improved CamShift algorithm based on Kalman filter has been greatly improved. In conclusion, the improved algorithm proposed in this study is highly practical.

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Section: B Establishment Of a Kalman Filter-based Signal Processingmentioning

confidence: 99%

Section: Time Update (Forecast)mentioning

confidence: 99%