Construction of the Kalman Filter Algorithm on the Model Reduction

Arif, Didik Khusnul; Widodo, Widodo; Salmah, Salmah; Apriliani, Erna

doi:10.14257/ijca.2014.7.9.22

Cited by 15 publications

(5 citation statements)

References 3 publications

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“…Currently, the Kalman filter has been widely used in many fields due to its simple algorithm and excellent state estimation [4,5].…”

Section: Standard Kalman Filtermentioning

confidence: 99%

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A Kalman Filter with Noise Covariances Estimation and Its Stability Analysis

Lv¹

2017

IJCA

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“…Currently, the Kalman filter has been widely used in many fields due to its simple algorithm and excellent state estimation [4,5].…”

Section: Standard Kalman Filtermentioning

confidence: 99%

“…The extensions of the Kalman filter have also received many attentions [2,3]. Thereafter, with the rapid development of computer technology, Kalman filter algorithm and its extensions have been applied in many fields successfully [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Kalman Filter with Noise Covariances Estimation and Its Stability Analysis

Lv¹

2017

IJCA

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“…Therefore, we need a simplification of large order systems into smaller orders without significant errors. Simplification of this system is called model reduction [1]. Currently, there are many developed methods of model reduction such as balanced truncation methods [2,3,7,8,9,11] and singular perturbation approximation [10].…”

Section: Introductionmentioning

confidence: 99%

“…In [1], Kalman filter algorithm has been developed in the reduced model and applied to the heat conduction distribution problem. The simulation results show that the Kalman filter estimation on the reduced system is more accurate and faster compared to Kalman filter on the original system.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Kalman filter algorithm on models reduced using singular pertubation approximation method and its application to measurement of water level

Rachmawati

Arif

Adzkiya

2018

J. Phys.: Conf. Ser.

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Abstract. The systems contained in the universe often have a large order. Thus, the mathematical model has many state variables that affect the computation time. In addition, generally not all variables are known, so estimations are needed to measure the magnitude of the system that cannot be measured directly. In this paper, we discuss the model reduction and estimation of state variables in the river system to measure the water level. The model reduction of a system is an approximation method of a system with a lower order without significant errors but has a dynamic behaviour that is similar to the original system. The Singular Perturbation Approximation method is one of the model reduction methods where all state variables of the equilibrium system are partitioned into fast and slow modes. Then, The Kalman filter algorithm is used to estimate state variables of stochastic dynamic systems where estimations are computed by predicting state variables based on system dynamics and measurement data. Kalman filters are used to estimate state variables in the original system and reduced system. Then, we compare the estimation results of the state and computational time between the original and reduced system.

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“…In this paper, a pedestrian tracking program that works even at night is proposed. Existing methods include the conventional pedestrian tracking method to track and analyze the human silhouette characteristic parameters [2]; using the image from the color information of the video by analyzing the division and outlining how to connect only similar colors to complete the blob [3]; the learning and classification algorithms Support Vector Machine (SVM) [4][5].Kalman Filtering Algorithm Based-on tracking System [6][7]. Expensive infrared cameras for night vision image are used for the improvement of existing methods.…”

Section: Introductionmentioning

confidence: 99%

Lightness Pretreatment with Pedestrian Detection Studies Using CH&K Algorithm for a Night Environment

Park¹,

Kang²

2015

IJSH

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This paper presents a real-time pedestrian detection algorithm for a night environment. By first converting the nighttime image data to the L * a * b * color space, it can be extracted in the area L * with robust noise reduction and contrast adjustment. This data is used to generate pre-data through image subtraction. A background image is generated using the data, and the Cascade Histogram of Oriented Gradient &Kalman filter (CH & K) algorithm is proposed to track the movement of pedestrians. In addition, pre-processing algorithms and the proposed algorithm can replace Histograms of Oriented Gradients (HOGs) with rather heavy computations, and sensitive Haar-like features in the night can be used for real-time pedestrian detection and brightness.

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Construction of the Kalman Filter Algorithm on the Model Reduction

Abstract: Abstract

Cited by 15 publications

References 3 publications

A Kalman Filter with Noise Covariances Estimation and Its Stability Analysis

A Kalman Filter with Noise Covariances Estimation and Its Stability Analysis

Implementation of Kalman filter algorithm on models reduced using singular pertubation approximation method and its application to measurement of water level

Lightness Pretreatment with Pedestrian Detection Studies Using CH&K Algorithm for a Night Environment

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