Robust Kalman filtering via Krein space estimation

Lee, T.H.; Ra, Won‐Sang; Yoon, Tae Ho; Park, J.B.

doi:10.1049/ip-cta:20040064

Cited by 32 publications

(16 citation statements)

References 13 publications

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“…Again, the assumption is made that the relationship is linear. So the measurement z k can be expressed in terms of the state x k [10] with…”

Section: Kalman Filtermentioning

confidence: 99%

“…Step 3: Compute the Kalman gain: Step 4: Update estimation with measurements: (10) Step 5: Update the error covariance:…”

Section: Figure 5 Kalman Filter Cyclementioning

confidence: 99%

“…equation (9) in (step 3) is chosen to be the gain that minimizes the posteriori error covariance. The next step is to actually measure the process to obtain z k , and then to generate a posteriori state estimate by incorporating the measurement as in equation (10). The final step is to obtain a posteriori error covariance estimate via equation (11).…”

Section: Figure 5 Kalman Filter Cyclementioning

confidence: 99%

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An Efficient Implementation of Tracking Using Kalman Filter for Underwater Robot Application

Modalavalasa¹

2012

IJCSEIT

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The exploration of oceans and sea beds is being made increasingly possible through the development of Autonomous Underwater Vehicles (AUVs). This is an activity that concerns the marine community and it must confront the existence of notable challenges. However, an automatic detecting and tracking system is the first and foremost element for an AUV or an aqueous surveillance network. In this paper a method of Kalman filter was presented to solve the problems of objects track in sonar images. Region of object was extracted by threshold segment and morphology process, and the features of invariant moment and area were analysed. Results show that the method presented has the advantages of good robustness, high accuracy and real-time characteristic, and it is efficient in underwater target track based on sonar images and also suited for the purpose of Obstacle avoidance for the AUV to operate in the constrained underwater environment.

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“…Again, the assumption is made that the relationship is linear. So the measurement z k can be expressed in terms of the state x k [10] with…”

Section: Kalman Filtermentioning

confidence: 99%

“…Step 3: Compute the Kalman gain: Step 4: Update estimation with measurements: (10) Step 5: Update the error covariance:…”

Section: Figure 5 Kalman Filter Cyclementioning

confidence: 99%

Section: Figure 5 Kalman Filter Cyclementioning

confidence: 99%

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An Efficient Implementation of Tracking Using Kalman Filter for Underwater Robot Application

Modalavalasa¹

2012

IJCSEIT

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“…A wellknown approach is traditional Kalman filtering, which is based on the assumption that the external disturbances are Gaussian white noises [9][10][11][12]. In the past few decades, as one of the most important problems in control theory, much attention has been paid to the filtering problem.…”

mentioning

confidence: 99%

“…In the past few decades, as one of the most important problems in control theory, much attention has been paid to the filtering problem. A wellknown approach is traditional Kalman filtering, which is based on the assumption that the external disturbances are Gaussian white noises [9][10][11][12]. However, when the external disturbances are not precisely known, Kalman filtering is no longer valid.…”

mentioning

confidence: 99%

Robust L₂−L_∞ Filtering for Stochastic Systems with Discrete and Distributed Time‐Varying Delays

Jia

2011

Asian Journal of Control

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This paper is devoted to the problem of robust L 2 .L ∞ filtering for a class of stochastic systems with both discrete and distributed time-varying delays. The objective is to design a full-order filter such that the resulting filtering error system is stochastically asymptotically stable with a prescribed L 2 .L ∞ performance satisfied. Delay-dependent sufficient condition for the existence of the filter is obtained in terms of linear matrix inequalities (LMIs). And the filter design method is proposed, while the explicit expression for the desired filter is also given. Numerical examples are included to illustrate the benefit and the effectiveness of the proposed method.

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Krein Space and Krein Space Based Optimization

Zhong¹,

Xue²,

Ding³

et al. 2022

Fault Diagnosis for Linear Discrete Time-Varying Systems and Its Applications

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Robust Kalman filtering via Krein space estimation

Cited by 32 publications

References 13 publications

An Efficient Implementation of Tracking Using Kalman Filter for Underwater Robot Application

An Efficient Implementation of Tracking Using Kalman Filter for Underwater Robot Application

Robust L₂−L_∞ Filtering for Stochastic Systems with Discrete and Distributed Time‐Varying Delays

Krein Space and Krein Space Based Optimization

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Robust Kalman filtering via Krein space estimation

Cited by 32 publications

References 13 publications

An Efficient Implementation of Tracking Using Kalman Filter for Underwater Robot Application

An Efficient Implementation of Tracking Using Kalman Filter for Underwater Robot Application

Robust L2−L∞ Filtering for Stochastic Systems with Discrete and Distributed Time‐Varying Delays

Krein Space and Krein Space Based Optimization

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Product

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Robust L₂−L_∞ Filtering for Stochastic Systems with Discrete and Distributed Time‐Varying Delays