Kalman Filtering with Uncertain Process and Measurement Noise Covariances with Application to State Estimation in Sensor Networks

Shi, Ling; Johansson, Karl Henrik; Murray, Richard M.

doi:10.1109/isic.2007.4359680

Cited by 6 publications

(7 citation statements)

References 6 publications

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“…Instead, a combination of different sensor types can be fused, i.e., Kalman filter, which provides a likelihood estimation of the measured OiW concentrations based on the different sensors [ 61 ]. As traditional Kalman filter requires the sensor measurement uncertainties, knowing the uncertainty of the OiW monitors will benefit the Kalman filter prediction [ 62 ]. This is usually executed by predicting the true value by producing estimates of the current state variables, along with their uncertainties [ 63 ].…”

Section: Discussionmentioning

confidence: 99%

Uncertainty Analysis of Fluorescence-Based Oil-In-Water Monitors for Oil and Gas Produced Water

Hansen

Jespersen

Bram

et al. 2020

Sensors

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Offshore oil and gas facilities are currently measuring the oil-in-water (OiW) concentration in the produced water manually before discharging it into the ocean, which in most cases fulfills the government regulations. However, as stricter regulations and environmental concerns are increasing over time, the importance of measuring OiW in real-time intensifies. The significant amount of uncertainties associated with manual samplings, that is currently not taken into consideration, could potentially affect the acceptance of OiW monitors and lower the reputation of all online OiW measurement techniques. This work presents the performance of four fluorescence-based monitors on an in-house testing facility. Previous studies of a fluorescence-based monitor have raised concerns about the measurement of OiW concentration being flow-dependent. The proposed results show that the measurements from the fluorescence-based monitors are not or insignificantly flow-dependent. However, other parameters, such as gas bubbles and droplet sizes, do affect the measurement. Testing the monitors’ calibration method revealed that the weighted least square is preferred to achieve high reproducibility. Due to the high sensitivity to different compositions of atomic structures, other than aromatic hydrocarbons, the fluorescence-based monitor might not be feasible for measuring OiW concentrations in dynamic separation facilities with consistent changes. Nevertheless, they are still of interest for measuring the separation efficiency of a deoiling hydrocyclone to enhance its deoiling performance, as the separation efficiency is not dependent on OiW trueness but rather the OiW concentration before and after the hydrocyclone.

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Section: Discussionmentioning

confidence: 99%

Uncertainty Analysis of Fluorescence-Based Oil-In-Water Monitors for Oil and Gas Produced Water

Hansen

Jespersen

Bram

et al. 2020

Sensors

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“…The development of the noise CM estimation methods has been vastly motivated by the need of applications of the optimum signal processing and decision‐making methods in diverse technical and nontechnical areas. In the following areas, the noise CM estimation methods have attracted significant attention of researchers and engineers: navigation, positioning, and tracking; econometry; speech and image processing; robotics and sensor networks; hydrology, meteorology, and weather forecasting; and chemistry, material science, and industrial processes …”

Section: Discussion and Comparison Of Noise CM Estimation Methodsmentioning

confidence: 99%

“…In the literature, an extensive number of various noise CM estimation methods can be found, which were often a component of adaptive control and signal processing systems . The methods differ in assumptions related to the considered model, underlying ideas and principles, properties of the estimates, and number and essence of the design parameters.…”

Section: Introductionmentioning

confidence: 99%

Noise covariance matrices in state‐space models: A survey and comparison of estimation methods—Part I

Duník

Straka

Kost

et al. 2017

Adaptive Control & Signal

118

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This paper deals with the estimation of the noise covariance matrices of systems described by state-space models. Stress is laid on the systematic survey and classification of both the recursive and batch processing methods proposed in the literature with a special focus on the correlation methods. Besides the correlation methods, representatives of other groups are introduced also with respect to their basic idea, estimate properties, assumptions and possible extensions, and user-defined parameters. Common and dual properties of the methods are highlighted, and a simulation comparison using exemplary MATLAB implementations of the methods is provided. KEYWORDSadaptive systems, noise covariance matrix, state estimation, state-space model, system identification INTRODUCTIONKnowledge of a system model is a key prerequisite for many state estimation, signal processing, fault detection, and optimal control problems. The model is often designed to be consistent with random behaviour of the system quantities and properties of the measurements. While the deterministic part of the model often arises from mathematical modelling on the basis of physical, chemical, or biological laws governing the behaviour of the system, the statistics of the stochastic part are often difficult to find by the modelling and have to be identified using the measured data. Incorrect description of the noise statistics may result in significant worsening of estimation, signal processing, detection, or control quality or even in a failure of the underlying algorithms.In the last 5 decades, therefore, a significant research interest has been focused on a design of the methods for the estimation of the properties of the stochastic part of the model. The attention has been devoted to both the input-output models 1-5 and the state-space (SS) models 6-14 and both recursive and batch processing methods. This paper focuses on the methods estimating the properties of the stochastic part of the system described by an SS model discrete in time. In particular, the methods estimating the covariance matrices (CMs) † of noises in the state and measurement equation from a sequence of measured data are of interest. The methods are further denoted as the noise CM estimation methods.

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“…Shi et al [17] derived a new performance-bound for a sensor fusion scheme that explicitly takes the model uncertainty of the underlying processes and sensors into account. Based on the classical Kalman filter, the estimation error covariance is computed for given uncertainties of the process and measurement noise covariance.…”

Section: Kalman Filter and Related Workmentioning

confidence: 99%

Error and Noise Analysis in an IMU using Kalman Filter

Quadri¹,

Sidek²

2014

IJHIT

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Kalman filtering is a well-established methodology used in various multi-sensor data fusion applications. In our experiment, we first obtain measurements from the accelerometer and gyroscope and fuse them using Kalman filter in an inertial measurement unit (IMU). We estimate Kalman filter output and estimation error. The affect of process noise and measurement noise on estimation error is tested. It is explored that the measurement noise has significant role to increase estimation error in the data fusion process.

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Kalman Filtering with Uncertain Process and Measurement Noise Covariances with Application to State Estimation in Sensor Networks

Cited by 6 publications

References 6 publications

Uncertainty Analysis of Fluorescence-Based Oil-In-Water Monitors for Oil and Gas Produced Water

Uncertainty Analysis of Fluorescence-Based Oil-In-Water Monitors for Oil and Gas Produced Water

Noise covariance matrices in state‐space models: A survey and comparison of estimation methods—Part I

Error and Noise Analysis in an IMU using Kalman Filter

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