Superresolution Algorithm Applied to Optimal Linear Filter Design

2016 ◽  
Author(s):  
Markus E. Testorf ◽  
Michael A. Fiddy
Keyword(s):  
Filter Design ◽  
Linear Filter ◽  
Optimal Linear

scholarly journals Discrete-Time Kalman Filter Design for Linear Infinite-Dimensional Systems

Processes ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 451
Author(s):  
Junyao Xie ◽  
Stevan Dubljevic
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Discrete Time ◽  
Filter Design ◽  
Order Reduction ◽  
Time Discretization ◽  
Linear Filter ◽  
Infinite Dimensional ◽  
Optimal Linear ◽  
Spatial Approximation

As the optimal linear filter and estimator, the Kalman filter has been extensively utilized for state estimation and prediction in the realm of lumped parameter systems. However, the dynamics of complex industrial systems often vary in both spatial and temporal domains, which take the forms of partial differential equations (PDEs) and/or delay equations. State estimation for these systems is quite challenging due to the mathematical complexity. This work addresses discrete-time Kalman filter design and realization for linear distributed parameter systems. In particular, the structural- and energy-preserving Crank–Nicolson framework is applied for model time discretization without spatial approximation or model order reduction. In order to ensure the time instance consistency in Kalman filter design, a new discrete model configuration is derived. To verify the feasibility of the proposed design, two widely-used PDEs models are considered, i.e., a pipeline hydraulic model and a 1D boundary damped wave equation.

A NEW DATA‐PROCESSING TECHNIQUE FOR THE ELIMINATION OF GHOST ARRIVALS ON REFLECTION SEISMOGRAMS

Geophysics ◽  
1964 ◽  
Vol 29 (5) ◽  
pp. 783-805 ◽  
Author(s):  
William A. Schneider ◽  
Kenneth L. Larner ◽  
J. P. Burg ◽  
Milo M. Backus
Keyword(s):  
Data Processing ◽  
Filter Design ◽  
Random Noise ◽  
Wide Band ◽  
Processing Technique ◽  
Linear Filter ◽  
Least Mean Square ◽  
Near Surface ◽  
Surface Reflection ◽  
Data Processing Technique

A new data‐processing technique is presented for the separation of initially up‐traveling (ghost) energy from initially down‐traveling (primary) energy on reflection seismograms. The method combines records from two or more shot depths after prefiltering each record with a different filter. The filters are designed on a least‐mean‐square‐error criterion to extract primary reflections in the presence of ghost reflections and random noise. Filter design is dependent only on the difference in uphole time between shots, and is independent of the details of near‐surface layering. The method achieves wide‐band separation of primary and ghost energy, which results in 10–15 db greater attenuation of ghost reflections than can be achieved with conventional two‐ or three‐shot stacking (no prefiltering) for ghost elimination. The technique is illustrated in terms of both synthetic and field examples. The deghosted field data are used to study the near‐surface reflection response by computing the optimum linear filter to transform the deghosted trace back into the original ghosted trace. The impulse response of this filter embodies the effects of the near‐surface on the reflection seismogram, i.e. the cause of the ghosting. Analysis of these filters reveals that the ghosting mechanism in the field test area consists of both a surface‐ and base‐of‐weathering layer reflector.

scholarly journals Network-Based RobustH∞Filtering for the Uncertain Systems with Sensor Failures and Noise Disturbance

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Mingang Hua ◽  
Pei Cheng ◽  
Juntao Fei ◽  
Jianyong Zhang ◽  
Junfeng Chen
Keyword(s):  
Filter Design ◽  
Design Method ◽  
Sufficient Conditions ◽  
Uncertain System ◽  
Linear Matrix ◽  
Linear Filter ◽  
Parameter Uncertainties ◽  
Globally Asymptotically Stable ◽  
Sensor Failures ◽  
Filter Design Method

The network-based robustH∞filtering for the uncertain system with sensor failures and the noise is considered in this paper. The uncertain system under consideration is also subject to parameter uncertainties and delay varying in an interval. Sufficient conditions are derived for a linear filter such that the filtering error systems are robust globally asymptotically stable while the disturbance rejection attenuation is constrained to a given level by means of theH∞performance index. These conditions are characterized in terms of the feasibility of a set of linear matrix inequalities (LMIs), and then the explicit expression is then given for the desired filter parameters. Two numerical examples are exploited to show the usefulness and effectiveness of the proposed filter design method.

RF tomography based optimal linear filter

2017 ◽  
Author(s):  
Muftah Akroush ◽  
Michael C. Wicks ◽  
Hamdi Abdelbagi ◽  
Turki Alanazi ◽  
Abdunaser Abdusamad ◽  
...  
Keyword(s):  
Linear Filter ◽  
Optimal Linear
Sign in / Sign up

Export Citation Format

Share Document