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Mutagenesis and Analysis of Genetic Mutations in the GC-rich KISS1 Receptor Sequence Identified in Humans with Reproductive Disorders

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A new interpretation of ROMKF.

J Kim¹, J W Woods

¹Dept. of Syst. Eng., Rensselaer Polytech. Inst., Troy, NY.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1997

Summary

This summary is machine-generated.

The reduced order model Kalman filter (ROMKF) is a special case of the approximate reduced updated Kalman filter (RUKF). This finding offers a method to estimate a crucial error variance for ROMKF in image processing applications.

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Area of Science:

Engineering
Computer Science
Signal Processing

Background:

The approximate reduced updated Kalman filter (RUKF) and reduced order model Kalman filter (ROMKF) are established techniques in image processing.
Both filters are utilized for state estimation in dynamic systems.

Purpose of the Study:

To demonstrate that the ROMKF is a specific instance of the approximate RUKF.
To provide a method for estimating the error variance parameter required by the ROMKF.

Main Methods:

Mathematical derivation showing the relationship between RUKF and ROMKF.
Analysis of filter structures and their underlying assumptions.

Main Results:

The ROMKF can be mathematically represented as a special case of the approximate RUKF.
A procedure is presented for estimating the necessary error variance input for the ROMKF.

Conclusions:

This work unifies the understanding of RUKF and ROMKF.
The proposed method enhances the practical application of ROMKF by providing a way to determine a critical parameter.