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State Space Representation

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Upsampling

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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

An Inverse Generalized Conversion Filter for State Estimation in Nonlinear Adversarial Sensing Systems.

Yi-An Xi¹, Xin-Hao Dong¹, Sun-Yong Wu^1,2,3

¹Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China.

Sensors (Basel, Switzerland)

|May 27, 2026

Summary

This summary is machine-generated.

This study introduces an inverse generalized-conversion-based filter (I-GCF) to improve opponent perception estimation in intelligent sensing systems. The I-GCF enhances the exploitation of nonlinear sensor information, boosting accuracy and stability in adversarial games.

Keywords:

counter adversarial games generalized conversion-based transformation inverse filtering nonlinear sensing systems sensor signal processing

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Last Updated: May 28, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Area of Science:

Intelligent Sensing Systems
Adversarial Game Theory
Nonlinear Filtering

Background:

Inverse filtering is crucial for defender decision-making in intelligent sensing systems.
Existing inverse nonlinear filters, like inverse quadrature Kalman filter and inverse extended Kalman filter, struggle to fully utilize higher-order nonlinear information.
Limitations in current methods hinder accurate opponent perception estimation.

Purpose of the Study:

To propose a novel inverse generalized-conversion-based filter (I-GCF) for enhanced nonlinear information extraction.
To improve the estimation accuracy and stability of inverse filtering in nonlinear sensing environments.
To address the limitations of conventional inverse filters in exploiting higher-order nonlinear sensor data.

Main Methods:

Developed an inverse generalized-conversion-based filter (I-GCF).
Employed deterministic sampling for nonlinear information extraction.
Constructed a generalized optimal decorrelating transformation function to capture nonlinear observation information beyond linear minimum mean-square error (LMMSE) estimation.
Derived general expressions for the time complexities of GCF and I-GCF.

Main Results:

The proposed I-GCF effectively extracts higher-order nonlinear sensor information.
I-GCF demonstrates improved estimation accuracy compared to conventional inverse filters.
Enhanced stability in nonlinear sensing environments was observed with I-GCF.
Numerical results validated the superior performance of I-GCF in nonlinear environments.

Conclusions:

The I-GCF significantly enhances the exploitation of nonlinear sensor information.
The proposed filter offers superior estimation accuracy and stability for inverse filtering in nonlinear sensing scenarios.
I-GCF represents a theoretical and practical advancement in adversarial game sensing.