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Generation and Coherent Control of Pulsed Quantum Frequency Combs

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Quantized State Estimation for Linear Dynamical Systems.

Ramchander Rao Bhaskara¹, Manoranjan Majji¹, Felipe Guzmán²

¹Department of Aerospace Engineering, Texas A&M University, College Station, TX 77843, USA.

Sensors (Basel, Switzerland)

|October 16, 2024

Summary

This summary is machine-generated.

This study enhances state estimation for embedded systems by accounting for finite-precision errors. Optimized algorithms improve performance and accuracy in resource-constrained applications.

Keywords:

FPGA Kalman filter finite-precision optical sensors quantized filtering state estimation

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Area of Science:

Control Systems Engineering
Embedded Systems
Numerical Analysis

Background:

State estimation is crucial for dynamical systems but challenging on resource-constrained embedded systems.
Finite-precision arithmetic in embedded systems introduces numerical errors impacting estimation accuracy.

Purpose of the Study:

To reformulate minimum mean square estimation algorithms to include finite-precision numerical errors.
To develop and evaluate quantized estimation algorithms for fixed-point implementations.
To analyze performance trade-offs between numerical precision and filter accuracy.

Main Methods:

Quantized versions of least squares batch estimation, sequential Kalman, and square-root filtering algorithms were proposed.
Numerical simulations were conducted to compare performance against standard formulations.
Steady-state covariance analysis was employed to assess performance trade-offs with numerical precision.
A fixed-point acceleration state estimation architecture was implemented on FPGA-SoC hardware.

Main Results:

Proposed quantized algorithms demonstrated performance improvements over standard filters.
Steady-state covariance analysis provided insights into achievable filter accuracy based on numerical precision.
Hardware implementation on FPGA-SoC showed comparable performance to double-precision MATLAB implementations.
Experimental results validated the significance of modeling quantization errors.

Conclusions:

Modeling quantization errors is essential for accurate state estimation in fixed-point embedded systems.
The developed fixed-point acceleration architecture offers a viable solution for optomechanical sensing.
The study provides a framework for optimizing state estimation in resource-constrained environments.