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Supplemental data for the paper "low-complexity detection of small frequency deviations by the generalized LMPU test"
Eyal Levy1, Tirza Routtenberg1
1School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.
Data in Brief
|February 3, 2021
Summary
This study details the proof for the locally most powerful unbiased (LMPU) test, a method for signal detection with nuisance parameters. It also provides MATLAB code for detecting frequency deviations in sinusoidal signals.
Area of Science:
- Signal Processing
- Statistical Inference
Background:
- Nuisance parameters are common in statistical detection problems.
- Locally most powerful unbiased (LMPU) tests offer a robust method for signal detection.
Purpose of the Study:
- To present the mathematical proof for Theorem 1 concerning the LMPU test.
- To provide practical MATLAB implementations of LMPU and generalized LMPU tests.
- To address the specific application of detecting small frequency deviations in sinusoidal signals.
Main Methods:
- Derivation of the LMPU test for local detection with known nuisance parameters.
- Development of MATLAB code for implementing LMPU and generalized LMPU tests.
- Application to the detection of frequency shifts in sinusoidal signal processing.
Main Results:
- The proof of Theorem 1, defining the LMPU test, is provided.
- MATLAB code for LMPU and generalized LMPU is available for signal processing applications.
- The code facilitates the detection of small frequency deviations in sinusoidal signals.
Conclusions:
- The LMPU test provides a theoretical framework for signal detection in the presence of nuisance parameters.
- The provided code enables practical application of these detection methods, particularly for frequency deviation analysis in sinusoidal signals.
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