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Published on: February 12, 2014
Sidelobe reduction via adaptive FIR filtering in SAR imagery
1Environ. Res. Inst. of Michigan, Ann Arbor, MI.
Summary
This study introduces adaptive sidelobe reduction (ASR) to significantly decrease noise and interference in Fourier transform data. ASR offers a computationally efficient method for improving signal quality, especially in synthetic aperture radar (SAR) imaging.
Area of Science:
- Signal Processing
- Data Analysis
- Image Formation
Background:
- Fourier transform data often suffers from sidelobes, interference, and noise.
- Existing methods like Minimum Variance Method (MVM) can be computationally intensive.
Purpose of the Study:
- To introduce a novel adaptive weighting function class for Fourier transform data.
- To develop a computationally efficient method for sidelobe and noise reduction.
Main Methods:
- Developed an adaptive sidelobe reduction (ASR) procedure.
- Represented adaptively weighted Fourier transform data as a convolution with a data adaptive FIR filter.
- Selected FIR filter coefficients to maximize signal-to-interference ratio.
Main Results:
- ASR significantly reduces sidelobes, interference, and noise in Fourier transform data.
- ASR provides a single-realization complex-valued estimate, unlike MVM's statistical estimate.
- ASR demonstrates dramatically lower computational complexity compared to MVM.
Conclusions:
- ASR is a powerful technique for enhancing signal quality in Fourier transform data.
- The method is particularly advantageous for large, multidimensional problems like SAR image formation.
- ASR's performance can be tuned by adjusting filter order and constraints.