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LMS and matched digital filters for optical clutter suppression.

M S Longmire, E H Takken

|June 10, 2010

Summary

New methods for computing impulse-response weights effectively suppress electrooptic sensor clutter using matched and least-mean-squares (LMS) filters. These techniques enhance signal detection against variable backgrounds for various sensor types.

Area of Science:

Signal Processing
Electrooptic Systems
Image Analysis

Background:

Clutter in electrooptic sensor outputs degrades signal detection.
Effective clutter suppression is crucial for accurate analysis of sensor data.
Existing methods may have limitations in handling variable backgrounds and signal types.

Purpose of the Study:

To develop and illustrate methods for computing impulse-response weights for clutter suppression filters.
To provide techniques applicable to both 1- and 2-D matched filters and least-mean-squares (LMS) filters.
To address clutter suppression for various sensor types and signal characteristics.

Main Methods:

Developed methods for computing impulse-response weights for 1- and 2-D matched filters.

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Developed methods for computing impulse-response weights for LMS filters.

Illustrated techniques with examples for point and extended sources against variable backgrounds.

Main Results:

The proposed methods are applicable to scanning or staring sensors with known signal shapes and orientations.
Matched-filter design relies on known isotropic power-spectral clutter models.
LMS filter design utilizes deterministic polynomial clutter models and performs explicit signal amplitude estimation.

Conclusions:

The developed methods offer effective strategies for clutter suppression in electrooptic sensor outputs.
LMS filter design does not require prior knowledge of clutter parameters, simplifying its application.
The techniques enhance the ability to detect signals against complex and variable backgrounds.