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Computer tracking of moving point targets in space.

¹Aeronutronic Division, Ford Aerospace and Communications Corporation, Newport Beach, CA 92663; The Aerospace Corporation, P.O. Box 92957, Los Angeles, CA 90009.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This study introduces an adaptive algorithm for detecting and tracking low-intensity targets using a charged-coupled device (CCD) mosaic sensor. The method effectively identifies target positions and paths from noisy data.

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Area of Science:

Signal processing
Image analysis
Astrophysical instrumentation

Background:

Detecting faint celestial objects is crucial for astronomical surveys.
Charged-coupled device (CCD) mosaic sensors are widely used for light detection.
Existing algorithms may struggle with low signal-to-noise ratios.

Purpose of the Study:

To develop and present an adaptive algorithm for detecting and tracking low-intensity targets.
To utilize maximum likelihood ratio for improved target detection accuracy.
To process data from CCD mosaic sensors for astronomical observations.

Main Methods:

An adaptive algorithm based on the maximum likelihood ratio was developed.
Noise statistics were computed, and target patterns were simulated.

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Received data were correlated with simulated target patterns.

A threshold comparison was performed to identify targets.

Main Results:

The algorithm successfully detects low-intensity targets.
The paths of detected targets are accurately tracked.
The positions and paths of targets are reliably determined from sensor data.

Conclusions:

The proposed adaptive algorithm is effective for detecting and tracking faint targets in noisy CCD data.
This method enhances the capabilities of astronomical surveys relying on CCD mosaic sensors.
The algorithm provides a robust solution for identifying celestial object trajectories.