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Signal detection using the radial basis function coupled map lattice.

H Leung¹, G Hennessey, A Drosopoulos

¹Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada.

IEEE Transactions on Neural Networks

|February 6, 2008

Summary

This summary is machine-generated.

This study introduces a novel spatial-temporal predictor, the radial basis function coupled map lattice (RBF-CML), to improve marine radar detection of small targets in chaotic sea clutter. The RBF-CML method significantly outperforms conventional constant false alarm rate (CFAR) detectors.

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Area of Science:

Marine radar technology
Signal processing
Chaos theory

Background:

Conventional marine radar struggles with detecting small targets in complex sea clutter.
Sea clutter exhibits chaotic dynamics, deviating from traditional random models.
Electromagnetic wave scattering is fundamentally a spatio-temporal process.

Purpose of the Study:

To develop an advanced method for reconstructing chaotic sea clutter dynamics.
To enhance the detection of small targets in challenging marine environments.
To evaluate the efficacy of a novel spatial-temporal predictor against existing techniques.

Main Methods:

Utilized a radial basis function coupled map lattice (RBF-CML) as a spatial-temporal predictor.
Employed a linear combiner to fuse spatial domain measurements and RBF predictions.
Applied the RBF-CML predictor within a constant false alarm rate (CFAR) framework for target detection.
Validated the approach using real-life marine radar data.

Main Results:

The RBF-CML effectively reconstructs the chaotic dynamics of sea clutter.
The RBF-CML predictor demonstrated superior performance in small target detection compared to conventional methods.
Theoretical and experimental analyses confirmed the advantages of the spatial-temporal approach.

Conclusions:

The RBF-CML predictor offers a significant advancement for marine radar systems.
This method provides enhanced capability for detecting small targets in cluttered sea conditions.
The spatial-temporal modeling of sea clutter is crucial for improving radar detection performance.