Target Recognition for Ultra-Wideband Radio Fuzes Using 1D-CGAN-Augmented 1D-CNN.

Area of Science:

• Electrical Engineering
• Signal Processing
• Artificial Intelligence

Background:

• Ultra-wideband (UWB) radio fuzes require precise signal processing for reliable detonation in battlefield conditions.
• Electronic warfare environments introduce interference, leading to false alarms and missed detections in UWB fuzes.
• Current detection methods are vulnerable to jamming, compromising fuze reliability.

Purpose of the Study:

• To develop a novel signal processing architecture for UWB radio fuzes to enhance reliability and accuracy in contested electromagnetic environments.
• To address the vulnerability of energy-threshold detection methods to deliberate jamming.
• To establish a new technical framework for UWB fuze operation in complex spectra.

Main Methods:

• Integration of fixed-parameter Least Mean Squares (LMS) front-end filtering for interference suppression.
• Implementation of a One-Dimensional Convolutional Neural Network (1D-CNN) for target recognition.
• Augmentation of training datasets using a One-Dimensional Conditional Generative Adversarial Network (1D-CGAN).

Main Results:

• The novel system achieved 0% false alarm and miss detection rates on test samples.
• A segment recognition accuracy of 97.66% was attained, a 5.32% improvement over the baseline.
• The system demonstrated resilience against deliberate jamming, overcoming limitations of traditional methods.

Conclusions:

• The developed signal processing architecture significantly enhances UWB fuze performance in electronic warfare.
• This breakthrough offers a robust solution for reliable UWB fuze operation in jammed and contested spectra.
• The study establishes a new benchmark for UWB fuze signal processing and target recognition.