Crossroads of Machine Learning and Signal Processing
Abstract
Traditional signal processing techniques often rely on models that accurately depict the system under consideration. However, the complexity and dynamic nature of modern systems limit the effectiveness of these model-based approaches. In this context, machine learning techniques offer data-driven alternatives that do not require explicit prior model information. The proposed pre-ICASSP workshop aims to explore various aspects of learning-based signal processing, such as high-dimensional statistics, quickest detection, sampling theory, online learning, tensor signal processing, stochastic filtering, and multi-agent signal processing and learning.The United States Navy operates in increasingly complex and dynamic environments where rapid decision-making and adaptability are paramount. Conventional model-based signal processing techniques often struggle to keep pace with the evolving challenges posed by modern warfare, such as electronic countermeasures, cyber threats, and the need for real-time intelligence across multiple domains. Machine learning (ML) offers transformative potential for ONRG programs by providing data-driven approaches that can learn and adapt without requiring explicit system models. Learning-based signal processing can enhance capabilities in areas critical to ONRG, including: Surveillance and reconnaissance (improved target detection and classification in cluttered or contested environments), electronic warfare (adaptive jamming and signal interception techniques that respond in real-time to adversarial actions), communications (robust and secure communication links that can adjust to spectrum congestion and interference), autonomous systems (enhanced sensor processing for autonomous platforms requiring real-time situational awareness), and cyber operations (detection of anomalous signals and patterns indicative of cyber threats or network intrusions). By exploring learning-based signal processing, the proposed pre-ICASSP workshop directly supports US Navy objectives to maintain technological superiority.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 10, 2025
- Source ID
- N629092512031
Entities
People
- Shobha Ram
Organizations
- Office of Naval Research
- United States Navy