Key research themes
1. How can cognitive radar systems optimally adapt waveform and sensing strategies to dynamic spectral environments?
This theme investigates algorithms and system architectures enabling cognitive radars to sense their environment, learn from it, and adapt transmission and reception parameters in real-time to optimize detection, tracking, and spectral coexistence. It is critical given the increasing spectral congestion and the need for multifunctional radar systems to operate in spectrally dense environments while maintaining performance.
2. What roles do machine learning and metacognition play in enhancing cognitive radar system performance and adaptability?
This theme explores advanced cognitive engine designs embedding machine learning and metacognitive frameworks to enable faster adaptation, performance predictability, and dynamic algorithm selection in cognitive radars. These approaches address limitations of static or single-method cognitive engines by providing flexible and self-aware systems capable of optimizing operational parameters based on environmental understanding and historical experience.
3. How can optimized waveform design improve cognitive MIMO radar performance in target detection and interference mitigation?
This theme focuses on design and optimization of orthogonal polyphase waveforms and other coding techniques tailored for cognitive MIMO radar systems to enhance resolution, reduce sidelobes, and mitigate interference. Employing evolutionary and nature-inspired algorithms supports waveform adaptability consistent with cognitive radar principles, leading to improved multi-target detection capabilities.