arXiv:2507.17506v4 Announce Type: replace-cross Abstract: This work presents a cognitive radar (CR) framework designed to track multiple aircraft under unknown disturbances using massive multiple-input multiple-output (MMIMO) systems. Since uniform power allocation is suboptimal across varying signal-to-noise ratios (SNRs), we couple an adaptive waveform design driven by Partially Observable Monte Carlo Planning (POMCP). By assigning an independent POMCP tree to each target, the system efficiently predicts target states. These predictions inform a constrained optimization problem that actively
The increasing sophistication of cognitive radar systems and advancements in AI-driven tracking necessitate more robust and adaptive solutions for defence applications.
This development enhances the autonomy and effectiveness of multi-target tracking in complex, disturbed environments, critical for modern defence and aerospace operations.
Radar systems can now dynamically allocate power and adapt waveforms based on AI predictions, improving tracking accuracy for multiple targets under challenging conditions.
- · Defence contractors
- · Air forces
- · Aerospace industry
- · AI/ML defence solution providers
- · Legacy radar systems
- · Adversarial aircraft relying on traditional evasion
Improved detection and tracking capabilities for military and civilian air traffic control.
Increased demand for advanced AI processors and specialized hardware for cognitive radar systems.
Potential for integration into broader autonomous defence systems, leading to more automated aerial defence strategies.
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Read at arXiv cs.LG