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.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2606. 01325v1 Announce Type: cross Abstract: Unmanned Aerial Vehicles (UAVs) equipped with radar sensors are deployed for target search missions in diverse environments, where targets exhibit characteristic signatures (e.
arXiv:2607. 13891v1 Announce Type: new Abstract: Multi-object detection and tracking from noisy point clouds remain challenging in many data-scarce radar applications.
Multi-object detection and tracking from noisy point clouds remain challenging in many data-scarce radar applications. Current Bayesian trackers based on Poisson measurement models offer a training-free solution but struggle to achieve accuracy and efficiency under severe clutter, large object populations, and full-resolution Doppler point clouds.
arXiv:2607. 13573v1 Announce Type: cross Abstract: Maneuvering target tracking in three-dimensional space remains a challenging problem due to complex motion dynamics and model mismatch.
arXiv:2607. 00056v1 Announce Type: cross Abstract: This paper studies energy efficient tracking of power-limited mobile users with the assistance of a Reconfigurable Intelligent Surface (RIS).
arXiv:2605. 22775v2 Announce Type: replace-cross Abstract: Real-time cognitive load assessment from eye-tracking signals could enable adaptive human-centered AI in safety-critical applications such as driver vigilance monitoring or automated flight deck assistance, yet two challenges persist: handling frequent data missingness from blinks and tracking failures, and efficiently modeling long-range temporal dependencies.