The paper presents a method for training neural networks on synthetic data to approximate the optimal Bayes estimator for dense emitter localization. By demonstrating that the networks can closely match this theoretical optimum, the authors provide evidence that such training approaches can be effective for high‑throughput, large‑field‑of‑view super‑spatiotemporal resolution single‑molecule localization microscopy (SMLM).
arXiv:2606. 01446v1 Announce Type: cross Abstract: Radio frequency spectrum awareness requires the ability to detect, localize, and characterize emitters in dense and contested wireless environments.
By H. Nazim Bicer, J. Nick Laneman
arXiv:2605. 12569v2 Announce Type: replace-cross Abstract: Global navigation satellite system (GNSS) interference poses a serious threat to reliable positioning, especially in indoor and multipath-rich environments where source localization is highly challenging.
By M. Shamail J. Khan, Nisha L. Raichur, Lucas Heublein, Christian Wielenberg, Alexander Mattick, Tobias Feigl, Christopher Mutschler, Felix Ott
arXiv:2609.26214v1 Announce Type: new
Abstract: We present a methodology that places a 3D digital twin (DT) of the environment as the main enabler behind the development of radio sensing at scale. Th...
By \'Eloi Sainte-Beuve (Orange Research), Guillaume Larue (Orange Research), Louis-Adrien Dufr\`ene (Orange Research), Quentin Lampin (Orange Research), Ali Al Khansa (Orange Research)
arXiv:2605. 13092v2 Announce Type: replace-cross Abstract: Density estimation in high-dimensional settings is an important and challenging statistical problem.
By Ruitong Zhang, Ke Deng
arXiv:2608. 09285v1 Announce Type: cross Abstract: Learning-based wireless localizers often fail to utilize geometric information about the propagation environment, limiting their ability to exploit non-line-of-sight (NLoS) propagation and generalize across scenes.
By Chenghong Bian, Chaozheng Wen, Hongze Chen, Jun Zhang