04.11.2025

Last week, we presented our paper "Posterior Cramér–Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM" [1] at the #Asilomar Conference. The paper introduces the mapping error bound (MEB)—a lower bound that quantifies how accurately specular surfaces can be estimated in radio-frequency (RF) simultaneous localization and mapping (SLAM). The bound covers single- and double-bounce multipath components and distributed anchors, and we show a state-of-the-art RF-SLAM algorithm [2] asymptotically approaches this bound.

In positioning, map information improves user localization. In communications, map information provides a geometric representation of the multipath channel, enabling the prediction and tracking of channel state information. The bounds presented in our paper enable benchmarking of the performance of SLAM algorithms that jointly estimate the user position and the environment map.

1. arxiv.org/pdf/2506.19957
2. https://arxiv.org/pdf/2506.21798

Contact: LinkedIn

13.10.2025

From wireless communication networks to medical imaging and radar systems, many modern technologies depend on the ability to detect just a few meaningful signals buried in massive amounts of data. This task — known as sparse signal recovery — is essential for making sense of complex measurements, but existing algorithms can be painfully slow, especially when the signals come in naturally grouped patterns, known as block sparsity.

In our recent work [1], we developed a new algorithm called Fast Variational Block-Sparse Bayesian Learning (F-BSBL) that makes this process dramatically faster and more efficient. By rethinking how uncertainty and structure are handled in sparse models, we achieved up to a hundredfold speedup over existing approaches — without sacrificing accuracy. Our method also unifies several previously separate learning strategies into a single, elegant mathematical framework. Tested on challenging problems such as direction-of-arrival estimation, F-BSBL delivers both speed and precision, paving the way for real-time signal analysis in next-generation communication, sensing, and data-driven technologies.

1. J. Möderl, E. Leitinger, B. H. Fleury, F. Pernkopf and K. Witrisal, "Fast Variational Block-Sparse Bayesian Learning," in IEEE Transactions on Signal Processing, doi: 10.1109/TSP.2025.3611234

Contact:
Jakob Möderl