Non-Line-of-Sight Detection for Radio Localization using Deep State Space Models

Brasseler L, Stahlke M, Altstidl TR, Feigl T, Mutschler C (2024)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2024

Pages Range: 1-6

Conference Proceedings Title: The fourteenth International Conference on Indoor Positioning and Indoor Navigation 2024 (IPIN 2024)

Event location: Hong Kong

Abstract

Localization based on channel impulse responses

(CIRs) of radio frequency (RF) signals yields centimeter-accurate

positions under optimal line-of-sight (LOS) propagation condi-

tions. However, in real indoor environments, e.g., in car manufac-

turing, non-line-of-sight (NLOS) situations dominate. Here, mul-

tipath propagation affects the time-of-arrival (ToA) estimation

and downstream multilateration and localization accuracy. The

detection and subsequent mitigation of NLOS per transceiver line

compensates for these effects. To detect NLOS, the state-of-the-

art employs either supervised or unsupervised learning methods

that require the acquisition of expensive reference data or do not

generalize to changes or unknown environments. This is due to,

among other things, the fact that they cannot exploit spatial and

temporal information from CIR signal streams.

Thus, we propose a generative deep state space model (SSM)

for NLOS detection on CIRs that exploits time and space. Our

ultra-wideband (UWB) experiments show that our dynamical

variational autoencoder (DVAE) detects NLOS signals from

sequences of CIRs more accurately than the state-of-the-art and

is robust to unknown environments.

Authors with CRIS profile

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) Germany (DE)

How to cite

APA:

Brasseler, L., Stahlke, M., Altstidl, T.R., Feigl, T., & Mutschler, C. (2024). Non-Line-of-Sight Detection for Radio Localization using Deep State Space Models. In The fourteenth International Conference on Indoor Positioning and Indoor Navigation 2024 (IPIN 2024) (pp. 1-6). Hong Kong.

MLA:

Brasseler, Leon, et al. "Non-Line-of-Sight Detection for Radio Localization using Deep State Space Models." Proceedings of the 14th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Hong Kong 2024. 1-6.

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