Delay Estimation in Dense Multipath Environments using Time Series Segmentation

Kram, Sebastian; Kraus, Christopher; Stahlke, Maximilian; Feigl, Tobias; Thielecke, Jörn; Mutschler, Christopher

doi:10.1109/wcnc51071.2022.9771875

Cited by 5 publications

(23 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• We demonstrate that our algorithm robustly and accurately fuses the information contained the presented hybrid model. It outperforms state-of-the-art methods for NLOS mitigation [15], [23], [43] and constantly attains the posterior Cramér-Rao lower bound (P-CRLB) [57].…”

Section: B Problem Statement and Contributionsmentioning

confidence: 92%

“…Similar to other "two-step approaches" [16], [19], [47], [48], the proposed algorithm uses signal component measurements consisting of delays and corresponding amplitudes estimated out of the received baseband signal by a snapshot-based parametric channel estimation and detection algorithm (CEDA). Additionally, our hybrid method uses feature measurements extracted out of the received baseband signal by an autoencoder deep neural network (AE-DNN) [43], [54]. Using the measurements provided by the CEDA, our physics-based model allows the algorithm to facilitate the position-related information contained in the LOS component with high accuracy and without the need of training data.…”

Section: B Problem Statement and Contributionsmentioning

confidence: 99%

“…n , and a (j) n , which denote the normalized amplitude, LOS probability and association variable, respectively, and are modeled separately for all anchors. The presented measurement model consists of a physics-based measurement model, which uses the signal component measurements z [47] and i [43], [64] given the state variables.…”

Section: A Random Variables and Assumptionsmentioning

confidence: 99%

See 2 more Smart Citations

Graph-based Simultaneous Localization and Bias Tracking for Robust Positioning in Obstructed LOS Situations

Venus

Leitinger

Tertinek³

et al. 2022

2022 56th Asilomar Conference on Signals, Systems, and Computers

View full text Add to dashboard Cite

This paper presents a neural-enhanced probabilistic model and corresponding factor graph-based sum-product algorithm for robust localization and tracking in multipathprone environments. The introduced hybrid probabilistic model consists of physics-based and data-driven measurement models capturing the information contained in both, the line-of-sight (LOS) component as well as in multipath components (NLOS components). The physics-based and data-driven models are embedded in a joint Bayesian framework allowing to derive from first principles a factor graph-based algorithm that fuses the information of these models. The proposed algorithm uses radio signal measurements from multiple base stations to robustly estimate the mobile agent's position together with all model parameters. It provides high localization accuracy by exploiting the position-related information of the LOS component via the physics-based model and robustness by exploiting the geometric imprint of multipath components independent of the propagation channel via the data-driven model. In a challenging numerical experiment involving obstructed LOS situations to all anchors, we show that the proposed sequential algorithm significantly outperforms state-of-the-art methods and attains the posterior Cramér-Rao lower bound even with training data limited to local regions.

show abstract

Section: B Problem Statement and Contributionsmentioning

confidence: 92%

Section: B Problem Statement and Contributionsmentioning

confidence: 99%

Section: A Random Variables and Assumptionsmentioning

confidence: 99%

See 1 more Smart Citation

Graph-based Simultaneous Localization and Bias Tracking for Robust Positioning in Obstructed LOS Situations

Venus

Leitinger

Tertinek³

et al. 2022

2022 56th Asilomar Conference on Signals, Systems, and Computers

View full text Add to dashboard Cite

show abstract

“…To calculate the distance metric, the estimation of the time delays of every arriving path is necessary. However, as the extraction of the delay of the MPCs from measured CIRs is very challenging due to the bandwidth limited signal s(t) [46], we consider a simple approximation by subtracting the time-aligned CIRs:…”

Section: A Local Cir Distancementioning

confidence: 99%

Indoor Localization With Robust Global Channel Charting: A Time-Distance-Based Approach

Stahlke

Yammine

Feigl

et al. 2023

Trans. Mach. Learn. Comm. Netw.

View full text Add to dashboard Cite

show abstract

“…n,ij in ( 12) denotes the timedifference observed at the (k) th BS. However, as the extraction of the delay of the MPCs from measured CIRs is very challenging due to the bandwidth limited signal s(t) [40], we consider a simple approximation by subtracting the timealigned CIRs:…”

Section: A Local Cir Distancementioning

confidence: 99%

Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach

Stahlke¹,

Yammine²,

Feigl³

et al. 2022

Preprint

View full text Add to dashboard Cite

Fingerprinting-based positioning significantly improves the indoor localization performance in non-line-of-sightdominated areas. However, its deployment and maintenance is cost-intensive as it needs ground-truth reference systems for both the initial training and the adaption to environmental changes. In contrast, channel charting (CC) works without explicit reference information and only requires the spatial correlations of channel state information (CSI). While CC has shown promising results in modelling the geometry of the radio environment, a deeper insight into CC for localization using multi-anchor large-bandwidth measurements is still pending.We contribute a novel distance metric for time-synchronized single-input/single-output CSIs that approaches a linear correlation to the Euclidean distance. This allows to learn the environment's global geometry without annotations. To efficiently optimize the global channel chart we approximate the metric with a Siamese neural network. This enables full CC-assisted fingerprinting and positioning only using a linear transformation from the chart to the real-world coordinates. We compare our approach to the state-of-the-art of CC on two different realworld data sets recorded with a 5G and UWB radio setup. Our approach outperforms others with localization accuracies of 0.69 m for the UWB and 1.4 m for the 5G setup. We show that CC-assisted fingerprinting enables highly accurate localization and reduces (or eliminates) the need for annotated training data.

show abstract

Delay Estimation in Dense Multipath Environments using Time Series Segmentation

Cited by 5 publications

References 22 publications

Graph-based Simultaneous Localization and Bias Tracking for Robust Positioning in Obstructed LOS Situations

Graph-based Simultaneous Localization and Bias Tracking for Robust Positioning in Obstructed LOS Situations

Indoor Localization With Robust Global Channel Charting: A Time-Distance-Based Approach

Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach

Contact Info

Product

Resources

About