Vehicular Positioning and Tracking in Multipath Non-Line-of-Sight Channels

Chen, Zhong; Vinogradova, Julia; Fodor, Gábor; Hammarberg, Peter

doi:10.1109/vtc2022-spring54318.2022.9860711

Cited by 6 publications

(2 citation statements)

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“…Several methods have been proposed to solve the problem of target tracking in NLOS environments as well, such as utilizing the hybrid of LOS and NLOS for tracking [22], [23], [24], multiple BS assisted tracking [25], and single BS tracking based on multiple paths [26], [27]. The example hybrid usage of LOS and NLOS channels is introduced in [24], [42], which uses NLOS paths assisted LOS channel to track UE and utilize enhanced Kalman Filter (EKF) for estimation.…”

Section: Introductionmentioning

confidence: 99%

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Positioning and Tracking with ODE-LSTM Algorithm for Emerging Smart Rail Systems

Zhao,

Tian,

Shao

2024

Preprint

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In this paper, we propose a NLOS positioning and tracking method in order to be applied in the emerging smart rail systems. By analyzing MIMO scatter channels, the geometric relationship for positioning between each UE, BS and scattering point can be modeled that includes information of AOA, AOD, TOA. The accuracy of positioning can be improved by forming an optimization problem with bias in time and orientation. Further, in order to track the mobile UE, an ODE-LSTM algorithm is proposed, which is combined by ODE derivation solver and LSTM network. It puts the positioning information into an ODE-LSTM network to achieve continuous tracking in arbitrary time instance. Simulation Works validate that the proposed ODE-LSTM method shows better performance in nonlinear tracking than traditional Kalman filter or enhanced Kalman filter.

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Section: Introductionmentioning

confidence: 99%

“…However, the exchange of information between multi-BS may bring heavy burden to communication system and this method have a serious delay in tracking. Single BS tracking method is used in paper [26]. To address the challenge posed by the time-varying nature of the channel, it introduces a two-state Kalman filter (KF) approach.…”

Section: Introductionmentioning

confidence: 99%