Vehicle Positioning Using 5G Millimeter-Wave Systems

Cui, Xuerong; Gulliver, T. Aaron; Li, Juan; Zhang, Hao

doi:10.1109/access.2016.2615425

Cited by 53 publications

(28 citation statements)

References 27 publications

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“…of auto-driving is vehicular positioning [2]- [4], namely positioning nearby vehicles and tracking other parameters such as sizes and trajectories. The information then serves as inputs for computing and control tasks such as navigation and accidence avoidance.…”

Section: Hidden Vehiclementioning

confidence: 99%

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Hidden Vehicle Sensing via Asynchronous V2V Transmission: A Multi-Path-Geometry Approach

Han

Chae

et al. 2019

IEEE Access

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Accurate vehicular sensing is a basic and important operation in autonomous driving. Unfortunately, the existing techniques have their own limitations. For instance, the communication-based approach (e.g., transmission of GPS information) has high latency and low reliability while the reflection-based approach (e.g., RADAR) is incapable of detecting hidden vehicles (HVs) without line-of-sight. This is arguably the reason behind some recent fatal accidents involving autonomous vehicles. To address this issue, this paper presents a novel HV-sensing technology that exploits multi-path transmission from a HV to a sensing vehicle (SV). The powerful technology enables the SV to detect multiple HV-state parameters including position, orientation of driving direction, and size. Its implementation does not even require transmitterreceiver synchronization like conventional mobile positioning techniques. Our design approach leverages estimated information on multi-path [namely their angles-of-arrival (AoA), angles-of-departure (AoD), and time-of-arrival (ToA)] and their geometric relations. As a result, a complex system of equations or optimization problems, where the desired HV-state parameters are variables, can be formulated for different channel-noise conditions. The development of intelligent solution methods ranging from leastsquare estimator to disk/box minimization yields a set of practical HV-sensing techniques. We study their feasibility conditions in terms of the required number of paths. Furthermore, practical solutions, including sequential path combining and random directional beamforming, are proposed to enable HVsensing given insufficient paths. Last, realistic simulation of driving in both highway and rural scenarios demonstrates the effectiveness of the proposed techniques. In summary, the proposed technique will enhance the capabilities of existing vehicular sensing technologies (e.g., RADAR and LIDAR) by enabling HV-sensing.

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Section: Hidden Vehiclementioning

confidence: 99%

“…This is feasible by standardizing the vehicular arrays' topology. In addition, the Doppler effect is ignored based 2 It is implicitly assumed that enough orthogonal waveforms are given for HV's multi-antenna transmission a.k.a. orthogonal multiple access (OMA).…”

Section: B Hidden Vehicle Transmissionmentioning

confidence: 99%

Hidden Vehicle Sensing via Asynchronous V2V Transmission: A Multi-Path-Geometry Approach

Han

Chae

et al. 2019

IEEE Access

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“…Consider a typical HV antennas cluster array. Based on the far-field propagation model [10] , the cluster response vector is represented as a function of AoD ϕ as where f c denotes the carrier frequency and α m (ϕ) refers to the difference in propagation time to the corresponding scatterer between the m-th HV antenna and the 1-st HV antenna in the same cluster, i.e., α 1 (ϕ) = 0. Similarly, the response vector of SV array is expressed in terms of AoA θ as…”

Section: B Hidden Vehicle Transmissionmentioning

confidence: 99%

Sensing Hidden Vehicles by Exploiting Multi-Path V2V Transmission

Han

Chae³

et al. 2018

2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)

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This paper presents a technology of sensing hidden vehicles by exploiting multi-path vehicle-to-vehicle (V2V) communication. This overcomes the limitation of existing RADAR technologies that requires line-of-sight (LoS), thereby enabling more intelligent manoeuvre in autonomous driving and improving its safety. The proposed technology relies on transmission of orthogonal waveforms over different antennas at the target (hidden) vehicle. Even without LoS, the resultant received signal enables the sensing vehicle to detect the position, shape, and driving direction of the hidden vehicle by jointly analyzing the geometry (AoA/AoD/propagation distance) of individual propagation path. The accuracy of the proposed technique is validated by realistic simulation including both highway and rural scenarios.

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“…The forthcoming 5G new radio (NR) developments open new carrier frequencies for mobile communications which together with the wide millimeter-wave channels allow to design a dedicated high-speed train (HST) multi-gigabit wireless network [1]. Moreover, the new 5G networks introduce a unique opportunity for high-accuracy and high-availability positioning [2], [3]. Depending on the considered use case for the position information, a submeter positioning accuracy is pursued with a use-case-specific availability requirement.…”

Section: Introductionmentioning

confidence: 99%

“…Wireless network-based positioning has been widely studied in literature, see, for example, [9]. Positioning with current 4G Long Term Evolution (LTE) networks is considered, for example, in [10] and [11], and positioning with forthcoming 5G networks, for example, in [2], [3], [12]. However, due to absence of detailed descriptions of 5G signal and frame structures, the 5G positioning studies have been limited to consider only assumed signal models.…”

Section: Introductionmentioning

confidence: 99%

Positioning of high-speed trains using 5G new radio synchronization signals

Talvitie

Levanen

Koivisto

et al. 2018

2018 IEEE Wireless Communications and Networking Conference (WCNC)

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We study positioning of high-speed trains in 5G new radio (NR) networks by utilizing specific NR synchronization signals. The studies are based on simulations with 3GPP-specified radio channel models including path loss, shadowing and fast fading effects. The considered positioning approach exploits measurement of Time-Of-Arrival (TOA) and Angle-Of-Departure (AOD), which are estimated from beamformed NR synchronization signals. Based on the given measurements and the assumed train movement model, the train position is tracked by using an Extended Kalman Filter (EKF), which is able to handle the nonlinear relationship between the TOA and AOD measurements, and the estimated train position parameters. It is shown that in the considered scenario the TOA measurements are able to achieve better accuracy compared to the AOD measurements. However, as shown by the results, the best tracking performance is achieved, when both of the measurements are considered. In this case, a very high, sub-meter, tracking accuracy can be achieved for most (>75%) of the tracking time, thus achieving the positioning accuracy requirements envisioned for the 5G NR. The pursued high-accuracy and high-availability positioning technology is considered to be in a key role in several envisioned HST use cases, such as mission-critical autonomous train systems.

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Vehicle Positioning Using 5G Millimeter-Wave Systems

Cited by 53 publications

References 27 publications

Hidden Vehicle Sensing via Asynchronous V2V Transmission: A Multi-Path-Geometry Approach

Hidden Vehicle Sensing via Asynchronous V2V Transmission: A Multi-Path-Geometry Approach

Sensing Hidden Vehicles by Exploiting Multi-Path V2V Transmission

Positioning of high-speed trains using 5G new radio synchronization signals

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