TOA-based localization error modeling of distributed MIMO radar for positioning accuracy enhancement

Zhu, Kaihui; Yan, Wei; Ru, Xu

doi:10.1109/radar.2016.8059316

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…The scenario parameters are shown in Figure 18a. As is shown, radar positioning at (11,2) is placed at the corner of a corridor of 2.4 m width. The pedestrian marked in red in the figure is the target, and the target moves from NLOS region to LOS region along the centreline of the corridor, which starts at (6, 4.2) and end at (10, 4.2).…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Time-of-arrival (TOA), angle-of-arrival (AOA), or received signal strength are the most commonly used measurements in classic radar localisation techniques [1]. These approaches based on LOS propagation models use two or more sensors to get the intersection of different measurements so that they can locate a target without ambiguity [2]. Conventional methods are sensitive to the LOS measurements, so these methods would involve considerable errors and induce inaccurate localisation results in multipath scenarios.…”

Section: Introductionmentioning

confidence: 99%

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A target localisation method with monostatic radar via multi‐observation data association

Ding

Wang

Jiang

et al. 2022

IET Radar Sonar & Navi

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Traditionally, radar-based localisation systems are designed to deal with targets in line-ofsight (LOS) scenarios. However, the performance of those radar systems is limited by multipath echoes reflected from non-line-of-sight (NLOS) targets and walls in the urban areas. In recent years, there has been an increasing interest in multipath exploitation methods aided by urban environmental knowledge. Based on the exploitation of space diversity provided by the multipath effect, this article proposes a novel two-stage localisation method using a single monostatic radar via association of multipath time-ofarrivals (TOAs). In the offline stage, reference TOA data at different locations is generated by a ray-tracing method to avoid the heterogeneity of multipath propagation at different locations, assuming that the environment information is completely known prior. In the online stage, the TOA set of multipath echoes from the target is estimated by a sparse reconstruction method to achieve super-resolution rather than the matched filter. Then based on the finite-set statistics (FISST) theory, a grid-based multi-observation data association (GMODA) algorithm is present as the likelihood function of the multipath measurement given a candidate location, which is followed by a maximum likelihood estimation to derive the location of the target. Furthermore, the global nearest neighbour approximation of GMODA is introduced to avoid combinatorial explosion in the association algorithm. Simulation and experiment results on a single antenna millimetrewave radar in various scenarios show that the proposed method achieves high localisation accuracy in both LOS and NLOS conditions while maintaining low computational cost at the same time. The presented results suggest that our method can be applied to target localisation applications in the complex urban environment. K E Y W O R D Smulti-observation data association, multipath exploitation, non-line-of-sight (NLOS), target localisation, timeof-arrival (TOA) estimationThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Experiments Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A target localisation method with monostatic radar via multi‐observation data association

Ding

Wang

Jiang

et al. 2022

IET Radar Sonar & Navi

View full text Add to dashboard Cite

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“…Considering the characteristics of 5G massive antenna systems, Han leveraged the improved angle estimation precision brought by massive MIMO to propose a direct AOA positioning method for both far-field and near-field scenarios, achieving sub-meter positioning performance [20]. Zhu, in the context of massive MIMO systems, presented a TOA-based localization model that defines intersecting lines of identical delay profiles, utilizing local root mean square error as a new metric to evaluate positioning performance [21].…”

Section: Introductionmentioning

confidence: 99%

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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“…However, the lengths of received NLOS signals are longer than the range of the direct path from radar to the target in multipath propagations, and the received DOA of NLOS signals are different from the DOA of LOS path. Therefore the NLOS propagations lead to considerable errors in range and angle measurements in multipath scenarios [6]. The performances of classic radar target localization methods based on LOS geometry of free space propagation degrade significantly in such multipath environments.…”

Section: Introductionmentioning

confidence: 99%

Radar Target Localization with Multipath Exploitation in Dense Clutter Environments

et al. 2023

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The performance of classic radar geometry based on the line-of-sight (LOS) signal transmitted from radar to the target in the free space is affected by multipath echoes in urban areas, where non-line-of-sight (NLOS) signals reflected by obstacles are received by the radar. Based on prior information of the urban situation, this article proposes a novel two-stage localization algorithm with multipath exploitation in a dense clutter environment. In the offline stage, multipath propagation parameters of uniformly distributed samples in the radar field of view are predicted by the ray-tracing technique. In the online stage, a rough location of the target is estimated by the maximum similarity between measurements and the predicted parameters of reference samples at different locations. The similarity is described by the likelihood between measurements and the predicted multipath parameters with respect to all possible associated hypotheses. A gating threshold is derived to exclude less likely hypotheses and reduce the computational burden. The accurate target location is acquired by a non-linear least squares (NLS) optimization of the associated multipath components. Simulation results in various noise conditions show that the proposed method provides robust and accurate target localization results under dense clutter conditions, and the offline pre-calculation of ray-tracing ensures the real-time performance of the proposed localization algorithm. The root mean square error (RMSE) of simulation results shows the advantage of the proposed method over the existing method. The presented results suggest that the proposed method can be applied to NLOS target localization applications in complex environments.

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TOA-based localization error modeling of distributed MIMO radar for positioning accuracy enhancement

Cited by 9 publications

References 8 publications

A target localisation method with monostatic radar via multi‐observation data association

A target localisation method with monostatic radar via multi‐observation data association

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

Radar Target Localization with Multipath Exploitation in Dense Clutter Environments

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