Multistatic pseudolinear target motion analysis using hybrid measurements

Nguyen, Ngoc Hung; Doğançay, Kutluyıl

doi:10.1016/j.sigpro.2016.06.004

Cited by 46 publications

(20 citation statements)

References 26 publications

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“…Remark 2: 5G mmWave uplink localization can be executed during the initial access stage, as explained in Section II-B. (11) and (35).…”

Section: Remarkmentioning

confidence: 99%

“…Study in [9] analyzed the localization performance by accounting for the distortion caused by fronthaul links quantization, however, the work was limited to the twodimensional (2-D) scenario with coarse positioning accuracy. The works of [10], [11] estimated the location and velocity of the moving source in radar systems. The algorithm in [10] estimated the position and velocity separately, and the research in [11] was limited to 2-D localization.…”

Section: Introductionmentioning

confidence: 99%

“…The works of [10], [11] estimated the location and velocity of the moving source in radar systems. The algorithm in [10] estimated the position and velocity separately, and the research in [11] was limited to 2-D localization. The work in [12] studied three-dimensional (3-D) downlink positioning with single reference station, which requires both AoA and AoD measurements (obtained after beam training) and abundant single-bounce multipath components.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

3-D Position and Velocity Estimation in 5G mmWave CRAN with Lens Antenna Arrays

Yang

Jin

Han

et al. 2019

2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)

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5G millimeter-wave (mmWave) cloud radio access network (CRAN) provides new opportunities for accurate multilateration: large bandwidth, large antenna arrays, and increased densities of base stations allow for unparalleled delay and angle resolution. However, combining localization into communications and designing joint position and velocity estimation algorithms are challenging. This paper considers the joint estimation in three-dimensional (3-D) lens antenna array based mmWave CRAN architecture. We embed multilateration into communications and explain its benefits to the initial access and beam training stages. We propose a closed-form solution for the joint estimation problem by forming the pseudo-linear matrix representation and designing the weighted least squares estimator with hybrid measurements. The proposed method is proven asymptotically unbiased and confirmed by simulations to achieve the Cramer-Rao lower bound and attain the sub-decimeter level accuracy.

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“…Remark 2: 5G mmWave uplink localization can be executed during the initial access stage, as explained in Section II-B. (11) and (35).…”

Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

3-D Position and Velocity Estimation in 5G mmWave CRAN with Lens Antenna Arrays

Yang

Jin

Han

et al. 2019

2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)

View full text Add to dashboard Cite

show abstract

“…Many TDOA/FDOA localization algorithms have been studied in the literatures, e.g., two-step weighted least square (WLS) method, constrained quadratic programming [4], pseudolinear estimation [5] and the constrained weighted least squares (CWLS) method [6]. Kalman filters based on TDOA and FDOA measurements are also applied in [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Optimal Sensor Deployment and Velocity Configuration With Hybrid TDOA and FDOA Measurements

et al. 2019

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Source localization based on the hybrid time-difference-of-arrival (TDOA) and frequencydifference-of-arrival (FDOA) measurements from distributed sensors is an essential problem in wireless sensor networks (WSNs). In this paper, we mainly study the optimal sensor deployment and velocity configuration of UAV swarms mounted with TDOA and FDOA based sensors. Explicit solutions of optimal sensor deployment and velocity configuration are acquired in both static and movable source scenarios based on the Fisher information matrix (FIM). Both centralized and decentralized localization are explored to meet different types of localization methods. Path planning problem of UAV swarms in TDOA/FDOA localization is also studied with constraints. Simulations verify its efficiency with path planning in TDOA and FDOA localization. INDEX TERMS Time-difference-of-arrival (TDOA), frequency-difference-of-arrival (FDOA), sensor deployment, fisher information matrix, path planning.

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“…In recent years, the problem of target localisation in multistatic radar has drawn considerable attentions [1, 2]. The multistatic system is composed of multiple transmitter–receiver pairs, in which the transmitters normally refer to the existing illuminators of opportunity like FM, DVB‐T, and mobile base station signals [3].…”

Section: Introductionmentioning

confidence: 99%