Least Square Cooperative Localization

Nguyen, Thang Van; Jeong, Youngmin; Shin, Hyundong; Win, Moe Z.

doi:10.1109/tvt.2015.2398874

Cited by 90 publications

(42 citation statements)

References 35 publications

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“…According to (17), the corresponding energy efficiency performance increases from 1.41 to 1.67 by adopting the adaptive sounding scheme in this case.…”

Section: Energy Efficiency Performancesmentioning

confidence: 92%

“…Then, introducing a secondary antenna to the mobile node may be another effective way to improve localization accuracy without the use of expensive hardware [16]. Also, a cooperative network architecture is proposed for improving the target detection and tracking [17] [18]. In addition, the approach to deploy the anchor nodes for localization is investigated in [19].…”

Section: Related Workmentioning

confidence: 99%

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Energy-Efficient Localization and Tracking of Mobile Devices in Wireless Sensor Networks

Zheng

Wang

et al. 2017

IEEE Trans. Veh. Technol.

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Wireless sensor networks (WSNs) are effective for locating and tracking people and objects in various industrial environments. Since energy consumption is critical to prolonging the lifespan of WSNs, we propose an energy-efficient LOcalization and Tracking (eLOT) system, using low-cost and portable hardware to enable highly accurate tracking of targets. Various fingerprint-based approaches for localization and tracking are implemented in eLOT. In order to achieve high energy efficiency, a network-level scheme coordinating collision and interference is proposed. On the other hand, based on the location information, mobile devices in eLOT can quickly associate with the specific channel in a given area, while saving energy through avoiding unnecessary transmission. Finally, a platform based on TI C-C2530 and the Linux operating system is built to demonstrate the effectiveness of our proposed scheme in terms of localization accuracy and energy efficiency.

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“…According to (17), the corresponding energy efficiency performance increases from 1.41 to 1.67 by adopting the adaptive sounding scheme in this case.…”

Section: Energy Efficiency Performancesmentioning

confidence: 92%

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Localization and Tracking of Mobile Devices in Wireless Sensor Networks

Zheng

Wang

et al. 2017

IEEE Trans. Veh. Technol.

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“…Since (15) is a convex optimization problem and strictly feasible, strong duality holds [40]. Problem (15) can be solved through its dual problem. The Lagrangian dual function of (15) is…”

Section: Dual Problemmentioning

confidence: 99%

“…Another widely used algorithm is least squares estimator (LS) [15], [16], which minimizes the squared error between range measurements and range estimates. LS does not need the knowledge of measurement error distribution.…”

Section: Introductionmentioning

confidence: 99%

Robust Localization Using Range Measurements With Unknown and Bounded Errors

Shi

Mao

Anderson

et al. 2017

IEEE Trans. Wireless Commun.

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Cooperative geolocation has attracted significant research interests in recent years. A large number of localization algorithms rely on the availability of statistical knowledge of measurement errors, which is often difficult to obtain in practice. Compared with the statistical knowledge of measurement errors, it can often be easier to obtain the measurement error bound. This work investigates a localization problem assuming unknown measurement error distribution except for a bound on the error. We first formulate this localization problem as an optimization problem to minimize the worst-case estimation error, which is shown to be a non-convex optimization problem. Then, relaxation is applied to transform it into a convex one. Furthermore, we propose a distributed algorithm to solve the problem, which will converge in a few iterations. Simulation results show that the proposed algorithms are more robust to large measurement errors than existing algorithms in the literature. Geometrical analysis providing additional insights is also provided.

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“…In concrete terms, we use the Fisher Information Matrix (FIM) [12][13][14] to measure the information of the JMART and then rely on the Cramer-Rao Lower Bound (CRLB) [15] to solve the corresponding unbiased localization problem [16].…”

Section: Introductionmentioning

confidence: 99%

Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error

et al. 2016

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Abstract:The emerging techniques in the Fifth Generation (5G) communication system, like the millimeter-Wave (mmWave) and massive Multiple Input Multiple Output (MIMO), make it possible to measure the Angle-Of-arrival (AOA), Receive Signal Strength (RSS) and Time-Of-flight (TOF) by using various types of mobile devices. At the same time, there is always significant interest in the high-precision localization techniques based on the joint metric of AOA/RSS/TOF, which enable one to overcome the drawback of the single metric-based localization. Motivated by this concern, we rely on the Cramer-Rao Lower Bound (CRLB) to analyze the localization errors of RSS/AOA, RSS/TOF, AOA/TOF and the Joint Metric of AOA/RSS/TOF (JMART)-based localization. The error bounds derived in this paper can be selected as the benchmarking results to evaluate the indoor localization performance. Finally, extensive simulations are conducted to support our claim.

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Least Square Cooperative Localization

Cited by 90 publications

References 35 publications

Energy-Efficient Localization and Tracking of Mobile Devices in Wireless Sensor Networks

Energy-Efficient Localization and Tracking of Mobile Devices in Wireless Sensor Networks

Robust Localization Using Range Measurements With Unknown and Bounded Errors

Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error

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