Multidimensional scaling approach for node localization using received signal strength measurements

Lin, Lanxin; So, Hing-Cheung; Chan, Frankie K. W.

doi:10.1016/j.dsp.2014.07.008

Cited by 11 publications

(8 citation statements)

References 25 publications

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“…To achieve a better accuracy at a reasonable cost of complexity, subspace localization using TOA [23] and RSSI measurements [24] is presented as follows. Assume that the WSN is fully cooperative, i.e., all the anchors and agents are in the communication ranges, or the anchors know the positions of each other.…”

Section: Rssi Localizationmentioning

confidence: 99%

Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks

Tran

Huang

et al. 2020

Sensors

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Source positioning using hybrid angle-of-arrival (AOA) estimation and received signal strength indicator (RSSI) is attractive because no synchronization is required among unknown nodes and anchors. Conventionally, hybrid AOA/RSSI localization combines the same number of these measurements to estimate the agents’ locations. However, since AOA estimation requires anchors to be equipped with large antenna arrays and complicated signal processing, this conventional combination makes the wireless sensor network (WSN) complicated. This paper proposes an unbalanced integration of the two measurements, called 1AOA/nRSSI, to simplify the WSN. Instead of using many anchors with large antenna arrays, the proposed method only requires one master anchor to provide one AOA estimation, while other anchors are simple single-antenna transceivers. By simply transforming the 1AOA/1RSSI information into two corresponding virtual anchors, the problem of integrating one AOA and N RSSI measurements is solved using the least square and subspace methods. The solutions are then evaluated to characterize the impact of angular and distance measurement errors. Simulation results show that the proposed network achieves the same level of precision as in a fully hybrid nAOA/nRSSI network with a slightly higher number of simple anchors.

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Section: Rssi Localizationmentioning

confidence: 99%

Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks

Tran

Huang

et al. 2020

Sensors

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“…The log-normal shadowing model [15, 16] is employed to reveal the relationship between the received signal strength and the distance. In LOS condition, the RSS of the i th node at time k can be expressed as where P0 is the path loss at d0 meter and d0=1 m in this paper; n is the path loss exponent.…”

Section: Problem Statementmentioning

confidence: 99%

“…RSS Model. The log-normal shadowing model [15,16] is employed to reveal the relationship between the received signal strength and the distance. In LOS condition, the RSS of the th node at time can be expressed as…”

Section: Tdoa Ranging Modelmentioning

confidence: 99%

A Kalman Framework Based Mobile Node Localization in Rough Environment Using Wireless Sensor Network

Chu

2015

International Journal of Distributed Sensor Networks

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Since the wireless sensor network (WSN) has the performance of sensing, processing, and communicating, it has been widely used in various environments. The node localization is a key technology for WSN. The accuracy localization results can be achieved in ideal environment. However, the measurement may be contaminated by NLOS errors in rough environment. The NLOS errors could result in big localization error. To overcome this problem, we present a mobile node localization algorithm using TDOA and RSS measurements. The proposed method is based on Kalman framework and utilizes the general likelihood ratio method to identify the propagation condition. Then the modified variational Bayesian approximation adaptive Kalman filtering is used to mitigate the NLOS error. It could estimate the mean and variance of measurement error. The simulation results demonstrate that the proposed method outperforms the other methods such as Kalman filter and ∞ filter.

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“…Recently, the MDS performance was investigated in the range‐based localisation [31] for the results [14–22] and in the TDOA‐based localisation [32] for the results [23–25]. All of above studies [14–27, 31, 32] have promoted the MDS technique development of source localisation.…”

Section: Introductionmentioning

confidence: 99%

“…Multidimensional scaling (MDS) [11,12] is of extensive application for TDOA/FDOA-based localisation [13] and other related scheme localisation, such as range-based localisation [14][15][16][17][18][19][20][21][22], TDOA-based localisation [23][24][25] and cognitive radio network localisation [26,27]. The MDS analysis in the moving source localisation provided a closed-form estimator [13], but its optimality was only verified empirically by simulation examples.…”

Section: Introductionmentioning

confidence: 99%

On optimality of multidimensional scaling for time differences of arrival/frequency differences of arrival based moving source localisation

Wei

Wen

Cheng

2022

IET Signal Processing

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Multidimensional scaling (MDS) is an attractive technique for a moving source localisation from time and frequency difference of arrival (time differences of arrival (TDOA)/ frequency differences of arrival (FDOA)) measurements. However, its optimality has not yet been proven theoretically because of the difficult Moore-Penrose pesudo-inverse operation. In addition to the theoretical incompleteness of the MDS technique, the sensor uncertainties are not considered in the MDS framework for the moving source localisation either. A closed-form estimator is proposed for the TDOA/FDOA-based localisation with senor uncertainties by exploiting the MDS technique. Furthermore, based on the fundamental corollaries in the MDS analysis, an elegant and detailed analytical proof is also presented for the optimality of the MDS estimator thoroughly in the presence and absence of sensor uncertainties. The theoretical derivation is corroborated by numerical examples.

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Multidimensional scaling approach for node localization using received signal strength measurements

Cited by 11 publications

References 25 publications

Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks

Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks

A Kalman Framework Based Mobile Node Localization in Rough Environment Using Wireless Sensor Network

On optimality of multidimensional scaling for time differences of arrival/frequency differences of arrival based moving source localisation

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