Received Signal Strength-Based Wireless Localization via Semidefinite Programming: Noncooperative and Cooperative Schemes

Ouyang, Robin Wentao; Wong, Albert Kai-Sun; Lea, Chin-Tau

doi:10.1109/tvt.2010.2040096

Cited by 254 publications

(216 citation statements)

References 24 publications

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“…As mentioned previously, the cost function in (4) is severely nonlinear and nonconvex [22], requiring involved computations. By using semidefinite relaxation (SDR), we convert the optimization problem into a convex semidefinite programming (SDP) problem.…”

Section: Semidefinite Programming Solovermentioning

confidence: 99%

RSS-based sensor localization in underwater acoustic sensor networks

Zhang

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Since the global positioning system (GPS) is not applicable underwater, source localization using wireless sensor networks (WSNs) is gaining popularity in oceanographic applications. Unlike terrestrial WSNs (TWSNs) which uses electromagnetic signaling, underwater WSNs (UWSNs) require underwater acoustic (UWA) signaling. Received signal strength (RSS)-based source localization is considered in this paper due to its practical simplicity and the constraint of low-cost sensor devices, but this area received little attention so far because of the complicated UWA transmission loss (TL) phenomena. In this paper, we address this issue and propose two novel semidefinite programming (SDP) approaches which can be solved more efficiently. The numerical results validate our proposed SDP solvers in underwater environments, and indicate that the placement of the anchor nodes influences the RSS-based localization accuracy similarly as in the terrestrial counterpart. We also highlight that adopting traditional terrestrial RSS-based localization methods will fail in underwater scenarios.

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Section: Semidefinite Programming Solovermentioning

confidence: 99%

RSS-based sensor localization in underwater acoustic sensor networks

Zhang

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…For both methods, we propose a novel SDP approach to transform the ML or the nonlinear least squares (NLS) cost function to a convex one by using approximations and relaxation techniques. Our proposed SDP approach is different from those studied in [4,5] since there is no information about the transmit power. We further linearize the measurement model and apply the least squares (LS) solution to the linear model.…”

Section: Introductionmentioning

confidence: 99%

“…To compute ML solution, it is required to minimize a nonconvex cost function which is computationally intensive. Convergence problems of the ML estimator can be addressed by using semidefinite programming (SDP) techniques, in which the ML cost function is approximated with a convex function [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

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RSS-based sensor localization with unknown transmit power

Vaghefi

Gholami

Ström

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Received signal strength (RSS)-based single source localization when there is not a prior knowledge about the transmit power of the source is investigated. Because of nonconvex behavior of maximum likelihood (ML) estimator, convoluted computations are required to achieve its global minimum. Therefore, we propose a novel semidefinite programming (SDP) approach by approximating ML problem to a convex optimization problem which can be solved very efficiently. Computer simulations show that our proposed SDP has a remarkable performance very close to ML estimator. Linearizing RSS model, we also derive the partly novel least squares (LS) and weighted total least squares (WTLS) algorithms for this problem. Simulations illustrate that WTLS improves the performance of LS considerably.

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“…The initial stage for localization is based on distance angle estimation between nodes. Different techniques for localization using distance/angle estimation are classified into: time of arrival [5], time difference of arrival [6], received signal strength indicator [7]- [10], and angle of arrival [11]. Currently, the Wi-Fi RSS-based positioning is considered to have great importance for localization [9].…”

Section: Introductionmentioning

confidence: 99%

Intruder Identification in IEEE 802.11 Wireless Infrastructure Using Localization

Dalai¹,

Jena²

2014

IJFCC

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Abstract-The method of obtaining the physical co-ordinates of a device is known as localization. Location information of the wireless devices in a network has many applications. Generally we use GPS (Global Positioning System) to get the location but it fails to provide the service in an indoor environment. Therefore we need a local positioning system, which can provide the relative location information in a network. Many approaches has been made for wireless localization using the distance obtained through

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Received Signal Strength-Based Wireless Localization via Semidefinite Programming: Noncooperative and Cooperative Schemes

Cited by 254 publications

References 24 publications

RSS-based sensor localization in underwater acoustic sensor networks

RSS-based sensor localization in underwater acoustic sensor networks

RSS-based sensor localization with unknown transmit power

Intruder Identification in IEEE 802.11 Wireless Infrastructure Using Localization

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