A Closed-Form Solution for RSS/AoA Target Localization by Spherical Coordinates Conversion

Tomic, Slaviša; Beko, Marko; Dinis, Rui; Montezuma, Paulo

doi:10.1109/lwc.2016.2615614

Cited by 88 publications

(95 citation statements)

References 15 publications

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“…Taking advantage of the existing technologies, e.g., terrestrial radio frequency sources, to provide a solution to the realtime object localization problem is strongly encouraged, due to cost minimization. These include for example, extracting time of arrival, received signal strength (RSS), angle of arrival (AoA) information from the received radio signal, or a combination of them [5]- [11]. Which approach to use depends mainly on the available hardware.…”

Section: Motivationmentioning

confidence: 99%

“…Works [6]- [11] investigated the classical target localization problem, where the proposed estimators were based on radio measurements only, and prior knowledge was disregarded. The authors in [2], [3] and [5] considered a problem of tracking of a moving target, and they combined the available radio measurements with prior knowledge given from the target state model.…”

Section: Related Workmentioning

confidence: 99%

“…As far as the prior knowledge part in (5) is concern, we can assume that has Gaussian distribution with mean and covariance [17]. Then, according to (3), we obtain (11) where is a constant, and and are respectively the mean and covariance of the one-step predicted state, obtained through (1) as (12) Therefore, (5) can be written as MAP Estimator for Target Tracking in Wireless Sensor Networks 329 (13) Similarly as in (9), the problem in (13) can be rewritten as (14) where , ,…”

Section: The Proposed Map Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

MAP Estimator for Target Tracking in Wireless Sensor Networks for Unknown Transmit Power

Tomic¹,

Beko²,

Dinis³

et al. 2017

IFIP Advances in Information and Communication Technology

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This paper addresses the target tracking problem, by extracting received signal strength (RSS) and angle of arrival (AoA) information from the received radio signal, in the case where the target transmit power is considered unknown. By combining the radio observations with prior knowledge given by the target transition state model, we apply the maximum a posteriori (MAP) criterion to the marginal posterior distribution function (PDF). However, the derived MAP estimator cannot be solved directly, so we tightly approximate it for small noise power. The target state estimate is then easily obtained at any time step by employing a recursive approach, typical for Bayesian methods. Our simulations confirm the effectiveness of the proposed algorithm, offering good estimation accuracy in all considered scenarios.

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Section: Motivationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: The Proposed Map Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

MAP Estimator for Target Tracking in Wireless Sensor Networks for Unknown Transmit Power

Tomic¹,

Beko²,

Dinis³

et al. 2017

IFIP Advances in Information and Communication Technology

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“…Hybrid approaches combining different types of measurements have also been proposed. For instance, references [26][27][28][29][30][31][32] investigated novel localization schemes based on the combined use of RSS and AOA measurements, which are able to achieve improved estimation performance. Although interesting, these techniques require enhanced hardware and processing capabilities that are usually not available on-board low-cost sensor nodes employed in typical WSN deployments; more precisely, AOA-based methods are not appealing in such contexts owing to the increased complexity and costs associated with multiple antennas.…”

Section: Introductionmentioning

confidence: 99%

Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks

Coluccia

Fascista

2019

JSAN

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The paper addresses the problem of localization based on hybrid received signal strength (RSS) and time of arrival (TOA) measurements, in the presence of synchronization errors among all the nodes in a wireless network, and assuming all parameters are unknown. In most existing schemes, in fact, knowledge of the model parameters is postulated to reduce the high dimensionality of the cost functions involved in the position estimation process. However, such parameters depend on the operational wireless context, and change over time due to the presence of dynamic obstacles and other modification of the environment. Therefore, they should be adaptively estimated “on the field”, with a procedure that must be as simple as possible in order to suit multiple real-time re-calibrations, even in low-cost applications, without requiring human intervention. Unfortunately, the joint maximum likelihood (ML) position estimator for this problem does not admit a closed-form solution, and numerical optimization is practically unfeasible due to the large number of nuisance parameters. To circumvent such issues, a novel two-step algorithm with reduced complexity is proposed: A first calibration phase exploits nodes in known positions to estimate the unknown RSS and TOA model parameters; then, in a second localization step, an hybrid TOA/RSS range estimator is combined with an iterative least-squares procedure to finally estimate the unknown target position. The results show that the proposed hybrid TOA/RSS localization approach outperformed state-of-the-art competitors and, remarkably, achieved almost the same accuracy of the joint ML benchmark but with a significantly lower computational cost.

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“…Range-based methods make use of the received signal in order to estimate the distance between the source and the receiving sensor node [14]. The distance information can be extracted from different measurements of the received signal, such as time of arrival, or time difference of arrival and received signal strength [15,16,17,18,19]. Nowadays, these different measurements are commonly integrated together, or combined with angle of arrival observations in order to enhance the localization accuracy [20,21,22].…”

Section: Introductionmentioning

confidence: 99%

Elephant Herding Optimization for Energy-Based Localization

Correia

Beko

Cruz

et al. 2018

Sensors

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This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations, we approach it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics are applied to this type of problem. More specifically, an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as a performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.

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A Closed-Form Solution for RSS/AoA Target Localization by Spherical Coordinates Conversion

Cited by 88 publications

References 15 publications

MAP Estimator for Target Tracking in Wireless Sensor Networks for Unknown Transmit Power

MAP Estimator for Target Tracking in Wireless Sensor Networks for Unknown Transmit Power

Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks

Elephant Herding Optimization for Energy-Based Localization

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