Cramer–Rao lower bounds for nonhybrid and hybrid localisation techniques in wireless networks

Laaraiedh, Mohamed; Avrillon, Stéphane; Uguen, Bernard

doi:10.1002/ett.1530

Cited by 13 publications

(14 citation statements)

References 35 publications

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“…Hybrid localization techniques [10], [11], such as the hybrid TOA/RSS technique, are proposed to enhance localization accuracy and fix the deficiencies of one type of measurements, such as the lack of measurements to uniquely localize the agent. Although the variances of the measurements for closer ranges between the agent and the anchor is likely smaller than that for non-hybrid localization techniques, it is possible that the variance of the shortest range measurement based on one type ranging technique is considerably larger than some longer range measurements based on another type ranging technique, e.g., RSS-based ranging commonly has larger variance than TOA-based ranging [3].…”

Section: Introductionmentioning

confidence: 99%

Reference Selection for Hybrid TOA/RSS Linear Least Squares Localization

Wang

Zheng

Wiemeler

et al. 2013

2013 IEEE 78th Vehicular Technology Conference (VTC Fall)

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Linear least squares (LLS) estimation is a suboptimum but low-complexity localization method based on measurements of location-related parameters. It has been proved that selection of the reference anchor influences the LLS localization accuracy. In addition, hybridization of different types of measurements can fix the deficiencies of one type of measurements. In this paper, we proposed a new reference selection criterion for the hybrid TOA/RSS LLS localization technique (called H-LLS-RS), which considers both measured ranges and the information about their coarse variances. Moreover, we consider a general scenario that variances of range measurements are different, and derive a weighted LLS (WLLS) estimator for hybrid TOA/RSS localization according to the information about the accurate ranging variances and the correlations among the observations.Simulation results show that if the RSS-based ranging variances are considerably larger than the TOA-based ranging variances, the H-LLS-RS localization technique yields better accuracy than the conventional LLS localization techniques. Furthermore, reference selection has no effect on the accuracy of WLLS localization technique.

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Section: Introductionmentioning

confidence: 99%

Reference Selection for Hybrid TOA/RSS Linear Least Squares Localization

Wang

Zheng

Wiemeler

et al. 2013

2013 IEEE 78th Vehicular Technology Conference (VTC Fall)

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“…The Cramer-Rao lower bound provides a lower bound on the variance of any unbiased estimator [8] and it is a very useful tool for evaluating the localization accuracy. The root mean square error (RMSE) performance of the proposed TOA based estimators is compared to the CRLB and presented in this paper.…”

Section: Introductionmentioning

confidence: 99%

Optimal source localization problem based on TOA measurements

Rosic¹,

Simić²,

Pejović³

et al. 2017

Serb J Electr Eng

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Determining an optimal emitting source location based on the time of arrival (TOA) measurements is one of the important problems in Wireless Sensor Networks (WSNs). The nonlinear least-squares (NLS) estimation technique is employed to obtain the location of an emitting source. This optimization problem has been formulated by the minimization of the sum of squared residuals between estimated and measured data as the objective function. This paper presents a hybridization of Genetic Algorithm (GA) for the determination of the global optimum solution with the local search Newton-Raphson (NR) method. The corresponding Cramer-Rao lower bound (CRLB) on the localization errors is derived, which gives a lower bound on the variance of any unbiased estimator. Simulation results under different signal-to-noise-ratio (SNR) conditions show that the proposed hybrid Genetic Algorithm-Newton-Raphson (GA-NR) improves the accuracy and efficiency of the optimal solution compared to the regular GA.

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“…Hybrid localization techniques [19,20], such as the hybrid TOA/RSS or AOA/RSS techniques, are proposed to enhance localization accuracy as well as fix the deficiencies of one type of measurements, such as the insufficiency of measurements to uniquely localize the agent. The effect of inaccurate measurements on hybrid AOA/RSS LLS localization was discussed in [21].…”

Section: Introductionmentioning

confidence: 99%

Linear least squares localization in sensor networks

Wang

2015

J Wireless Com Network

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Localization in sensor networks is critical for search and rescue. Linear least squares (LLS) estimation is a sub-optimum but low-complexity localization algorithm based on measurements of location-related parameters. Commonly, there are two types of LLS localization algorithms using range measurements; one is based on introducing a dummy variable (called LLS-I), and the other is based on the subtraction of the reference measured range (called LLS-II). Moreover, their respective weighted LLS (WLLS) algorithms (called WLLS-I and WLLS-II) can be adopted to further improve the localization accuracy. In addition, hybridization of different types of measurements can fix the deficiencies of one type of measurements. In this paper, we compare the localization performances of different LLS and WLLS algorithms in both non-hybrid time-of-arrival (TOA) and hybrid TOA/received signal-strength (RSS) networks. Simulation results show that if the variances of measurements are unavailable, the LLS-II localization algorithm should be adopted in both non-hybrid and hybrid networks using their respective reference selection criterions. If the variances of measurements are available, the two-step WLLS-I algorithm should be utilized to localize the agent in both non-hybrid and hybrid networks.

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Cramer–Rao lower bounds for nonhybrid and hybrid localisation techniques in wireless networks

Abstract: International audienc

Cited by 13 publications

References 35 publications

Reference Selection for Hybrid TOA/RSS Linear Least Squares Localization

Reference Selection for Hybrid TOA/RSS Linear Least Squares Localization

Optimal source localization problem based on TOA measurements

Linear least squares localization in sensor networks

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