TOA-Based Passive Localization in Quasi-Synchronous Networks

Wang, Yuan; Ma, Shaodan; Chen, C. L. Philip

doi:10.1109/lcomm.2014.021214.132662

Cited by 74 publications

(31 citation statements)

References 19 publications

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“…Recently, the close relationship between localization and synchronization is unveiled by simultaneously solving the two problems [18]- [23]. Passive localization in a synchronous network was studied in [24], [25], and in a quasi-synchronous network (all receivers experience the same clock offset) in [26]. In [27], TOA measurements were used for acquiring time difference of arrival (TDOA) estimates, when the receivers are asynchronous.…”

Section: Introductionmentioning

confidence: 99%

“…• We extend the quasi-synchronous network assumption of [26] and propose a novel EM-based passive localization technique for asynchronous receivers (EMpLaR) operating in a mixed time-variant LOS/NLOS environment based on TOA measurements. • We invoke the classic Taylor expansion for approximating the conditioned expectation of the log-likelihood function in the E-step, so that the M-step can be formulated analytically.…”

Section: Introductionmentioning

confidence: 99%

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Expectation–Maximization-Based Passive Localization Relying on Asynchronous Receivers: Centralized Versus Distributed Implementations

Yuan

Etzlinger

et al. 2019

IEEE Trans. Commun.

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This paper considers a passive localization scenario relying on a single transmitter, several receivers and multiple moving targets to be located. The so-called "passive" targets equipped with RFID reflectors are capable of reflecting the signals from the transmitter to the receivers. Existing approaches assume that the transmitter and receivers are synchronous or quasi-synchronous, which is not always realistic in practical scenarios. Hence, an asynchronous wireless network is considered, where different clock offsets are assumed at different receivers. We propose a centralized expectation maximizationbased passive localization method for asynchronous receivers (EMpLaR) by treating the clock offsets as hidden variables. Thereby, the proposed algorithm makes use of Taylor expansions to arrive at a closed-form maximization. Furthermore, to improve the robustness to link failures and to reduce the energy consumption, we propose a distributed localization approach based on average consensus formulation to locate the target at each receiver. By applying a quadratic polynomial approximation of the function on which consensus has to be reached, both the computational complexity and the communications overhead are significantly reduced. The Cramér-Rao bound of the target location is derived as a benchmark of our proposed algorithms. Our simulation results show that the proposed centralized and distributed EMpLaR algorithms match the Cramér-Rao bound and significantly improve the localization performance compared to the conventional methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Expectation–Maximization-Based Passive Localization Relying on Asynchronous Receivers: Centralized Versus Distributed Implementations

Yuan

Etzlinger

et al. 2019

IEEE Trans. Commun.

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“…As a classical problem, passive source localization using time of arrival (TOA) measurements has gained considerable interests in various fields, such as radar, sonar, wireless communication and so on [1][2][3]. Localization techniques have been extensively investigated for deducing the target position from TOAs of the target signal received at spatially distributed receivers [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Weighted Multidimensional Scaling Localization of a Known Altitude Object Using TOA Measurements

Deng¹,

Weng²,

Sun³

2018

dtcse

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Abstract. This paper considers the problem of geolocating a target on the Earth surface whose altitude is known using the target signal time of arrival (TOA) measurements. The geolocation Cramer-Rao lower bound (CRLB) is derived and the performance improvement due to the availability of target altitude information is quantified. An weighted multidimensional scaling geolocation solution is developed. Its sensitivity to the target altitude error is also studied. Simulations verify the theoretical developments and illustrate the good performance of the proposed geolocation method.

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“…For passive localization in wireless networks, a TOA-based two-step estimation (TSE) algorithm is proposed in [12]. Passive localization in quasi-synchronous networks is studied in [13] based on TSE algorithm. In [14], differential TOA measurements are employed to perform passive localization in asynchronous network.…”

Section: Introductionmentioning

confidence: 99%

“…Particle swarm optimization (PSO) processing 8: for q = 1 to N P SO do 9: update the positions and velocities of particles according to (15) and (16); 10: update the local best position of each particle and the global best position according to (17) and (18); 11: end for; 12: end PSO 13:…”

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confidence: 99%

TOA-based passive localization of multiple targets with inaccurate receivers based on belief propagation on factor graph

Yuan

Wang

et al. 2016

Digital Signal Processing

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Location awareness is now becoming a vital requirement for many practical applications. In this paper, we consider passive localization of multiple targets with one transmitter and several receivers based on time of arrival (TOA) measurements. Existing studies assume that positions of receivers are perfectly known. However, in practice, receivers' positions might be inaccurate, which leads to localization error of targets. We propose factor graph (FG)-based belief propagation (BP) algorithms to locate the passive targets and improve the position accuracy of receivers simultaneously. Due to the nonlinearity of the likelihood function, messages on the FG cannot be derived in closed form. We propose both sample-based and parametric methods to solve this problem. In the sample-based BP algorithm, particle swarm optimization is employed to reduce the number of particles required to represent messages. In parametric BP algorithm, the nonlinear terms in performs slightly worse than the sample-based BP method, it could be more attractive in practical applications due to the significantly lower computational complexity.

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TOA-Based Passive Localization in Quasi-Synchronous Networks

Cited by 74 publications

References 19 publications

Expectation–Maximization-Based Passive Localization Relying on Asynchronous Receivers: Centralized Versus Distributed Implementations

Expectation–Maximization-Based Passive Localization Relying on Asynchronous Receivers: Centralized Versus Distributed Implementations

Weighted Multidimensional Scaling Localization of a Known Altitude Object Using TOA Measurements

TOA-based passive localization of multiple targets with inaccurate receivers based on belief propagation on factor graph

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