New Efficient Indoor Cooperative Localization Algorithm With Empirical Ranging Error Model

Li, Shenghong; Hedley, Mark; Collings, Iain B.

doi:10.1109/jsac.2015.2430273

Cited by 53 publications

(41 citation statements)

References 26 publications

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“…For the general measurement model in Eq. (19), our purpose is to sample from the normalized likelihood function Z −1 f (r ij x i , x l ′ j ). The sampling strategy proposed by us is essentially an importance sampler combined with random variable transformation.…”

Section: B Sampling From a Normalized Likelihood Functionmentioning

confidence: 99%

Bayesian Cooperative Localization Using Received Signal Strength With Unknown Path Loss Exponent: Message Passing Approaches

Jin

Yin

Fritsche

et al. 2020

IEEE Trans. Signal Process.

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We propose a Bayesian framework for the receivedsignal-strength-based cooperative localization problem with unknown path loss exponent. Our purpose is to infer the marginal posterior of each unknown parameter: the position or the path loss exponent. This probabilistic inference problem is solved using message passing algorithms that update messages and beliefs iteratively. To enable the numerical tractability, we combine the variable discretization and Monte-Carlo-based numerical approximation schemes. To further improve computational efficiency, we develop an auxiliary importance sampler that updates the beliefs with the help of an auxiliary variable. To sample from a normalized likelihood function, which is an important ingredient of the proposed auxiliary importance sampler, we develop a stochastic sampling strategy that mathematically interprets and corrects an existing heuristic strategy. The proposed message passing algorithms are analyzed systematically in terms of computational complexity, demonstrating the computational efficiency of the proposed auxiliary importance sampler. Various simulations are conducted to validate the overall good performance of the proposed algorithms.

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Section: B Sampling From a Normalized Likelihood Functionmentioning

confidence: 99%

Bayesian Cooperative Localization Using Received Signal Strength With Unknown Path Loss Exponent: Message Passing Approaches

Jin

Yin

Fritsche

et al. 2020

IEEE Trans. Signal Process.

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“…ξ k,i denotes the associated ranging error, which is non-Gaussian distributed due to NLOS and multipath propagation [6]- [9], [14].…”

Section: A Frame Definition and Measurement Modelmentioning

confidence: 99%

“…which is derived from ranging data between anchors, with λ N = 0.6, λ P = 2.03 (see Section III of [9]). • RTS-Proposed: The offline tracking algorithm obtained by extending the KF in Algorithm 2 to a RTS smoother, as described in Section III-C.…”

Section: A Experimental Platform and Setupmentioning

confidence: 99%

“…However, the algorithms used in [2]- [5] are based on a KF, which assumes Gaussian error distributions. While this assumption is valid under LOS conditions, it does not hold in indoor applications since the ranging error distributions are non-Gaussian due to NLOS and multipath propagation [6]- [9], which compromises the performance of these systems in practice. This issue is addressed in [10] by using a nonlinear ranging error transition model and a particle filter for position Shenghong estimation.…”

Section: Introductionmentioning

confidence: 99%

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Integration of IMU in indoor positioning systems with non-Gaussian ranging error distributions

Hedley

Collings

et al. 2016

2016 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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The performance of a wireless positioning system can be improved through the integration with an inertial measurement unit (IMU). Conventional sensor fusion algorithms based on a Kalman filter (KF) are not accurate for indoor positioning systems since the ranging errors in indoor environments are non-Gaussian distributed due to non-line-of-sight (NLOS) and multipath propagation. In this paper, we propose a novel sensor fusion scheme for integrating range-based indoor positioning systems with IMUs, which supports non-Gaussian ranging error distributions at a low computational cost by using a pseudo position measurement for the measurement update step in the KF. The performance of the proposed fusion scheme is evaluated experimentally using an indoor positioning platform in an office environment, and is shown to be significantly better than conventional approaches. Particularly, the median positioning error is reduced by 52% using the proposed scheme, and the maximum positioning error is reduced to 0.36 m using an offline tracking algorithm extended from the proposed approach.

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“…Localization is very important in wireless sensor networks (WSNs) [1,4]. A important application for WSNs is the indoor localization of the blind node [2][3].…”

Section: Introductionmentioning

confidence: 99%

Study on Weighted Centroid Localization Algorithm for an Indoor Localization

Xie¹,

Ai²

2016

Proceedings of the 2016 4th International Conference on Electrical &Amp; Electronics Engineering and Computer Science (ICEEECS

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Abstract. Localization algorithm is very important in node localization. The received signal strength (RSS) can be used to reduce positioning time. In this paper, an improved weighted centroid localization algorithm based on RSSI (IWCL-RSSI) is proposed for an indoor localization. Theoretical analysis shows know that the proposed algorithm has the advantage of lower complexity, less prior information and lower power consumption. The simulation results show that the proposed algorithm is more accurate than AMWCL-RSSI (anchor_optimized modified weighted centroid localization algorithm based on RSSI), and at least as good as WCL (weighted centroid localization) in terms of the localization accuracy. Real experimental results show that IWCL-RSSI is better than WCL in terms of the localization accuracy.

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New Efficient Indoor Cooperative Localization Algorithm With Empirical Ranging Error Model

Cited by 53 publications

References 26 publications

Bayesian Cooperative Localization Using Received Signal Strength With Unknown Path Loss Exponent: Message Passing Approaches

Bayesian Cooperative Localization Using Received Signal Strength With Unknown Path Loss Exponent: Message Passing Approaches

Integration of IMU in indoor positioning systems with non-Gaussian ranging error distributions

Study on Weighted Centroid Localization Algorithm for an Indoor Localization

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