Efficient cooperative localization algorithm in LOS/NLOS environments

Jin, Di; Yin, Feng; Fritsche, Carsten; Zoubir, Abdelhak M.; Gustafsson, Fredrik

doi:10.1109/eusipco.2015.7362370

Cited by 9 publications

(9 citation statements)

References 11 publications

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“…space-alternating generalized expectationmaximization (SAGE) [33] and Kalman enhanced super resolution tracking (KEST) [34], iteratively estimate all MPCs for multipath mitigation. The NLOS bias effect can be mitigated by exploiting the NLOS bias distribution [30], [35], [36] or applying identify-and-discard techniques in either signal [37] or location domain [38]. Most NLOS bias mitigation techniques require a-priori information or training data and may be subject to NLOS classification failure.…”

Section: ) Multi-link Fusionmentioning

confidence: 99%

Distributed Direct Localization Suitable for Dense Networks

Zhang

Staudinger

Jost

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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Traditional network localization algorithms contain ranging and localization steps, which have systematic disadvantages. We propose an algorithm dubbed direct particle filter based distributed network localization (DiPNet). A node's location is directly estimated from the received signals, incorporating location uncertainty of neighboring nodes. The propagation effects on DiPNet become insignificant for dense networks, due to the massive-link collective physical layer processing. DiPNet achieves a near-optimal performance with low complexity, which is particularly attractive for realtime dense-network localization.

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Section: ) Multi-link Fusionmentioning

confidence: 99%

Distributed Direct Localization Suitable for Dense Networks

Zhang

Staudinger

Jost

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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“…for the sampling problem in Eq. (20) can be designed in a bottom-up manner, meaning that we first develop a sampling strategy and then derive the associated distribution q(x i x l ′ j , r ij ). Given r ij , x l ′ j and the measurement model in Eq.…”

Section: ) Position Variable Xmentioning

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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“…Initialization: Self-Localization: Every node i with |Γa(i)| ≥ 3 self-localizes by solving (26) and estimates α via (25) and p 0 via (23), thus obtaining (p (i) 0 ,α (i) ,x i ); Broadcast: Every node which self-localized broadcasts its local estimates (p (i) 0 ,α (i) ,x i ); Consensus: All the nodes agree on global values of (p 0 ,α) by averaging all the local estimates (p (i) 0 ,α (i) ); Iterative scheme: Start with n ← 1; Update position: Every node i with |Γa(i)| + |Γ as a benchmark for both RD-ML and D-ML. In the following, this algorithm will be denoted by C-MLE.…”

Section: Algorithm 2 Distributed Maximum Likelihood (D-ml)mentioning

confidence: 99%

“…Expectation-Maximization (EM) [21], and its variant, Expectation-Conditional Maximization (ECM) [22], the unknown positions are treated as deterministic, while in Bayesian algorithms, e.g. Nonparametric Belief Propagation (NBP) [23], Sum-Product Algorithm over Wireless Networks (SPAWN) [24,25] and its variant Sigma-Point SPAWN [26], the unknown positions are assumed to be random variables with a known prior distribution.…”

Section: Introductionmentioning

confidence: 99%

Robust distributed cooperative RSS-based localization for directed graphs in mixed LoS/NLoS environments

Carlino

Jin

Muma

et al. 2019

J Wireless Com Network

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The accurate and low-cost localization of sensors using a wireless sensor network is critically required in a wide range of today's applications. We propose a novel, robust maximum likelihood-type method for distributed cooperative received signal strength-based localization in wireless sensor networks. To cope with mixed LoS/NLoS conditions, we model the measurements using a two-component Gaussian mixture model. The relevant channel parameters, including the reference path loss, the path loss exponent and the variance of the measurement error, for both LoS and NLoS conditions, are assumed to be unknown deterministic parameters and are adaptively estimated. Unlike existing algorithms, the proposed method naturally takes into account the (possible) asymmetry of links between nodes. The proposed approach has a communication overhead upper-bounded by a quadratic function of the number of nodes and computational complexity scaling linearly with it. The convergence of the proposed method is guaranteed for compatible network graphs and compatibility can be tested a priori by restating the problem as a graph coloring problem. Simulation results, carried out in comparison to a centralized benchmark algorithm, demonstrate the good overall performance and high robustness in mixed LoS/NLoS environments.

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Efficient cooperative localization algorithm in LOS/NLOS environments

Cited by 9 publications

References 11 publications

Distributed Direct Localization Suitable for Dense Networks

Distributed Direct Localization Suitable for Dense Networks

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

Robust distributed cooperative RSS-based localization for directed graphs in mixed LoS/NLoS environments

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