Cooperative Localization in WSNs Using Gaussian Mixture Modeling: Distributed ECM Algorithms

IEEE Trans. Signal Process.

Yin

Fritsche

et al. 2020

Self Cite

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.

Section: Introductionmentioning

confidence: 99%

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

IEEE Trans. Signal Process.

Yin

Fritsche

et al. 2020

Self Cite

“…In non-Bayesian algorithms, e.g. 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%

“…Among various works handling the NLoS effect, a majority of them have treated the NLoS meaures as outliers and tried to neglect or mitigate their effect, including the Maximum Likelihood (ML)-based approach [31,32], the Weighted Least-Squares (WLS) estimator [33,32], the constrained localization techniques [34,35], robust estimators [36,37] and the method based on virtual stations [38]. In contrast to these works, several approaches, including [39,21,22], have proposed specific probabilistic models for the NLoS measures, therewith exploiting the NLoS measures for the localization purpose. In the light of these considerations, our aim is to develop an RSS-based, cooperative localization framework that works in mixed LoS/NLoS environments, requires no knowledge on parameters of the propagation model, and can be realized in a distributed manner.…”

Section: Introductionmentioning

confidence: 99%

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

Carlino

Muma

et al. 2019

J Wireless Com Network

Self Cite

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.

“…Among different types of methods as surveyed in [1], the SPAWN algorithm [2] is a promising solution. To reduce computational complexity and communication load, many variants have been built upon it.…”

Section: Introductionmentioning

confidence: 99%

Cooperative localization based on severely quantized RSS measurements in wireless sensor network

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Yin

Fritsche

et al. 2016

Self Cite

We study severely quantized received signal strength (RSS)-based cooperative localization in wireless sensor networks. We adopt the well-known 'sum-product algorithm over a wireless network' (SPAWN) framework in our study. To address the challenge brought by severely quantized measurements, we adopt the principle of importance sampling and design appropriate proposal distributions. Moreover, we propose a parametric SPAWN in order to reduce both the communication overhead and the computational complexity. Experiments with real data corroborate that the proposed algorithms can achieve satisfactory localization accuracy for severely quantized RSS measurements. In particular, the proposed parametric SPAWN outperforms its competitors by far in terms of communication cost. We further demonstrate that knowledge about non-connected sensors can further improve the localization accuracy of the proposed algorithms.