Statistical Trilateration With Skew-t Distributed Errors in LTE Networks

Müller, Philipp L.; Peral-Rosado, José A. del; Piché, Robert

doi:10.1109/twc.2016.2597836

Cited by 21 publications

(18 citation statements)

References 33 publications

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“…For instance, characterizing range measures in NLOS conditions in ultra-wideband (UWB) localization [7] or in multipath channels when ranging is computed with long-term evolution (LTE) networks [8]. Interestingly, this distribution allows a Gaussian meanscale mixture (GMSM) representation, which implies that the distribution can be reformulated as hierarchically (conditionally) Gaussian [9,10].…”

Section: State-of-the-artmentioning

confidence: 99%

“…In general, these non-Gaussian behaviors can be effectively characterized by parametric heavy-tailed and positive-skewed noise distributions. It has been recently shown experimentally that TOA-based positioning under NLOS conditions [7] and multipath ranging error distributions in LTE networks [8] can be well approximated by a skew t-distribution [9]. Taking into account the problem at hand, we are interested in measurement models with independent observation components and measurement noise models where the noise components are independently univariate skew t-distributed…”

Section: Algorithm 1 Sigma-point Kalman Filter/smoothermentioning

confidence: 99%

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NLOS mitigation in indoor localization by marginalized Monte Carlo Gaussian smoothing

Vilà‐Valls

Closas

2017

EURASIP J. Adv. Signal Process.

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One of the main challenges in indoor time-of-arrival (TOA)-based wireless localization systems is to mitigate non-line-of-sight (NLOS) propagation conditions, which degrade the overall positioning performance. The positive skewed non-Gaussian nature of TOA observations under LOS/NLOS conditions can be modeled as a heavy-tailed skew t-distributed measurement noise. The main goal of this article is to provide a robust Bayesian inference framework to deal with target localization under NLOS conditions. A key point is to take advantage of the conditionally Gaussian formulation of the skew t-distribution, thus being able to use computationally light Gaussian filtering and smoothing methods as the core of the new approach. The unknown non-Gaussian noise latent variables are marginalized using Monte Carlo sampling. Numerical results are provided to show the performance improvement of the proposed approach.

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Section: State-of-the-artmentioning

confidence: 99%

Section: Algorithm 1 Sigma-point Kalman Filter/smoothermentioning

confidence: 99%

NLOS mitigation in indoor localization by marginalized Monte Carlo Gaussian smoothing

Vilà‐Valls

Closas

2017

EURASIP J. Adv. Signal Process.

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“…External reference options, such as the well-known Global Positioning System (GPS), optical motion capture systems, and the trendy ultra-wideband (UWB) anchors, have powered MASs for recreational or professional use [5]- [7]. Terrestrial wireless communications and the broadcast signals of opportunity (SOPs), such as cellular signals [8]- [11] and the signals from Iridium system [12], have also been demonstrated to navigate MASs in recent studies. In all cases, by comparing absolute position and time relative to the external reference, the relative localization and clock synchronization among each agent can be straightforwardly determined.…”

Section: Introductionmentioning

confidence: 99%

“…Extract the TOA measurement: (8). 8: Calculate the reception interval: ∆t = T j i rx,k −T j i rx,k−1 .…”

mentioning

confidence: 99%

BLAS: Broadcast Relative Localization and Clock Synchronization for Dynamic Dense Multiagent Systems

Shi

Cui

Zhao

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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The spatiotemporal information plays crucial roles in a multi-agent system (MAS). However, for a highly dynamic and dense MAS in unknown environments, estimating its spatiotemporal states is a difficult problem. In this paper, we present BLAS: a wireless broadcast relative localization and clock synchronization system to address these challenges. Our BLAS system exploits a broadcast architecture, under which a MAS is categorized into parent agents that broadcast wireless packets and child agents that are passive receivers, to reduce the number of required packets among agents for relative localization and clock synchronization. We first propose an asynchronous broadcasting and passively receiving (ABPR) protocol. The protocol schedules the broadcast of parent agents using a distributed time division multiple access (D-TDMA) scheme and delivers interagent information used for joint relative localization and clock synchronization. We then present distributed state estimation approaches in parent and child agents that utilize the broadcast inter-agent information for joint estimation of spatiotemporal states. The simulations and real-world experiments based on ultra-wideband (UWB) illustrate that our proposed BLAS cannot only enable accurate, high-frequency and real-time estimation of relative position and clock parameters but also support theoretically an unlimited number of agents.

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“…Related studies have also modeled this presence of delayed measurements using skew-t distributions (Nurminen et al, 2015;Müller et al, 2016) and Gaussian mixture models (Müller et al, 2014). It would be an interesting topic for future work to study how these approaches compare to each other.…”

Section: Combining Uwb With Inertial Sensorsmentioning

confidence: 99%

Probabilistic modeling for sensor fusion with inertial measurements

Kok¹

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In recent years, inertial sensors have undergone major developments. The quality of their measurements has improved while their cost has decreased, leading to an increase in availability. They can be found in stand-alone sensor units, so-called inertial measurement units, but are nowadays also present in for instance any modern smartphone, in Wii controllers and in virtual reality headsets.The term inertial sensor refers to the combination of accelerometers and gyroscopes. These measure the external specific force and the angular velocity, respectively. Integration of their measurements provides information about the sensor's position and orientation. However, the position and orientation estimates obtained by simple integration suffer from drift and are therefore only accurate on a short time scale. In order to improve these estimates, we combine the inertial sensors with additional sensors and models. To combine these different sources of information, also called sensor fusion, we make use of probabilistic models to take the uncertainty of the different sources of information into account. The first contribution of this thesis is a tutorial paper that describes the signal processing foundations underlying position and orientation estimation using inertial sensors.In a second contribution, we use data from multiple inertial sensors placed on the human body to estimate the body's pose. A biomechanical model encodes the knowledge about how the different body segments are connected to each other. We also show how the structure inherent to this problem can be exploited. This opens up for processing long data sets and for solving the problem in a distributed manner.Inertial sensors can also be combined with time of arrival measurements from an ultrawideband (uwb) system. We focus both on calibration of the uwb setup and on sensor fusion of the inertial and uwb measurements. The uwb measurements are modeled by a tailored heavy-tailed asymmetric distribution. This distribution naturally handles the possibility of measurement delays due to multipath and non-line-of-sight conditions while not allowing for the possibility of measurements arriving early, i.e. traveling faster than the speed of light.Finally, inertial sensors can be combined with magnetometers. We derive an algorithm that can calibrate a magnetometer for the presence of metallic objects attached to the sensor. Furthermore, the presence of metallic objects in the environment can be exploited by using them as a source of position information. We present a method to build maps of the indoor magnetic field and experimentally show that if a map of the magnetic field is available, accurate position estimates can be obtained by combining inertial and magnetometer measurements.v Populärvetenskaplig sammanfattningNär regissören Seth MacFarlane animerade teddybjörnen Ted i den storsäljande filmen med samma namn, lånade han ut inte bara sin röst utan också sin kropp till Ted. Genom att montera en mängd sensorer på kroppen, kan man lagra rörelse-mönster digita...

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Statistical Trilateration With Skew-t Distributed Errors in LTE Networks

Cited by 21 publications

References 33 publications

NLOS mitigation in indoor localization by marginalized Monte Carlo Gaussian smoothing

NLOS mitigation in indoor localization by marginalized Monte Carlo Gaussian smoothing

BLAS: Broadcast Relative Localization and Clock Synchronization for Dynamic Dense Multiagent Systems

Probabilistic modeling for sensor fusion with inertial measurements

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