49 research outputs found
5G Positioning and Mapping with Diffuse Multipath
5G mmWave communication is useful for positioning due to the geometric
connection between the propagation channel and the propagation environment.
Channel estimation methods can exploit the resulting sparsity to estimate
parameters(delay and angles) of each propagation path, which in turn can be
exploited for positioning and mapping. When paths exhibit significant spread in
either angle or delay, these methods breakdown or lead to significant biases.
We present a novel tensor-based method for channel estimation that allows
estimation of mmWave channel parameters in a non-parametric form. The method is
able to accurately estimate the channel, even in the absence of a specular
component. This in turn enables positioning and mapping using only diffuse
multipath. Simulation results are provided to demonstrate the efficacy of the
proposed approach
Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization
Ultra-Wide Bandwidth (UWB) and mm-wave radio systems can resolve specular multipath components (SMCs) from estimated channel impulse response measurements. A geometric model can describe the delays, angles-of-arrival, and angles-of-departure of these SMCs, allowing for a prediction of these channel features. For the modeling of the amplitudes of the SMCs, a data-driven approach has been proposed recently, using Gaussian Process Regression (GPR) to map and predict the SMC amplitudes. In this paper, the applicability of the proposed multipath-resolved, GPR-based channel model is analyzed by studying features of the propagation channel from a set of channel measurements. The features analyzed include the energy capture of the modeled SMCs, the number of resolvable SMCs, and the ranging information that could be extracted from the SMCs. The second contribution of the paper concerns the potential applicability of the channel model for a multipath-resolved, single-anchor positioning system. The predicted channel knowledge is used to evaluate the measurement likelihood function at candidate positions throughout the environment. It is shown that the environmental awareness created by the multipath-resolved, GPR-based channel model yields higher robustness against position estimation outliers
A Graph-based Algorithm for Robust Sequential Localization Exploiting Multipath for Obstructed-LOS-Bias Mitigation
This paper presents a factor graph formulation and particle-based sum-product
algorithm (SPA) for robust sequential localization in multipath-prone
environments. The proposed algorithm jointly performs data association,
sequential estimation of a mobile agent position, and adapts all relevant model
parameters. We derive a novel non-uniform false alarm (FA) model that captures
the delay and amplitude statistics of the multipath radio channel. This model
enables the algorithm to indirectly exploit position-related information
contained in the MPCs for the estimation of the agent position. Using simulated
and real measurements, we demonstrate that the algorithm can provide
high-accuracy position estimates even in fully obstructed line-of-sight (OLOS)
situations, significantly outperforming the conventional amplitude-information
probabilistic data association (AIPDA) filter. We show that the performance of
our algorithm constantly attains the posterior Cramer-Rao lower bound (PCRLB),
or even succeeds it, due to the additional information contained in the
presented FA model.Comment: corrected small errors, changed titl
Fast Variational Block-Sparse Bayesian Learning
We present a fast update rule for variational block-sparse Bayesian learning
(SBL) methods. Using a variational Bayesian framework, we show how repeated
updates of probability density functions (PDFs) of the prior variances and
weights can be expressed as a nonlinear first-order recurrence from one
estimate of the parameters of the proxy PDFs to the next. Specifically, the
recurrent relation turns out to be a strictly increasing rational function for
many commonly used prior PDFs of the variances, such as Jeffrey's prior. Hence,
the fixed points of this recurrent relation can be obtained by solving for the
roots of a polynomial. This scheme allows to check for convergence/divergence
of individual prior variances in a single step. Thereby, the the computational
complexity of the variational block-SBL algorithm is reduced and the
convergence speed is improved by two orders of magnitude in our simulations.
Furthermore, the solution allows insights into the sparsity of the estimators
obtained by choosing different priors.Comment: 10 pages, 2 figures, submitted to IEEE Transactions on Signal
Processing on 1st of June, 202
Impact of Rough Surface Scattering on Stochastic Multipath Component Models
Multipath-assisted positioning makes use of specular multipath components (MPCs), whose parameters are geometrically related to the positions of the transceiver nodes. Diffuse scattering from rough surfaces affects the observed specular reflections in the angular and delay domains. Based on the effective roughness approach, the angular delay power spectrum can be calculated as a function of location parameters, which-in a next step-could be useful to accurately characterize the position-related information of MPCs. The calculated power spectra follow reported characteristics of stochastic multipath models, i.e. Gaussian shape in the angular domain and an exponential shape in the delay domain. The resulting angular and delay spreads are in an equivalent range to values reported in literature