NLOS Mitigation in TOA-based Indoor Localization by Nonlinear Filtering under Skew t-distributed Measurement Noise

Wireless localization by time-of-arrival (TOA) measurements is typically corrupted by non-line-of-sight (NLOS) conditions, causing biased range measurements that can degrade the overall positioning performance of the system. In this article, we propose a localization algorithm that is able to mitigate the impact of NLOS observations by employing a heavy-tailed noise statistical model. Modeling the observation noise by a skew t-distribution allows us to, on the one hand, employ a computationally light sigma-point Kalman filtering method while, on the other hand, be able to effectively characterize the positive skewed non-Gaussian nature of TOA observations under LOS/NLOS conditions. Numerical results show the enhanced performance of such approach

Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density

This paper deals with the problem of inferring the signals and parameters that cause neural activity to occur. The ultimate challenge being to unveil brain’s connectivity, here we focus on a microscopic vision of the problem, where single neurons (potentially connected to a network of peers) are at the core of our study. The sole observation available are noisy, sampled voltage traces obtained from intracellular recordings. We design algorithms and inference methods using the tools provided by stochastic filtering that allow a probabilistic interpretation and treatment of the problem. Using particle filtering, we are able to reconstruct traces of voltages and estimate the time course of auxiliary variables. By extending the algorithm, through PMCMC methodology, we are able to estimate hidden physiological parameters as well, like intrinsic conductances or reversal potentials. Last, but not least, the method is applied to estimate synaptic conductances arriving at a target cell, thus reconstructing the synaptic excitatory/inhibitory input traces. Notably, the performance of these estimations achieve the theoretical lower bounds even in spiking regimes.Postprint (published version

Frame Synchronization for Next Generation Uplink Coding in Deep Space Communications

In this paper we develop two new approaches for frame synchronization in the binary-input AWGN channel, in which we account for the sign ambiguity of the received symbols and exploit knowledge of an alternating sequence which precedes the synchronization word. We present an approach based on an extended sliding window and the appropriate decision metric. For the common case that the synchronization word is followed by encoded data we present a solution which exploits the error detection capability of the channel decoder and applies a list decoding approach for frame synchronization. The proposed methods are validated through computer simulations in the deepspace communication uplink and show significant performance gains compared to current solutions

Potential Game for Energy-Efficient RSS-based Positioning in Wireless Sensor Networks

Positioning is a key aspect for many applications in wireless sensor networks. In order to design practical positioning algorithms, employment of efficient algorithms that maximize the battery lifetime while achieving a high degree of accuracy is crucial. The number of participating anchor nodes and their transmit power have an important impact on the energy consumption of positoning a node. This paper proposes a game theoretical algorithm to optimize resource usage in obtaining location information in a wireless sensor network. The proposed method provides positioning and tracking of nodes using RSS measurements. We use the Geometric Dilution of Precision as an optimization metric for our algorithm, with the aim of minimizing the number and power of anchor nodes that collaborate in positioning, thus saving energy. The algorithm is shown to be a potential game, therefore convergence is guaranteed. A distributed low complexity solution for the implementation is presented. The game is applied to WSN and results show the trade-off between power saving and positioning error

Importance Gaussian Quadrature

Importance sampling (IS) and numerical integration methods are usually employed for approximating moments of complicated target distributions. In its basic procedure, the IS methodology randomly draws samples from a proposal distribution and weights them accordingly, accounting for the mismatch between the target and proposal. In this work, we present a general framework of numerical integration techniques inspired by the IS methodology. The framework can also be seen as an incorporation of deterministic rules into IS methods, reducing the error of the estimators by several orders of magnitude in several problems of interest. The proposed approach extends the range of applicability of the Gaussian quadrature rules. For instance, the IS perspective allows us to use Gauss-Hermite rules in problems where the integrand is not involving a Gaussian distribution, and even more, when the integrand can only be evaluated up to a normalizing constant, as it is usually the case in Bayesian inference. The novel perspective makes use of recent advances on the multiple IS (MIS) and adaptive (AIS) literatures, and incorporates it to a wider numerical integration framework that combines several numerical integration rules that can be iteratively adapted. We analyze the convergence of the algorithms and provide some representative examples showing the superiority of the proposed approach in terms of performance

Sequential estimation of neural models by Bayesian filtering

Un dels reptes més difícils de la neurociència és el d'entendre la connectivitat del cervell. Aquest problema es pot tractar des de diverses perspectives, aquí ens centrem en els fenòmens locals que ocorren en una sola neurona. L'objectiu final és, doncs, entendre la dinàmica de les neurones i com la interconnexió amb altres neurones afecta al seu estat. Les observacions de traces del potencial de membrana constitueixen la principal font d'informació per a derivar models matemàtics d'una neurona, amb cert sentit biofísic. En particular, la dinàmica de les variables auxiliars i els paràmetres del model són estimats a partir d'aquestes traces de voltatge. El procés és en general costós i típicament implica una gran varietat de blocatges químics de canals iònics, així com una certa incertesa en els valors dels paràmetres a causa del soroll de mesura. D'altra banda, les traces de potencial de membrana també són útils per obtenir informació valuosa sobre l'entrada sinàptica, un problema invers sense solució satisfactòria a hores d'ara. En aquesta Tesi, estem interessats en mètodes d'estimació seqüencial, que permetin evitar la necessitat de repeticions que podrien ser contaminades per la variabilitat neuronal. En particular, ens concentrem en mètodes per extreure l'activitat intrínseca dels canals iònics, és a dir, les probabilitats d'obertura i tancament de canals iònics, i la contribució de les conductàncies sinàptiques. Hem dissenyat un mètode basat en la teoria Bayesiana de filtrat per inferir seqüencialment aquestes quantitats a partir d'una única traça de voltatge, potencialment sorollosa. El mètode d'estimació proposat està basat en la suposició d'un model de neurona conegut. Això és cert fins a cert punt, però la majoria dels paràmetres en el model han de ser estimats per endavant (això és valid per a qualsevol model). Per tant, el mètode s'ha millorat pel cas de models amb paràmetres desconeguts, incloent-hi un procediment per estimar conjuntament els paràmetres i les variables dinàmiques. Hem validat els mètodes d'inferència proposats mitjançant simulacions realistes. Les prestacions en termes d'error d'estimació s'han comparat amb el límit teòric, que s'ha derivat també en el marc d'aquesta Tesi

Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks

Multitemporal hyperspectral unmixing (MTHU) is a fundamental tool in the analysis of hyperspectral image sequences. It reveals the dynamical evolution of the materials (endmembers) and of their proportions (abundances) in a given scene. However, adequately accounting for the spatial and temporal variability of the endmembers in MTHU is challenging, and has not been fully addressed so far in unsupervised frameworks. In this work, we propose an unsupervised MTHU algorithm based on variational recurrent neural networks. First, a stochastic model is proposed to represent both the dynamical evolution of the endmembers and their abundances, as well as the mixing process. Moreover, a new model based on a low-dimensional parametrization is used to represent spatial and temporal endmember variability, significantly reducing the amount of variables to be estimated. We propose to formulate MTHU as a Bayesian inference problem. However, the solution to this problem does not have an analytical solution due to the nonlinearity and non-Gaussianity of the model. Thus, we propose a solution based on deep variational inference, in which the posterior distribution of the estimated abundances and endmembers is represented by using a combination of recurrent neural networks and a physically motivated model. The parameters of the model are learned using stochastic backpropagation. Experimental results show that the proposed method outperforms state of the art MTHU algorithms

Design of a simpler ampere-hourmeter

In any application in which working with batteries is a must, the knowledge of the batteries’ state-of-charge (SOC) is a fundamental parameter for anyone, so it determines the remaining capacity in the battery. There exist several methods for the estimation of this SOC in Lead-acid batteries; however, when the requisites of the measuring method must offer, besides precision and reliability, the possibility to integrate the results into an automatized system, the Coulomb’s counter is the method that prevails. This paper presents, then, the design of a simpler Ampere-hourmeter based lead-acid battery SOC estimating system. Supported by previous studies in the field of SOC estimation in Hybrid Electric Vehicles and by experimental tests carried out by the researchers, the modelling of the simpler measuring system has been parameterized by following Peukert’s Equation, and afterwards it has been integrated into a data acquisition and processing system designed through a CSNX25 current sensor and a low-cost and low-consume 16F877 microcontroller. The fundamental conclusion of this paper is that obtaining an accurate result while estimating the SOC of a lead-acid battery with an simpler Ampere-hourmeter developed through a microcontroller based system is achievable but only if the used microcontroller holds enough memory to include the whole data treatment algorithms.Peer ReviewedPostprint (published version