4 research outputs found
Least-Squares Filtering Algorithm in Sensor Networks with Noise Correlation and Multiple Random Failures in Transmission
This paper addresses the least-squares centralized fusion estimation problem of discrete-time random signals from measured outputs, which are perturbed by correlated noises. These measurements are obtained by different sensors, which send their information to a processing center, where the complete set of data is combined to obtain the estimators. Due to random transmission failures, some of the data packets processed for the estimation may either contain only noise (uncertain observations), be delayed (randomly delayed observations), or even be definitely lost (random packet dropouts). These multiple random transmission uncertainties are modelled by sequences of independent Bernoulli random variables with different probabilities for the different sensors. By an innovation approach and using the last observation that successfully arrived when a packet is lost, a recursive algorithm is designed for the filtering estimation problem. The proposed algorithm is easily implemented and does not require knowledge of the signal evolution model, as only the first- and second-order moments of the processes involved are used. A numerical simulation example illustrates the feasibility of the proposed estimators and shows how the probabilities of the multiple random failures influence their performance
Centralized, distributed and sequential fusion estimation from uncertain outputs with correlation between sensor noises and signal
This paper focuses on the least-squares linear fusion filter design
for discrete-time stochastic signals from multisensor measurements
perturbed not only by additive noise, but also by different uncertainties
that can be comprehensively modeled by random parameter
matrices. The additive noises from the different sensors are assumed
to be cross-correlated at the same time step and correlated with
the signal at the same and subsequent time steps. A covariancebased
approach is used to derive easily implementable recursive
filtering algorithms under the centralized, distributed and sequential
fusion architectures. Although centralized and sequential estimators
both have the same accuracy, the evaluation of their computational
complexity reveals that the sequential filter can provide a significant
reduction of computational cost over the centralized one. The
accuracy of the proposed fusion filters is explored by a simulation
example, where observation matrices with random parameters are
used to describe different kinds of sensor uncertainties.This research is supported by Ministerio de EconomĂa, Industria y Competitividad, Agencia Estatal
de InvestigaciĂłn and Fondo Europeo de Desarrollo Regional FEDER [grant number MTM2017-
84199-P]
Networked Fusion Filtering from Outputs with Stochastic Uncertainties and Correlated Random Transmission Delays
This paper is concerned with the distributed and centralized fusion filtering problems in sensor networked systems with random one-step delays in transmissions. The delays are described by Bernoulli variables correlated at consecutive sampling times, with different characteristics at each sensor. The measured outputs are subject to uncertainties modeled by random parameter matrices, thus providing a unified framework to describe a wide variety of network-induced phenomena; moreover, the additive noises are assumed to be one-step autocorrelated and cross-correlated. Under these conditions, without requiring the knowledge of the signal evolution model, but using only the first and second order moments of the processes involved in the observation model, recursive algorithms for the optimal linear distributed and centralized filters under the least-squares criterion are derived by an innovation approach. Firstly, local estimators based on the measurements received from each sensor are obtained and, after that, the distributed fusion filter is generated as the least-squares matrix-weighted linear combination of the local estimators. Also, a recursive algorithm for the optimal linear centralized filter is proposed. In order to compare the estimators performance, recursive formulas for the error covariance matrices are derived in all the algorithms. The effects of the delays in the filters accuracy are analyzed in a numerical example which also illustrates how some usual network-induced uncertainties can be dealt with using the current observation model described by random matrices
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Wireless indoor localisation within the 5G internet of radio light
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonNumerous applications can be enhanced by accurate and efficient indoor localisation using wireless
sensor networks, however trade-offs often exist between these two parameters. In this thesis, realworld
and simulation data is used to examine the hybrid millimeter wave and Visible Light
Communications (VLC) architecture of the 5G Internet of Radio Light (IoRL) Horizon 2020 project.
Consequently, relevant localisation challenges within Visible Light Positioning (VLP) and asynchronous
sampling networks are identified, and more accurate and efficient solutions are developed.
Currently, VLP relies strongly on the assumed Lambertian properties of light sources.
However, in practice, not all lights are Lambertian. To support the widespread deployment of VLC
technology in numerous environments, measurements from non-Lambertian sources are analysed to
provide new insights into the limitations of existing VLP techniques. Subsequently, a novel VLP
calibration technique is proposed, and results indicate a 59% accuracy improvement against existing
methods. This solution enables high accuracy centimetre level VLP to be achieved with non-
Lambertian sources.
Asynchronous sampling of range-based measurements is known to impact localisation
performance negatively. Various Asynchronous Sampling Localisation Techniques (ASLT) exist to
mitigate these effects. While effective at improving positioning performance, the exact suitability of
such solutions is not evident due to their additional processes, subsequent complexity, and increased
costs. As such, extensive simulations are conducted to study the effectiveness of ASLT under variable
sampling latencies, sensor measurement noise, and target trajectories. Findings highlight the
computational demand of existing ASLT and motivate the development of a novel solution. The
proposed Kalman Extrapolated Least Squares (KELS) method achieves optimal localisation
performance with a significant energy reduction of over 50% when compared to current leading ASLT.
The work in this thesis demonstrates both the capability for high performance VLP from non-
Lambertian sources as well as the potential for energy efficient localisation for sequentially sampled
range measurements.Horizon 202