Least‐Squares Filtering Algorithm in Sensor Networks with Noise Correlation and Multiple Random Failures in Transmission

Caballero-Águila, Raquel; Hermoso-Carazo, Aurora; Linares-Pérez, Josefa

doi:10.1155/2017/1570719

Cited by 5 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, assuming that the signal process and the observation noises are N-step auto-and crosscorrelated, the estimation problem has been investigated in Tian et al (2019). On the other hand, assuming that the evolution model generating the signal process is not available, filtering algorithms have been also derived when the measured outputs are perturbed by correlated noises in Caballero-Águila et al (2017c), considering multiple random transmission uncertainties, and in García-Ligero et al (2017) for the case of multiple packet dropouts.…”

Section: Introductionmentioning

confidence: 99%

Least-squares estimators for systems with stochastic sensor gain degradation, correlated measurement noises and delays in transmission modelled by Markov chains

García-Ligero

Hermoso-Carazo

Linares-Pérez

2020

International Journal of Systems Science

Self Cite

View full text Add to dashboard Cite

This paper addresses the linear least-squares estimation of a signal from measurements subject to stochastic sensor gain degradation and random delays during the transmission. These uncertainty phenomena, common in network systems, have traditionally been described by independent Bernoulli random variables. We propose a model that is more general and therefore has greater applicability to real-life situations. The model has two particular characteristics: firstly, the sensor gain degradation is represented by a white sequence of random variables with values in [0,1]; in addition, the absence or presence of delays in the transmission is described by a homogeneous three-state Markov chain, which reflects a possible correlation of delays at different sampling times. Furthermore, assuming that the measurement noise is one-step correlated, we obtain recursive prediction, filtering and fixed-point smoothing algorithms using the first and second-order moments of the signal and the processes present in the observation model. Simulation results for a scalar signal are provided to illustrate the feasibility of the proposed algorithms, using the estimation error variances as a measure of the quality of the estimators.

show abstract

Section: Introductionmentioning

confidence: 99%

Least-squares estimators for systems with stochastic sensor gain degradation, correlated measurement noises and delays in transmission modelled by Markov chains

García-Ligero

Hermoso-Carazo

Linares-Pérez

2020

International Journal of Systems Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, in wireless communication, speech enhancement systems or global navigation satellite systems, the noises are usually correlated and cross-correlated. For this reason, research into sensor network systems under the assumption of correlated and/or cross-correlated noises is a promising field of activity, and numerous papers incorporating this assumption are now appearing (see, for example, [29][30][31][32][33]).…”

Section: Introductionmentioning

confidence: 99%

Distributed Fusion Estimation with Sensor Gain Degradation and Markovian Delays

García-Ligero¹,

Hermoso-Carazo²,

Linares-Pérez³

2020

Mathematics

Self Cite

View full text Add to dashboard Cite

This paper investigates the distributed fusion estimation of a signal for a class of multi-sensor systems with random uncertainties both in the sensor outputs and during the transmission connections. The measured outputs are assumed to be affected by multiplicative noises, which degrade the signal, and delays may occur during transmission. These uncertainties are commonly described by means of independent Bernoulli random variables. In the present paper, the model is generalised in two directions: (i) at each sensor, the degradation in the measurements is modelled by sequences of random variables with arbitrary distribution over the interval [0, 1]; (ii) transmission delays are described using three-state homogeneous Markov chains (Markovian delays), thus modelling dependence at different sampling times. Assuming that the measurement noises are correlated and cross-correlated at both simultaneous and consecutive sampling times, and that the evolution of the signal process is unknown, we address the problem of signal estimation in terms of covariances, using the following distributed fusion method. First, the local filtering and fixed-point smoothing algorithms are obtained by an innovation approach. Then, the corresponding distributed fusion estimators are obtained as a matrix-weighted linear combination of the local ones, using the mean squared error as the criterion of optimality. Finally, the efficiency of the algorithms obtained, measured by estimation error covariance matrices, is shown by a numerical simulation example.

show abstract

“…In refs. [ 7 , 8 , 9 , 10 ], systems with failures during transmission (such as uncertain observations, random delays, and packet dropouts) are considered. Also, recent advances in the estimation, filtering, and fusion of networked systems with network-induced phenomena can be reviewed in refs.…”

Section: Introductionmentioning

confidence: 99%

Centralized Fusion Approach to the Estimation Problem with Multi-Packet Processing under Uncertainty in Outputs and Transmissions

Caballero-Águila

Hermoso-Carazo

Linares-Pérez

2018

Sensors

Self Cite

View full text Add to dashboard Cite

This paper is concerned with the least-squares linear centralized estimation problem in multi-sensor network systems from measured outputs with uncertainties modeled by random parameter matrices. These measurements are transmitted to a central processor over different communication channels, and owing to the unreliability of the network, random one-step delays and packet dropouts are assumed to occur during the transmissions. In order to avoid network congestion, at each sampling time, each sensor’s data packet is transmitted just once, but due to the uncertainty of the transmissions, the processing center may receive either one packet, two packets, or nothing. Different white sequences of Bernoulli random variables are introduced to describe the observations used to update the estimators at each sampling time. To address the centralized estimation problem, augmented observation vectors are defined by accumulating the raw measurements from the different sensors, and when the current measurement of a sensor does not arrive on time, the corresponding component of the augmented measured output predictor is used as compensation in the estimator design. Through an innovation approach, centralized fusion estimators, including predictors, filters, and smoothers are obtained by recursive algorithms without requiring the signal evolution model. A numerical example is presented to show how uncertain systems with state-dependent multiplicative noise can be covered by the proposed model and how the estimation accuracy is influenced by both sensor uncertainties and transmission failures.

show abstract

Least‐Squares Filtering Algorithm in Sensor Networks with Noise Correlation and Multiple Random Failures in Transmission

Cited by 5 publications

References 18 publications

Least-squares estimators for systems with stochastic sensor gain degradation, correlated measurement noises and delays in transmission modelled by Markov chains

Least-squares estimators for systems with stochastic sensor gain degradation, correlated measurement noises and delays in transmission modelled by Markov chains

Distributed Fusion Estimation with Sensor Gain Degradation and Markovian Delays

Centralized Fusion Approach to the Estimation Problem with Multi-Packet Processing under Uncertainty in Outputs and Transmissions

Contact Info

Product

Resources

About