Maximum Likelihood estimation for non-minimum-phase noise transfer function with Gaussian mixture noise distribution

Orellana, Rafael; Bittner, Gustavo; Carvajal, Rodrigo; Agüero, Juan C.

doi:10.1016/j.automatica.2021.109937

Cited by 11 publications

(5 citation statements)

References 37 publications

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“…Te ML method can be deployed to examine the behaviour of channel data parameters. Tis study employs the likelihood function [40][41][42] to determine the ML estimation parameters in the measured pathloss data. Specifcally, the likelihood function of the lognormal distribution for P i (i � 1, 2, 3, .…”

Section: Maximum Likelihood Estimatorsmentioning

confidence: 99%

Statistical Characterization and Modeling of Radio Frequency Signal Propagation in Mobile Broadband Cellular Next Generation Wireless Networks

Isabona

Ibitome

Imoize

et al. 2023

Computational Intelligence and Neuroscience

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An accurate assessment of the spatial and temporal radio frequency channel characteristics is essential for complex signal processing and cellular network optimization. Current research has employed numerous models to figure out how much signal propagation loss occurs along the propagation paths. However, there are issues in finding the right model for a particular terrain because these models are not universally applicable. By employing the lognormal function and the Maximum Likelihood model, a hybrid probabilistic statistical distribution model was evolved. Three LTE cell site locations in Port Harcourt, Nigeria, were used to create a hybrid model that describes the functional stochastic signal propagation loss in the area. The evaluated Maximum Likelihood model accurately estimates the relevant wireless channel properties based on observed field data. The minor square regression approach and the proposed hybrid parameter estimation methodology are compared. When it comes to estimating standard deviation errors as well as the root mean square errors, the ML-based approach consistently outperforms the least square regression model. Finally, the proposed hybrid probabilistic statistical distribution model would be useful for mobile broadband network planning in related wireless propagation conditions.

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Section: Maximum Likelihood Estimatorsmentioning

confidence: 99%

Statistical Characterization and Modeling of Radio Frequency Signal Propagation in Mobile Broadband Cellular Next Generation Wireless Networks

Isabona

Ibitome

Imoize

et al. 2023

Computational Intelligence and Neuroscience

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show abstract

“…We consider that there exists a vector of parameters β = β 0 that defines the "true" system. In order to formulate the ML estimation algorithm, we introduce the following standing assumptions [18], [42], [46]: Assumption 1. The vector of parameters β 0 , the input signal u t , the noise-free input u 0 t , the noise ũt and ỹt in (6) satisfy regularity conditions, guaranteeing that the ML estimate β ML converges (in probability or a.s.) to the true solution β 0 as N → ∞.…”

Section: B Standing Assumptionsmentioning

confidence: 99%

“…The motivations to consider the noise-free input PDF as GMM are i) to satisfy the condition of identifiability [9], [10] and ii) because the GMM approximates any PDF with compact support [33], which allows for identifying the FIR-EIV system with any noisefree input distribution. Furthermore, GMMs have been used in many applications such as filtering [34]- [37], static EIV system identification [38], Bayesian estimation [39], [40], linear dynamic systems estimation [41], [42], uncertainty modeling for FIR systems [43], and astronomy [44]. We use the approximated likelihood given in [32] in order to reduce the computational complexity that is produced by correlated data corresponding to the input and output measurements.…”

Section: Introductionmentioning

confidence: 99%

Finite Impulse Response Errors-in-Variables System Identification Utilizing Approximated Likelihood and Gaussian Mixture Models

et al. 2023

Self Cite

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In this paper a Maximum likelihood estimation algorithm for Finite Impulse Response Errors-in-Variables systems is developed. We consider that the noise-free input signal is Gaussian-mixture distributed. We propose an Expectation-Maximization-based algorithm to estimate the system model parameters, the input and output noise variances, and the Gaussian mixture noise-free input parameters. The benefits of our proposal are illustrated via numerical simulations.

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“…The maximum likelihood principle is widely utilized in system modellng and parameter estimation for the reason that it has good statistical properties and it can be applied for both linear models and nonlinear models [ 16 , 17 ]. In order to obtain the estimated values, the maximum likelihood principle is to maximize the probability of the occurrence of the experimental data [ 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Moisture Ratio Model of Cantaloupe Slices by Maximum Likelihood Principle-Based Algorithms

Zhu

Raghavan

2023

Plants

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As an agricultural plant, the cantaloupe contains rich nutrition and high moisture content. In this paper, the estimation problem of the moisture ratio model during a cantaloupe microwave drying process was considered. First of all, an image processing-based cantaloupe drying system was designed and the expression of the moisture ratio with regard to the shrinkage was built. Secondly, a maximum likelihood principle-based iterative evolution (MLP-IE) algorithm was put forward to estimate the moisture ratio model. After that, aiming at enhancing the model fitting ability of the MLP-IE algorithm, a maximum likelihood principle-based improved iterative evolution (MLP-I-IE) algorithm was proposed by designing the improved mutation strategy, the improved scaling factor, and the improved crossover rate. Finally, the MLP-IE algorithm and MLP-I-IE algorithm were applied for estimating the moisture ratio model of cantaloupe slices. The results showed that both the MLP-IE algorithm and MLP-I-IE algorithm were effective and that the MLP-I-IE algorithm performed better than the MLP-IE algorithm in model estimation and validation.

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Maximum Likelihood estimation for non-minimum-phase noise transfer function with Gaussian mixture noise distribution

Cited by 11 publications

References 37 publications

Statistical Characterization and Modeling of Radio Frequency Signal Propagation in Mobile Broadband Cellular Next Generation Wireless Networks

Statistical Characterization and Modeling of Radio Frequency Signal Propagation in Mobile Broadband Cellular Next Generation Wireless Networks

Finite Impulse Response Errors-in-Variables System Identification Utilizing Approximated Likelihood and Gaussian Mixture Models

Estimating the Moisture Ratio Model of Cantaloupe Slices by Maximum Likelihood Principle-Based Algorithms

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