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This paper presents the design of a new robust nonlinear estimator for estimation of states of nonlinear systems.
That means this study utilises Markov random sub-class of ERG models for modelling cross-sectional PCNs and maximum Monte Carlo Markov chain maximum (MCMC) estimator for estimation purpose.
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A linear fixed order state estimator for state estimation is presented for various estimation error performance criteria in a unified framework.
Observed indicators were declared as categorical and we relied on the robust mean and variance adjusted weighted least squares estimator (WLSMV) for estimation.
To solve this new problem, we propose an estimator for joint estimation of fundamental frequencies and DOAs of multiple sources.
The properties of PCA are exploited to provide the most sensitive dynamic temperature measurement information of the process to the estimator for accurate estimation of compositions.
In this work we have designed a signal based K-exponentially stable frequency estimator for the estimation of the wave encounter frequency at sea.
Here, a robust estimator for state estimation of a class of noisy nonlinear gene networks with uncertain process noise is presented.
To design a stochastic estimator for online estimation of Wiener system of second order, this paper utilizes differential mean value theorem and the results of stochastic contraction theory.
The challenges of the classical correlation-based estimator for TOA estimation are also addressed in Section 'OFDM-based communication system with positioning capability'.
Using the proposed technique, we derive a computationally efficient expectation maximization (EM) estimator for simultaneous estimation of both SI and communication channels.
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