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The comparative analysis of the efficiency of processing of signals information parameters by the discriminators of tracking meters when exposed non-Gaussian broadband and band-limited noise is carried out. It is shown that when band-limited noise influences the discriminator, the efficiency of non-linear processing is higher and it the band-limited path and it increases with the increase of the signal/noise...
This paper studies the linear quadratic Gaussian (LQG) control problem for wireless networked control systems in which control inputs are randomly dropped without the packet acknowledgment The packet acknowledgment is based on a signal scheme between the actuator and the estimator, which make the estimator know whether control packets are dropped or not. For such systems, the calculation of the optimal...
In order to improve the posture estimation accuracy of small ship, a regularized particle filter based on multidimensional autoregressive model (MARM) is proposed in this paper. The small ship has the characteristics of nonlinear stochastic dynamical systems. Particle filter algorithm has been proposed to deal with nonlinear problems for many years. Although it is effective, two problems often arise:...
In this paper we study the initialization step of decoding algorithms for efficient error-control coding techniques, and its dependence of the channel characteristics over which transmission is performed. LDPC and Polar codes are selected as efficient error-control codes operating over different channels. Channels under study are the classic Additive White Gaussian noise channel, the Rayleigh fading...
This paper investigates wind power potential assessment of Kano, located at North-Western region of Nigeria where the wind is highly variable and energy demand is high. Weibull probability density function is used to analyze Kano wind speed data obtained from Nigerian Meteorological Agency (NIMET). The analysis shows that average monthly wind power density of Kano varies from 213.913 W/m2 to 735.925...
This paper deals with a modeling of data by several mixtures of different distributions within a task of clustering. This issue can be required from a practical point of view, e.g., for a multi-modal system, which generates measurements described by different distributions. The approach is based on the partition of the data on several parts, the factorization of the joint probability density function...
This paper considers the problem of range-based decentralized localization in wireless sensor networks when the impulsive measurement noise is present. We develop a robust localization estimator requiring no a priori knowledge of the noise distribution. The approach to robust localization presented here follows the concept of M-estimation and is implemented in a decentralized manner thus suiting the...
This paper proposes a new TDOA estimation based on phase-voting cross correlation and circular standard deviation. Based on phase delay and kernel function, the proposed method generates a probability density function (PDF) of TDOA for each frequency bin. TDOA estimate is determined by voting the PDFs generated for all frequency bins. Peak positions of the bin-wise PDFs for the target signal are concentrated...
Currently the number of applications where the data generation function is not known has been growing, making necessary the use of non-parametric estimation techniques to describe such model. Therefore, relevant questions emerge regarding the quality of the model that represents some dataset and how to quantify this quality. This article aims to evaluate some of the measurements presented in the literature...
The modeling of speech can be used for speech synthesis and speech recognition. We present a speech analysis method based on pole-zero modeling of speech with mixed block sparse and Gaussian excitation. By using a pole-zero model, instead of the all-pole model, a better spectral fitting can be expected. Moreover, motivated by the block sparse glottal flow excitation during voiced speech and the white...
In this paper, a generalization of the Misspecified Cramér-Rao Bound (MCRB) and of the Constrained MCRB (CMCRB) to complex parameter vectors is presented. Our derivation aims at providing lower bounds on the Mean Square Error (MSE) for both circular and non-circular, MS-unbiased, mismatched estimators. A simple toy example is also presented to clarify the theoretical findings.
The last decades have witnessed the development of degradation modeling based remaining useful life (RUL) estimation, especially for Wiener process based degradation models. However, most researchers paid their attention to the drift coefficient representing the degradation rate, while much fewer eyes focus on the diffusion coefficient. The under consideration of diffusion process may cause bias in...
In this paper, we consider the nonlinear filtering by using information geometric approach. Under the principle of Bayesian, the filtering problem has been converted to Bayesian estimation. Based on the estimation conditional on the measurement, the posterior probability density functions (PDFs) have constructed a statistical manifold. With the information geometric approach, the nonlinear characteristic...
In this paper, an improved estimation of distribution algorithm (EDA) is proposed and applied to the identification of ARMA model parameters. The system parameter identification problem is transformed into the optimization problem in high dimensional parameter space. Based on the traditional EDA algorithm, the parameters of preliminary estimation and data selection are added to improve the speed of...
Target detection experiments with a novel non-parametric detector are carried out exploiting the availability of a new hyperspectral data set featuring a suburban scene with several different targets. Benefiting from its non-parametric nature and from its data adaptivity deriving from the variable-bandwidth approach, the detector is shown to provide promising results for the detection of the targets...
In this paper, we investigate the Bayesian filtering problem for discrete nonlinear dynamical systems which contain random parameters. An augmented cubature Kalman filter (CKF) is developed to deal with the random parameters, where the state vector is enlarged by incorporating the random parameters. The corresponding number of cubature points is increased, so the augmented CKF method requires more...
For nonlinear estimation, the Gaussian sum filter (GSF) provides a flexible and effective framework. It approximates the posterior probability density function (pdf) by a Gaussian mixture in which each Gaussian component is obtained using a linear minimum mean squared error (LMMSE) estimator. However, for a highly nonlinear problem with large measurement noise, the estimation performance of the LMMSE...
Bayesian filters are often used in statistical inference and consist of recursively alternating between two steps: prediction and correction. Most commonly the Gaussian distribution is used within the Bayes filtering framework, but other distributions, which could model better the nature of the estimated phenomenon like the von Mises-Fisher distribution on the unit sphere, have also been subject of...
Fault estimation with performance analysis is investigated in the least squares sense for a class of time-varying systems with event-triggered measurement transmission and biased process noise covariances. In most other work, process noise covariance is assumed as either completely known. In this paper, we relax the assumption of knowing the exact process noise covariance and apply an event-triggered...
Offloading traffic from the macro cells (MCs) with limited frequency resources to the high frequency small cells (SCs) improves the spectral efficiency and the energy efficiency in the inter-frequency heterogeneous networks (HetNets). Inter-frequency measurement (IFM) is an essential process prior to offloading terminals and their traffic to the SCs, and has a great impact on the utilization of the...
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