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Non-Gaussian statistical models fit SAR data better than Gaussian-based statistics, in most cases, but are complicated and time-consuming to use for unsupervised image segmentation via probabilistic clustering. The more advanced the model, the more complicated and slow the clustering. The U-distribution has been demonstrated to be one of the most flexible models, capturing the Gaussian/Wishart, the...
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 our analysis, we consider daily rainfall records y(n) from 3825 areas covering entire China. The daily rainfall records is 50 years long from 1961 to 2011. We study the statistical properties of the daily rainfall data and return intervals Tq between two consecutive rainfall records above some threshold q. Using the detrended fluctuation analysis (DFA) method to analyze the long-term correlation...
We present a new algorithm for detecting forest disturbances from a pair of dual-polarimetric synthetic aperture radar (SAR) data. The algorithm uses the mean dual-polarimetric alpha angle in conjunction with its probability distribution to isolate forest structural changes from statistical noise fluctuations. In contrast to radar backscatter, the dual-polarimetric alpha angle is estimated from the...
A near-real-time algorithm to process satellite synthetic aperture radar (SAR) imagery for urban flood detection still remains a challenge. In this paper, we developed a new floodwater detection algorithm based on SAR backscattered intensity using pre- and post-flood data observed by the Japan Aerospace Exploration Agency's Advanced Land Observing Satellite-2, which provided very high-spatial-resolution...
Spectrum sensing is the important aspect of cognitive radio (CR). In order to use the vacant spectrum, cognitive radio user must be able to identify the presence of empty spectrum efficiently. A non-cooperative spectrum sensing faces the problem of shadowing and hidden terminal due to which the CR user fails to monitor the vacant spectrum. To solve the problem of hidden terminal and shadowing in non-cooperative...
We consider the problem of PSK-n signal reception over linear radio channel in the presence of strong intersymbol interference. Particularly we provide results of probability of error estimation within the problem. It's shown that probability of error depends on initial shift of signal constellation and recommendations for the initial shift are provided.
Considering the uncertain and stochastic of intermittent distributed generations (DGS) in active distribution network (ADN), a scenario method using Wasserstein distance metric and K-means cluster scenes reduction technology to generate optimal scene is proposed in this paper. So the stochastic problem is transformed into a deterministic problem. The multi-scenario tree models of wind-photovoltaic-load...
In order to improve the positioning accuracy and reliability of ship dynamic positioning system, a method of combing Ensemble Adjustment Kalman Filter (EAKF) and Particle Filter was proposed. It's according to the use of the max of posterior probability density to generate the importance density function of particle. So that the importance probability density function could integrate into the latest...
A Takagi-Sugeno (T-S) fuzzy model is applied to approximate the nonlinear dynamics of stochastic distribution control (SDC) systems, in which linear radial basis function (RBF) net work is adopted to approximate the output probability density function (PDF) for non-Gaussian SDC systems. Considering the situation that disturbance and multiple actuator faults may occur at the same time, fault detection,...
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...
In this paper, the parameters and reliability characteristics of the mixture of the failure time distribution are estimated based on a complete sample using both Markov chain Monte Carlo (MCMC) method and maximum likelihood estimation via cross-entropy (CE) algorithm. While maximum likelihood estimation is the most frequently used method for parameter estimation, MCMC has recently emerged as a good...
Detection with multiple distributions is considered. Rather than formulating the problem with multiple hypotheses, we formulate the problem in a binary hypothesis testing framework by a multiple model approach. Three classes of the Multi-Model Detection (MMD) problems are considered: simplex, compound, and mixture. Three concepts of optimality are given for these three problems, including Uniformly...
Belief fusion consists of taking into account multiple sources of belief about a domain of interest. This paper describes cumulative and averaging multi-source belief fusion in the formalism of subjective logic, which represent generalisations of binary-source belief fusion operators previously described. The advantage of this approach is that we can model and analyse belief fusion situations involving...
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...
Despite the wide body of literature on the sizing of energy storage devices available in the domain of electrical energy systems, the problem has not drawn much attention in the area of battery-powered electronic systems.
In this paper, consensus-based Kalman filtering is extended to deal with the problem of joint target tracking and sensor self-localization in a distributed wireless sensor network. The average weighted Kullback-Leibler divergence, which is a function of the unknown drift parameters, is employed as the cost to measure the discrepancy between the fused posterior distribution and the local distribution...
When mounted on a vehicle bumper, ultrasonic transducer signal contains information from valid objects as well as ground reflections. In order to remove ground echoes, the classic approach is to use thresholds to filter reflections of small amplitude. However, valid object reflections can frequently occur beneath the ground thresholds, reducing the detection rate of the sensor. We present an approach...
Some concerns are raised on the prevailing generalized covariance intersection (GCI) based Gaussian mixture probability hypothesis density (GM-PHD) fusion for distributed multiple target tracking under cluttered environments, which is both communicative and computation expensive, and generates a large amount of Gaussian components (GCs) of little physical significance. The problems become more serious...
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...
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