The performance of conventional constant false alarm rate (CFAR) detectors may degrade in nonhomogeneous clutter environments, as accurately estimating the clutter distribution in the cell under test (CUT) using refer...
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The performance of conventional constant false alarm rate (CFAR) detectors may degrade in nonhomogeneous clutter environments, as accurately estimating the clutter distribution in the cell under test (CUT) using reference cells becomes challenging. In this article, a CFAR detector based on clutter segmentation with spatial continuity constraints is proposed for target detection within nonhomogeneous weather clutter backgrounds. Analysis of real weather clutter collected by a high-resolution phased array radar indicates that the Rayleigh mixture model can precisely characterize the amplitude distribution of nonhomogeneous weather clutter in spatial domain. The hidden Markov random field model is employed to capture the spatial correlation of weather clutter. Based on this model, clutter segmentation is implemented using the variational expectation-maximization algorithm, which provides the posterior class of clutter in each range cell and the estimated parameter of each class. Simulation results indicate that introducing the spatial continuity improves the accuracy of clutter segmentation and parameter estimation. A CFAR detection scheme is proposed, which utilizes the segmentation results to estimate the clutter distribution of the CUT and set the detection threshold accordingly. Experiments conducted using both simulated data and real weather clutter have demonstrated that the proposed method improves detection performance. The proposed method exhibit a maximum increase in detection probability of 8.97% compared to the best-performing benchmark method when the false alarm rate is 10(-6) in real weather clutter.
In this paper, we explore the multiple testing problem of paired null hypotheses, for which the data are collected on pairs of entities and tests have to be performed for each pair. Typically, for each pair (i, j), we...
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In this paper, we explore the multiple testing problem of paired null hypotheses, for which the data are collected on pairs of entities and tests have to be performed for each pair. Typically, for each pair (i, j), we observe some interaction/association score between i and j and the aim is to detect the pairs with a significant score. In this setting, it is natural to assume that the true/false null constellation is structured according to an unobserved graph, where present edges correspond to a significant association score. The point of this work is to build an improved multiple testing decision by learning the graph structure. Our approach is in line with the seminal work of Sun and Cai [46], that uses the hidden Markov model to structure the dependencies between null hypotheses. Here, we adapt this strategy by considering the stochastic block model for the latent graph. Under appropriate assumptions, the new proposed procedure is shown to control the false discovery rate, up to remainder terms that vanish when the size of the number of hypotheses increases. The procedure is also shown to be nearly optimal in the sense that it is close to the procedure maximizing the true discovery rate. Numerical experiments reveal that our method outperforms state-of-the-art methods and is robust to model mis-specification. Finally, the applicability of the new method is demonstrated on data concerning the usage of self-service bicycles in London.
3D point cloud data has been widely used in surface quality inspection to measure fabricated artifacts, allowing the high density and precision of measurements and providing quantitative 3D geometric characteristics f...
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3D point cloud data has been widely used in surface quality inspection to measure fabricated artifacts, allowing the high density and precision of measurements and providing quantitative 3D geometric characteristics for anomalies. Unlike structured 3D point cloud data, unstructured 3D point cloud data can capture the surface geometry completely. However, anomaly detection by using unstructured 3D point cloud data is more challenging, due to the nonexistence of global coordinate ordering and the difficulty of mathematically modeling anomalies and discriminating outliers. To deal with these challenges, this article formulates the anomaly detection problem into a probabilistic framework. By categorizing points into three types, i.e., reference surface point, anomaly point, and outlier point, a novel Bayesian network is proposed to model the unstructured 3D point cloud data. The variational expectation-maximization algorithm is used to estimate parameters and make inference on the unknown types of points. Both simulation and real case studies demonstrate the accuracy and robustness of the proposed method.
We propose an evolutionary optimization method for maximum likelihood and approximate maximum likelihood estimation of discrete latent variable models. The proposal is based on modified versions of the expectation-max...
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We propose an evolutionary optimization method for maximum likelihood and approximate maximum likelihood estimation of discrete latent variable models. The proposal is based on modified versions of the expectation-maximization (EM) and variational EM (VEM) algorithms, which are based on the genetic approach and allow us to accurately explore the parameter space, reducing the chance to be trapped into one of the multiple local maxima of the log-likelihood function. Their performance is examined through an extensive Monte Carlo simulation study where they are employed to estimate latent class, hidden Markov, and stochastic block models and compared with the standard EM and VEM algorithms. We observe a significant increase in the chance to reach global maximum of the target function and a high accuracy of the estimated parameters for each model. Applications focused on the analysis of cross-sectional, longitudinal, and network data are proposed to illustrate and compare the algorithms.
作者：
Chengyu TaoJuan DuTzyy-Shuh Changc Interdisciplinary Programs Office
The Hong Kong University of Science and Technology Hong Kong SAR Chinad Guangzhou HKUST Fok Ying Tung Research Institute Guangzhou China a Smart Manufacturing Thrust
The Hong Kong University of Science and Technology (Guangzhou) Guangzhou Chinab Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong SAR Chinac Interdisciplinary Programs Office The Hong Kong University of Science and Technology Hong Kong SAR Chinad Guangzhou HKUST Fok Ying Tung Research Institute Guangzhou China e OG Technologies
Ann Arbor MI USA
3D point cloud data has been widely used in surface quality inspection to measure fabricated artifacts, allowing the high density and precision of measurements and providing quantitative 3D geometric characteristics f...
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3D point cloud data has been widely used in surface quality inspection to measure fabricated artifacts, allowing the high density and precision of measurements and providing quantitative 3D geometric characteristics for anomalies. Unlikestructured3D point cloud data,unstructured3D point cloud data can capture the surface geometry completely. However, anomaly detection by using unstructured 3D point cloud data is more challenging, due to the nonexistence of global coordinate ordering and the difficulty of mathematically modeling anomalies and discriminating outliers. To deal with these challenges, this article formulates the anomaly detection problem into a probabilistic framework. By categorizing points into three types, i.e., reference surface point, anomaly point, and outlier point, a novel Bayesian network is proposed to model the unstructured 3D point cloud data. The variational expectation-maximization algorithm is used to estimate parameters and make inference on the unknown types of points. Both simulation and real case studies demonstrate the accuracy and robustness of the proposed method.
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