Adaptive detection of range-spread targets in compound Gaussian clutter with the square root of inverse Gaussian texture

The compound Gaussian clutter with the square root of inverse Gaussian texture component has been successfully used for modeling the heavy-tailed non-Gaussian clutter measured by high-resolution radars. In high-resolution radars, the targets may extend along multiple consecutive range cells, which are called range-spread targets. In this paper, we consider the range-spread target detection problem in the compound Gaussian clutter with the square root of inverse Gaussian texture. Three adaptive detectors are proposed based on Bayesian one-step generalized likelihood ratio test, maximum a posteriori generalized likelihood ratio test and Bayesian two-step generalized likelihood ratio test, respectively. Finally, the detection performances of the proposed detectors are evaluated by the Monte Carlo simulation. The simulation results show that the proposed detectors have better detection performance of range-spread target than the conventional generalized likelihood ratio test detector.     

A review of time–frequency matched filter design with application to seizure detection in multichannel newborn EEG

This paper presents a novel design of a time–frequency (t–f) matched filter as a solution to the problem of detecting a non-stationary signal in the presence of additive noise, for application to the detection of newborn seizure using multichannel EEG signals. The solution reduces to two possible t–f approaches that use a general formulation of t–f matched filters (TFMFs) based on the Wigner–Ville and cross Wigner–Ville distributions, and a third new approach based on the signal ambiguity domain representation; referred to as Radon-ambiguity detector. This contribution defines a general design formulation and then implements it for newborn seizure detection using multichannel EEG signals. Finally, the performance of different TFMFs is evaluated for different t–f kernels in terms of classification accuracy using real newborn EEG signals.Experimental results show that the detection method which uses TFMFs based on the cross Wigner–Ville distribution outperforms other approaches including the existing TFMF-based ones. The results also show that TFMFs which use high-resolution kernels such as the modified B-distribution, achieve higher detection accuracies compared to the ones which use other reduced-interference t–f kernels.     

应用阶数自学习自回归隐马尔可夫模型对控制过程异常数据的在线检测

针对过程工业中强噪声环境下实时采集的控制过程海量数据难以在线精确检测的问题,提出了基于阶数自学习自回归隐马尔可夫模型(ARHMM)的工业控制过程异常数据在线检测方法.该算法采用自同归(AR)模型对时间序列进行拟合,利用隐马尔科夫模型(HMM)作为数据检测的工具,避免了传统检测方法中需要预先设定检测阈值的问题,并将传统的...     

网页查重技术在企业数据仓库中的应用

介绍了处理网页排重的三类通用方法，并介绍了在企业数据仓库系统中，通过利用相似性检索技术实现情报资料自动排重的应用。通过对测试结果的评估表明，这种基于相似性检索技术的自动排重的方法能够达到较好的效果，实现了企业情报资料智能化预处理的应用。     

数据挖掘技术用于间歇蒸馏过程转馏分点的在线预报

对于间歇蒸馏过程,提前准确判断从低馏分到主馏分的转馏分点是影响最终产品质量和产量的关键环节,设计基于数据挖掘技术的转馏分点在线预报软测量系统是一项重要的过程质量控制手段,为提高生产的综合自动化水平创造了重要条件。根据混沌理论,温度能较高程度的反映体系内反应及分离情况,因此选取间歇蒸馏上升气温度为考察变量。针对数据非线性、动态、数据长度短、不同批次数据不等长等特点,提出了将不同批次数据按照随机的顺序首尾相接组成长数据集的数据重构策略;采用自回归求和滑动平均方法和最小二乘支持向量机方法建立了组合时间序列预测模型;通过对理论转馏分温度与实际转馏分温度的差值和预测曲线近似斜率的统计分析,建立了转馏分点在线预报系统,经过在实际生产中的验证,实现了对转馏分点提前1min的准确预报。