Detecting Signals in a Non-stationary Environment Modeled by a TVAR process, from Data Corrupted by an Additive White Noise
Detecting Signals in a Non-stationary Environment Modeled by a TVAR process, from Data Corrupted by an Additive White Noise
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从被加性白噪声损坏的数据中检测由 TVAR 过程建模的非平稳环境中的信号
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
É. Grivel
中科院分区：
文献类型：
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作者：
Ijima Hiroshi;É. Grivel
本文提出了一种在非平稳环境中检测未知信号的方法。此外，由于传感器的作用，数据会受到加性测量平稳零均值白噪声的干扰。该方法分三步进行：首先，假设非平稳环境被建模为时变自回归(TVAR)过程；其次，基于变量中误差(EIV)方法的进化方法估计TVAR参数以及加性测量白噪声和驾驶过程的方差。第三，信号检测在于研究TVAR模型的归一化预测误差过程。仿真结果表明了该方法的实用性。关键词：-信号检测，非平稳噪声，时变自回归模型，参数估计，进化方法，变量中误差方法，预测误差过程。
In this paper, a method to detect unknown signals ina non-stationaryenvironmentis proposed. In addition, due to the sensor, the data are corrupted by an additive measurement stationary zero-mean white noise.Our approach, which can be useful in a wide range of situations such as the analysis of the object passing by, anomaly detection and digital communications, operates in three steps.Firstly, the nonstationaryenvironmentis assumed to be modeled by a time-varying autoregressive (TVAR) process.Secondly, the TVAR parameters and both the variances of the additive measurement white noise and the driving process are estimated by an evolutive method based on an errors-in-variables (EIV) approach. Thirdly, signal detection consists in studying the normalized prediction-error process of the TVAR model. Simulation results point out the relevance of the approach. Key-Words: -Signal detection, non-stationary noise, time-varying autoregressive model, parameter estimation, evolutivemethod, errors-in-variable approach, prediction-error process.
通过结合卡尔曼滤波器的数据平稳化检测非平稳随机噪声中的信号
DOI：
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发表时间：
2007
期刊：
Proc. 7th IEEE International Symposium on Signal Processing and Information Technology(ISSPIT 2007), Cairo, Egypt
影响因子：
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作者：
H. Ijima;Y. Yamashita;and A. Ohsumi
通讯作者：
and A. Ohsumi