Generalized Likelihood Ratio Test Technique Research Articles

Online reduced gaussian process regression based generalized likelihood ratio test for fault detection

In this paper we consider a new fault detection approach that merges the benefits of Gaussian process regression (GPR) with a generalized likelihood ratio test (GLRT). The GPR is one of the most well-known machine learning techniques. It is simpler and generally more robust than other methods. To deal with both high computational costs for large data sets and time-varying dynamics of industrial processes, we consider a reduced and online version of the GPR method. The online reduced GPR (ORGPR) aims to select a reduced set of kernel functions to build the GPR model and apply it for online fault detection based on GLRT chart. Compared with the conventional GPR technique, the proposed ORGPR method has the advantages of improving the computational efficiency by decreasing the dimension of the kernel matrix. The developed ORGPR-based GLRT (ORGPR-based GLRT) could improve the fault detection efficiency since it is able to track the time-varying characteristics of the processes. The fault detection performance of the developed ORGPR-based GLRT method is evaluated using a Tennessee Eastman process. The simulation results show that the proposed method outperforms the conventional GPR-based GLRT technique.

New Robust Statistics for Change Detection in Time Series of Multivariate SAR Images

This paper explores the problem of change detection in time series of heterogeneous multivariate synthetic aperture radar images. Classical change detection schemes have modeled the data as a realization of Gaussian random vectors and have derived statistical tests under this assumption. However, when considering high-resolution images, the heterogeneous behavior of the scatterers is not well described by a Gaussian model. In this paper, the data model is extended to spherically invariant random vectors where the heterogeneity of the images is accounted for through a deterministic texture parameter. Then, three separate detection problems are considered and generalized likelihood ratio test technique is used to derive statistical tests for each problem. The constant false alarm rate property of the new statistics are studied both theoretically and through simulation. Finally, the performance of the new statistics are studied both in simulation and on real synthetic aperture radar data and compared to Gaussian-derived ones. The study yields promising results when the data are heterogeneous.

Robust Target Identification Using a Modified Generalized Likelihood Ratio Test

In order to correctly identify a remote target, an efficient and robust target signature identification technique is required. Radar target identification based on complex natural resonances (CNRs) has drawn the interest of many researchers following the development of the singularity expansion method (SEM). As evident from the literature, statistical techniques such as the generalized likelihood ratio test (GLRT) have produced a better identification result, in the presence of noise, compared to some other SEM-based identification methods such as the extinction pulse (E-pulse) technique. However, one of the issues related to a resonance based target classifier is that it requires the commencement of the late time period for the unknown target response to be determined accurately in order to avoid false alarms during target classification process. For automatic target recognition (ATR) applications, usually such information is not known a priori. In view of this problem, a modified GLRT technique that utilizes time-frequency analysis is presented in this paper. The improved GLRT method does not require prior knowledge of the beginning of the late time period for the transient response of the unknown target. Simulation results using various targets show that our method is comparable to the original GLRT technique when the commencement of the late time period for the unknown target response is correctly determined and outperforming the original GLRT technique when the commencement of the late time period for the unknown target response is incorrectly determined.

Monitoring signal integrity of atomic clocks by means of the GLRT

The generalized likelihood ratio test (GLRT) is a statistical fault detection method used for revealing fault occurrences in electronic systems. In this paper, the GLRT technique is analysed and customized for a rubidium frequency standard in order to reveal mean or standard deviation changes in the clock frequency. Experimental results are presented that confirm the effectiveness of the technique also when it is applied to data acquired from a rubidium clock. Monte Carlo simulations are shown in order to characterize the proposed method and to give a simple interpretation of the obtained results. The effectiveness of the GLRT has been already compared with standard tools such as the Allan variance (Nunzi E et al 2007 IEEE Trans. Instrum. Meas. 56 523–8). In particular, the sensitivity of the method with respect to the jump size has been analysed. In this paper, the fault detection technique is characterized with respect to its readiness in terms of the number of samples employed for obtaining a failure occurrence when applied to clock frequency. Results obtained are employed for giving practical indications on the design of this failure test.