Generalized Likelihood Ratio Algorithm Research Articles

Sequential Transient Detection for RF Fingerprinting

In this paper, a sequential transient detection method for radio frequency (RF) fingerprinting used in the identification of wireless devices is proposed. To the best knowledge of the authors, sequential detection of transient signals for RF fingerprinting has not been considered in the literature. The proposed method is based on an approximate implementation of the generalized likelihood ratio algorithm. The method can be implemented online in a recursive manner with low computational and memory requirements. The transients of wireless transmitters are detected by using the likelihood ratio of the observations without the requirement of any a priori knowledge about the transmitted signals. The performance of the method was evaluated using experimental data collected from 16 Wi-Fi transmitters and compared to those of two existing methods. The experimental test results showed that the proposed method can be used to detect the transient signals with a low detection delay. Our proposed method estimates transient starting points 20-times faster compared to an existing robust method, as well as providing a classification performance of a mean accuracy close to 95%.

Model and Verification of Medical English Machine Translation Based on Optimized Generalized Likelihood Ratio Algorithm

Phrase identification plays an important role in medical English machine translation. However, the phrases in medical English are complicated in internal structure and semantic relationship, which hinders the identification of machine translation and thus affects the accuracy of translation results. With the aim of breaking through the bottleneck of machine translation in medical field, this paper designed a machine translation model based on the optimized generalized likelihood ratio (GLR) algorithm. Specifically, the model in question established a medical phrase corpus of 250,000 English and 280,000 Chinese words, applied the symbol mapping function to the identification of the phrase’s part of speech, and employed the syntactic function of the multioutput analysis table structure to correct the structural ambiguity in the identification of the part of speech, eventually obtaining the final identification result. According to the comprehensive verification, the translation model employing the optimized GLR algorithm was seen to improve the speed, accuracy, and update performance of machine translation and was seen to be more suitable for machine translation in medical field, therefore providing a new perspective for the employment of medical machine translation.

Issues of an improving the accuracy of energy carriers production forecasting in a computer-aided system for monitoring the operation of a gas-fired cogeneration plant

The article presents issues related to improving the accuracy of forecasting of combined electricity and heat production in a selected gas-fired CHP plant equipped with two gas turbines with heat exchangers and a heat accumulator. To improve the accuracy of forecasting the production of energy carriers in the considered CHP plant, it has been proposed to extend the existing computer system for supervising operation with an additional calculation module. The task of this module is to correct the forecasts for electricity and heat production obtained using gas turbine model based on correction curves. In the additional computational module of the computer system, it is proposed to use empirical functions developed with the use of operational measurements of the analysed gas turbines. In the process of preparing operational measurement data for the construction of static empirical functions, the GLR (Generalized Likelihood Ratio) algorithm was used to detect the process changes and identify the periods of operation of gas turbines in a close to steady-state. The obtained results of the analyses confirm the possibility of their use in the existing computer system of operation supervision of the CHP plant. The developed empirical models are characterized by the high quality of prediction, which is confirmed by the obtained high values of determination coefficients and low error values of the model accuracy measures. The values of the coefficient of determination R^2 are within the following ranges: from 0.9754 to 0.9936 (for gas turbine GT-1) and from 0.9138 to 0.9939 (for gas turbine GT-2). The developed empirical functions can be implemented in an additional calculation module of the computer system, increasing the accuracy of planning the production of energy carriers in a gas-fired power plant.

Detection Algorithm for Weak Signals of Multiple Input and Output Radars

In order to detect weak signals generated by multiple-input and output radars, algorithms used for detecting weak signal are to be studied. The target echo model of multiple input and output radars is constructed by the transmit signal model, the target response model and the clutter fluctuation. Then, sparse decomposition is utilized to remove the noise present in the signal. A generalized likelihood ratio algorithm based on the spatial scattering density is used to weight-accumulate the amplitude of the observed echo, resulting in smaller echo amplitude. The influence of the distance resolution unit of SNR and distance resolution unit with clutters on the detection performance of the detector is reduced to complete the detection of weak signals of multiple input and output radars. The experimental results show that the proposed method has strong anti-interference and the accuracy of the detection is extraordinary.

An Information Geometric Viewpoint on the Detection of Range Distributed Targets

The paper adopts the information geometry, to put forward a new viewpoint on the detection of range distributed targets embedded in Gaussian noise with unknown covariance. The original hypothesis test problem is formulated as the discrimination between distributions of the measurements and the noise. The Siegel distance, which is exactly the well-known geodesic distance between images of the original distributions via embedding into a higher-dimensional manifold, is given as an intrinsic measure on the difference between multivariate normal distributions. Without the assumption of uncorrelated measurements, we propose a set of geometric distance detectors, which is designed based on the Siegel distance and different from the generalized likelihood ratio algorithm or other common criterions in statistics. As special cases, the classical optimal matched filter, Rao test, and Wald test, which have been proven to have the CFAR property, belong to the set. Moreover, it is also accessible to an intuitively geometric analysis about how strongly the data contradict the null hypothesis.

Combined Signal and Model-Based Sensor Fault Diagnosis for a Doubly Fed Induction Generator

The problem of multiplicative and/or additive fault detection and isolation (FDI) in the current sensors of a doubly fed induction generator (DFIG) is considered in the presence of model uncertainty. A residual generator based on the DFIG model is proposed using the structure of the classical generalized observer scheme. However, each observer in this scheme is replaced by a robust H_/H∞ fault detection filter followed by a Kalman-like observer. The latter further attenuates the effect of the modeling uncertainties on the residuals. It exploits the specific pattern induced by the balanced three-phase nature of all the electric signals. It turns out that the FDI problem then amounts to detecting an abrupt change in the mean of the residual vector in the additive fault case, or the appearance of sine waves superimposed on a white noise vector in the multiplicative fault case. A decision algorithm made of a combination of generalized likelihood ratio algorithms allows us to detect and isolate the additive and multiplicative sensor faults. The complete FDI system is tested through simulations on a controlled DFIG.

Oestrus Detection in Dairy Cows Using Likelihood Ratio Tests

This paper addresses detection of oestrus in dairy cows using methods from statistical change detection. The activity of the cows was measured by a necklace attached sensor. Statistical properties of the activity measure were investigated. Using data sets from 17 cows, diurnal activity variations were identified for the ensemble and for the individual cows. A diurnal filter was adapted to remove the daily variation of the individual. Change detection algorithms were designed for the actual probability densities, which were Rayleigh distributed with individual parameters for each cow. A generalized likelihood ratio algorithm was derived for the compensated activity signal and detection algorithm was tested on 2323 days of activity, which contained 42 oestruses on 12 cows in total. The application of statistical change detection methods is a new approach for detecting oestrus in dairy cows and the results are shown to outperform earlier approaches in respect to combined statistics of false alarms and missed detections.

Robust recursive AR speech analysis

In this paper a new robust recursive method of estimating the linear prediction parameters of an auto-regressive speech signal model using weighted least squares with variable forgetting factors (VVFs) is described. The proposed robust recursive least-squares (RRLS) method differs from the conventional recursive least-squares (RLS) method by the insertion of a suitably chosen nonlinear transformation of the prediction residuals. The RRLS algorithm takes into account the contaminated Gaussian nature of the excitation for voiced speech, and the effect of nonlinearity is to assign less weight to the small portions of large residuals so that the spiky excitation will not greatly influence the final AR parameter estimates, while giving unity weight to the bulk of small to moderate residuals generated by the nominal Gaussian distribution. In addition, the VFF is adapted to a nonstationary speech signal by a generalized likelihood ratio algorithm, which accounts for the nonstationarity of a speech signal. The proposed method has a good adaptability to the nonstationary parts of a speech signal, and gives low bias and low variance at the stationary signal segments. The feasibility of the robust approach is demonstrated with both synthesized and natural speech.

A persymmetric multiband GLR algorithm

By exploring the covariance structure information to reduce the uncertainty in adaptive processing, a persymmetric generalized likelihood ratio algorithm (PGLR) is developed together with the closed-form expressions of probabilities of detection and false alarm. This multiband algorithm, which requires less computation, can significantly outperform the corresponding unstructured multiband GLR algorithm, especially in a severely nonstationary and/or nonhomogeneous interference environment. Simulation shows that the constant false alarm rate (CFAR) performance of the new algorithm is as insensitive as that of the unstructured multiband GLR to the departure of interference distribution from Gaussian. >

Performance comparisons of modified SMI and GLR algorithms

Adaptive filtering for signal detection in colored interference of unknown statistics is addressed. The detection performance of a modified version of the well-known sample matrix inversion (SMI) algorithm, called the modified SMI (MSMI), is compared with that of the generalized likelihood ratio (GLR) algorithm in colored Gaussian interference. The performance sensitivity of the MSMI and GLR in colored Weibull and log-normal interference is studied via simulation. It is found that there is almost no need to use the more complicated GLR algorithm in Gaussian interference, while in Weibull or log-normal interference the GLR should be preferable to the MSMI. >

A Computationally Efficient GLR Algorithm for Detecting and Estimating State Jumps in Linear Systems

A generalized likelihood ratio (GLR) algorithm which is computationally efficient is developed for detecting and estimating state jumps in discrete linear stochastic systems. A jump error state variable concept is used to decompose the failure signature matrix into a new failure signature matrix which is independent of the jump time and another matrix which depends only on the jump time. The product of the latter matrixwith the jump vector provides a constant jump error state variable. The nondependency of the new failure signature matrix on the jump time and the transformation of the jump error state to a constant provides for the development of a GLR algorithm with considerably reduced computational and storage requirements. The new algorithm uses the recursive least squares approach together with an efficient search technique for locating the maximum of the log likelihood ratio. It avoids much of the updating and storage of large matrices for past observation times.

On GLR detection and estimation of unexpected inputs in linear discrete systems

In this paper, we present a recursive generalized likelihood ratio (GLR) test algorithm for detecting sudden changes in linear discrete systems. We demonstrate the application of linear filtering techniques to obtain a recursive GLR algorithm so that the requirement for matrix inversions in the previously known GLR algorithms can be reduced or avoided. Furthermore, the GLR algorithm is extended to the case when the sudden change follows known linear dynamics. An adaptive filtering scheme which uses the input estimate to correct the state estimate is also presented for the time-varying input case.