Likelihood Ratio Test Algorithm Research Articles

Spectral domain isolation of ballistic component in visible light OCT based on random matrix description

Visible light optical coherence tomography (vis-OCT) provides a unique tool for imaging both structure and oxygen metabolism in ophthalmology. Working in visible light bandwidth, it suffers from noises due to strong scattering, especially in the blood. This work established the random matrix (RM) description of vis-OCT’s k-space data as ballistic and multiple scattering components. The eigenvalue density of the hybrid RM follows a low-rank biased Marčenko–Pastur law. The ballistic component can thus be separated out using a generalized likelihood ratio test algorithm. The RM-based method was validated by both the Monte Carlo simulation and ex vivo pure blood phantom study. We further demonstrated that the RM-based method could significantly improve the imaging quality in the human fundus, showing more details of the layered structure than current vis-OCT with ∼23.6% increase of signal-to-noise ratio, measuring the blood oxygen value more accurately, and enabling better structure visualization than the traditional method, a 1.6-fold higher contrast-to-noise ratio in raster scan mode. The isolated ballistic component also fits the Beer–Lambert law better, giving more accurate oxygen saturation in arc scan mode. The RM-based method significantly improves the reconstruction quality in 3D and facilitates clinical diagnostics. As a general framework, random matrix description also provides a new separation strategy to estimate the ballistic component in other spectral domain OCT techniques.

Real-Time Detection of Incipient Inter-Turn Short Circuit and Sensor Faults in Permanent Magnet Synchronous Motor Drives Based on Generalized Likelihood Ratio Test and Structural Analysis.

This paper presents a robust model-based technique to detect multiple faults in permanent magnet synchronous motors (PMSMs), namely inter-turn short circuit (ITSC) and encoder faults. The proposed model is based on a structural analysis, which uses the dynamic mathematical model of a PMSM in an frame to evaluate the system’s structural model in matrix form. The just-determined and over-determined parts of the system are separated by a Dulmage–Mendelsohn decomposition tool. Subsequently, the analytical redundant relations obtained using the over-determined part of the system are used to form smaller redundant testable sub-models based on the number of defined fault terms. Furthermore, four structured residuals are designed based on the acquired redundant sub-models to detect measurement faults in the encoder and ITSC faults, which are applied in different levels of each phase winding. The effectiveness of the proposed detection method is validated by an in-house test setup of an inverter-fed PMSM, where ITSC and encoder faults are applied to the system in different time intervals using controllable relays. Finally, a statistical detector, namely a generalized likelihood ratio test algorithm, is implemented in the decision-making diagnostic system resulting in the ability to detect ITSC faults as small as one single short-circuited turn out of 102, i.e., when less than 1% of the PMSM phase winding is short-circuited.

Performance Improvement for SAR Tomography Based on Local Plane Model

Multilook approaches have been applied in synthetic aperture radar (SAR) tomography (TomoSAR), for improving the density and regularity of persistent scatterers reconstructed from multipass SAR images in both rural and urban regions. Multilook operations assume that all scatterers in a given neighborhood are similar in height, thereby providing additional data for recovering the position and reflectivity of a single scatterer, so that a higher signal-to-noise ratio can be achieved. This is equivalent to assuming that scatterers belonging to a local neighborhood of range–azimuth cells are located on horizontal planes. The present article generalizes this approach by adopting the so-called local plane (LP) model for TomoSAR imaging in urban areas, accounting for local variations in the height of scatterers that are not negligible. Furthermore, an LP-generalized likelihood ratio test (LP-GLRT) algorithm is developed to implement the previous idea. Compared with the multilook generalized likelihood ratio test algorithm, LP-GLRT shows better performance in the case of urban structures and terrains in experiments based on both simulated data and TerraSAR-X images.

Using extended Kalman filter for failure detection and prognostic of degradation process in feedback control system

In this article, we study a new approach to predict failures in feedback control system and particularly in actuators. However, we use two-tank control system with a proportional–integral–derivative controller for controlling a process variable. In practice, the actuator is a dynamic operating component in a random environment. Moreover, its capacity decreases over time and becomes valuable information for reliability analysis. The loss of capacity which is related to degradation, either normally or in an accelerated manner, depends on different operational conditions of the feedback control system and environmental factors. For this reason, to improve its working condition, a service life time analysis is necessary. Obviously, one has to predict the trend of future system characteristics, such as the reliability, which is measured by the estimate value of remaining useful life. In this situation, we use the stochastic gamma process model to describe the degradation behavior of the actuator. Generally, the algorithm of extended Kalman filter is a widely used method to overcome the difficulties of estimating the state vector in a nonlinear model of two-tank control system. This algorithm gives an innovation vector or prediction residual which contains fault information, when the system is failed. The prediction residuals can be recursively computed for diagnosis by the generalized likelihood ratio test. However, we use the generalized likelihood ratio test algorithm to estimate the moment at which the prognostic started. Finally, a practical case study is given to show the effectiveness of the proposed approaches for failure detection. Obviously, the simulation results show that the degradation path of the actuator capacity is estimated and the reliability based on remaining useful life predicted is analyzed.

Adaptive Two-Step Bayesian Generalized Likelihood Ratio Test Algorithm for Low-Altitude Detection

The low-altitude target detection remains a difficult problem in MIMO radar. In this paper, we propose a novel adaptive two-step Bayesian generalized likelihood ratio test (TB-GLRT) detection algorithm for low-altitude target detection. By defining the compound channel scattering coefficient and applying the K distributed clutter model, the signal models for different radars in low-altitude environment are established. Then, aiming at the problem that the integrals are too complex to yield a closed-form Neyman-Pearson detector, we assume prior knowledge of the channel scattering coefficient and clutter to design an adaptive two-step Bayesian GLRT algorithm for low-altitude target detection. Monte Carlo simulation results verify that the proposed detector has better performance than the square law detector, GLRT detector or Bayesian GLRT detector in low-altitude environment. With the TB-GLRT detector, the maximum detection probability can reach 70% when SNR=0dB and ν=1. Simulations also verify that the multipath effect shows positive influence on detection when SNR<5dB, and when SNR>10dB, the multipath effect shows negative influence on detection. When SNR>0dB, the MIMO radar, which keeps a detection probability over 70% with the proposed algorithm, has the best detection performance. Besides, the detection performance gets improved with the decrease of sea clutter fluctuation level.

OFDM chirp radar for adaptive target detection in low grazing angle

Low-altitude target detection is of great importance in radar applications, especially for military radar to counter the targets penetrated from low grazing angles. However, it is a challenge to detect the targets in low grazing angle area due to severe multipath reflection effects. In this study, the authors propose orthogonal frequency division multiplexing (OFDM) chirp waveform for target detection in low grazing angle region. The OFDM chirp waveform has better peak-to-average-power ratio level and provides larger time-bandwidth product than that of conventional OFDM waveform, which is beneficial for target detection. Two different radar schemes, namely, OFDM chirp radar and OFDM chirp multiple-input multiple-output (MIMO) radar, are studied under the Earth's curvature geometry model with the consideration of realistic multipath reflection effects. The generalised likelihood ratio test algorithm is developed by jointly exploiting the advantages of OFDM chirp waveform and MIMO configuration in increased degrees of freedom. In addition, an adaptive algorithm is further applied to enhance the target detection performance. All proposed schemes are verified by numerical results.

Experimental infrared point-source detection using an iterative generalized likelihood ratio test algorithm.

This work documents the performance of a recently proposed generalized likelihood ratio test (GLRT) algorithm in detecting thermal point-source targets against a sky background. A calibrated source is placed above the horizon at various ranges and then imaged using a mid-wave infrared camera. The proposed algorithm combines a so-called "shrinkage" estimator of the background covariance matrix and an iterative maximum likelihood estimator of the point-source parameters to produce the GLRT statistic. It is clearly shown that the proposed approach results in better detection performance than either standard energy detection or previous implementations of the GLRT detector.

Bias change detection‐based sensor selection approach for target tracking in large‐scale distributed sensor networks

In large-scale distributed sensor networks, in the presence of the possible changing sensor biases, this study proposes a bias change detection-based sensor selection (SS) approach to address this multi-sensor target tracking problem. The authors only need to detect the bias change and select those reliable sensors (sensors with constant biases) for data fusion, rather than the traditional approach, which try to directly estimate the changing biases. The proposed method mainly contains the following three steps. First, the sliding window marginalised likelihood ratio test algorithm is proposed to detect the bias change. Then based on the bias change detection results, they adopt an SS strategy to select those reliable sensors. Finally, for these selected distributed sensors, the federated filter algorithm is used here for target tracking and bias registration. Simulation results show the effectiveness of these algorithms.

Secondary User Access Control in Cognitive Radio Networks

Spectrum sharing and aggregation among authorized secondary users (A-SUs) are important tasks in operating effective cognitive radio networks. Furthermore, in protecting spectrum sharing/aggregation against unauthorized secondary users (UA-SUs), secondary user access control (SUAC) is needed, which is investigated in this paper. A jamming signal is injected to degrade the spectrum sensing performance of UA-SUs, while reliable spectrum sensing performance for A-SUs can be achieved through an oblique projection-based jamming cancellation method. An orthogonal frequency division multiplexing-based transmission model is considered in this paper. The generalized likelihood ratio test algorithm is used for both authorized and unauthorized SUs in spectrum sensing. Numerical results show the effectiveness of the proposed SUAC in degrading the spectrum sensing performance of the unauthorized SUs.

Classification of Imperfectly Time-Locked Image RSVP Events with EEG Device

Classification based on EEG data in an RSVP experiment is considered. Although the latency in neural response relative to the stimulus onset time may be more realistically considered to vary across trials due to factors such as subject fatigue and environmental distractions, it is nevertheless assumed to be time-locked to the stimulus in most of the existing work as a means to alleviate the computational complexity. We consider here a more practical scenario that allows variation in response latency and develop a rigorous statistical formulation for modeling the uncertainty within the varying latency coupled with a likelihood ratio test (LRT) for classification. The new model not only improves the EEG classification performance, but also may predict the true stimulus onset time when this information is not precisely available. We test the proposed LRT algorithm on an EEG data set from an image RSVP experiment and show that, by admitting the latency variation, the proposed approach consistently outperforms a method that relies on perfect time-locking (AUC: 0.88 vs 0.86), especially when the stimulus onset time is not precisely available (AUC: 0.84 vs 0.71). Furthermore, the predicted stimulus onset times are highly enriched around the true onset time with p-value = 5.2 x 10(44).

Theoretical performance analysis of radio frequency fingerprinting under receiver distortions

AbstractRadio frequency fingerprinting (RFF) is used to uniquely identify individual emitters by exploiting the radio frequency characteristics, which are originated from transmitter imperfections. The theoretical performance is analyzed to evaluate the feasibility of RFF with channel noise and receiver imperfections. The distortions from oscillators and analog‐to‐digital converter (ADC) are considered, and a RFF signal model describing the discrepancies of transmitter imperfections, channel noise, and receiver distortions are constructed. A likelihood ratio test algorithm for RFF is proposed to analyze the theoretical performance. The upper and lower bound of theoretic performance is derived through the analysis of the likelihood ratio test statistic, which enlightens how to design a “just enough” receiver to meet the demands of RFF. The simulation results are in agreement with the theoretical evaluation. Copyright © 2013 John Wiley &amp; Sons, Ltd.

A Class of Monte-Carlo-Based Statistical Algorithms for Efficient Detection of Repolarization Alternans

Cardiac repolarization alternans is an electrophysiologic condition identified by a beat-to-beat fluctuation in action potential waveform. It has been mechanistically linked to instances of T-wave alternans, a clinically defined ECG alternation in T-wave morphology, and associated with the onset of cardiac reentry and sudden cardiac death. Many alternans detection algorithms have been proposed in the past, but the majority have been designed specifically for use with T-wave alternans. Action potential duration (APD) signals obtained from experiments (especially those derived from optical mapping) possess unique characteristics, which requires the development and use of a more appropriate alternans detection method. In this paper, we present a new class of algorithms, based on the Monte Carlo method, for the detection and quantitative measurement of alternans. Specifically, we derive a set of algorithms (one an analytical and more efficient version of the other) and compare its performance with the standard spectral method and the generalized likelihood ratio test algorithm using synthetic APD sequences and optical mapping data obtained from an alternans control experiment. We demonstrate the benefits of the new algorithm in the presence of Gaussian and Laplacian noise and frame-shift errors. The proposed algorithms are well suited for experimental applications, and furthermore, have low complexity and are implementable using fixed-point arithmetic, enabling potential use with implantable cardiac devices.

Comparison of the maximum likelihood ratio test algorithm and linear filters for target location in binary images

We consider the problem of estimating the position of objects in binary images corrupted with nonoverlapping and spatially nonhomogeneous noise. For this application, we compare classical linear filters with the recently proposed maximum likelihood ratio test (MLRT) algorithm, which is optimal in the maximum likelihood sense for object detection. We first demonstrate that the MLRT algorithm can be approximated with a good precision by the square of a correlation product. We then compare the MLRT with the Classical Matched, Phase-only and Optimal Tradeoff filters in terms of probability of correct location. We conclude that if they are properly regularized, linear filters can achieve a performance level comparable to that of the MLRT. This result is important in the design of optical correlators, which often implement linear filters with binary input spatial light modulators.