Test Statistics For Detection Research Articles

Study of γ-Ray Emission from a Compact Radio Galaxy with the Fermi Large Area Telescope

The radio galaxy PKS 1007+142 is classified as a compact steep-spectrum source (CSS) and belongs to the class of young active galactic nuclei (AGNs). In this paper, we investigate the γ-ray emission from this CSS by conducting a comprehensive analysis of the 15 yr Fermi Large Area Telescope (Fermi-LAT) observation data. The Fermi-LAT latest Source Catalog, 4FGL-DR4, includes an unassociated γ-ray source, 4FGL J1010.0+1416, located at 0.°24 away from the radio position of PKS 1007+142. Using the 15 yr Fermi-LAT observation data, we reestimate the best-fit position of the γ-ray source and find that PKS 1007+142 is in close proximity to the γ-ray source and falls within its 68% error circle. Therefore, we conclude that PKS 1007+142 is the most plausible counterpart to the unassociated LAT source with detection test statistics ∼ 43.4 (∼6.6σ). PKS 1007+142 exhibits a steep power-law spectrum in the 0.1–300 GeV band, with a photon spectral index (Γ γ ) of 2.86 ± 0.17. The average flux in the considered time interval is (2.14 ± 0.34) × 10−12 erg cm−2 s−1. Comparing PKS 1007+142 with other γ-ray emitting AGNs in both the L γ –Γ γ and L γ –L 1.4 GHz planes, it shows a softer γ-ray spectrum and lower luminosity compared to other γ-ray emitting CSSs. Furthermore, the possible origins of γ-ray in PKS 1007+142 are also discussed.

Do methods of estimation matter in detecting outliers and forecasting macroeconomic variables?

ABSTRACT Outliers in time-series data are crucial in model estimation and forecasting. Understanding the importance of false detection of outliers in various autoregressive processes, the present study aims to evaluate test statistics’ performance by utilizing multiple models through robust estimation of errors. In this study, numerous data-generating techniques have been employed via simulation at different values of the estimates, location, and size of an outlier, sample sizes, and classical cutoff to access the power of the test statistics for false detection of outlier type. The findings of the simulation reveal that the location and size of outliers, parameter values, and, to some extent, the size of the series influence the behavior of test statistics in detecting the type of outliers. Overall, the estimation method of residual standard deviation influences the sampling behavior of the test statistics. Outliers also affect the forecasting performance in the model. All results are also validated empirically.

Underwater Target Detection by Measuring Water-Surface Vibration With Millimeter-Wave Radar

Underwater target detection has been a significant challenge in environment aware. In this letter, an underwater target detection scheme is proposed by measuring the water-surface vibrations caused by acoustic waves with a millimeter-wave radar. Firstly, the power of the water-surface vibration due to the reflection of acoustic waves by the target is derived, which gives a quantitative relationship between the perturbations on water-surface vibration and the position of the target. Then, the signal model to accurately measure the water-surface vibration with millimeter-wave radar is established. A method to estimate the power spectrum of the phase of water-surface vibrations is proposed, based on which a test statistic for underwater target detection is formulated. Finally, the effectiveness of the proposed scheme is validated by water-tank experiments using a frequency modulated radar with a center frequency of 77 GHz.

False discovery rate approach to dynamic change detection

In multiple data stream surveillance, the rapid and sequential identification of individuals whose behavior deviates from the norm has become particularly important. In such applications, the state of a stream can alternate, possibly multiple times, between a null state and an alternative state. To balance the ability to detect two types of changes, that is, a change from the null to the alternative and back to the null, we propose a new multiple testing procedure based on a penalized version of the generalized likelihood ratio test statistics for change detection. The false discovery rate (FDR) at each time point is shown to be controlled under some mild conditions on the dependence structure of data streams. A data-driven approach is developed for selection of the penalization parameter. Its advantage is demonstrated via simulation and a data example.

Asgard/NOTT: L-band nulling interferometry at the VLTI

Context. NOTT (formerly Hi-5) is a new high-contrast L′ band (3.5–4.0 µm) beam combiner for the VLTI designed with an ambitious aim to be sensitive to young giant exoplanets down to 5 mas separation around nearby stars. The performance of nulling interferometers in these wavelengths is affected both by fundamental noise from the background and contributions of instrumental noise. This motivates the development of end-to-end simulations to optimize these instruments. Aims. The aim of this study is to enable a performance evaluation of NOTT and inform the design of such instruments with current and future infrastructures in mind, taking into account the different sources of noise and their correlation. Methods. SCIFYsim is an end-to-end simulator for single-mode-filtered beam combiners, with an emphasis on nulling interferometers. We use it to compute a covariance matrix of the errors. We then use statistical detection tests based on likelihood ratios to compute compound detection limits for the instrument. Results. With the current assumptions as to the performance of the wavefront correction systems, the errors are dominated by correlated instrumental errors down to stars of magnitude 6–7 in the L band, beyond which thermal background from the telescopes and relay system becomes dominant. Conclusions. SCIFYsim is suited to anticipating some of the challenges of design, tuning, operation, and signal processing for integrated-optics beam combiners. The detection limits found for this early version of NOTT simulation with the unit telescopes are compatible with detections at contrasts up to 105 in the L band at separations of 5–80 mas around bright stars.

Sequential advanced receiver autonomous integrity monitoring method considering the optimal sample size

The integrity monitoring performance of Receiver Autonomous Integrity Monitoring (RAIM) degrades when insufficient satellites are visible, which may cause RAIM to fail to support the approach procedure with vertical guidance service. We propose a sequential advanced receiver autonomous integrity monitoring (S-ARAIM) method to improve the integrity monitoring capability and availability. We first introduce a sample size to a dominant snapshot technique, advanced RAIM (ARAIM), to formulate cumulative test statistics for fault detection. The sample size is then determined to maximize the fault detection capability, including the response speed and sensitivity to minor faults. Meanwhile, the optimal sample size minimizes the undetected fault magnitude bound when no fault is declared. In this manner, S-ARAIM combines the advantage of snapshot RAIM and sequential RAIM in detecting severe pulse and small persistent faults. Additionally, we derive a protection level to envelop the estimate error under the constraints of continuity and integrity requirements. Experiments validate the fault detection performance under single-satellite-fault and dual-satellite-fault scenarios. Compared with least square residual RAIM and ARAIM, S-ARAIM improves the integrity availability by 45.91% and 5.64%, respectively.

Spectrum Sensing by Cepstral Covariance Detection

This work presents the cepstral covariance detector for spectrum sensing in cognitive radio. The power cepstral peaks of digitally modulated signals are exploited for spectrum sensing. To enhance the process of signal detection in low signal-to-noise ratio environments and with the knowledge of the data rate of the primary user signal, the cepstral covariance of the received signal is evaluated. The detection test statistic of the proposed detector is evaluated and an expression for the detection threshold is obtained under the null hypothesis. The detection performance of the proposed cepstrum-based detector is compared to covariance-based detectors and the obtained results validate its detection reliability at low signal-to-noise ratio levels with relatively low computational complexity.

Time-Domain Frequency Estimation With Application to Fault Diagnosis of the Unmanned Aerial Vehicles’ Blade Damage

In this article, a novel time-domain frequency estimation approach is proposed based on the well-known subspace identification method. Differing from the fast Fourier transformation method, the nonlinear identification for frequency estimation is reformulated into the eigenvalue identification problem, which avoids the spectrum peak search. The recursive frequency estimation approach is proposed based on the updating/downdating of the Cholesky decomposition, where the extended observability matrix can be identified from a small matrix with lower computation cost. In addition, a gap metric-oriented performance indicator is proposed as a test statistic for fault detection as well as the evaluation of fault severity. The effectiveness of the proposed methods is verified for frequency estimation and fault diagnosis performance through numerical simulations and the experimental measurements from a laboratory unmanned aerial vehicle rig with partial blade damage.

A modified between‐receiver single‐difference‐based fault detection and exclusion algorithm for real‐time kinematic positioning

With the increasing number of available satellites from multi-global navigation satellite systems (GNSS) and their applications in complicated environments, an increased number of faults in measurements are inevitable. Fault detection and exclusion (FDE) is an effective way to reject observation faults in order to guarantee the integrity of a GNSS positioning and navigation system. We propose a modified between-receiver single-differencing (SD)-based FDE algorithm for real-time kinematic (RTK) positioning. First, a modified between-receiver SD model-based FDE is proposed for testing the estimated float ambiguities. This is helpful in rejecting observations with unreliable float ambiguities and obtaining reliable double-differencing (DD) integer ambiguities, denoted as the SD float model in the sequel. Second, DD ambiguities are resolved by the DD model and used to update the previous SD float ambiguity. After that, a modified SD fix model, taking the DD ambiguities as the known parameters, is further developed to detect any unreliable or fault-resolved ambiguities. To verify the modified SD model-based FDE algorithm, a kinematic vehicle test is conducted. The fault detection test statistics of the SD float model and the SD fix model, time-to-first-fix, ambiguity-fix rate, SD residuals, and positioning performances are examined. The results show that the modified FDE method can detect and exclude fault satellites accurately and effectively, which is verified by the SD residuals of fault satellites. The ambiguity-fix rate of the proposed modified FDE algorithm is improved by approximately 3% with 70 more epochs of fixes. Regarding the 3D positioning performance, the modified proposed SD-based FDE algorithm can achieve 54.11%, 57.78%, and 73.11% improvements for standard deviation, root mean square, and mean bias, respectively, when compared with the processing strategy without the use of the proposed FDE.

A systematic review and meta-analysis of minimally invasive surgery in children with occult tethered cord syndrome.

BackgroundAt present, the surgical treatment of occult tethered cord syndrome (OTCS) in children is mostly two types of minimally invasive surgery: filum terminalis laxity or filum terminalectomy. The clinical efficacy and safety of minimally invasive treatment and conservative treatment are still unclear. Therefore, this study will use the advantages of systematic review and meta-analysis to evaluate the objectivity, and explore the effect of minimally invasive surgery on children with occult tethered cord syndrome.MethodsA computer search was used to search PubMed, Embase, CNKI, Wanfang Database and other literature search websites about the randomized controlled trials (RCTs) of minimally invasive surgery in children with occult tethered cord syndrome and spinal lipoma. Professional journals were manually searched to avoid omissions. The search keywords were: occult myelolipoma, occult tethered cord syndrome, surgical treatment of tethered cord syndrome, occult tethered cord syndrome.ResultsA total of 6 relevant literatures that could be used for meta-analysis were selected. A total of 425 subjects were included in the article, of which 132 were treated conservatively and 293 were treated surgically. The heterogeneity detection test statistics of the included studies were Chi2 (Chi-squared test) =8.18, df (degree of freedom) =5, I2=39%<50%, Z=2.53, and the homogeneity of the included studies was good. The number of unimproved cases under conservative treatment was 40, accounting for 30.30%; the number of unimproved cases under surgical treatment was 33, accounting for 11.26%, and the total unimproved rate of the two groups accounted for 17.17%. The unimproved rate of the experimental group was significantly lower than that of the control group, and the difference was statistically significant (P=0.01). The results of bias analysis showed that there was no significant bias in the literature included in this study.DiscussionMeta-analysis results confirmed that minimally invasive surgery has a significant effect on the treatment of occult children with tethered cord syndrome. However, due to the small sample size of the included literature, further evaluation of the treatment risk is required.

Code phase tracking error based autonomous integrity monitoring for GNSS/INS ultra-tightly integrated system

The ultra-tight integration of GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System) may suffer from faults in GNSS, INS and integration filter. Both the faults occurred at the current epoch and at previous epochs affect the position solution deviation of ultra-tight integration at the current epoch, and the effect is reflected in GNSS code phase tracking errors. In this paper, a new autonomous integrity monitoring algorithm is proposed for the federated GNSS/INS ultra-tight integrations in which the code phases of local GNSS signal replicas for GNSS signal tracking are completely derived from the integrated navigation solution. In the proposed algorithm, GNSS code phase tracking errors are employed to construct the test statistics for fault detection through a MHSS (Multiple Hypothesis Solution Separation) method improved from the classical MHSS used in ARAIM (Advanced Receiver Autonomous Integrity Monitoring). With the improved MHSS, the proposed algorithm can account for GNSS nominal biases, multiple GNSS faults and the faults in INS and integration filter. The protection levels under multiple hypothesis are also derived. A semi-physical simulation of the federated ultra-tight integration of GPS (Global Positioning System) and INS in airplane approaching case and a vehicle-based field test of the federated ultra-tight integration of GPS, Galileo and INS are conducted to validate the proposed algorithm. The experiments show that the designed fault detection can effectively detect the occurred faults and the derived protection levels can correctly bound the caused position solution error.

Amateur radio sensing technique using a combination of energy detection and waveform classification

&lt;p&gt;A critical problem in spectrum sensing is to create a detection algorithm and test statistics. The existing approaches employ the energy level of each channel of interest. However, this feature cannot accurately characterize the actual application of public amateur radio. The transmitted signal is not continuous and may consist only of a carrier frequency without information. This paper proposes a novel energy detection and waveform feature classification (EDWC) algorithm to detect speech signals in public frequency bands based on energy detection and supervised machine learning. The energy level, descriptive statistics, and spectral measurements of radio channels are treated as feature vectors and classifiers to determine whether the signal is speech or noise. The algorithm is validated using actual frequency modulation (FM) broadcasting and public amateur signals. The proposed EDWC algorithm's performance is evaluated in terms of training duration, classification time, and receiver operating characteristic. The simulation and experimental outcomes show that the EDWC can distinguish and classify waveform characteristics for spectrum sensing purposes, particularly for the public amateur use case. The novel technical results can detect and classify public radio frequency signals as voice signals for speech communication or just noise, which is essential and can be applied in security aspects.&lt;/p&gt;

Sieve bootstrap test for multiple change points in the mean of long memory sequence

&lt;abstract&gt;&lt;p&gt;In this paper, the sieve bootstrap test for multiple change points in the mean of long memory sequence is studied. Firstly, the ANOVA test statistics for change points detection is obtained. Secondly, sieve bootstrap statistics is constructed and the consistency under the Mallows measure is proved. Finally, the effectiveness of the method was illustrated by simulation and example analysis. Simulation results show that our method can not only control the empirical size well but also have reasonable good power.&lt;/p&gt;&lt;/abstract&gt;

Test Statistics for Reflection Symmetry: Applications to Quad-Polarimetric SAR Data for Detection of Man-Made Structures

In polarimetric synthetic aperture radar (SAR) images, speckle is removed by multilooking and the local covariance matrix is the main parameter of interest. In the covariance matrix from a backscatter with reflection symmetry, the terms <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\langle \boldsymbol{S}_{hh}\boldsymbol{S}_{hv}^*\rangle }$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\langle \boldsymbol{S}_{vv}\boldsymbol{S}_{hv}^*\rangle }$</tex-math></inline-formula> , and their complex conjugates are 0. The backscatter from natural covers, such as fields and forested areas, is typically reflection-symmetric, as these four elements have near-zero values. The backscatter from urban areas and man-made structures is substantially different, and the backscatter from buildings not aligned with the radar line of sight usually does not have reflection symmetry. A novel block-diagonality test statistic for reflection symmetry with a constant false alarm rate property is proposed. It is compared to an approximate test built on a change detection test statistic for Wishart-distributed covariance matrices. Their use on quad-polarimetric data in different situations shows their high potential for man-made structure detection. Applied after an orientation correction of the covariance matrices, these test statistics highlight with high-contrast buildings and urban areas. We also apply this test for ship detection at sea, and show that while the results are unconvincing at X-band, it can also be applied at longer wavelengths such as L-band.

Detection of LFM Radar Signals and Chirp Rate Estimation Based on Time-Frequency Rate Distribution.

Linear frequency-modulated (LFM) signals are the most significant example of waveform used in low probability of intercept (LPI) radars, synthetic aperture radars and modern communication systems. Thus, interception and parameter estimation of the signals is one of the challenges in Electronic Support (ES) systems. The methods, which are widely used to accomplish this task are mainly based on transformations from time to time-frequency domain, which concentrate the energy of signals along an instantaneous frequency (IF) line. The most popular examples of such transforms are the short time Fourier transform (STFT) and Wigner-Ville distribution (WVD). However, for LFM waveforms, methods that concentrate signal energy along a line in the time-frequency rate domain may allow to obtain better detection and estimation performance. This type of transformation can be obtained using the cubic phase (CP) function (CPF). In the paper, the detection of LFM waveform and its chirp rate (CR) parameter estimation based on the extended forms of the standard CPF is proposed. The CPF was originally introduced for instantaneous frequency rate (IFR) estimation for quadratic frequency modulated (QFM) signals i.e., cubic phase signals. Summation or multiplication operations on time cross-sections of the CPF allow to formulate the extended forms of the CPF. Based on these forms, detection test statistics and the estimation procedure of LFM signal parameters have been proposed. The widely known estimation methods assure satisfying accuracy for high SNR levels, but for low SNRs the reliable estimation is a challenge. The proposed approach based on joint analysis of detection and estimation characteristics allows to increase the reliability of chirp rate estimates for low SNRs. The results of Monte-Carlo simulation investigations on LFM signal detection and chirp rate estimation evaluated by the mean squared error (MSE) obtained by the proposed methods with comparisons to the Cramer-Rao lower bound (CRLB) are presented.

Influence measures and outliers detection in linear mixed measurement error models with Ridge estimation

In this article, we have concentrated on the influence diagnostics based on the mean shift outlier models (MSOM) and case deletion methods in the linear mixed measurement error models with Ridge estimation using the corrected likelihood of Nakamura (1990). We have derived the corrected score test statistic for outliers detection according to MSOM. Then, several case deletion measures are constructed as a tool for influence diagnostics. A parametric bootstrap procedure is used to obtain empirical distributions of the test statistics, and a simulation study has been used to show the performance of the proposed score test statistic and cook distance. Finally, a numerical example is given to illustrate the theoretical results.

Evaluation of Test Statistics for Detection of Outliers and Shifts

Existence of outliers and structural breaks having mutually unknown nature, in time series data, offer challenges to data analysts in model identification, estimation and validation. Detection of these outliers has been an important area of research in time series since long. To analyze the impact of these structural breaks and outliers on model identification, estimation and their inferential analysis, we use two data generating processes: MA(1) and ARMA(1,1). The performance of the test statistics for detecting additive outlier(AO), innovative outlier(IO), level shift(LS) and transient change(TC) is investigated using simulation strategy through power of a test, empirical level of significance, empirical critical values, misspecification frequencies and sampling distribution of estimators for the two models. The empirical critical values are found higher than the theoretical cut-off points, empirical power of the test statistics is not satisfactory for small sample size, large cut-off points and large model coefficient. We have explored confusion between LS, AO, TC and IO at different critical values(c) by varying sample size. We have also collected empirical evidence from time series data for Pakistan using 3-stage iterative procedure to detect multiple outliers and structural breaks. We find that neglecting shocks lead to wrong identification, biased estimation and excess kurtosis.&#x0D; JEL Classification Codes: C15, C18, C63, C32, C87, C51, C52, C82&#x0D; AMS Classification Codes: 62, 65, 91, DI, 62-08, 62J20, 00A72, 91-08, 91-10, 91-11 62P20, 91B82, 91B84, 62M07, 62M09, 62M10, 62M15, 62M20

Artificial neural network design for improved spectrum sensing in cognitive radio

Dynamic Spectrum Access/Cognitive Radio systems access the channel in an opportunistic, non-interfering manner with the primary network. These systems utilize spectrum sensing techniques to sense the occupancy of the primary user. In this paper, an artificial neural network based hybrid spectrum sensing technique is proposed, which considers sensing as a binary classification problem to detect whether the primary user is idle or busy. The proposed scheme utilizes energy detection and likelihood ratio test statistic as features to train the neural network. Moreover, we demonstrate the impact of hyperparameter tuning and carry out the detailed study of it, yielding a combination of best-suited hyperparameters. The performance of the proposed sensing scheme is validated on primary signals of various real world radio technologies acquired with an empirical testbed setup. We conclude that the best performing configuration results in an increase of approximately 63% in detection performance compared to classical energy detection and improved energy detection sensing schemes when averaged over all the radio technologies considered in this work.

Reproducibility-optimized detection of differential DNA methylation.

Aim: DNA methylation is a key epigenetic mechanism regulating gene expression. Identifying differentially methylated regions is integral to DNA methylation analysis and there is a need for robust tools reliably detecting regions with significant differences in their methylation status. Materials & methods: We present here a reproducibility-optimized test statistic (ROTS) for detection of differential DNA methylation from high-throughput sequencing or array-based data. Results: Using both simulated and real data, we demonstrate the ability of ROTS to identify differential methylation between sample groups. Conclusion: Compared with state-of-the-art methods, ROTS shows competitive sensitivity and specificity in detecting consistently differentially methylated regions.

Data-driven fault diagnosis for dynamic traction systems in high-speed trains

Traction systems are an important aspect of high-speed trains, and their reliable operation is crucial. With data available from trains, this paper proposes an optimal fault detection and diagnosis (FDD) strategy for dynamic traction systems. Based on the established dynamic model, using sensor measurements, a correlation-aided subspace identification technique is proposed to formulate residual signals and corresponding test statistics for fault detection. Then, a modified support vector machine (SVM) is designed for optimally solving the diagnosis bias caused by the difference in the apparent probabilities of multiple fault scenarios. The feasibility and effectiveness of the proposed optical FDD performance are illustrated in the CRRC experimental platforms.