Probability Of False Detection Research Articles

Air quality in the Metropolitan Zone of the Valley of Puebla: Comparative evaluation of CAMS and persistence forecasts

Background on air quality in the Metropolitan Zone of the Valley of Puebla shows that suspended particles smaller than 10 micrometers (PM10) and smaller than 2.5 micrometers (PM2.5) represent a health risk. Puebla’s automatic air quality monitoring system measures PM10 and PM2.5 at five stations in the municipalities of Puebla and Coronango. These measurements allow for determining the Air and Health Index according to the NOM-172-SEMARNAT-2019 standard for these pollutants. The advancement of global pollutant modeling techniques represents an opportunity for air quality management in areas with scarce terrestrial measurements. However, it is necessary to validate global forecasts with ground measurements from georeferenced monitoring stations to reduce uncertainties and determine reliability. The Copernicus Atmospheric Monitoring Service (CAMS) forecast allows atmospheric pollution exploration processes in the study region. This study presents an analysis of the CAMS forecast against the Persistence forecast. The results show that the persistence forecast performs better than the CAMS forecast in general, both for PM10 and for PM2.5. However, using the CAMS forecast for a preliminary evaluation of the prediction of PM2.5 is feasible due to its acceptable values in the comparison criteria of the dichotomous statistics ACCURACY, probability of detection (POD), false alarm rate (FAR), probability of false detection (POFD), success ratio (SR), threat score (TS), equitable threat score (ETS), Heidke skill score (HSS), and odds ratio skill score (ORSS). This work provides valuable insights to both the population and decision-makers, aiding in the enhancement of air quality management and public health strategies.

A diffusion-based super resolution model for enhancing sonar images.

Improved hardware and processing techniques such as synthetic aperture sonar have led to imaging sonar with centimeter resolution. However, practical limitations and old systems limit the resolution in modern and legacy datasets. This study proposes using single image super resolution based on a conditioned diffusion model to map between images at different resolutions. This approach focuses on upscaling legacy, low-resolution sonar datasets to enable backward compatibility with newer, high-resolution datasets, thus creating a unified dataset for machine learning applications. The study demonstrates improved performance for classifying upscaled images without increasing the probability of false detection. The increased probability of detection was 7% compared to bicubic interpolation, 6% compared to convolutional neural networks, and 2% compared to generative adversarial networks. The study also proposes two sonar specific evaluation metrics based on acoustic physics and utility to automatic target recognition.

基于轻量化的 HCES-YOLO 的鸡蛋品质检测算法

The quality detection of eggs based on deep learning faced many problems, such as similar feature colors and low computational efficiency, which resulted in an increased probability of false detection or missed detection. To effectively solve these problems, this paper proposed an egg quality detection method based on YOLOv8n, which integrated the ContextGuideFusionModule, EfficientHead, and SIOU loss functions by improving the backbone network. The recognition rate from the field test was 88.4%, indicating that the algorithm could meet the real-time monitoring requirements, effectively identify the quality status of eggs, and provide support for intelligent poultry house management.

Adaptive Spectrum Sensing in Cognitive Radio Employing Multitaper Method and Higher-Order STBC Techniques

The effects of the non-parametric multitaper method (MTM) and symmetrical higher-order space-time block-code (STBC) with full diversity techniques on the performance of spectrum sensing (SS) in cognitive radio (CR) systems are studied. The overall wireless link performance can be improved by using the STBC techniques to combat the multipath fading effects in wireless channels. The MTM is integrated with the STBC for spectrum estimation (SE), which will be termed multitaper spectrum estimation (MTSE)-STBC, and the given analysis of which is developed using the quadrature form approximation. Also, classical SE algorithms commonly focus on a fixed performance assessment method based on predefined false alarms or detection probabilities. Licensed users need to be protected against interference that opportunistic users might cause. An appropriate thresholding policy that varies concerning the designated values of false alarm and detection rates can achieve such protection. Utilization factors are instrumental in providing further protection for licensed users or occupied spectrum holes, and they need to be determined in advance and adopted in the detection policy. If utilization factors are not desirable, then a fine selection of appropriate threshold values will need to be decided as they have a major impact on the overall error probability performance. Using analytical and simulation methods, the assessment results revealed improved performance of the MTSE-STBC compared to other classical methods such as the Periodogram especially in the aspects of efficient transmission and less error probability. The proposed adaptive thresholding technique also proved useful in coping with different SE settings.

A comparative study on sequential detection of random mean change in multivariate normal data stream

For the detection of random mean changes in a multivariate normal data stream, we explore and compare various direction-invariant monitoring charts. These include the (adaptive) largest eigenvalue charts employing exponential weighted moving variance (EWMV), the moving largest eigenvalue (MLE) based on moving sample variance, and both adaptive and windowed cumulative sum (CUSUM) charts with a reference value for the largest eigenvalue. The comparison involves assessing the power of detection (POD) given false detection probability (FDP) at a signal length and the average delay detection time (ADDT) given a false alarm rate (FAR) at a change time. Our primary contribution involves introducing the moving generalized likelihood ratio (MGLR) chart, which relies solely on the chi-square property of the adjusted generalized likelihood ratio. We provide simple approximations for FDP and FAR for design purposes. Our results indicate that the EWMV chart is recommended, and the MGLR chart performs effectively. Detection of volatility change in 30 Dow Jones Industrial stock prices is used for illustration.

Improved PHY-layer authentication utilizing multi-modal features for mmWave MIMO UAV-enabled systems

This paper exploits multi-modal Physical (PHY)-layer features in terms of artificial fingerprint, In-phase/Quadrature (IQ) imbalance and Angle of Arrival (AoA) to propose a novel PHY-layer authentication framework for a Millimeter Wave (mmWave) Multiple-Input Multiple-Output (MIMO) Unmanned Aerial Vehicle (UAV)-enabled communication system. First, we resort to the AoA-based spatial fingerprint to effectively address the challenge of channel fingerprint instability induced by high-speed UAV mobility. To further enhance the low discriminability of hardware fingerprints caused by refined manufacturing techniques, artificial Gaussian noise is injected into the transmission signals to assist the receiver in better distinguishing between legitimate and illegitimate UAVs. Then, we jointly combine with inherent IQ imbalance and AoA features to design a hybrid authentication scheme and thus construct a multi-dimensional fingerprint space for a comprehensive characterization of UAV identities. To theoretically evaluate the effectiveness of the proposed authentication framework, the analytical closed-form expressions of performance metrics like false alarm and detection probabilities are also exactly derived based on the statistical signal processing technology and composite hypothesis testing. Finally, we provide large simulation results to validate the correctness and feasibility of the proposed theoretical models, and also discuss the relation between system security and communication service quality under different artificial fingerprint level.

MODELS OF FALSE AND CORRECT DETECTION OF INFORMATION LEAKAGE SIGNALS FROM MONITOR SCREENS BY A SPECIALIZED TECHNICAL MEANS OF ENEMY INTELLIGENCE

The calculation expressions for the probability of false detection of a leakage signal, taking into account the discrete representation of time in the models of detection of side radiation signals from static images on the monitor screen on liquid crystal structures by a specialised technical means of enemy intelligence, which implements an asymptotically optimal joint algorithm for detecting side radiation signals and estimating the duration of the image on the monitor screen, are refined. For this purpose, a large independent array of noise was generated in the Mathcad software environment, and the process of false signal detection by a specialised enemy intelligence vehicle was modelled in several Excel workbooks under conditions of a priori uncertainty about the duration of a static image on the monitor screen.

Spectrum Sensing Using Wavelet Transforms and Filtering Under Signal Frequency Distortion and Fading Conditions

This article explores improving the accuracy and reliability of spectrum sensing methods within cognitive radio networks. The primary focus is on how signal fading and frequency distortion influence the results of spectral analysis. These issues can severely impact the precision of signal detection, making adaptive methods and filters indispensable for accurately detecting changes in the spectral landscape. The purpose of the paper is to evaluate the effectiveness of various adaptive methods and filters – such as wavelet transforms, along with Butterworth, Chebyshev, and Kaiser filters—in improving the detection of changes within the spectral environment across different signal-to-noise ratio (SNR) levels. The research spans a broad frequency range, concentrating on pivotal technologies like 5G NR, Wi-Fi 6, DVB-T2, and GPS, each having unique requirements for signal precision and dependability. The spectrum sensing approach described in the article achieves high signal detection accuracy under favorable conditions, particularly when the SNR is strong. Experiments revealed that with SNR values above 1 dB, the signal detection accuracy (True Positive Rate, or TPR) for all technologies examined remains at or above 0.90. For instance, the TPR for 5G NR is 0.92 at an SNR of 1 dB, while for Wi-Fi 6, it stands at 0.90. However, the effectiveness of the method declines as the SNR decreases. For example, with 5G NR, the TPR drops to 0.70 at an SNR of -21 dB, indicating a heightened probability of false signal detection. Similar patterns are observed with Wi-Fi 6, where the TPR falls to 0.65, with DVB-T2 to 0.68, and GPS to 0.66. Additionally, the average noise level rises as SNR diminishes, making accurate signal detection increasingly challenging and emphasizing the need for further refinement of these methods. The findings underscore the need for ongoing advancements in spectrum monitoring, especially under low SNR conditions. Future research should prioritize developing new or refining existing adaptive algorithms capable of operating effectively in complex spectral environments. Exploring the impact of other filtering and transformation methods could also yield valuable insights. Moreover, the incorporation of machine learning techniques offers a promising path for boosting the adaptability and accuracy of spectrum monitoring in real-world telecommunication systems.

Virtual Array-Based Signal Detection and Carrier Frequency Offset Estimation in a Multistatic Collaborative Passive Detection System

To tackle the problem of difficult signal detection and carrier frequency synchronization faced by wireless communication among stations of the multistatic passive detection system in interference environments, an adaptive signal detection and carrier frequency offset (CFO) estimation method based on a virtual array is proposed in this paper. This is a data-aided method that utilizes a training sequence composed of three segments of sub-training sequences with different symbols. This method first uses spatial spectrum estimation to obtain the coarse frequency estimations of interference signals and CFO from virtual array signals constructed from the first two sub-training sequences. Then, beamforming is conducted accordingly on the virtual array signals constructed from the third sub-training sequence to suppress the in-band interferences and protrude the expected signal. Finally, improved performance of signal detection and CFO estimation is obtained with the beamformed signals. Simulation experiments show that a missed detection probability as low as 1 × 10−4, with a false detection probability of 1 × 10−3, can be obtained under a signal-to-interference ratio (SIR) of −10 dB and Eb/N0 of 1 dB. Moreover, the proposed method can also simultaneously achieve a CFO estimation error that is lower than 3%, with the condition of Eb/N0 being as low as −5 dB under different SIRs. Simulation results validate the proposed method and demonstrate the promising application prospects of the proposed method in networked passive detection scenarios.

High Sensitivity and High Throughput Magnetic Flow CMOS Cytometers With 2D Oscillator Array and Inter-Sensor Spectrogram Cross-Correlation.

In the paper, we present an integrated flow cytometer with a 2D array of magnetic sensors based on dual-frequency oscillators in a 65-nm CMOS process, with the chip packaged with microfluidic controls. The sensor architecture and the presented array signal processing allows uninhibited flow of the sample for high throughput without the need for hydrodynamic focusing to a single sensor. To overcome the challenge of sensitivity and specificity that comes as a trade off with high throughout, we perform two levels of signal processing. First, utilizing the fact that a magnetically tagged cell is expected to excite sequentially an array of sensors in a time-delayed fashion, we perform inter-site cross-correlation of the sensor spectrograms that allows us to suppress the probability of false detection drastically, allowing theoretical sensitivity reaching towards sub-ppM levels that is needed for rare cell or circulating tumor cell detection. In addition, we implement two distinct methods to suppress correlated low frequency drifts of singular sensors-one with an on-chip sensor reference and one that utilizes the frequency dependence of the susceptibility of super-paramagnetic magnetic beads that we deploy as tags. We demonstrate these techniques on a 7 ×7 sensor array in 65 nm CMOS technology packaged with microfluidics with magnetically tagged dielectric particles and cultu lymphoma cancer cells.

Характеристики обнаружения когерентной системы при рассогласовании квадратурных каналов

Introduction – The operation of an adaptive coherent detection system is considered. The structure of a processing device is presented, containing a series-connected shaper of quadrature signal components that convert high-frequency input radio signals to video frequency, an adaptive multi-channel coherent storage device with threshold devices. Goal – To study the influence of amplitude and phase mismatches of quadrature channel parameters, which are inevitable in practice, on the operation of an adaptive coherent detection system. Formulation of the problem – Determine the spectral-correlation characteristics of the processed signals when the parameters of the quadrature channels are mismatched. Assess the influence of amplitude and phase deviations of the parameters of quadrature channels on the characteristics of a coherent detection system. Method – The complex variable method is used, in which the input high-frequency signals are represented at the video frequency by a pair of complex conjugate real components, shifted in phase by ninety degrees. Results – A model of the processed signal is presented at the video frequency, taking into account possible mismatches in the parameters of quadrature channels in practice. Expressions are obtained and graphs of the spectral density of the complex signal are plotted separately for the amplitude and phase mismatch of the quadrature channels. It is shown that the mismatch of quadrature channels leads to the appearance in the spectrum of the output signal of phase detectors of a component at a frequency that is the mirror frequency of the signal, which leads to an increase in the probability of false detection. The results of calculations of the probability of false detection of a signal depending on the deviation of the transmission coefficients of the quadrature channels and the phase mismatch of the reference voltages of the quadrature channels are presented. Conclusion – The significant influence of amplitude and phase mismatches of the parameters of quadrature channels on the spectral-correlation characteristics of the complex signal is shown. The mismatch of the parameters of the quadrature channels leads to deterioration in the detection characteristics of the coherent processing system. The research methodology used allows us to develop requirements for the quality of tuning quadrature channels.

A non-zero Doppler amplitude is not enough: revisiting the putative radial velocity detection of sub-Venus exoplanet L 98-59b

ABSTRACT L 98-59b is a transiting exoplanet with radius ${0.85^{+0.061}_{-0.047}}$R⊕ and orbital period ${2.2531136^{+1.2e-06}_{-1.5e-06}}$ d. In 2021, a Doppler detection of L 98-59b was announced, with radial velocity (RV) semi-amplitude $K_{\rm b}=0.46^{+0.20}_{-0.17}$$\textrm {m}\, \textrm {s}^{-1}$ inferred from 164 High Accuracy Radial velocity Planet Searcher and 66 Échelle SPectrograph for Rocky Exoplanets and Stable Spectroscopic Observation RVs. This translated into a mass $M_{\rm b}=0.40^{+0.16}_{-0.15}$M⊕, or half the mass of Venus, making L 98-59b then (by far) the lowest mass planet with a putative RV detection. To illuminate the difficulties in securely detecting <1 $\textrm {m}\, \textrm {s}^{-1}$ signals, we argue here that the published RV detection of L 98-59b is not supported by sufficient statistical evidence. We show, under the published modelling assumptions, that Bayesian model comparison (BMC) suggests an $\sim 50~{{\ \rm per\ cent}}$ false-detection probability. We also often infer ∼40 $\textrm {cm}\, \textrm {s}^{-1}$ semi-amplitudes for Keplerians with periods not corresponding to any known planet; importantly, though, BMC rejects all these ‘detections’. By implementing a more sophisticated stellar activity model and more realistic parameter priors, we infer a mass Mb = 0.47 ± 0.14M⊕ from semi-amplitude Kb = 0.56 ± 0.16 $\textrm {m}\, \textrm {s}^{-1}$, with BMC now indicating substantial yet still not decisive evidence for the detection. Definitive detection and characterization may require many additional RVs, alongside careful modelling and stringent statistical analysis. Our case study has implications for current extreme-precision RV campaigns and the longer term quest to discover the first Earth-twins.

A locational false data injection attack detection method in smart grid based on adversarial variational autoencoders

Stealthy FDIA (False Data Injection Attack) is a serious cyber threat that can modify state estimation of smart grid through maliciously altering the measurement data, but can’t be detected by traditional bad data detection system in smart grid. There exist two weakpoints for numerous deep neural networks (DNNs) based data-driven schemes against FDIA. First, they mainly focus on detecting the presence of FDIA, but fail to localize the specific bus/nodes affected. Second, their performance is not sufficiently desirable under small attack, i.e., anomalies caused by the attack closely resemble normal data. To address the above issues, this paper proposes an effective locational FDIA detection framework based on data reconstruction, AT-GVAE, which seamlessly integrates the variational autoencoders (VAEs) and generative adversarial network paradigm. Specifically, our contributions are threefold. First, the proposed AT-GVAE framework is novelly composed of two main modules: the generative VAE_G and discriminative VAE_D that both play dual roles: reconstruct data from jointly learning distributions of data and latent feature space, and meanwhile play Minmax game with adversarial way. Second, multiple-layer gated recurrent units (GRUs) are utilized as the basic structure of the encoder and decoders in both VAEs, to characterize the temporal correlations of measurement data sequence. Additionally, the self-attention mechanism is used to enhance the expressive ability of GRU based VAEs. Then, the anomaly score for each busbar in the smart grid is determined by comparing the residual between the observed measurement and the outputs of VAE_G and VAE_D, enabling the localization of FDIA. Finally, thorough experiments on multiple power systems with series data of total 3456 measurements demonstrate that, in terms of multiple typical metrics, including false alarm rate vs. detection probability, AUC-ROC, and AUC-PR, our proposed framework outperforms the state-of-the-art GRU, VAE and adversarial training based FDIA detection schemes.

GLRT‐based compressive subspace detectors in single‐frequency multistatic passive radar systems

AbstractThe authors study the problem of compressive target detection in a single‐frequency network (SFN)‐based multistatic passive radar system (MS‐PRS) consisting of multiple illuminators of opportunity (IOs) and one receiver. Firstly, a generalised likelihood ratio test (GLRT)‐based SFN‐based compressive subspace detector (SFN‐CSD) is derived by exploiting the sparsity of the target echoes for the case of known noise variance. When the noise variance is unknown, an SFN‐based unknown‐noise (UN) compressive subspace detector is proposed, referred to as the SFN‐UNCSD. Moreover, closed‐form expressions of the probability of false alarm and detection of the proposed detectors are deriived. It is proved that the SNF‐UNCSD has a constant false alarm rate (CFAR) property. Finally, numerical simulations are conducted to verify the theoretical analysis and illustrate the performance of the proposed detector relative to several benchmark detectors.

Joint detection algorithm for multiple cognitive users in spectrum sensing

Spectrum sensing technology is a crucial aspect of modern communication technology, serving as one of the essential techniques for efficiently utilizing scarce information resources in tight frequency bands. This paper first introduces three common logical circuit decision criteria in hard decisions and analyzes their decision rigor. Building upon hard decisions, the paper further introduces a method for multi-user spectrum sensing based on soft decisions. Then the paper simulates the false alarm probability and detection probability curves corresponding to the three criteria. The simulated results of multi-user collaborative sensing indicate that the simulation process significantly reduces false alarm probability and enhances detection probability. This approach effectively detects spectrum resources unoccupied during idle periods, leveraging the concept of time-division multiplexing and rationalizing the redistribution of information resources. The entire computation process relies on the calculation principles of power spectral density in communication theory, involving threshold decision detection for noise power and the sum of noise and signal power. It provides a secondary decision detection, reflecting the perceptual decision performance of logical detection methods with relative accuracy.

Tag-Based PHY-Layer Authentication for RIS-Assisted Communication Systems

This paper proposes a tag-based approach for physical (PHY)-layer authentication in a reconfigurable intelligent surface (RIS) communication system. We first extract the intrinsic PHY-layer features of RIS communication systems in terms of channel gain and background noise, and then apply these PHY-layer features, a random signal as well as the private key of the transmitter to construct a robust cover tag signal against the impersonation attack. We adopt an asymmetric cryptography technique to encrypt tagged signals and to resist against unauthorized detection and tampering attacks during the transmission process. The receiver then applies the maximum a-posteriori (MAP) ratio test to conduct authentication based on the received tag signal, a reference tag signal transmitted in training phase and the knowledge of distributions of the channel gain, background noise and the random signal. We also provide security analysis to demonstrate how the proposed scheme can resist unauthorized detection, tampering attacks, etc. With the help of tools of the MAP ratio test, maximum likelihood estimation, we further analyze the distribution of the test statistics and derive analytical models for the false alarm and detection probabilities. Finally, extensive simulations are conducted to verify the theoretical results and to illustrate the performance of the proposed scheme.

Real-Time Traffic Flow Statistics Based on Dual-Granularity Classification

Article Real-Time Traffic Flow Statistics Based on Dual-Granularity Classification Yanchao Bi, Yuyan Yin, Xinfeng Liu *, Xiushan Nie, Chenxi Zou, and Junbiao Du 1 School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China * Correspondence: liuxinfeng18@sdjzu.edu.cn Received: 17 June 2023 Accepted: 13 September 2023 Published: 26 September 2023 Abstract: Traffic detection devices can cause accuracy degradation over time. Considering problems such as time-consuming and laborious manual statistics, high misdetection probabilities, and model tracking failures, there is an urgent need to develop a deep learning model (which can stably achieve detection accuracy over 90%) to evaluate whether the device accuracy still satisfies the requirements or not. In this study, based on dual-granularity classification, a real-time traffic flow statistics method is proposed to address the above problems. The method is divided into two stages. The first stage uses YOLOv5 to acquire all the motorized and non-motorized vehicles appearing in the scene. The second stage uses EfficientNet to acquire the motorized vehicles obtained in the previous stage and classify such vehicles into six categories. Through this dual-granularity classification, the considered problem is simplified and the probability of false detection is reduced significantly. To correlate the front and back frames of the video, vehicle tracking is implemented using DeepSORT, and vehicle re-identification is implemented in conjunction with the ResNet50 model to improve the tracking accuracy. The experimental results show that the method used in this study solves the problems of misdetection and tracking effectively. Moreover, the proposed method achieves 98.7% real-time statistical accuracy by combining the two-lane counting method.

Evaluation of Seasonal Differences among Three NOAA Climate Data Records of Precipitation

Abstract Three satellite precipitation datasets—CMORPH, PERSIANN-CDR, and GPCP—from the NOAA/Climate Data Record program were evaluated in their ability to capture seasonal differences in precipitation for the period 2007–18 over the conterminous United States. Data from the in situ U.S. Climate Reference Network (USCRN) provided reference precipitation measurements and collocated atmospheric conditions (temperature) at the daily scale. Satellite precipitation products’ (SPP) performance with respect to cold season precipitation was compared to warm season and full-year analysis for benchmarking purposes. Considering an ensemble of typical performance metrics including accuracy, false alarm ratio, probability of detection, probability of false detection, and the Kling–Gupta efficiency (KGE) that combines correlation, bias, and variability, we found that the three SPPs displayed better performances during the warm season than during the cold season. Among the three datasets, CMORPH displayed better performance—smaller bias, higher correlation, and a better KGE score—than the two other SPPs on an annual basis and during the warm season. During the cold season, CMORPH showed the worst performance at higher latitudes over areas experiencing recurring snow or frozen and mixed precipitation. CMORPH’s performances were further degraded compared to PERSIANN-CDR and GPCP when considering freezing temperatures (T < 0°C) due to the inability to microwave sensors to retrieve precipitation over snow-covered surface. However, for cold rainfall events detected simultaneously by the satellite and the USCRN stations (i.e., conditional case), CMORPH performance noticeably improved but remained inferior to the two other datasets. The quantification of seasonal precipitation errors and biases for three satellite precipitation datasets presented in this work provides an objective basis for the improvement of rainfall retrieval algorithms of the next generation of satellite precipitation products.

Adaptive Channel Detection for Full-Duplex IAB Systems on Unlicensed Bands

The integration of integrated access and backhaul (IAB) and full-duplex communications on unlicensed bands can improve the spectrum efficiency and mitigate the spectrum resource shortage. To guarantee fair coexistence on unlicensed spectrum, the listen-before-talk mechanism needs to be executed at the IAB nodes, where the clear channel assessment (CCA) is applied before transmission. However, the residual self-interference (RSI) brought by the full-duplex communication may degrade the CCA detection performance tremendously. Therefore, in this paper we propose an adaptive channel detection scheme for the full-duplex IAB network to minimize the impact of RSI on the CCA detection, which can enhance the spectrum efficiency. In particular, the closed-form expressions of false alarm probability and detection probability are derived. Accordingly, the energy detection (ED) threshold applied in CCA is adjusted adaptively to improve the system throughput. Then, a novel timing sequence with respect to the self-interference cancellation and CCA detection is proposed. The corresponding optimal initial threshold is also derived to realize the adaptive ED threshold. Furthermore, an enhanced request to send/clear to send mechanism is proposed to solve the hidden terminal problem. Simulation results demonstrate that the proposed schemes can effectively reduce the impact of RSI on the CCA detection accuracy and significantly improve the system throughput.

Adaptive Jamming Attack Detection Under Noise Uncertainty in mmWave Massive MIMO Systems

This paper addresses jamming attack detection issue in a millimeter Wave (mmWave) massive MIMO system under noise uncertainty. Specifically, we apply the generalized likelihood ratio test (GLRT) to develop a one-step GLRT scheme for detecting jamming attack under the homogeneous and partially homogeneous noise environments, and exploit training data to estimate the unknown noise statistical information and replace it with resulting estimation in deriving GLRT procedure to obtain adaptive GLRT detection scheme. We also design a two-step GLRT scheme, where we first assume the unknown noise statistical information is known, and derive GLRT based on test data, and then replace it by the sample covariance matrix based on training data only to achieve a fully adaptive jamming attack detector. With the help of statistical signal subspace analysis and composite hypothesis testing theories, we further examine the statistical distributions of the jamming attack detection schemes and present the closed-form expressions of false alarm and detection probabilities for the proposed schemes under different noise environments. Finally, we implement extensive simulations to validate the theoretical results and evaluate the detection efficiency under various parameters.