Probability Of False Alarm Research Articles

Evaluating Satellite Precipitation Products in Capturing the Spatio-temporal Rainfall Variability Across North Darfur State, Sudan

Abstract Accurate rainfall measurement is vital when investigating spatio-temporal precipitation variability, especially in arid lands. However, there are regions worldwide where only a few ground-based observations are made. This research evaluated the applicability of six satellite precipitation products (SPPs) in detecting rainfall variability in North Darfur State, Sudan. The SPPs were Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), African Rainfall Climatology (ARC), Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), Integrated Multi-satellitE Retrievals for Global Precipitation Measurements (GPM) Final Run (GPMIMERG), Precipitation Estimation from Remote Sensing Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT). The SPPs were assessed at daily, monthly, and annual timescales for 2000–2019. Four categorical indices, i.e., the probability of detection (POD), probability of false alarm (POFA), bias in detection (BID) and Heidke skill score (HSS), and four continuous indices, i.e., the Pearson correlation coefficient (r), the root mean square error (RMSE), the per cent bias (Pbias), and the Nash–Sutcliffe model efficiency coefficient (NSE) were used to evaluate the accuracy of the SPPs. Results of the statistical analysis showed that (1) at the daily timescale, the SPPs underestimate daily rainfall by 6.53–17.61%, and CHIRPS was the best for detecting rainy days, while PERSIANN-CDR performed poorly; (2) monthly and annual scales performed better than daily timescale, and TAMSAT and CHIRPS portrayed better performance than the other SPPs. Therefore, the two could reasonably estimate rainfall amounts in North Darfur State.

Research on Target Detection Algorithm for Complex Traffic Scenes Based on ADVI-CFAR

To address the issue of reduced target detection accuracy due to interfering targets and clutter reference cells in complex traffic scenarios, we propose the ADVI-CFAR (Adaptive Discriminant Variation Index Constant False Alarm Rate) detection algorithm. Considering that the non-uniformity of the background environment leads to significant variations in signal power magnitude, we introduce a background power transition point to evaluate the uniformity of the background environment within the reference window. Moreover, in complex background environments, clutter distributions often exhibit skewness rather than a Gaussian distribution. We incorporate the higher-order statistical skewness of the clutter to calculate the background power threshold index, thereby improving the accuracy of background power estimation. Then, based on the transition points and clutter power index, the background environment is classified, and an appropriate detection threshold calculation method is chosen for target detection. We conduct a simulation analysis in uniform, non-uniform, and clutter edge environments, and the results show that the identification accuracy exceeds 95% for all three background environments. At a detection probability of 50%, the performance loss is 0.08 dB in uniform environments and 0.36 dB in multi-target environments. When the false alarm probability is set to 10−4, the ADVI-CFAR algorithm significantly suppresses false alarms, with the false alarm peak occurring at 10−3.52. Real data from urban traffic scenarios validate the method, showing that it achieves a high detection accuracy for target detection in real traffic scenarios and effectively meets the radar target detection requirements in practical traffic environments.

Performance Analysis of Dynamic Resource Assignment Technique for 6G Wireless Networks

ABSTRACTThe sixth generation (6G) wireless networks will implement dynamic spectrum access methods to ensure efficient spectrum sensing, which will in turn reduce the probability of false alarms and maximize the detection probability. Hence, time for sensing (TfS) will be a key parameter as it controls both probabilities. However, there exists a trade‐off in setting the TfS value, which has an effect on network performance. Also, the implementation of an efficient spectrum sensing technique will mandate the use of efficient resource assignment (RA) to achieve high throughput. Therefore, there exists motivation to formulate an efficient spectrum sensing technique that jointly optimizes TfS and RA. In the current article, a dynamic RA (DRA) technique is proposed for assigning key resources, that is, sub‐carriers, power, remote radio heads, and baseband units, dynamically within the network. The DRA technique implements an opportunistic spectrum sharing (SS) method which uses cooperative SS to enable secondary users (SUs) to detect any vacant spectrum slots not being currently utilized by primary users (PUs). The aim of the DRA technique is to maximize overall throughput of the SUs while simultaneously ensuring the desired quality of service for the PUs. To achieve this aim, DRA adjusts the time for spectrum sensing in accordance with the detection probabilities of targets and the false alarms. To find solutions to the formulated problem in reasonable times, an iterative heuristic method is proposed. The results reveal that (i) the DRA technique is effective in obtaining the solutions, and (ii) it is mandatory to adjust the time for sensing.

Improving early detection of gravitational waves from binary neutron stars using CNNs and FPGAs

Abstract The detection of gravitational waves (GWs) from binary neutron stars (BNSs) with possible telescope follow-ups opens a window to ground-breaking discoveries in the field of multi-messenger astronomy. With the improved sensitivity of current and future GW detectors, more BNS detections are expected in the future. Therefore, enhancing low-latency GW search algorithms to achieve rapid speed, high accuracy, and low computational cost is essential. One innovative solution to reduce latency is the use of machine learning (ML) methods embedded in field-programmable gate arrays (FPGAs).
In this work, we present a novel WaveNet-based method, leveraging the state-of-the-art ML model, to produce early-warning alerts for BNS systems. Using simulated GW signals embedded in Gaussian noise from the Advanced LIGO and Advanced Virgo detectors’ third observing run (O3) as a proof-of-concept dataset, we demonstrate significant performance improvements. Compared to the current leading ML-based early-warning system, our approach enhances detection accuracy from 66.81% to 76.22% at a 1% false alarm probability. Furthermore, we evaluate the time, energy, and economical cost of our model across CPU, GPU, and FPGA platforms, showcasing its potential for deployment in real-time gravitational wave detection pipelines.

Memorial K-means clustering for cooperative spectrum sensing in cognitive wireless sensor networks at low SNR regimes

PurposeThe spectrum resources are becoming increasingly scarce and underutilized, and cooperative spectrum sensing (CSS) in cognitive wireless sensor networks (CWSNs) offers many solutions with good results, but this paper aims to address the significant issue of CSS in the context of low signal-to-noise ratio (SNR).Design/methodology/approachThis study proposes Pearson Correlation Coefficient (PCC) to obtain value feature values under the Rayleigh channel model, which are then used for Memorial K-means Clustering (MKC) analysis of CSS in CWSNs at low SNR regimes. In addition, MKC algorithm is used for training and converted it into supervised model.FindingsA series of numerical simulation results demonstrate that the correctness and effectiveness of the proposed MKC, especially the detection and false alarm probabilities in a low SNR condition. The detection probability is increased by 5%–12% at low SNR compared with other methods.Originality/valueThe MKC algorithm can reduce the impact of randomness on the clustering centers for multiple groups, which combined with PCC can effectively reduce the influence of noise at low SNR, and the unsupervised transformed model effectively reducing the complexity of re-discrimination.

Waveform Reconstruction of Core-Collapse Supernova Gravitational Waves with Improved Multisynchrosqueezing Transform

Abstract Gravitational waves (GWs) from core-collapse supernovae (CCSNe) have been proposed as a means to probe the internal physical properties of supernovae. However, due to their complex time-frequency structure, effectively searching for and extracting GW signals from CCSNe remains an unsolved challenge. In this paper, we apply the improved multisynchrosqueezing transform (IMSST) method to reconstruct simulated GW data based on the advanced LIGO (aLIGO) and Einstein Telescope (ET) detectors. These data are generated by the rotational and neutrino-driven mechanisms, and we use the match score as the criterion for evaluating the quality of the reconstruction. To assess whether the reconstructed waveforms correspond to true GW signals, we calculate the false alarm probability of reconstruction (FAPR). For GW sources located at 10 kpc and datasets where the waveform amplitudes are normalized to 5 × 10−21 observed by aLIGO, FAPR are 2.1 × 10−2 and 6.2 × 10−3, respectively. For GW sources at 100 kpc and with waveform amplitudes normalized to 5 × 10−21 observed by ET, FAPR are 1.3 × 10−1 and 1.5 × 10−2, respectively. When the GW strain reaches 7 × 10−21 and the match score threshold is set to 0.75, the IMSST method achieves maximum reconstruction distances of approximately 37 kpc and 317 kpc for aLIGO and ET, respectively. Finally, we compared the performance of IMSST and STFT in waveform reconstruction based on the ET. The results show that the maximum reconstructable distance using STFT is 186 kpc.

Acoustic source detection and localization using generalized likelihood ratio test in the spherical harmonic domain

Acoustic source detection and localization using generalized likelihood ratio test in the spherical harmonic domain

Fast and Efficient Bayesian Method to Search for Strongly Lensed Gravitational Waves

Abstract A small fraction of the gravitational-wave (GW) signals from binary black holes observable by ground-based detectors will be strongly lensed by intervening objects such as galaxies and clusters. Strong lensing will produce nearly identical copies of the GW signals separated in time. These lensed signals must be identified against a background of unlensed pairs GW events, some of which may appear similar by accident. This is usually done using fast, but approximate methods that, for example, check for the overlap between the posterior distributions of a subset of binary parameters, or using slow, but accurate joint Bayesian parameter estimation. In this work, we present a modified version of the posterior overlap method dubbed “PO2.0” that is mathematically equivalent to joint parameter estimation while still remaining fast. We achieve a significant gain in efficiency by incorporating informative priors about the binary and lensing populations, selection effects, and all the inferred parameters of the binary. For binary black hole signals lensed by galaxies, our improved method can detect 65% lensed events at a pairwise false alarm probability of ∼2 × 10−6. Consequently, we have a 13% probability of detecting a strongly lensed event above 2.25σ significance during 18 months of observation by the LIGO-Virgo detectors at their current sensitivity. We also show how we can compute the joint posteriors of the lens and source parameters from a pair of lensed events by reweighting the posteriors of individual events in a computationally inexpensive way.

Comparative Analysis of Prior and Posterior Integrity Monitoring Techniques for Enhanced Global Navigation Satellite System Positioning Continuity and Accuracy

GNSS integrity is an essential component for ensuring the reliability of safety-critical applications using Global Navigation Satellite Systems (GNSSs). These applications, such as use in aviation and autonomous vehicles, demand high precision and dependability. There are two major GNSS integrity monitoring techniques, namely prior and posterior integrity monitoring. The principles of the two approaches, however, differ significantly, each influencing the GNSS positioning system’s continuity and accuracy performance in unique ways. In this study, we conduct a thorough evaluation and comparison of these two approaches to integrity monitoring, focusing on their effects on continuity and accuracy performance. We assess the probability of false alarms and continuity risks associated with posterior integrity monitoring by defining specific geometric spheres, both inside and outside the contours of the parity set, where the integrity risk requirement is satisfied. By using these defined spheres, we determine the lower and upper bounds for the probability of false alarms and continuity risks in posterior integrity monitoring. These spheres provide a novel and effective framework for comparing the continuity performance between the Chi-squared residual-based prior and posterior integrity monitoring. Our analysis highlights that, under fault-free scenarios, posterior integrity monitoring offers superior accuracy compared with the Chi-squared residual-based prior integrity monitoring approach. This finding underscores the critical importance of selecting an appropriate integrity monitoring strategy to enhance GNSS positioning system performance, particularly in environments where safety and precision are paramount. The insights gained from this study contribute to the advancement of GNSS technologies, supporting their implementation in an increasingly wide range of safety-critical applications.

Analysis of 6G and B5G waveforms using hybrid MF-ED and ECG-ED spectrum sensing techniques

The rapid evolution of wireless communication has necessitated advanced waveform analysis for beyond-fifth-generation (B5G) and sixth-generation (6G) radio networks, focusing on efficient spectrum utilization. There is a need for greater spectrum allotment in data-intensive applications, and new technologies require faster data rates and reduced latency. This study explores hybrid spectrum sensing techniques, combining matched filter (MF) energy detection (ED) and an equal-gain combining-based energy detection Neyman-Pearson threshold estimation technique (EGC-ED-PTh) to enhance waveform detection accuracy in complex environments. The proposed method offers an enhanced signal-to-noise ratio (SNR) by optimizing the detection performance, particularly in low-SNR environments, thereby improving the signal reliability. The proposed algorithms are evaluated in comparison with traditional SS methods, including ED, MF, and cyclostationary feature detection (CFD). Additionally, characteristics including bit error rate (BER), power spectral density (PSD), probability of detection (pd), and probability of false alarm (pfa) were researched and evaluated for 500 and 1000 samples. The simulation findings show that the projected algorithms perform better than the traditional algorithms with minimum sidelobes of – 3024 and pfa effects and achieve a throughput gain of 5 and 4.7 dB compared with the conventional algorithms.

EWMA control chart framework for efficient Maxwell quality characteristic monitoring: An application to the aerospace industry

EWMA control chart framework for efficient Maxwell quality characteristic monitoring: An application to the aerospace industry

Eyewitness Lineup Identity (ELI) database: Crime videos and mugshots for eyewitness identification research

There is a long history of experimental research on eyewitness identification, and this typically involves staging a crime for participants to witness and then testing their memory of the “culprit” by administering a lineup of mugshots. We created an Eyewitness Lineup Identity (ELI) database, which includes crime videos and mugshot images of 231 identities. We arranged the mugshots into 6-, 9-, and 12-member lineups, and then we tested the stimuli in an eyewitness experiment. Participants (N = 1584) completed six trials of viewing a crime video and completing a lineup identification task. In lineups that included the culprit, the average probability of correction identification was 59.0%, 95% CI [55.9, 62.0]. In lineups that did not include the culprit, the average probability of false alarm was 29.9% [27.8, 32.0]. These outcomes indicate that the ELI database is suitable for eyewitness identification research, and the large number of crime videos would enable stimulus sampling. The database is available for research approved by a research ethics board and can be requested at https://osf.io/vrj3u.

Performance analysis of capsule networks for detecting GPS spoofing attacks on unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) are prone to several cyber-attacks, including global positioning system (GPS) spoofing. The use of machine learning and deep learning are becoming increasingly common for UAV GPS spoofing attack detection; however, these approaches have some limitations, such as a high rate of false alarm and misdetection. We propose using capsule networks to detect and classify UAV-focused GPS spoofing attacks. This paper compares simple capsule networks, efficient capsule networks, dual attention capsule networks, and convolutional neural network in terms of accuracy, probability of detection, probability of misdetection, probability of false alarm, prediction time, training time per sample, and memory size. The results indicate that the Efficient-capsule network outperforms the other models, as demonstrated by an accuracy of 99.1%, a probability of detection of 99.9%, a probability of misdetection of 0.1%, a probability of false alarm of 0.37%, a prediction time of 0.5 seconds, a training time per sample of 0.2 seconds, and a memory size of 123 mebibytes for binary classification.

An Optimization Coverage Strategy for Wireless Sensor Network Nodes Based on Path Loss and False Alarm Probability.

In existing coverage challenges within wireless sensor networks, traditional sensor perception models often fail to accurately represent the true transmission characteristics of wireless signals. In more complex application scenarios such as warehousing, residential areas, etc., this may lead to a large gap between the expected effect of actual coverage and simulated coverage. Additionally, these models frequently neglect critical factors such as sensor failures and malfunctions, which can significantly affect signal detection. To address these limitations and enhance both network performance and longevity, this study introduces a perception model that incorporates path loss and false alarm probability. Based on this perception model, the optimization objective function of the WSN node optimization coverage problem is established, and then the intelligent optimization algorithm is used to solve the objective function and finally achieve the optimization coverage of sensor nodes. The study begins by deriving a logarithmic-based path loss model for wireless signals. It then employs the Neyman-Pearson criterion to formulate a maximum detection probability model under conditions where the cost function and prior probability are unknown, constraining the false alarm rate. Simulated experiments are conducted to assess the influence of various model parameters on detection probability, providing comparative analysis against traditional perception models. Ultimately, an optimization model for WSN coverage, based on combined detection probability, is developed and solved using an intelligent optimization algorithm. The experimental results indicate that the proposed model more accurately captures the signal transmission and detection characteristics of sensor nodes in WSNs. In the coverage area of the same size, the coverage of the model constructed in this paper is compared with the traditional 0/1 perception model and exponential decay perception model. The model can achieve full coverage of the area with only 50 nodes, while the exponential decay model requires 54 nodes, and the coverage of the 0/1 model is still less than 70% at 60 nodes. According to the simulation experiments, it can be basically proved that the WSN node optimization coverage strategy based on the proposed model provides an effective solution for improving network performance and extending network lifespan.

Machine Learning Approach to Energy Detection Based Spectrum Sensing for Cognitive Radio Networks

AbstractCognitive radio is an intelligent technology for wireless communication that optimizes the use of available frequency bands. Machine learning techniques can play an important role in spectrum sensing for cognitive radio networks to meet the rising traffic demand of wireless communication systems. The reliability of spectrum sensing methods depends on the prior knowledge of the noise to set a threshold. On the other hand, the success of a machine learning model relies on both the datasets and the accuracy of its learning algorithms. In this paper, we propose a spectrum sensing method for cognitive radio based on a machine learning algorithm in the conventional energy detection technique that removes the requirement to calculate the threshold. Initially, we introduce a method to build the dataset using the general concept of spectrum sensing based on the energy detection technique. The Naive Bayes supervised machine learning classification algorithm is implemented on the generated dataset for training, validation, and testing to sense the available spectrum. The proposed method is evaluated and tested using performance metrics such as confusion matrix, accuracy, precision, recall, F1 score, probability of detection, and probability of false alarm. In the simulation, the quadrature phase‐shift keying (QPSK) modulation scheme over the additive white Gaussian noise (AWGN) channel is considered. The experimental outcomes of the proposed method provide satisfactory and acceptable performance for spectrum sensing in cognitive radio networks. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Agile Detection of DRM Signal via GLRT

AbstractDigital Radio Mondiale (DRM) is a digital Orthogonal Frequency Division Multiplexing (OFDM) broadcasting standard that has been employed all over the world. The operating carrier frequency and time of DRM station change with the scheduling period. The user terminal commonly uses channel decoding and audio decoding to detect the DRM radio. This conventional signal detection method always fail when the station is not working, or the signal‐to‐noise ratio (SNR) is weak, or the designated frequency band is illegally occupied. Besides, the conventional method needs at least one DRM transmission super frame with a duration of 1.2 s, which causes delay and brings additional computations. To solve the challenges faced by conventional method, this paper has proposed an agile and efficient signal detection method based on the Generalized Likelihood Ratio Test. The proposed method coherently integrates the frequency pilots of successive OFDM symbols via discrete Fourier transform, then propose a sufficient statistic to detect the DRM radio. The required number of OFDM symbols, which is much smaller than that of one transmission super frame, is adaptively chosen from the given probabilities of false alarm and correct detection. The computations and SNR requirement of the proposed method are both smaller than the conventional method, which help the user terminal quickly detect the DRM signal over the given frequency band. The proposed method also provides a new perspective for electromagnetic spectrum management.

Cautious parameter learning for a joint mean and variance monitoring CUSUM scheme with guaranteed in-control performance

Joint monitoring of the mean and variance of a normal process is crucial in quality engineering for assessing homoscedasticity, determining process capability, and recognizing that changes in scale often accompany changes in location. A CUSUM scheme for monitoring both mean and variance must ensure overall performance, even with estimated parameters. Approaches guaranteeing minimum in-control performance are standard, but they lose power with small Phase I samples. Learning approaches allow parameter re-estimation with new data, while cautious schemes define update rules to minimize sample contamination from out-of-control observations. This article presents a CUSUM scheme incorporating cautious parameter learning and guaranteed in-control performance for joint monitoring of mean and variance with normal, independent observations. Our novel approach compares parameter estimation procedures based on conditional expected delay and false alarm probability across a series of change points for a profile assessment, rather than a point-based assessment. Computational results show that the cautious learning procedure using a conservative external updating rule is preferred even when the initial sample size is small, as it provides the best balance between conditional expected delay and false alarm probability from a practical perspective.

USING NEURAL NETWORKS FOR LANDMINE DETECTION

The issue of large mined areas and their expansion is critical due to their impact on safety and the economy. This article focuses on enhancing the efficiency of optical methods for detecting surface-laid landmines in humanitarian demining tasks. It demonstrates that one of the most promising optical methods for mine detection is hyperspectral imaging, which provides exceptionally precise analysis of the spectral composition of the radiation in the background-target environment. However, practical application of hyperspectral methods is significantly complicated by the vast size of mined territories. Therefore, mine detection must be automated. To address this challenge, the potential of artificial neural networks was investigated. The study proposes the integration of artificial intelligence, specifically RBF (Radial Basis Function) networks, to improve the efficiency and accuracy of object detection. The work compares traditional classification methods with innovative machine learning approaches. Algorithms were tested on both simulated and real data, enabling an evaluation of their ability to identify objects under varying spectral content conditions. The specific characteristics of the study area define the requirements for assessing the performance of technical tools: foremost, the probability of missing a signal must be minimized. At the same time, given the large-scale computations involved, the probability of false alarms should remain low. The study shows that RBF neural networks can detect mines with a low rate of false alarms. During network training with a large spread parameter, the sensitivity of the output to the spectral variability of pixels decreases. This allows the network to detect a target even at low fill coefficients. Thus, the research results indicate that the proposed methods significantly reduce false alarms and ensure high performance under real-world conditions.

Selected issues of the probability of false alarms in electronic security systems used in building objects

The article presents selected issues concerning problems related to the probability of false alarms generated by electronic security systems (ESS) during technical monitoring states. ESSs are used in building objects. The authors of the article conducted a theoretical analysis regarding the causes of false signals in security systems, with particular attention to Fire Alarm Systems (FAS). FAS are the most important security systems that are responsible for fire safety in buildings and large areas – e.g. airports, railway stations, etc. FAS are responsible for the life and health of people using the objects, but also for property and the surrounding natural environment. In accordance with CPR Regulation No. 305 of March 9, 2011, of the European Union, FAS elements and devices are required to obtain certification and are treated as generally available construction materials.

Method for detecting radio signal boundaries in the time-frequency domain based on a multidimensional matrix window operator

Formulation of the problem. The use of artificial neural networks to solve the problem of detecting and identifying signal structures as objects in the image is not oriented to work in a real, highly dynamic and saturated radio-electronic environment, characterized by intense fluctuations in radio signal parameters and changes in radio link parameters. The fundamental difficulty in creating an up-to-date, complete and reliable reference description of the radio technical parameters of detected radio links determines the need to create highly universal and adaptive algorithms for identifying radio links, which significantly limits the range of applications of the neural network approach to solving these problems. Target. Develop and analyze a method for detecting the boundaries of a radio signal in the time-frequency domain. Results. The paper proposes a new multidimensional matrix window operator and a corresponding statistical rule for adaptive detection of the corner of a rectangular signal region in a time-frequency panorama. An exact analytical expression is obtained for the distribution of the decisive detection statistics, the probabilities of false alarms and missed signals. Practical significance. The implementation of the proposed approach makes it possible to detect and distinguish radio signals from modern radio communication and data transmission lines in a real complex electromagnetic environment, characterized by mutual overlap of frequency-time domains of signals from different radio lines with a priori uncertainty regarding the noise level.