Probability Of False Alarm Research Articles

The knowledge-aided generalized multipath adaptive detector

In urban environments, the challenges brought about by multipath propagation are significant for traditional adaptive radar detection problems. To tackle this issue, a generalized multipath signal model combined with environmental geometric prior information (EGPI) is developed. This model can be utilized regardless of whether the multipath signals are fully, partially, or partially-resolvable in the range domain. During the process of designing a detector using the criteria of the one-step generalized likelihood ratio test (GLRT), an attempt to find a closed-form expression for the maximum likelihood estimator of the unknown parameters was unsuccessful. However, by utilizing an adaptive configuration for the initial value, a cyclic optimization algorithm was able to obtain the estimated value within just a few iterations, demonstrating a rapid convergence rate. Furthermore, a detector based on the two-step GLRT criteria is proposed. Compared to traditional detectors, the newly developed detectors make more efficient use of the energy in multipath signals, resulting in improved detection performance. It has been demonstrated that when the whitened angles between subspaces are set to two specific values, the GLRT detectors exhibit the Constant False Alarm Probability (CFAR) property. Simulation results show that the false alarm probability(Pfa) at these two specific angles provides upper and lower bounds of Pfa.

Noise-like signal detector, reducing the probability of false alarms and not giving false detections associated with the correlation properties of sequences modulating the phase of the signal

Noise-like signal detector, reducing the probability of false alarms and not giving false detections associated with the correlation properties of sequences modulating the phase of the signal

Semiparametric constant false alarm rate method for radar and sonar images

AbstractThis work proposed a novel constant false alarm rate detection method based on Gaussian mixture model (GMM). The main contribution contains two aspects. First, the Gabor wavelet is used to determine the GMM component number. Second, the expression of probability of false alarm is derived based on the GMM parameter.

Passive reputation method with hybrid optimized deep model for spectrum sensing in cognitive radio network based on blockchain

SummaryThe discovery of malevolent users in cognitive radio networks (CRNs) is done by using blockchain‐based technique. For diagnosing the availability of frequency resources, the spectrum sensing (SS) plays an essential role in CRN. It is critical to enable cognitive radio (CR) technology in wireless communication systems for the next generation. Due to the increasing demand of the users, some bands are overloaded and others become underutilized. The CR applied models for vigorously assigning unlicensed users. The missed detection problem occurs in the conventional spectrum sensing schemes, which hampers the proper utilization of the spectrum. These motivated the development of a newly devised passive reputation method with an optimized deep model for spectrum sensing in CRN‐based blockchain. The sensing spectrum in CRN is designed using the cooperative spectrum sensing algorithm. The spectrum sensing is done with the decision made using AlexNet where the weights of AlexNet are trained with a hybrid optimization algorithm, named Honey Badger Remora Optimization (HBRO) algorithm. Thus, the output attained from the Alexnet_HBRO algorithm is considered. Meanwhile, the data are recorded in the blockchain, and then, the passive reputation is performed, and the determined output is considered. At last, the spectrum sensing is carried out by fusing AlexNet and passive reputation output where weights are optimally generated using the proposed Snake Honey Badger Remora Optimization (SHBRO) algorithm. The proposed SHBRO offered a high detection probability of 1, false alarm probability of 0.003, sensing time of 551.567s, and computation time of 210.102s.

Target tracking using video surveillance for enabling machine vision services at the edge of marine transportation systems based on microwave remote sensing

AbstractAutomatic target tracking in emerging remote sensing video-generating tools based on microwave imaging technology and radars has been investigated in this paper. A moving target tracking system is proposed to be low complexity and fast for implementation through edge nodes in a mini-satellite or drone network enabling machine intelligence into large-scale vision systems, in particular, for marine transportation systems. The system uses a group of image processing tools for video pre-processing, and Kalman filtering to do the main task. For testing the system performance, two measures of accuracy and false alarms probability are computed for real vision data. Two types of scenes are analyzed including the scene with single target, and the scene with multiple targets that is more complicated for automatic target detection and tracking systems. The proposed system has achieved a high performance in our tests.

ATLAS: Latest Advancements and First Observations

The increasing amount of space debris poses a significant threat to operational satellites and space-based services. This article updates the community on the current status of the development of ATLAS, a tracking radar that is part of the EUSST network and aims to detect space objects in low Earth orbits. This article focuses on the latest activities performed: calibration of the pointing system and initial observations of space objects. The calibration procedure consisted of cross-scanning the Solar disk and yielded great results, obtaining an offset of 5.3° in azimuth and 0.10° in elevation. The first observation campaign resulted in 33 range detections of the International Space Station (ISS) with a probability of false alarm of 10−9. The observations were then used to readjust the radar equation to assess the real-world performance of the system.

Pre-Processing of Simulated Synthetic Aperture Radar Image Scenes Using Polarimetric Enhancement for Improved Ship Wake Detection

Ship wake detection using synthetic aperture radar (SAR) imagery provides a way to obtain small marine ship information, but it often becomes unavailable and unreliable during a high sea state. Polarimetric information provides a potential way to solve this problem, which can enhance the ship target as well as the ship wake features. However, three challenges still exist in ship wake detection in polarimetric SAR imagery: the unwanted influences of bright and singular points on ship wake detection, the lack of performance analysis of wake detection by new-type polarimetric enhancement methods, and the difficulty of using the assessment criteria for ship wake detection. In this paper, we try to solve the above problems. Firstly, fully polarized SAR imagery of both ship turbulent and Kelvin wake is simulated based on the two-scale composite model, and the Polarimetric Whitening Filter (PWF) and Polarimetric Detection Optimization Filter (PDOF) are applied to the simulated fully polarized SAR imagery to enhance the ship wake features. Secondly, since the bright and singular points resulting from the ship echoes and the polarimetric enhancement methods may lead to misdetections, a logarithm process and z-score normalization pre-processing has been applied to the images. Then, a new assessment criterion for wake detection performance has been formulated, and the probability of missing detections (PMDs) and the probability of false alarms (PFAs) have been defined for two different requirements. And a Radon transform-based ship wake detection method for both ship turbulence and Kelvin wake has been carried out in horizontal–horizontal (HH), vertical–vertical (VV), horizontal–vertical (HV), PWF and PDOF SAR imagery. Finally, an analysis of the wake detection performance has been carried out. The PWF and PDOF can improve the wake detection performance by an average of nearly 50 percent compared with the HH and VV.

Optimized Cluster-Based Cooperative Spectrum Sensing Over Weibull, Nakagami, Rician, and Rayleigh Fading Channels

This paper reports cluster-based cooperative spectrum sensing (CB-CSS) over Weibull, Nakagami, Rician, and Rayleigh fading channels in cognitive radio networks (CRNs). The performance assessment of centralized cooperative spectrum sensing (CCSS) and CB-CSS with different fusion rules over the Weibull fading channel is presented. The goal is to improve detection performance in a practical and efficient manner for CB-CSS. For this purpose, an expression for the optimal inter-fusion rule threshold (fusion rule between cluster head (CH) and fusion center (FC)) is proposed to minimize total error probability (TEP) over Weibull for CB-CSS. Simulations are conducted using analytical equations for missed detection and false alarm probability. The obtained expression for optimal inter-fusion rule threshold which minimizes TEP is verified by the results. The proposed model is validated with other fading channels such as Nakagami, Rician, and Rayleigh to show the superiority of the approach. It is observed that for a given range of detection threshold and for 4 × 6 cluster size (4 clusters and 6 CR users inside each cluster), the lowest total error probability is achieved over the Weibull fading channel. Minimum TEP over the Weibull fading channel for given network parameters is TEP = 1.656 × 10 − 6 when intra- and inter-fusion rules are l = 1 (OR fusion) and c = 3 . The least number of clusters and users essential to achieving target error bound over different fading channels are also determined.

Joint optimization of sampling point and sensing threshold for spectrum sensing

AbstractWith the continuous evolution and in‐depth integration between wireless communication and emerging technology such as internet of things (IoT), artificial intelligence (AI) etc., wireless terminals are growing exponentially, thus bringing great challenges to available spectrum resources. The contradiction between unlimited frequency needs and limited spectrum resources has become a bottleneck restricting the development of wireless communication technology. As an efficient way to improve spectrum efficiency, cognitive radio (CR) continues to be the focus of wireless communication within decades. To conduct CR, the main procedure is the discovery of available spectral holes by periodically monitoring the target authorized band, namely spectrum sensing (SS). Energy detector (ED) is widely accepted for SS due to its low complexity and high convenience. The essence of traditional ED based SS schemes consist in the adaptive variation of sensing threshold/sampling point with environmental signal‐to‐noise ratio (SNR) at the receiver of CR terminal, namely adaptive sensing threshold/sampling point based SS. However, the performance of both adaptive sensing threshold and adaptive sampling point based SS schemes are always at the expense of computation complexity due to the excessive sampling point. In addition, these two schemes are both about the optimization issue of a single variable under constraints. Actually, both detection probability and false alarm probability of ED are a two‐dimensional function of sensing threshold and sampling point for a given SNR. The optimal solution of sensing performance can not be obtained by optimizing sensing threshold or sampling point alone. Motivated by these, the joint optimization of sampling point and sensing threshold is considered for SS in this paper, where sampling point and sensing threshold are jointly adaptive with the variation of environmental SNR. In addition, Q‐learning is considered in this paper to obtain the sub‐optimal solution due to the non‐convexity of the considered optimization problem. Finally, the simulation experiments are made and the results validate the effectiveness of the proposed scheme.

The Channel Fading Influence of the Receiver Operating Characteristics of the TT&C Receiver Based on the Dual-Sequence Frequency Hopping

Aimed at the antijamming needs of the space tracking, telemetry command (TT&C) receiver under a low signal-to-noise ratio, the anti-interference advantage of the dual-sequence frequency hopping (DSFH) communication system is applied. The channel amplitude fading influence of the receiver operating characteristics (ROC) of the TT&C receiver based on the DSFH is studied. Firstly, based on the typical channel model of the Rayleigh fading without direct path transmission, the conditional Fokker-Planck equation (FPE) is obtained by analyzing the statistical independence of the Rayleigh fading signal and SR output particle moments. Secondly, the probability density function (PDF) of the DSFH signal via channel Rayleigh fading enhanced by stochastic resonance (SR) is obtained by introducing the decision time. Thirdly, the detection probability, false alarm probability, ROC, and system bit error rate (BER) of the DSFH signals enhanced by SR under the Rayleigh fading conditions are obtained, under the minimum BER criterion. Finally, the conclusions are reached: one is that the DSFH signals via channel Rayleigh fading can still be detected by the SR system under low SNR, and the other one is that the SNR can reach the -13 dB by the reception of DSFH signal enhanced by SR, when the Rayleigh fading parameter is 0.2042.

A series expansion formula of the scale matrix with applications in CUSUM analysis

We introduce a new Lévy fluctuation theoretic method to analyze the cumulative sum (CUSUM) procedure in sequential change-point detection. When observations are phase-type distributed and the post-change distribution is given by exponential tilting of its pre-change distribution, the first passage analysis of the CUSUM statistic is reduced to that of a certain Markov additive process. We develop a novel series expansion formula of the scale matrix for Markov additive processes of finite activity, and apply it to derive exact expressions of the average run length, average detection delay, and false alarm probability under the CUSUM procedure.

Multi-agent reinforcement learning based optimal energy sensing threshold control in distributed cognitive radio networks with directional antenna

In CRNs, it is crucial to develop an efficient and reliable spectrum detector that consistently provides accurate information about the channel state. In this work, we investigate a CSS in a fully-distributed environment where all secondary users (SUs) are equipped with directional antennas and make decisions based solely on their local knowledge without information sharing between SUs. First, we establish a stochastic sequential optimization problem, which is an NP-hard, that maximizes the SU’s detection accuracy by the dynamic and optimal control of the energy sensing/detection threshold. It can enable SUs to select an available channel and sector without causing interference to the primary network. To address it in a distributed environment, the problem is transformed into a decentralized partially observed Markov decision process (Dec-POMDP) problem. Second, in order to determine the best control for the Dec-POMDP in a practical environment without any prior knowledge of state–action transition probabilities, we develop a multi-agent deep deterministic policy gradient (MADDPG)-based algorithm, which is referred to as MA-DCSS. This algorithm adopts the centralized training and decentralized execution (CTDE) architecture. Third, we analyzed its computational complexity and showed the proposed approach’s scalability by the polynomial computational complexity, in terms of the number of channels, sectors, and SUs. Lastly, the simulation confirms that the proposed scheme provides enhanced performance in terms of convergence speed, accurate detection, and false alarm probabilities when it is compared to baseline algorithms.

Performance of Detectors for Cooperative Spectrum Sensing Under Laplacian Noise

Electronic systems in general can be impaired by impulsive noise generated by a variety of sources. Spectrum sensors are of particular interest herein, since their probabilities of detection and false alarm can be severely degraded under this impairment. Several models for impulsive noise have been studied in the literature, all of them having the common characteristic of being well represented by heavy-tailed probability density functions, like Laplace and some Stable distributions. This article addresses the performances of state-of-the-art detectors for cooperative spectrum sensing when the received signal is impaired by Laplacian noise. This is made by means of estimating the probability of detection for a fixed false alarm rate, when important system parameters are varied. It is demonstrated that the robustness against impulsive noise varies significantly depending on the adopted detection strategy.

The verification of periodicity with the use of recurrent neural networks

Abstract The ability to automatically and robustly self-verify periodicity present in time-series astronomical data is becoming more important as data sets rapidly increase in size. The age of large astronomical surveys has rendered manual inspection of time-series data less practical. Previous efforts in generating a false alarm probability to verify the periodicity of stars have been aimed towards the analysis of a constructed periodogram. However, these methods feature correlations with features that do not pertain to periodicity, such as light-curve shape, slow trends, and stochastic variability. The common assumption that photometric errors are Gaussian and well determined is also a limitation of analytic methods. We present a novel machine learning based technique which directly analyses the phase-folded light curve for its false alarm probability. We show that the results of this method are largely insensitive to the shape of the light curve, and we establish minimum values for the number of data points and the amplitude to noise ratio.

Multitargets Orientation Technique Based on Reflection Characteristic Analysis Using an Inverse Diffraction Parabolic Equation

In this article, the inverse diffraction parabolic equation (IDPE) model based on the finite difference method is proposed, which is first applied in the multiple nonradiation targets orientation technology. In principle, the electromagnetic signal propagating in the transmission path will produce a reflected signal back to the source end while encountering the discontinuous objects. The distribution of the reflection or refraction intensity is directly associated with the distances and heights of the objects, so the location can be determined by means of analyzing the distribution. Here, according to the profile data of field intensity at the source end, the distribution of backward propagating electromagnetic waves are calculated rapidly by the IDPE. Then, the local extreme searching method is applied to search the coordinate of the convergence point of field intensity and the positions of multiple objects are finally determined. The piecewise linear function is used to model the irregular terrain. The influence of discontinuous terrain slopes on the false alarm probability of objects localization is also analyzed. The results show that the localization accuracy of the IDPE algorithm is affected by multiple factors, such as the radio frequency and sampling interval of field intensity. It is proved that the IDPE is a novel and efficient algorithm for multiple nonradiation targets orientation technology in long-range complicated terrain environment.

Neurocognitive Features of Mild Cognitive Impairment and Distress Symptoms in Older Adults Without Major Depression.

Two distinct symptom dimensions were identified in older adults who did not have major depressive disorder (MDD): a) a dimension associated with mild cognitive dysfunction, and b) a dimension related to distress symptoms of old age (DSOA). It is uncertain whether previous findings regarding the features of amnestic mild cognitive impairment (aMCI) remain valid when patients with MDD are excluded. To examine the neurocognitive features of aMCI (n = 61) versus controls (n=59) and the objective cognitive characteristics of DSOA in participants without MDD. Neurocognition was evaluated utilizing the Cambridge Neurological Test Automated Battery (CANTAB) and memory tests. This research demonstrated that CANTAB tests may differentiate between aMCI and controls. The One Touch Stockings of Cambridge, probability solved on first choice (OTS_PSFC), Rapid Visual Information Processing, A prime (RVP_ A´), and the Motor Screening Task, mean latency, were identified as the significant discriminatory CANTAB tests. 37.6% of the variance in the severity of aMCI was predicted by OTS_PSFC, RVP_ A´, word list recognition scores, and education years. Psychosocial stressors (adverse childhood experiences, negative life events), subjective feelings of cognitive impairment, and RVP, the probability of false alarm, account for 40.0% of the DSOA score. When MDD is ruled out, aMCI is linked to deficits in attention, executive functions, and memory. Psychosocial stressors did not have a statistically significant impact on aMCI or its severity. Enhanced false alarm response bias coupled with heightened psychological stress (including subjective perceptions of cognitive decline) may contribute to an increase in DSOA among older adults.

A Relationship Between the False Alarm Probability and the Atomic Norm Denoising Regularization Parameter for Line Spectral Estimation

A Relationship Between the False Alarm Probability and the Atomic Norm Denoising Regularization Parameter for Line Spectral Estimation

On the False Alarm Probability of the Normalized Matched Filter for Off-Grid Targets: A Geometrical Approach and Its Validity Conditions

On the False Alarm Probability of the Normalized Matched Filter for Off-Grid Targets: A Geometrical Approach and Its Validity Conditions

Hybrid spectrum sensing enhancement for cognitive radio in 6G radio system

This article highlights the potential advantages of the proposed hybrid algorithm for 6G. The algorithm's adaptability addresses the dynamic spectral characteristics of 6G environments, enabling seamless transition between different spectral conditions. The hybrid approach holds promise for enhancing spectrum utilization, reducing interference, and optimizing overall communication system performance. As 6G technology evolves, the integration of Energy Detection and Cyclostationary Spectrum Sensing through the hybrid algorithm offers a glimpse into innovative techniques that can shape the future of wireless communication, unlocking the potential for enhanced connectivity, efficiency, and user experience. Several parameters such as Probability of detection (Pd), Probability of false alarm (pfa), Biter error rate (BER) and Power spectral density (PSD) are compared and analysed with the conventional spectrum sensing algorithms. It is seen that the proposed hybrid algorithms obtained a substantial detection performance at the low Signal to noise ratio (SNR).

A multi-user cognitive radio full-duplex channel under imperfect spectrum sensing: Achievable rates and energy efficiency

In this work, the achievable rates and energy efficiency of a multi-user cognitive radio (CR) uplink channel with full-duplex (FD) are studied. For this multi-user CR channel, secondary users can work in FD mode to simultaneously transmit and listen to the channel. We consider a realistic scenario in which both FD operation and spectrum sensing are imperfect. At first, we examine the sensing performance and the rate region through the evaluation of the probability of miss-detection and the probability of false alarm when the primary user (PU) activity is assumed to be non-time-slotted. Due to an imperfect sensing operation, the cognitive radio channel is modeled as a non-Gaussian channel impacted by a Gaussian-mixture (GM) noise plus PU interference. Under this practical assumption, it is not possible to calculate the achievable rates and the rate region directly. Instead, we derive simple approximations of these metrics which can be easily calculated with arbitrarily small errors. In the second part of the paper, we further evaluate the derivatives of these achievable rates at low-power regimes. This calculation helps us evaluate the energy efficiency and obtain the minimum energy per bit Eb/N0min when Gaussian and discrete input signals are used.