Probability Of Alarm Research Articles

Design and implementation of distribution-free Phase-II charting schemes based on unconditional run-length percentiles

Traditionally, the mean of the run-length distribution (ARL) of an in-control (IC) process is used to design and implement statistical process charting schemes. When standards are unknown (Case U), the unconditional ARL is considered during Phase-II monitoring—surprisingly, by suppressing the term “unconditional.” The literature has recently highlighted the difference between the unconditional and the conditional ARL in studying the properties of Phase-II charting schemes under the Case U. The effects of bias in the Phase-I sample may lead to remarkably high rates of early false alarms. We explore the idea of restricting the probability of unconditional early false alarms by using lower percentile points of the unconditional run-length distribution to design nonparametric charting schemes. This new approach is named “the lower percentile-based (LPL) design.” We consider the design and implementation of six distribution-free schemes: five precedence-type schemes and the rank-sum scheme. We carry out simulations to compare the six schemes with a prefixed value of some lower percentile point of the IC run-length distribution. The best scheme is the one with the lowest value for a specific higher percentile point of the out-of-control run-length distribution. We illustrate the new design and implementation strategies with real data, and offer a summary and concluding remarks.

Condition Classification of Fibre Ropes during Cyclic Bend over Sheave testing Using Machine Learning

Fibre ropes have been shown to be a viable alternative to steel wire rope for offshore lifting operations. Visual inspection remains a common method of fibre rope condition monitoring and has the potential to be further automated by machine learning. This would provide a valuable aid to current inspection frameworks to make more accurate decisions on recertification or retirement of fibre ropes in operational use. Three different machine learning algorithms: decision tree, random forest and support vector machine are compared to classical statistical approaches such as logistic regression, k-nearest neighbours and Naïve-Bayes for condition classification for fibre ropes under cyclic-bend-over-sheave (CBOS) testing. By measuring the rope global elongation throughout the CBOS tests, a binary classification system has been used to label recorded samples as healthy or close to rupture. Predictions are made on one rope through leave-one-out cross validation. The models are then assessed through calculating the accuracy, probability of detection, probability of false alarm and Matthew’s Correlation Coefficient, and ranked based on the results. The results show that both machine learning and classical statistical methods are effective options for condition classification of fibre ropes under CBOS regimes. Typical values for Matthews Correlation Coefficient (MCC) were shown to exceed 0.8 for the best performing methods.

Spectrum Sensing using Energy Measurement in Wireless Telemetry Networks using Logarithmic Adaptive Learning

<p>To identify primary user signals in cognitive radios spectrum sensing method is used. Due to statistical variances in received signal, noise is present in primary user signals, this noise powers are varied due to random nature of noise signals and leads to noise uncertainty problem in the performance of energy detection. The task of energy measurement and further detecting the unused frequency spectrum is a key task in cognitive radio applications. For avoiding these problems, least logarithmic absolute difference (LLAD) algorithm is proposed in which noise powers are adjusted at sensing point of licensed users. With help of proposed method, estimated noise signals are eliminated. Sign regressor version of LLAD algorithm is considered due to it reduces computational complexity and convergence rate is improved. Further probability of detection (<em>P</em><sub>od</sub>), probability of false alarm (<em>P</em><sub>ofa</sub>) is estimated to know threshold value. From results, it is clear that good performance in terms of <em>P</em><sub>ofa</sub> versus <em>P</em><sub>od</sub> in range of low signal to noise ratio in multiple nodes. Therefore, the proposed energy measurement-based spectrum sensing method is useful in remote health care monitoring, medical telemetry applications by sharing the un-used spectrum.</p>

Local modeling approach for cross-project defect prediction

Prediction approaches used for cross-project defect prediction (CPDP) are usually impractical because of high false alarms, or low detection rate. Instance based data filter techniques that improve the CPDP performance are time-consuming and each time a new test set arrives for prediction the entire filter procedure is repeated. We propose to use local modeling approach for the utilization of ever-increasing cross-project data for CPDP. We cluster the cross-project data, train per cluster prediction models and predict the target test instances using corresponding cluster models. Over 7 NASA Data sets performance comparison using statistical methods between within-project, cross-project, and our local modeling approach were performed. Compared to within-project prediction the cross-project prediction increased the probability of detection (PD) associated with an increase in the probability of false alarm (PF) and decreased overall performance Balance. The application of local modeling decreased the (PF) associated with a decrease in (PD) and an overall performance improvement in terms of Balance. Moreover, compared to one state of the art filter technique – Burak filter, our approach is simple, fast, performance comparable, and opens a new perspective for the utilization of ever-increasing cross-project data for defect prediction. Therefore, when insufficient within-project data is available we recommend training local cluster models than training a single global model on cross-project datasets.

WEAK SIGNAL DETECTION BASED ON MULTIPLE AUTO-CORRELATION TECHNIQUES

This study presents the performance of the auto-correlation methods for detecting weak signals, where the signal level is much less than the noise level. Double and triple auto-correlation techniques are used to improve the detection performance compared with the single autocorrelation. Simulation results obtained by MATLAB programs show that the multiple correlation techniques outperform the single correlation in terms of probability of detection and probability of false alarm versus signal to noise ratio SNR.

Using CME and FCME Algorithms for the Task of Detecting Human Steps

Currently, global trends in the development of perimeter security technology lead to the general miniaturization of devices and systems and the increase of their autonomy. The primary trend in developing these systems is the maximum processing and classification of signals by built-in tools. The article presents the study results on the possibility of using blind algorithms to calculate the level of operation of the threshold detector CME and FCME in detecting human steps in seismic perimeter security detectors using low-power computing modules. The algorithms were tested on actual data recorded during the experiments. As a result of applying algorithms for the seismic signal envelope, the human steps of the probability of false positives for the CME and FCME algorithms were 23% and 10%, respectively. Neutralizing the signal trend allowed obtaining values of false alarms of 16% for the CME algorithm and 7% for the FCME algorithm, and normalization of the signal amplitude within one analysis interval allowed obtaining the probability of false alarms at 0% for both algorithms. The obtained results give complete information when choosing the type of algorithm depending on the input data. In detecting seismic data by an autonomous sensor, it is most appropriate to use the SME algorithm with pre-normalization of the signal amplitude due to less computational complexity.

Enhancement of Spectrum Sensing Performance via Cooperative Cognitive Radio ‎Networks at Low SNR

The inefficient use of spectrum is the key subject to overcome the upcoming spectrum crunch issue. This paper presents a study of performance of cooperative cognitive network via hard combining of decision fusion schemes. Simulation results presented different cooperative hard decision fusion schemes for cognitive network. The hard-decision fusion schemes provided different discriminations for detection levels. They also produced small values of Miss-Detection Probability at different values of Probability of False Alarm and adaptive threshold levels. The sensing performance was investigated under the influence of channel condition for proper operating conditions. An increase in the detection performance was achieved for cognitive users (secondary users) of the authorized unused dynamic spectrum holes (primary users) while operating in a very low signal-to-noise ratio with the proper condition of minimum total error rate.

Novel filter bank-based cooperative spectrum sensing under practical challenges for beyond 5G cognitive radios

Cognitive radio (CR) technology with dynamic spectrum management capabilities is widely advocated for utilizing effectively the unused spectrum resources. The main idea behind CR technology is to trigger secondary communications to utilize the unused spectral resources. However, CR technology heavily relies on spectrum sensing techniques which are applied to estimate the presence of primary user (PU) signals. This paper firstly focuses on novel analysis filter bank (AFB) and FFT-based cooperative spectrum sensing (CSS) techniques as conceptually and computationally simplified CSS methods based on subband energies to detect the spectral holes in the interesting part of the radio spectrum. To counteract the practical wireless channel effects, collaborative subband-based approaches of PU signal sensing are studied. CSS has the capability to relax the problems of both hidden nodes and fading multipath channels. FFT- and AFB-based receiver side sensing methods are applied for OFDM waveform and filter bank-based multicarrier (FBMC) waveform, respectively, the latter one as a candidate beyond-OFDM/beyond-5G scheme. Subband energies are then applied for enhanced energy detection (ED)-based CSS methods that are proposed in the context of wideband, multimode sensing. Our first case study focuses on sensing potential spectral gaps close to relatively strong primary users, considering also the effects of spectral regrowth due to power amplifier nonlinearities. The study shows that AFB-based CSS with FBMC waveform is able to improve the performance significantly. Our second case study considers a novel maximum–minimum energy detector (Max–Min ED)-based CSS. The proposed method is expected to effectively overcome the issue of noise uncertainty (NU) with remarkably lower implementation complexity compared to the existing methods. The developed algorithm with reduced complexity, enhanced detection performance, and improved reliability is presented as an attractive solution to counteract the practical wireless channel effects under low SNR. Closed-form analytic expressions are derived for the threshold and false alarm and detection probabilities considering frequency selective scenarios under NU. The validity of the novel expressions is justified through comparisons with respective results from computer simulations.

Reliability of visual detections of cirrus over Poland

Earth is a cloudy planet—at every moment, 62–72% of the planet is covered by cloud. Its presence and properties affect the flow of energy between the sun, Earth and the atmosphere. More comprehensive information about cloudiness is important to improve our understanding of the impact of cloud on Earth’s radiation budget. The aim of our work is to assess the accuracy of ground-based high-level cloud detections over Poland compared with satellite data. This project is the very first evaluation of error in visual high-cloud observation. Specifically, we answer a fundamental question: how effective are ground-based observers in detecting high-level cloud? Our unprecedented analysis is based on two datasets: traditional, visual observations from 43 selected synoptic stations and satellite (lidar) data, for the years 2006–2017. Ground-based observations were obtained from the archives of the Polish National Weather Service (IMWM-NRI), while satellite data came from the CALIPSO mission. Our final dataset comprised 7548 ground-based and satellite observations. Accuracy was assessed using the following indicators: probability of detection (Pd), probability of false alarm (Pfa), and overall accuracy (ACC). Overall (day and night observations), in the absence of lower-level cloud, observers correctly detected 80% of high-level cloud. However, when lower-level cloud was present, observers could not detect around 70% of high-level cloud. In sum, ground-based, high-level cloud detection is a function of the presence of lower-level cloud, the time of day, and the size of the area over which cirrus are observed/detected.

Algorithms for detecting broadband signals with a constant level of false alarms

The use of broadband souding signals in radars, which has become real in recent years, leads to a significant reduction in the size of resolution elements in range and, accordingly, in the size of the window in which the training sample is formed, which is used to adapt the detection threshold in signal detection algorithms with a constant level of false alarms. In existing radars, such a window would lead to huge losses. The purpose of the work was to study the most rational options for constructing detectors with a constant level of false alarms in radars with broadband sounding signals. The problem was solved for the Rayleigh distribution of the envelope of the noise and a number of non-Rayleigh laws — Weibull and the lognormal, the appearance of which is associated with a decrease in the number of reflecting elements in the resolution volume. For Rayleigh interference, an algorithm is proposed with a multi-channel in range incoherent signal amplitude storage and normalization to the larger of the two estimates of the interference power in the range segments. The detection threshold in it adapts not only to the interference power, but also to the magnitude of the «power jump» in range, which allows reducing the number of false alarms during sudden changes in the interference power – the increase in the probability of false alarms did not exceed one order of magnitude. In this algorithm, there is a certain increase in losses associated with incoherent accumulation of signals reflected from target elements, and losses can be reduced by certain increasing the size of the distance segments that make up the window. Algorithms for detecting broadband signals against interference with non-Rayleigh laws of distribution of the envelope – Weibull and lognormal, based on the addition of the algorithm for detecting signals by non-linear transformation of sample counts into counts with a Rayleigh distribution, are studied. The structure of the detection algorithm remains unchanged in practice. The options for detectors of narrowband and broadband signals are considered. It was found that, in contrast to algorithms designed for the Rayleigh distribution, these algorithms provide a stable level of false alarms regardless of the values of the parameters of non-Rayleigh interference. To reduce losses due to interference with the distribution of amplitudes according to the Rayleigh law, detectors consisting of two channels are used, in which one of the channels is tuned for interference with the Rayleigh distribution, and the other for lognormal or Weibull interference. Channels are switched according to special distribution type recognition algorithms. In such detectors, however, there is a certain increase in the probability of false alarms in a rather narrow range of non-Rayleigh interference parameters, where their distribution approaches the Rayleigh distribution. It is shown that when using broadband signals, there is a noticeable decrease in detection losses in non-Rayleigh noise due to lower detection thresholds for in range signal amplitudes incoherent storage.

Weighted Circular Folding Cooperative Power Spectral Density Split Cancellation Algorithm

Low computation complexity and robustness against dynamical noise are attributes of the cooperative power spectral density split cancellation (CPSC) algorithm, developed for centralized data-fusion cooperative spectrum sensing. Recently, an improved version of the CPSC was proposed, named circular folding CPSC (CFCPSC), which outperforms the CPSC while maintaining low complexity and robustness against dynamical noise. This correspondence proposes the weighted CFCPSC (WCFCPSC), which improves the performance of the CFCPSC, retaining the attributes of its predecessors. Expressions are derived for the probability of false alarm, and for the probability density and the cumulative distribution functions of the main random variables that form the WCFCPSC test statistics. Simulations results are given to support the theoretical findings and to demonstrate the superior performance of the WCFCPSC.

SPECTRUM SENSING OF WIDE BAND SIGNALS BASED ON ENERGY DETECTION WITH COMPRESSIVE SENSING

Compressive sensing (CS) technique is used to solve the problem of high sampling rate with wide band signal spectrum sensing where high speed analogue to digital converter is needed to do that. This leads to difficult hardware implementation, large time of sensing and detection with high consumptions power. The proposed approach combines energy-based detection, with CS compressive sensing and investigates the probability of detection, and the probability of false alarm as a function of the SNR, showing the effect of compression to spectrum sensing performance of cognitive radio system. The Discrete Cosine Transform (DCT) is used as a sparse representation basis of the received signal, and random matrix as a compressive matrix.

Real-Time Plume Detection and Segmentation Using Neural Networks

Applications of artificial intelligence have been gaining extraordinary traction in recent years across innumerable domains. These novel approaches and technological leaps permit leveraging profound quantities of data in a manner from which to elucidate and ease the modeling of arduous physical phenomena. ExoAnalytic collects over 500,000 resident space object images nightly with an arsenal of over 300 autonomous sensors; extending the autonomy of collection to data curation, anomaly detection, and notification is of paramount importance if elusive events are desired to be captured and classified. Efforts begin with rigorous image annotation of observed glints, streaking stars, and resident space objects with plumes from debris shedding events. Preliminary results permitted the successful classification of observed debris generating events from AMC-9, Telkom-1, and Intelsat-29e. After initial proof-of-concept, these events are incorporated into the training pipeline in order to characterize potentially unknown debris generating or anomalous events in future observations. The inclusion of a visual tracking system aides in reducing false alarms by roughly 30%. Future efforts include applications on both historical datamining as well as real-time indications and warnings for satellite analysts in their daily operations while maintaining a low probability of false alarm through detection and tracking algorithm refinement.

Quickest change point detection with multiple postchange models

We study the sequential quickest change point detection for systems with multiple possible postchange models. A change point is the time instant at which the distribution of a random process changes. In many practical applications, the prechange model can be easily obtained, yet the postchange distribution is unknown due to the unexpected nature of the change. In this article, we consider the case that the postchange model is from a finite set of possible models. The objective is to minimize the average detection delay (ADD), subject to upper bounds on the probability of false alarm (PFA). Two different quickest change detection algorithms are proposed under Bayesian and non-Bayesian settings. Under the Bayesian setting, the prior probabilities of the change point and prior probabilities of possible postchange models are assumed to be known, yet this information is not available under the non-Bayesian setting. Theoretical analysis is performed to quantify the analytical performance of the proposed algorithms in terms of exact or asymptotic bounds on PFA and ADD. It is shown through theoretical analysis that when PFA is small, both algorithms are asymptotically optimal in terms of ADD minimization for a given PFA upper bound. Numerical results demonstrate that the proposed algorithms outperform existing algorithms in the literature.

Nonparametric Specification Testing for Signal Models

Given noisy samples of a signal, the problem of testing whether the signal belongs to a restricted class of parametrically specified class of signals is considered. Using a nonparametric kernel-based approach for reconstructing the signal we compare the parametric signal class to a broad alternative of signal classes that cannot be parametrized. For such a setup, we introduce testing procedures relying on nonparametric kernel-type sampling reconstruction algorithms properly adjusted for noisy data. First, we propose the testing procedures utilizing the $L_{2}$ -distance between the kernel estimate and signals from the target parametric class. Then, we make use of the maximum likelihood ratio test extended to the posed nonparametric signal specification problem. Central limit theorems of the test statistics are derived yielding consistent testing methods. Hence, we obtain testing procedures that keep asymptotically the desired level of the probability of false alarm and showing a power tending to one as the number of samples increases. The finite sample size properties of the introduced tests are also given.

Dynamic Threshold Based Throughput Enhancement in Cognitive Radio Network Using Hidden Markov Model with State Prediction

The enhancement of throughput is one of the key issues in the cognitive radio network. In this paper, a channel status prediction scheme based on hidden Markov model is proposed for enhancing the throughput of cognitive radio network. Unlike the conventional scheme which relies on channel sensing alone, the proposed scheme provides an additional advantage of being able to predict the primary user’s state along with channel sensing, thus increasing spectrum utilization and system throughput. For achieving enhanced reliability in spectrum sensing process of the network, a dynamic threshold based energy detection technique considering noise uncertainty and target detection probabilities is proposed. Comparative analyses of the performance of the dynamic threshold based energy detection technique with that of existing fixed and dynamic threshold based detection schemes are presented. The analyses reveal that the proposed detection scheme performs better than the existing detection schemes with regard to probability of detection and probability of false alarm. It is shown that the hidden Markov model-based prediction scheme makes the cognitive radio network more efficient in terms of throughput than existing schemes.

Statistical process monitoring in a specified period for the image data of fused deposition modeling parts with consistent layers

Statistical process monitoring (SPM) methods have been adopted and studied to detect variations in the fused deposition modeling (FDM) process in recent years. The FDM process that builds parts layer-by-layer is accomplished in a specified manufacturing period (number of layers) without interruption or suspension. Thus, traditional SPM methods, where the average run length is used for the calculation of the control limits and the measurement of the performance, are no longer applicable to the FDM process. In this paper, an SPM method is proposed based on the surface image data of FDM parts with consistent layers and a specified period. The probability of alarm in a specified period (PASP) and the cumulative PASP are introduced to determine the control limits and evaluate the monitoring performance. Regions of interest are determined in a fixed way to cover the sizes and locations of different defects. The statistics are calculated based on the generalized likelihood ratio. The control limit is determined based on the specified period and the nominal in-control PASP. A simulation study for different locations, sizes and magnitudes of the mean shift of defects is presented. In the case study, the proposed SPM method is applied to monitor the FDM process of a cuboid, which verifies the effectiveness of the proposed method.

Robust Control for the Detection Threshold of CFAR Process in Cluttered Environments.

The constant false alarm rate (CFAR) process is essential for target detection in radar systems. Although the detection performance of the CFAR process is normally guaranteed in noise-limited environments, it may be dramatically degraded in clutter-limited environments since the probabilistic characteristics for clutter are unknown. Therefore, sophisticated CFAR processes that suppress the effect of clutter can be used in actual applications. However, these methods have the fundamental limitation of detection performance because there is no feedback structure in terms of the probability of false alarm for determining the detection threshold. This paper presents a robust control scheme for adjusting the detection threshold of the CFAR process while estimating the clutter measurement density (CMD) that uses only the measurement sets over a finite time interval in order to adapt to time-varying cluttered environments, and the probability of target existence with finite measurement sets required for estimating CMD is derived. The improved performance of the proposed method was verified by simulation experiments for heterogeneous situations.

PCA-Based Matrix CFAR Detection for Radar Target.

In radar target detection, constant false alarm rate (CFAR), which stands for the adaptive threshold adjustment with variation of clutter to maintain the constant probability of false alarm during the detection, plays an important role. Matrix CFAR detection performed on the manifold of Hermitian positive-definite (HPD) covariance matrices is an efficient detection method that is based on information geometry. However, the HPD covariance matrix, which is constructed by a small bunch of pulses, describes the correlations among received data and suffers from severe information redundancy that limits the improvement of detection performance. This paper proposes a Principal Component Analysis (PCA) based matrix CFAR detection method for dealing with the point target detection problems in clutter. The proposed method can not only reduce dimensionality of HPD covariance matrix, but also reduce the redundant information and enhance the distinguishability between target and clutter. We first apply PCA to the cell under test, and construct a transformation matrix to map higher-dimensional matrix space to a lower-dimensional matrix space. Subsequently, the corresponding detection statistics and detection decision on matrix manifold are derived. Meanwhile, the corresponding signal-to-clutter ratio (SCR) is improved. Finally, the simulation experiment and real sea clutter data experiment show that the proposed method can achieve a better detection performance.

Research work under Visvesvaraya YFRF

This research work concentrates on developing new methodologies for weak signal detection, which are further used in the watermark detection application. The proposed detectors perform comparable to or better than most of the state-of-the-art techniques. Stochastic resonance plays a significant role in weak signal detection. Injection of precalculated noise is a big concern for us. Noises i.e., symmetric and asymmetric have been utilized for getting an improved version of the weak signal detector. With equality and non-equality constraints, we use the particle swarm optimization method to optimize the objective function. After that, we investigate a new detector based on the fractional operator. The noisy signal is convolved with the coefficients of fractional order filter. The proposed method has been tested with different standard performance parameters. The results of the proposed detector have been compared with state-of-the-art detectors such as a threshold detector (TD), neural network-based detector etc. The robustness of the proposed detector with respect to the parameters of signal and noise has also been explored. The problem of weak DC signal detection present in non-Gaussian noise is always a troublesome because of the non-linearity of the test statistic. We have used the concept of stochastic resonance in a conventional neural network-based detector. A predefined noise is injected during the back-propagation algorithm in order to minimize the error. The reduction in errors (in terms of enhancement in the probability of detection, $$P_D$$ at a constant value of the probability of false alarm, $$P_{FA}$$ ) and faster convergence give a right justification to use the concept of stochastic resonance in the neural network-based detector.