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• The maximum P H M I , q , j , p * basically determines the size of p T p = α. • Posterior integrity monitoring can outperform prior integrity monitoring in terms of the probability of false alarm under certain configurations. • The evaluation accuracy of probability of false alarm inversely correlates with the severity of the differences in solution separation variances. Compared to the prior integrity monitoring of global navigation satellite system (GNSS), the posterior integrity monitoring incorporates the used GNSS measurements into the computation of integrity risk, thereby providing a more accurate representation of the reliability of position solution. However, the continuity risk evaluation in posterior integrity monitoring remains an unresolved issue. After formulating the posterior integrity risk of each event hypothesis as a parity function, we propose a method to evaluate the posterior continuity risk by computing the cumulative probability of false alarm within a sphere constructed in the parity space. This approach guarantees a conservative estimate of the probability of false alarm, thereby establishing an upper bound for posterior continuity risk. Experiment and analysis based on positioning cases involving GPS, Galileo, and BeiDou satellites suggest that the posterior integrity monitoring can achieve a lower probability of false alarm compared to prior integrity monitoring under certain integrity monitoring configurations. Furthermore, the evaluation accuracy of probability of false alarm inversely correlates with the severity of the differences in solution separation variances. [ABSTRACT FROM AUTHOR]

This paper proposed a detection algorithm for sweeping jamming with low complexity under the constraint of the probability of false alarm and missed detection, and theoretically analyzed the performance of the detection algorithm to provide the basis for jamming detection of the actual communication system. Then, the paper proposed an anti-jamming learning algorithm based on Q-learning, which could autonomously select the best communication channel and the maximal reliable transmission time when threatened by the sweeping jamming. Finally, the simulation results show that the proposed detection algorithm can effectively detect the linear sweeping jamming, and the detection performance is basically consistent with the theoretical analysis results with low complexity. The anti-jamming learning algorithm can adaptively determine the optimal communication channel, and effectively avoid the sweeping jamming to realize the reliable information transmission. [ABSTRACT FROM AUTHOR]

The detection of weak monocycle sinusoidal signals is the abstract problem of the weak transient signal detection in many engineering applications. A low constant false alarm rate is often required on some occasions with serious false alarm consequences, such as the relay protection and the weapon trigger. The weak monocycle sinusoidal signal means a low signal-to-noise ratio (SNR), only one sinusoidal period, and an unknown initial phase. The support vector machine (SVM) is applied to detect the weak monocycle sinusoidal signal. The false alarm performance of the built SVM is attractive. The characteristics of the built SVM in white Gaussian noise are as follows. Firstly, the probability of false alarm is 0 and the probability of detection is 1 when the SNR is over −11 dB. Secondly, different unknown initial phases cannot influence the performance. Thirdly, the probability of false alarm is always 0, when the SNR is over −25 dB. Furthermore, the experimental results show that this method is also effective in coloured noise. When the SNR of coloured noise is −0.28 dB, the weak monocycle sinusoidal signal can be detected. When the SNR of coloured noise is over −60 dB, the probability of false alarm is always 0. [ABSTRACT FROM AUTHOR]

In this paper, the performance of an energy detector in cognitive radio, using different diversity combining techniques, is evaluated. Among many diversity combining techniques, maximal ratio combining (MRC) gives the best results but at the cost of the highest complexity. To design a simpler receiver, it is suggested to use less complex combining techniques, i.e. switched diversity, which provides one of the least complex solutions to combat fading. The paper analyzes two switched diversity schemes, switch examine combining (SEC), and switch examine combining with post examining selection (SECp). A closed form expression determining the probability of detection using MRC, SEC and SECp is derived for various numbers of branches. Detection performance with different diversity combining techniques is compared and the complexity trade-off is observed. [ABSTRACT FROM AUTHOR]

A system is constructed, which, on the basis of extensive experimental material and the use of eigenoscopy, has allowed us to detect anomalies in the spectra of uncorrelated components localized near the rotation frequencies and twice the rotation frequencies of relativistic binary star systems with vanishingly low probability of false alarm, not exceeding 10-17. [ABSTRACT FROM AUTHOR]

The current practice of multivariate process variability monitoring, when sub-group size is small, has nothing to do with probability of false alarm (PFA). Consequently, the reliability of the existing control charts remains undetermined. In this article, we propose a control chart which is reliable, very sensitive to the change in variance for small or moderate correlation, and provides a root causes analysis of an out-of-control signal. To illustrate these advantages, an industrial example is presented and the results are compared with those issued from the existing methods. [ABSTRACT FROM AUTHOR]

The objective of cognitive radio network is to enhance the wireless network spectrum utilization. In such a network, two types of users are enlisted, namely primary user (PU) and secondary user (SU). The PU can access any channel in case of its availability, but SU users have lower priority and can access a channel only when it is unused by PUs. The performance of such a network solely depends on two traffic parameters: probability of false alarm and probability of misdetection. In this paper the performance of such a network is analyzed based on two dimensional Markov chain including those parameters. The main contribution of this paper is to evaluate blocking probability and PU and SU throughput using the state transition chain instead of existing statistical analysis. [ABSTRACT FROM AUTHOR]

When commissioning or restarting a system after a maintenance action there is a need to properly tune the decision thresholds of the diagnostic system. Too low or too high thresholds may implicate either missed alarms or false alarm rates. This paper suggests an efficient data-driven approach to optimal setting of decision thresholds for a PEM fuel cell system based solely on data acquired from the system in reference state of health (i.e. under fault free operation). The only design parameter is the desired false alarm rate. Technically, the problem reduces to analytically determining the probability distribution of the fuel cell's complex impedance and its particular components. Employing pseudo-random binary sequence perturbation signals, the distribution of the impedance is estimated through the complex wavelet coefficients of the fuel cell voltage and current. The approach is validated on a PEM fuel cell system subjected to various faults. [ABSTRACT FROM AUTHOR]

In radar detection, many constant false alarm rate (CFAR) processors have been proposed in the literature. It is well known that a processor is optimal only for one type of environment and that its detection performances are seriously degraded in presence of unknown irregularities. In such situations, the main difficulty resides in the estimation of the background configuration. That is, depending upon the non-homogeneity of the environment, one would choose the adequate optimal detection algorithm among a variety of known conventional ones that offer the best detection probability. Based on unknown transitions; i.e. , in the presence of a priori unknown numbers of interfering targets and/or clutter edge, we propose an automatic censoring CFAR (AC-CFAR) detector for heterogeneous Gaussian clutter. The censoring technique used in this work offers a good discrimination between homogeneous and non-homogeneous environments. The proposed detector dynamically switches to the optimal conventional detector CA-, CMLD- or TM-CFAR. The performances of the proposed detector is evaluated and compared to existing detectors in various background situations. Monte Carlo simulations show that the AC-CFAR detector performs like the CA-CFAR in a homogeneous background. Moreover, the proposed detector exhibits considerable robustness in the presence of interfering targets and/or clutter-edge situations. [ABSTRACT FROM AUTHOR]

Cognitive Radio (CR) users need to sense the environment or channel at regular time interval for sharing the spectrum band of the primary users (PUs). Once find the spectrum idle, CR users start their transmission through it. Even while transmitting, they need to continue the sensing process so that they can leave the spectrum immediately whenever find a PU wanting to use the band. Therefore, detecting PUs is one of the main functions of cognitive radio before transmission and higher the detection probability ensures better protection to the primary users. However, it is not possible to attain a high detection probability (or a low miss detection probability) and low false alarm probability simultaneously as there is a tradeoff between false alarm probability (Pfa) and the probability of detection (Pd). In this paper, the author has provided a comprehensive study on different sensing techniques and discussed their advantages and disadvantages. Moreover, it is expected that, with this article, readers can have a through understanding of sensing techniques in CR and the current research trends in this area. [ABSTRACT FROM AUTHOR]

Existing active multi-tone interference (MTI) suppression methods used in direct sequence spread spectrum (DSSS) consider the interference to be time-invariant. However, in most practical scenarios, the interference is time-varying. These conventional analytic models cannot be directly applied to current realistic systems with time-varying interference. Hence, in this paper, we propose a novel approach based on frequency domain interference detection to suppress MTI experiencing a time-varying fading channel. Our approach operates in two steps. Firstly, we calculate the interference decision threshold by the probability of false alarm based on the Neyman-Pearson (NP) criterion. Secondly, we consider that the frequency bin is corrupted by interference, and the corresponding point is set to zero if the magnitude of the received signal exceeds the threshold. The simulation results demonstrate that our proposed method can mitigate time-varying MTI effectively, and can approach the interference-free performance limit over practical ranges of interference power levels and mobility levels. [ABSTRACT FROM AUTHOR]

To develop better image change detection algorithms, new models able to capture spatiotemporal regularities and geometries present in an image pair are needed. In this paper, we propose a multiscale formulation for modeling semilocal interimage interactions and detecting local or regional changes in an image pair. By introducing dissimilarity measures to compare patches and binary local decisions, we design collaborative decision rules that use the total number of detections obtained from the neighboring pixels for different patch sizes. We study the statistical properties of the nonparametric detection approach that guarantees small probabilities of false alarms. Experimental results on several applications demonstrate that the detection algorithm (with no optical flow computation) performs well at detecting occlusions and meaningful changes for a variety of illumination conditions and signal-to-noise ratios. The number of control parameters of the algorithm is small, and the adjustment is intuitive in most cases. [ABSTRACT FROM AUTHOR]

The matched filter can be used to identify the location of a specific signal embedded in background correlated noise. The maximum filter output indicates the likely location and, with appropriate statistical assumptions on the noise, it also serves as a test statistic in the probabilistic evaluation of the filter's performance. Different setups of the Neyman-Pearson statistical hypothesis test yield predictions of either the probability of a miss or that of a false alarm. The needed statistics of the maximum filter output are properly obtained using the distribution of order statistics. Through Monte Carlo simulations, we analyzed the ability of the matched filter to identify a sub-surface anomaly in typically correlated gravity fields using observations of elements of the gravity gradient tensor. We also evaluated the reliability of the hypothesis testing and the associated predicted probabilities of misses and false alarms. Our simulations and statistical analyses confirm that the power of the tests increases as the signal strength increases and as more gradient tensor components per observation point are included. We also found that the hypothesis test that is designed to predict the probability of a miss is more robust and powerful than the one for predicting a false alarm. Moreover, the probability of a miss is somewhat smaller than the probability of a false alarm under otherwise equal circumstances. [ABSTRACT FROM AUTHOR]

The development of automated monitoring systems for the detection of singularities, such as leakages in dikes, is indispensable to avoid mass disaster. An efficient solution for dike survey is the use of distributed temperature sensors (DTSs) based on optical fiber, offering a multitude of advantages such as low cost, extreme robustness, long-range measurement, etc. However, the temperature data acquired with DTSs, being not directly interpretable, require intervention of signal processing techniques. This paper addresses this signal processing aspect, exploiting the key idea that the temperature variations over the course of a day for singular zones are quite different from those for nonsingular zones. A daily reference temperature variation, which is representative of the nonsingular zones, is estimated using singular value decomposition (SVD). The residue subspace of SVD contains information linked to the deviations from this reference, thus allowing the degree of singularity to be quantified by a dissimilarity measure such as the L2-norm. To detect only the singularities in dikes, such as leakages or drains, a constant false alarm rate (CFAR) detector is proposed by modeling each daily dissimilarity measure with a mixture of Gamma and uniform distributions. The proposed automatic singularity detection system was validated under different scenarios on real data over periods from 2005 to 2007. The first scenario depicted the detection of percolation-type artificial leakages with their detection strength depending on their flow rates. Another scenario allowed detecting the presence of a real water leakage at the site, which was previously unobserved during manual inspections. The repeatability of the system was also verified by periodic analysis. [ABSTRACT FROM AUTHOR]

In this paper, an analytical framework is presented for device detection in an impulse radio (IR) ultra-wide bandwidth (UWB) system and its performance analysis is carried out. The Neyman–Pearson (NP) criteria is employed for this device-free detection. Different from the frequency-based approaches, the proposed detection method utilizes time domain concepts. The characteristic function (CF) is utilized to measure the moments of the presence and absence of the device. Furthermore, this method is easily extendable to existing device-free and device-based techniques. This method can also be applied to different pulse-based UWB systems which use different modulation schemes compared to IR-UWB. In addition, the proposed method does not require training to measure or calibrate the system operating parameters. From the simulation results, it is observed that an optimal threshold can be chosen to improve the ROC for UWB system. It is shown that the probability of false alarm, P F A , has an inverse relationship with the detection threshold and frame length. Particularly, to maintain P F A < 10 − 5 for a frame length of 300 ns, it is required that the threshold should be greater than 2.2 . It is also shown that for a fix P F A , the probability of detection P D increases with an increase in interference-to-noise ratio (INR). Furthermore, P D approaches 1 for INR > − 2 dB even for a very low P F A i.e., P F A = 1 × 10 − 7 . It is also shown that a 2 times increase in the interference energy results in a 3 dB improvement in INR for a fixed P F A = 0.1 and P D = 0.5 . Finally, the derived performance expressions are corroborated through simulation. [ABSTRACT FROM AUTHOR]

The present article reviews various criteria and approaches for an early detection of abrupt changes in the parameters of stochastic sequences. The discussion focuses on two issues: (i) the distributions of the stopping variables employed; and (ii) the probability of false alarm and the conditional expected delay characteristics. [ABSTRACT FROM AUTHOR]

Performance analysis of the normalized adaptive matched filter (NAMF) operating in homogeneous Gaussian clutter has received considerable attention in the recent years (see [1]-[6]). In this letter, for the first time, exact and explicit expressions are derived for the associated probability of false alarm and the probability of detection. The practical advantages of using the derived expressions are established. [ABSTRACT FROM AUTHOR]

Sensing the wideband spectrum is an important process for next-generation wireless communication systems. Spectrum sensing primarily aims at detecting unused spectrum holes over wide frequency bands so that secondary users can use them to meet their requirements in terms of quality-of-service. However, this sensing process requires a great deal of time, which is not acceptable for timely communications. In addition, the sensing measurements are often affected by uncertainty. In this paper, we propose an approach based on Bayesian compressive sensing to speed up the process of sensing and to handle uncertainty. This approach takes only a few measurements using a Toeplitz matrix, recovers the wideband signal from a few measurements using Bayesian compressive sensing via fast Laplace prior, and detects either the presence or absence of the primary user using an autocorrelation-based detection method. The proposed approach was implemented using GNU Radio software and Universal Software Radio Peripheral units and was tested on real-world signals. The results show that the proposed approach speeds up the sensing process by minimizing the number of samples while achieving the same performance as Nyquist-based sensing techniques regarding both the probabilities of detection and false alarm. [ABSTRACT FROM AUTHOR]