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In this paper, a novel and exact mathematical formulation of three mean level constant false alarm rate (CFAR) detectors has been developed considering a homogeneous gamma-distributed (GM) clutter. These detectors include the cell averaging (CA), the greatest-of (GO), and the smallest-of (SO)-CFAR detection schemes. To do so, we derived closed-form expressions for the probability density function (pdf), and the cumulative distribution function (cdf) of the sum of an exponential fluctuating target submerged in gamma-distributed sea clutter. Then, we developed exact and explicit expressions for the probability of false alarm (Pfa) and probability of detection (Pd) of the optimal fixed threshold detector and the three considered CFAR detectors. The correctness of our expressions is then examined and validated via numerical simulations. Assuming a homogeneous GM background, the detection performance of these three detectors is carried out, discussed, and compared with that of the optimal detector. Experimental results with Sentinel-1 synthetic aperture radar (SAR) data demonstrate the effectiveness of the proposed closed-form expressions and show that the considered CFAR schemes are efficient for ship detection in sea clutter backgrounds. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is an effective method to optimize the use of spectrum resources. Spectrum sensing plays significant role in finding free channels that can be used with CR. the most important One of issues in CR is interference to the primary user (PU). Consequently, there is a significant research gap for an algorithm that distributes channels dynamically and takes into consideration all kinds of interference.This study looks at a universal optimal voting rule to an algorithm to determine the optimum parameters λ opt , n opt that minimize the Bayes risk cost function which has different weights for the probability of false alarm Q f and the missing probability Q m in general. As discussed formerly, Q m is most important from Q f . On another side, an algorithm that determines other technique which gives a large priority for Q m over the Q f . In this technique, we determine the optimum parameters λ opt , n opt , Supposing that secondary users (K) are fixed, i.e. what is the optimum fusion rule n opt and optimum threshold λ opt that minimizes Q f with constraint on Q m .In this study, we introduced a technique that ensures Qm ​ remains below a specified threshold while minimizing Qf as quickly as possible. We developed an algorithm to determine the optimal fusion rule and threshold that minimize Qf while keeping Qm​below the given threshold. The performance of cooperative spectrum sensing was analyzed using hard combination fusion rules. Among these, the OR rule showed superior performance in cooperative spectrum sensing for Cognitive Radio, outperforming the AND rule. [ABSTRACT FROM AUTHOR]

Copyright of Informatics Control Measurement in Economy & Environment Protection / Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska is the property of Lublin University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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

In recent development and improvement of wireless communication system, the cognitive radio (CR) is a potential approach to utilize spectrum efficiently. Spectrum sensing technique arguably is the most significant component of cognitive radio. Cooperative spectrum sensing (CSS) is utilized to improve the detection performance of the system. Several fusion strategies of decision making are presented for sensing the primary user but they do not perform well under low signal to noise ratio (SNR) conditions. This paper proposes artificial neural network (ANN) based CSS under Rayleigh multipath fading channel in IEEE 802.22 wireless regional area network (WRAN). We implemented an ANN in the fusion centre. First, the energy of the received signal is calculated using discrete wavelet packet transform (DWPT). Then, calculated energy, SNR and false alarm probability are used jointly to make a data set of 2048 samples and are used to train Levenberg-Marquardt back propagation training algorithm-based feed-forward neural network (FFNN). Using this trained neural network, CSS in WRAN is simulated under Rayleigh multipath fading channel and results demonstrate the superiority of the proposed approach over traditional CSS with DWPT and Fast fourier transform (FFT) based energy detection schemes in low SNR environment.

Spectrum sensing is a critical component of cognitive radio systems, enabling the detection and utilization of underutilized frequency bands. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in particular, have showed promise in recent years for enhancing the precision and effectiveness of spectrum sensing. This paper presents an overview of spectrum sensing using CNNs and RNNs and their performance in cognitive radio systems. Furthermore, the paper delves into the spectrum sensing performance of RNNs, particularly for processing time-series data. RNNs are capable of capturing temporal dependencies in sequential data, which is essential for spectrum sensing tasks where signals evolve over time. Further, the parakeets such as probability of detection (Pd), Probability of false alarm (PFA), Bite Error rate (BER) and Power spectral density (PSD) are analysed for spectrum sensing algorithms. RNNs and CNNs, two examples of deep learning methods (DLM), performed better than more traditional approaches.

The problem of calculating the quality indicators of detection of side radiation signals from static images on the monitor screen by a specialized technical means of enemy intelligence is solved, which implements an asymptotically optimal compatible algorithm for detecting side radiation signals and estimating the duration of image immutability on the monitor screen. The reliability of the calculation results was confirmed by modeling detection processes in the Mathcad and Excel software environments.

This paper presents a performance analysis of centralized spectrum sensing based on compressed measurements. We assume cooperative sensing, where unlicensed users individually perform compressed sensing and send their results to a fusion center, which makes the final decision about the presence or absence of a licensed user signal. Several cooperation schemes are considered, such as and-rule, or-rule, majority voting, soft equal-gain combining (EGC). The proposed analysis provides simplified closed-form expressions that calculate the required number of sensors, the required number of samples, the required compression ratio, and the required signal-to-noise ratio (SNR) as a function of the probability of detection and the probability of the false alarm of the fusion center and of the sensors. The resulting expressions are derived by exploiting some accurate approximations of the test statistics of the fusion center and of the sensors, equipped with energy detectors. The obtained results are useful, especially for a low number of sensors and low sample sizes, where conventional closed-form expressions based on the central limit theorem (CLT) fail to provide accurate approximations. The proposed analysis also allows the self-computation of the performance of each sensor and of the fusion center with reduced complexity. [ABSTRACT FROM AUTHOR]

This article discusses the calculation and modeling of probabilities related to detecting side radiation signals from monitor screens. The text presents an algorithm for detecting these signals and estimating the duration of image immutability on the screen. The reliability of the algorithm is confirmed through modeling processes. The article also analyzes the effectiveness of specialized technical means of enemy intelligence and examines the quality indicators for signal detection. The text provides mathematical equations and formulas to support the analysis, but further modeling in professional software environments is recommended to ensure reliability. [Extracted from the article]

The generalized penalized constrained regression (G-PCR) is a penalized model for high-dimensional linear inverse problems with structured features. This paper presents a sharp error performance analysis of the G-PCR in the over-parameterized high-dimensional setting. The analysis is carried out under the assumption of a noisy or erroneous Gaussian features matrix. To assess the performance of the G-PCR problem, the study employs multiple metrics such as prediction risk, cosine similarity, and the probabilities of misdetection and false alarm. These metrics offer valuable insights into the accuracy and reliability of the G-PCR model under different circumstances. Furthermore, the derived results are specialized and applied to well-known instances of G-PCR, including l 1 -norm penalized regression for sparse signal recovery and l 2 -norm (ridge) penalization. These specific instances are widely utilized in regression analysis for purposes such as feature selection and model regularization. To validate the obtained results, the paper provides numerical simulations conducted on both real-world and synthetic datasets. Using extensive simulations, we show the universality and robustness of the results of this work to the assumed Gaussian distribution of the features matrix. We empirically investigate the so-called double descent phenomenon and show how optimal selection of the hyper-parameters of the G-PCR can help mitigate this phenomenon. The derived expressions and insights from this study can be utilized to optimally select the hyper-parameters of the G-PCR. By leveraging these findings, one can make well-informed decisions regarding the configuration and fine-tuning of the G-PCR model, taking into consideration the specific problem at hand as well as the presence of noisy features in the high-dimensional setting. [ABSTRACT FROM AUTHOR]

This letter presents an analytical study of the performance of the maximum-likelihood gamma constant false alarm rate (MLG-CFAR) detector operating over a homogenous gamma-distributed (GM) clutter. To accomplish this task, a novel closed-form expression for the probability of false alarm and a highly accurate approximation for the probability of detection are derived. Specifically, this proposed approximation is also given in a closed form. These expressions, which can be of great interest to the radar community, are given in terms of the Gauss hypergeometric function and the Tricomi function. Besides, the proposed expressions can be easily implemented and computed using most popular scientific software packages such as MATLAB, Maple, and Mathematica. Our propositions are then tested and validated using numerical computing and Monte Carlo simulations. The obtained results demonstrate a high accuracy and significant computational advantages of the proposed expressions compared to the alternative ones. Moreover, the detection performance of the MLG-CFAR detector is also conducted assuming a homogenous clutter with a known shape parameter. [ABSTRACT FROM AUTHOR]

Sea clutter has been a long-standing issue in old and modern radars. Under this context, the K-distribution has emerged as a promising clutter model to accurately mimic sea signal variations in a large variety of radar systems. To guarantee an adequate radar performance in the presence of sea clutter, the family of constant false-alarm rate (CFAR) detectors has been commonly used. In particular, due to its adequate balance between performance and implementation, the cell-averaging CFAR (CA-CFAR) detector has been considered an attractive detection mechanism to enhance radar performance over various clutter environments. In this work, we assess radar performance considering a CA-CFAR detector operating over K-distributed sea clutter with fully correlated texture. More precisely, we derive novel closed-form expressions for the probability of detection ($P_{\text {D}}$) and the probability of false alarm ($P_{\text {FA}}$) that can be readily evaluated using any mathematical software. Monte-Carlo (MC) simulations corroborate our analytical findings. [ABSTRACT FROM AUTHOR]

This study proposed a method to evaluate proper experimental conditions of eddy current tests under a low signal-to-noise ratio. This method was applied to quantify the detection reliability of the eddy current test to detect corrosion pits on the surface of austenitic stainless-steel weld cladding from the perspectives of probability of detection and probability of false alarm. The fluctuations of flaw signal and noise were described using truncated bivariate normal distributions of a two-dimensional vector composed of the real and imaginary components of a signal. The probabilities of detection and false alarms were formalized as cumulative distributions of the estimated distributions of the maximum flaw signal and the noise exceeding a decision threshold. The receiver operating characteristic curves were created by coupling the probabilities of detection and false alarms at various thresholds. The area under the curve was used to quantify the detection reliability, simultaneously considering the probabilities of detection and false alarms. The results obtained in this study support the applicability of this method in quantitatively evaluating the detection reliability of a non-destructive inspection under a low signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

• 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]

In this paper, we analyse the performance of infrastructure-based fixed relay with multi-antenna in cognitive radio. We derive the mathematical expression for detection probability of single cooperative relay system over Rayleigh fading channel. Relay and fusion centre may perform either selection combining or maximal ratio combining of the signals. Effect of number of relay antennas for all four combinations of combining schemes has been analysed and as expected, increase in relay diversity improves the sensing performance. This is because, on installing more number of receiving antennas on the relay, it's participation increases, so the detection performance also improves. In the non-cooperative system also, the performance improves on increasing the diversity order but on the other hand, this increment imposes burden on the fusion centre. Whereas in multi-antenna cooperative relay system, the burden of relay gets transferred on relay node and the fusion centre takes advantage of increased diversity order of relay. For cooperative spectrum sensing, we have used soft decision combining scheme in which secondary users send their test statistics calculated from their local observations. [ABSTRACT FROM AUTHOR]

Copyright of Karaelmas Science & Engineering Journal / Karaelmas Fen ve Mühendislik Dergisi is the property of Karaelmas Science & Engineering Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Integrity is an important index for GNSS-based navigation and positioning, and the receiver autonomous integrity monitoring (RAIM) algorithm has been presented for integrity applications. In the integrated navigation systems of a global navigation satellite system (GNSS) and inertial navigation system (INS),the conventional RAIM algorithm has been developed to extended receiver autonomous integrity monitoring (ERAIM). However, the ERAIM algorithm may fail and a false alarm may generate once the measurements are contaminated by significant outliers, and this problem is rarely discussed in the existing literatures. In this paper, a robust fault detection and the corresponding data processing algorithm are proposed based on the ERAIM algorithm and the robust estimation. In the proposed algorithm, weights of the measurements are adjusted with the equivalent weight function, and the efficiency of the outlier detection and identification is improved, therefore, the estimates become more reliable, and the probability of the false alarm is decreased. Experiments with the data collected under actual environments are implemented, and results indicate that the proposed algorithm is more efficient than the conventional ERAIM algorithm for multiple outliers and a better filtering performance is achieved.

Spectrum sharing alleviates the severe shortage of spectrum in sub-6 GHz frequency bands through the harmonious coexistence of two or more wireless technologies on the same frequency resources. In this work, we study underlay radar-massive MIMO cellular coexistence in LoS/near-LoS channels, where both systems have 3D beamforming capabilities. Using mathematical tools from stochastic geometry, we derive an upper bound on the average interference power at the radar due to the 3D massive MIMO cellular downlink under the worst-case ‘cell-edge beamforming’ conditions. To overcome the technical challenges imposed by asymmetric and arbitrarily large cells, we devise a novel construction in which each Poisson Voronoi (PV) cell is bounded by its circumcircle to bound the effect of the random cell shapes on average interference. Since this model is intractable for further analysis due to the correlation between adjacent PV cells’ shapes and sizes, we propose a tractable nominal interference model, where we model each PV cell as a circular disk with an area equal to the average area of the typical cell. We quantify the gap in the average interference power between these two models and show that the upper bound is tight for realistic deployment parameters. We also compare them with a more practical but intractable MU-MIMO scheduling model to show that our worst-case interference models show the same trends and do not deviate significantly from realistic scheduler models. Under the nominal interference model, we characterize the interference distribution using the dominant interferer approximation by deriving the equi-interference contour expression when the typical receiver uses 3D beamforming. Finally, we use tractable expressions for the interference distribution to characterize radar’s spatial probability of false alarm/detection in a quasi-static target tracking scenario. Our results reveal useful trends in the average interference as a function of the deployment parameters (BS density, exclusion zone radius, antenna height, transmit power of each BS, etc.). We also provide useful system design insights using radar receiver operating characteristic (ROC) curves by applying our analytical results to design the minimum exclusion zone radius in current and future radar-cellular spectrum sharing scenarios. [ABSTRACT FROM AUTHOR]

Acquisition and tracking systems form an important component of free-space optical communications due to directional nature of the optical signal. Acquisition subsystems are needed in order to search and locate the receiver terminal in an uncertainty/search region with very narrow laser beams. In this paper, we have proposed and analyzed two adaptive search schemes for acquisition systems that perform better—for the low probability of detection—than the spiral scanning approach. The first of these schemes, the adaptive spiral search, provides a better acquisition time performance by dividing the search region into a number of smaller subregions, and prioritizing search in regions of higher probability mass. The second technique—the shotgun approach—searches the region in a random manner by sampling the search region according to a Gaussian distribution. The adaptive spiral scheme outperforms the shotgun approach in terms of acquisition time, especially if the number of search subregions is large enough. However, a higher pointing accuracy is required by the adaptive spiral search in order to search the region precisely. On the other hand, the shotgun scanning approach does not require such stringent pointing accuracy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Pointing and acquisition are an important aspect of free-space optical communications because of the narrow beamwidth associated with the optical signal. In this paper, we have analyzed the pointing and acquisition problem in free-space optical communications for photon-counting detector arrays and Gaussian beams. In this regard, we have considered the maximum likelihood detection for detecting the location of the array, and analyzed the one-shot probabilities of missed detection and false alarm using the scaled Poisson approximation. Moreover, the upper/lower bounds on the probabilities of missed detection and false alarm for one complete scan are also derived, and these probabilities are compared with Monte Carlo approximations for a few cases. Additionally, the upper bounds on the acquisition time and the mean acquisition time are also derived. The upper bound on mean acquisition time is minimized numerically with respect to the beam radius for a constant signal-to-noise ratio scenario. Finally, the complementary distribution function of an upper bound on acquisition time is also calculated in a closed form. Our study concludes that an array of smaller detectors gives a better acquisition performance (in terms of acquisition time) as compared to one large detector of similar dimensions as the array. [ABSTRACT FROM AUTHOR]

Spectrum sensing is a critical component of cognitive radio systems, enabling the detection and utilization of underutilized frequency bands. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in particular, have showed promise in recent years for enhancing the precision and effectiveness of spectrum sensing. This paper presents an overview of spectrum sensing using CNNs and RNNs and their performance in cognitive radio systems. Furthermore, the paper delves into the spectrum sensing performance of RNNs, particularly for processing time-series data. RNNs are capable of capturing temporal dependencies in sequential data, which is essential for spectrum sensing tasks where signals evolve over time. Further, the parakeets such as probability of detection (Pd), Probability of false alarm (PFA), Bite Error rate (BER) and Power spectral density (PSD) are analysed for spectrum sensing algorithms. RNNs and CNNs, two examples of deep learning methods (DLM), performed better than more traditional approaches. [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, PFA, has an inverse relationship with the detection threshold and frame length. Particularly, to maintain PFA<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 PFA, the probability of detection PD increases with an increase in interference-to-noise ratio (INR). Furthermore, PD approaches 1 for INR >−2 dB even for a very low PFA i.e., PFA=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 PFA=0.1 and PD=0.5. Finally, the derived performance expressions are corroborated through simulation.

Seafloor terrain and water column target detection is of great significance in marine surveys. However, the complex clutter environment can dramatically affect the detection performance of a multi-beam echo sounder. To simultaneously and robustly detect the seafloor terrains and water column targets, this paper proposes a fast two-dimensional double selectivity index-constant false alarm rate (DSI-CFAR) detection method. A two-dimensional cross sliding window, which includes reference cells and guard cells, is used in this paper. The proposed method improves the detection performance by using the appropriate clutter power level estimation strategies in different directions. An exponential model is accurately built by fitting various distributions, so the DSI-CFAR detection performance is greatly ameliorated. To reduce the computational load of the method, this paper uses a fast algorithm based on the global threshold. The region of interest (ROI) is selected by the preset global threshold, and the DSI-CFAR detector is only performed in the region of interest, which significantly reduces the number of pixels that must be calculated. In this paper, the measured results of different sliding windows and CFAR detection methods validate the basic performance of the proposed method. [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]

Cognitive radio employs an opportunistic spectrum access approach to ensure efficient utilization of the available spectrum by secondary users (SUs). To allow SUs to access the spectrum opportunistically, the spectrum sensing process must be fast and accurate to avoid possible interference with the primary users. Previously, two-stage spectrum sensing methods were proposed that consider the sensing time and sensing accuracy parameters independently at the cost of a non-optimal spectrum sensing performance. To resolve this non-optimality issue, we consider both parameters in the design of our spectrum sensing scheme. In our scheme, we first derive optimal thresholds using an optimization equation with an objective function of maximizing the probability of detection, subject to the minimal probability of error. We then minimize the average spectrum sensing time using signal-to-noise ratio estimation. Our simulation results show that the proposed improved two-stage spectrum sensing (ITSS) scheme provides a 4%, 7%, and 6% better probability of detection accuracy rate than two-stage combinations of energy detection (ED) and maximum eigenvalue detection, energy detection and cyclostationary feature detection (CFD), and ED and combination of maximum-minimum eigenvalue (CMME) detection, respectively. The ITSS is superior also to single-stage ED by 19% and shows an improved average spectrum sensing time. [ABSTRACT FROM AUTHOR]

In the recent developments, the spectrum sensing and detection plays a major importance in day to day communication and it is very much essential for the user to utilize the spectrum bandwidth effectively in cognitive radio (CR) networks. The major performance metrics constraint that causes severe problems in spectrum sensing are probability of false alarm (P fa) and probability of miss detection (P md) . In the proposed paper, the authors made an attempt to enhance the characteristic performances compared to existing methods, matched filter detection, cyclostationary detection and hybrid filter detection. The three detection methods are incorporated in to this non cooperative detection method of CR systems. In the proposed research work, a simulation result are obtained by using MATLab of the modified detection methods and shows the better performance by improving probability of detection (P D) and reducing P fa , P md . [ABSTRACT FROM AUTHOR]

Recently, many studies on vital sign detection using a radar sensor related to Internet of Things(IoT) smart home systems have been conducted. Because vital signs such as respiration and cardiac rates generally cause micro-motions in the chest or back, the phase of the received echo signal from a target fluctuates according to the micro-motion. Therefore, vital signs are usually detected via spectral analysis of the phase. However, the probability of false alarms in cardiac rate detection increases as a result of various problems in the measurement environment, such as very weak phase fluctuations caused by the cardiac rate. Therefore, this study analyzes the difficulties of vital sign detection and proposes an efficient vital sign detection algorithm consisting of four main stages: 1) phase decomposition, 2) phase differentiation and filtering, 3) vital sign detection, and 4) reduction of the probability of false alarm. Experimental results using impulse-radio ultra-wideband radar show that the proposed algorithm is very efficient in terms of computation and accuracy. [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]

The analytical expressions for calculation of probabilities of correct detection and false alarms are dual inertial detector of pulsed optical radiation in the photon counting mode, containing the receiving optical system of two lens antennas and single-photon photoemission device.

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.

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.