An EMD-Based Double Threshold Detector for Spectrum Sensing in Cognitive Radio Networks

The development of tools for automated analysis of data collected for mine countermeasures (MCM) search, classify and map (SCM) has predominantly focused on automated target recognition for individual mines. As MCM target detection becomes more automated, detecting spatial patterns in the positioning of mines should follow. Natural restrictions on mine deployment such as depth and bottom contour as well as mine deployment methodology and protocol effectively create mine line patterns that are typical to coastal areas. We have developed a mine line detection algorithm that uses nonparametric spatial analysis to identify mine-like contacts (MLCs) that fit into mine line patterns dictated by standard mine laying protocol in coastal regions. In general, a set of MLCs reported from a mine detection sensor will consist of both actual mines and false alarms; the algorithm classifies every MLC in this set into one of three classes: fixed — the MLCψfits into a mine line pattern with fixed mine spacing; random -the MLCψfits into a mine line pattern with random mine spacing; or false alarm-ψ-the MLCψdoes not fit into a mine line pattern and is subsequently not considered a mine. The latest version of the algorithm utilizes nonparametric techniques and requires no knowledge of either the detection sensor's probability of detection (P d ) or probability of false alarm (P fa ). A nonparametric anomaly detector first identifies regions with unusually high MLC density for more detailed analysis. Suspected regions are subsequently analyzed for unusually regular patterns of MLCs. The probabilities that the observed target densities and target patterns could occur simply by chance are estimated by comparison with results from nearby areas using basic information about mine deployment. To ensure that a mine line's full extent is captured, the algorithm attempts to grow a line after it is detected. And a more detailed probability analysis distinguishes random mine lines from fixed-spaced mine lines. Extensive Matlab simulations characterize the algorithm's performance across a wide range of operating environments and simulated minefield configurations. In total, ten replications each of 1,367 different configurations have been evaluated. These configurations include three different water depths (10 ft., 6 ft. and 200 ft.), six false notional alarm rates (1, 2,5,10,15 and 20 false alarms per sq. NM), three false alarm distributions (uniform, sloped, and striped), three sensor probabilities of detections (0.4, 0.6 and 0.9), three target confidence scores (0.6,0.8 and 1.0) and four false alarm confidence scores (0.5, 0.6, 0.8 and 1.0). The range of simulation parameters was selected in part as an attempt to push the algorithm past its acceptable performance limits. However, the algorithm performed remarkably well even under the worst conditions. For MLC sets with false alarm rates as high as 20 per square nautical mile, the algorithm correctly classifies over 98% of the MLCs as mines and detects 97% of the mine lines. Furthermore, it reduces the false alarm rate from 20 to 0.2 per square nautical mile with a false mine line rate of 0.14 false lines per square nautical mile. The algorithm achieves even lower false alarm rates when the initial false alarm rate is less than 20. The automated mine line detection discovers many obscure lines that are virtually undetectable to the naked eye.

In the sense of likelihood ratio test (LRT), a new type of distributed constant false alarm rate (CFAR) scheme-CCAWCA(censored cell-averaging -R-weighted cell averaging) CFAR detector is presented. Its characteristic is that censored cell-averaging (CCA) CFAR algorithms are used in local processors to form the estimation of SNR of local observations, and then the estimation transmitted to the data fusion center (DFC). Finally, the fusion center makes the final decision based on the weighted cell averaging (WCA). Since the weights are adjusted according to different SNR adaptively, the proposed detector can be available in the case that the target echo and noise/clutter have different level for every sensor. In addition, it does not need a priori knowledge about the interference in order to perform well. Furthermore, unlike the OS-CFAF the tolerance of interfering targets is restricted in appointed k value’s. Under Swerling 2 assumption, the analytic expression of detection probability and false alarm probability are derived.

In this paper, we study the sensitivity of cell-averaging constant false alarm rate (CA CFAR) processors towards the change of input distribution (Poisson and binomial distribution) of pulse jamming (PJ). We offer the mathematical expression for calculating the parameters of CA CFAR detectors in the entire area of the probability for the appearance of strong pulse jamming. The original results in the present work concern the determination of the sensitivity expressions of the probability of detection, probability of false alarm and the average decision threshold (ADT) for CA CFAR detectors, towards the change of distributed pulse jamming. The results show that a CA CFAR processor has high losses when the scale factor is chosen incorrectly. Research is performed in a MATLAB environment.

In this paper, we propose to analyze the binary integration of the cell-averaging constant false-alarm rate (CA-CFAR) and order statistics constant false-alarm rate (OS-CFAR) detectors in the presence of non-Gaussian spiky clutter modeled as a Pearson distribution. We derive new closed form expressions for false alarm and detection probabilities for the CA-CFAR detector in the presence of Pearson-distributed clutter backgrounds. We first show that the use of binary integration improves the detection probabilities of the detectors considered. Secondly, the maximum of detection probability occurs for an optimum choice when the second threshold is set to be equal to M = (3/4) L. For this optimum M-out-of-L rule, the comparison analysis of the CA-CFAR and OS-CFAR binary integrators showed that the latter has better performance in homogeneous Pearson- distributed clutter.

The measurement of optical fibre vibration is a key part of optic fibre pre-warning system, which has gradually focused on phase-sensitive optical time-domain reflectometer. However, for this instrument, false alarm rate is very high and some unstable intrusion signals cannot be detected by using its fixed threshold method in the actual application. It needs to develop new vibration detection method to overcome the above defect. The vibration signals normally consist of three parts, that is, noise, interference and intrusion signals. After a large number of data analysis, the authors find that the system noise is time varying and follows the Rayleigh distribution. Hence, the authors innovatively use the constant false alarm rate (CFAR) method to detect this type of intrusion. Considering interference is also time varying and diverse, a good detection performance cannot be obtained only by using the conventional CFAR. For this reason, a background homogeneity adaptive CFAR (BHA-CFAR) method is further proposed to detect the vibration signals in this study. The BHA-CFAR consists of two detectors, cell averaging CFAR (CA-CFAR) detector and greatest-of/smallest-of CFAR (GO/SO-CFAR) detector. A parameter, homogeneity of background, is estimated first to classify the surrounding. Then CA-CFAR and GO/SO-CFAR are optionally used according to the surrounding is homogeneous or heterogeneous, respectively. This new detection method can adapt to any background surrounding and has a good detection performance. In order to check the feasibility and validity of the BHA-CFAR method, several experiments were carried out in Da Gang oilfield. The detection results show that the proposed method can provide a good tradeoff between the detection performance and computation time.

To overcome the limitations of Orthonormal Basis Function (OBF) methods in magnetic anomaly detection, including high false alarm rates and ambiguous target localization due to background noise, this paper introduces a high-confidence detection algorithm based on hierarchical clustering with an optimal cut height. The core of our approach is a theoretically derived optimal cut height, which is calculated from a physical model of the magnetic dipole's vertical gradient field. This model establishes the implicit functional relationship between the effective detection range and key parameters, including magnetic moment orientation, geomagnetic inclination, and sensor height. The calculated optimal cut height serves as the critical criterion in a complete-linkage hierarchical clustering algorithm, which processes the alarm point clouds generated by a preliminary Greatest-of Cell-Averaging Constant False Alarm Rate (GOCA-CFAR) detector. This effectively suppresses isolated false alarms caused by background fluctuations while preserving spatially coherent alarm clusters within the target's effective detection range, thereby significantly enhancing detection confidence. Results from both simulations and field experiments validate the efficacy of the proposed algorithm, demonstrating its superior capability to reliably discriminate genuine targets from false alarms compared to traditional one-dimensional CFAR detection.

In this paper, we analyze the performance of distributed constant false alarm rate (CFAR) detection with data fusion both in homogeneous and nonhomogeneous Gaussian backgrounds. We employ the ordered statistics (OS) CFAR detectors as local detectors. In the homogeneous background, we maximize the global probability of detection for a given fixed global probability of false alarm by optimizing both the threshold multipliers and the order numbers of the local OS-CFAR detectors. In the nonhomogeneous background with multiple targets or clutter edges, we analytically analyze the performance of the detection system and compare its performance with the performance of the distributed cell-averaging (CA) CFAR detection system.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper present a new distributed constant false alarm rate (CFAR) detector based on automatic censoring technique. In the scheme, every local decision of individual detector, resulting from the comparison between its sample level and the estimation of the background based on GOSCA-CFAR (generalized order statistic, cell averaging-CFAR) algorithm, takes the value zero or one. Then, the fusion center makes the final decision utilizing the total local decisions, which are transmitted from each local detector. The overall decision, which is zero or one, is obtained at the data fusion center based on “AND”, “OR”, and “k/N” fusion rule. The results show that for the nonhomogeneous background caused by multiple interfering targets, particularly in multiple target situation, it exhibits good robustness in distributed sensor network. Under Swerling 2 assumption, the analytic expression of detection probability and false alarm probability are derived.

A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm, such as Constant False Alarm Rate (CFAR). However, noise and multipath components often exist over the range-angle map, which could produce false alarms for an undesired location depending on the threshold setting. In other words, the threshold setting is sensitive in noisy range-angle maps. Therefore, if the noise is reduced, the threshold can be easily set to reduce the number of false alarms. In this paper, we propose a method that improves the CFAR threshold tolerance by denoising a range-angle map using Deep Image Prior (DIP). DIP is an unsupervised deep-learning technique that enables image denoising. In the proposed method, DIP is applied to the range-angle map calculated by the Curve-Length (CL) method, and then the object location is detected over the denoised range-angle map based on Cell-Averaging CFAR (CA-CFAR), which is a typical threshold setting algorithm. Through the experiments to estimate human locations in indoor environments, we confirmed that the proposed method with DIP reduced the number of false alarms and estimated the human location accurately while improving the tolerance of the threshold setting, compared to the method without DIP.

In this paper we perform an experimental analysis for assessing the constant false alarm rate (CFAR) behavior or four coherent adaptive radar detectors in the presence of experimentally measured clutter data. To this end we exploit several data files containing both land and lake clutter, collected by two radar systems (the MIT Lincoln Laboratory Phase-One radar and the McMaster IPIX radar) at different polarizations, range resolutions, and frequency bands. The results show that all the receivers, in the presence of real data, do not respect their nominal probability of false alarm (P/sub fa/) namely they exhibit a false alarm rate higher than the value preassigned at the design stage. Nevertheless one of them, the recursive persymmetric adaptive normalized matched filter (RPANMF) is very robust, in the sense that it presents an acceptable displacement from the nominal P/sub fa/, in correspondence of all the analyzed scenarios.

A convolutional neural network (CNN) detector with fixed probability of false alarm (P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FA</inf> ) for application to non-coherent wide area surveillance (WAS) maritime radars is proposed. This detector is trained using a novel cost function-based on Neyman-Pearson (NP) criterion. The use of machine learning allows the detector to learn a complex non-linear model of sea clutter and obviates the need for specifying complex, likely intractable, target plus clutter statistical models. The NP-CNN is shown to perform better than a simple cell-averaging constant false alarm rate (CA-CFAR) statistical detector and a CNN trained using the cross-entropy cost function.

The region of interest (ROI) contains a potential target in synthetic aperture radar (SAR) image. Picking out ROI chips before target recognizing will accelerate the whole automatic target recognition (ATR) system. This paper improves the traditional method to obtain ROI chips through constant false alarm rate (CFAR) algorithm. With the help of integral image, we reduce redundant calculation in traditional cell average CFAR (CA-CFAR) to increase its computing speed, which is denoted as the Integral Image CFAR (II-CFAR). Finally we compare the ROI extraction results of the traditional CA-CFAR and the proposed II-CFAR on the same real SAR image. The results show that the II-CFAR detector can obtain the same detection performance with the CA-CFAR detector but with a much shorter running time.

The authors develop the theory of CA-CFAR (cell-averaging constant false-alarm rate) detection using multiple sensors and data fusion, where detection decisions are transmitted from each CA-CFAR detector to the data fusion center. The overall decision is obtained at the data fusion center based on some k out of n fusion rule. For a Swerling target model I embedded in white Gaussian noise of unknown level, the authors obtain the optimum threshold multipliers of the individual detectors. At the data fusion center, they derive an expression for the overall probability of detection while the overall probability of false alarm is maintained at the desired value for the given fusion rules. An example is presented showing numerical results.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

Generally there are three mechanisms in ship detection in the Polarimetric Synthetic Aperture Radar (PolSAR), such as the optimal polarization, polarization decomposition and ship wake. The optimal polarimetric detector (OPD) provides the best possible detection performance achievable under ideal conditions. But it is very hard to derive the threshold in the constant false alarm rate (CFAR) process, because the expression of the probability of false alarm (PFA) is difficult to calculate. In this paper an approximation of the statistics of the OPD is presented, and the PFA and the probability of detection (PD) are both derived. This makes the threshold easy to be obtained via the bisection method. The derivations are validated by the simulated data and are helpful to the application of the OPD in PolSAR image.