Statistical Evaluation of Adaptive Threshold Alignment for a Multi-Carrier CDMA Code Synchronization

This paper presents an exact performance analysis for the evaluating a mean acquisition time in a cellular code division multiple access system. New expressions for the probabilities of detection, miss, and false alarm are derived in a frequency-selective Rician fading channel. From the numerical results, it is shown that our formula gives the accurate results of the mean acquisition time in the performance analysis of the parallel acquisition system with the reference filter.

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.

This paper presents a novel analytical derivation of the false alarm probability (FAP) and detection probability (DP) for non-line-of-sight (NLOS) detection based on LTE signals. Position estimation in indoor or urban area with cellular wireless communication systems is getting more and more important. However, especially in these environments NLOS propagation causes positioning errors in the order of hundreds of meters. Thus, detection and mitigation of NLOS signals is a critical task. In this paper, we derive the FAP and DP on power-scaled detectors based on LTE multipath signals. The derivation considers frequency-selective fading channels taking sub-sample multipath interference, channel dynamics and initial frequency offsets into account. The results show a simple way to calculate the DP versus FAP. Simulation results verify the analytical probabilities for the WINNER II C2 channel model. Additionally, the simulations show the improved FAP of an antenna array at the receiver.

The two primary measures of detection performance are probability of detection (PD) and false alarm rate (FAR). Since these are statistical measures, the large statistical uncertainties associated with measurements on small data sets impose a severe limitation on the interpretation and extrapolation of the test results. The main difficulty with the data collected from many of the landmine detection experiments is that the number of mine targets is small, and the clutter data is measured on small areas. As a result of analyzing data from several detection tests for both UXO and mine detection technologies, the Institute for Defense Analyses (IDA) has generated a number of suggestions to improve the results obtained from future demonstrations and tests. In the absence of a rigorous error analysis, we can estimate the uncertainty in a probability of detection measurement with a simple model. If detections can be described as a binomial process weighted by the true probability of detection, then uncertainty equals (root)N, where N is the number of mines detected, and uncertainty in PD (percent) equals (root)N/N X 100 for large N. A similar statistical uncertainty is encountered in the measurement of probability of false alarm. An opportunity for a false alarm can be defined by the amount of ground covered by the projected area of the target plus an allowable miss distance, say 1 m2. Thus, for each 1 m2 of ground that it passes over, the system has one opportunity to declare a false alarm. A test field that is 1 m by 20 m offers only 20 m2 of area and thus only 20 samples for probability of false alarm measurement. A site that is 4 m wide and 4 km long covers 16,000 m2. Thus, there will be a substantial increase in the clutter environment samples by the sensor and a concomitant improvement in false alarm measurements. Of course, false alarm density will be highly site dependent, so extrapolation of the result is still uncertain. Unfortunately, this limitation is insurmountable in single test and at a single site.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A non-coherent fast acquisition system is described for a DS-SS signal in the presence of a large Doppler shift. This technique utilises N complex correlators of length x such that Nx=m where m is the length of the spreading sequence. These complex correlators produce partial correlations of the DS-SS signal. The outputs of these correlators are fed to an N point FFT. The maximum output of the FFT is then selected and compared against a threshold. In situations with high Doppler shifts the FFT acquisition system described can test all frequency offsets for a given code phase simultaneously. The theoretical probability of detection and false alarm are derived in a Gaussian channel. We determine the mean acquisition time for an 8-point FFT using a single dwell system in the presence of 32 kHz Doppler shift. It is shown by choosing a suitable threshold level that the mean acquisition time is over 16 times lower than a standard single dwell non-coherent decorrelator.

This paper introduces analysis and simulations results of a DS spread spectrum system in which code acquisition is based on a matched filter, a matched filter and a median filter and a matched filter and a transversal filter. Noncoherent structures are used due to the lack of knowledge of the carrier phase in the acquisition process. The probabilities of detection and false alarms and the mean acquisition times are calculated in the AWGN channel and with continuous tone jamming with ideal autocorrelation of the spreading code being assumed. The probabilities are also measured by simulations. The effect of CW jamming can be eliminated by both methods, so the comparisons are made in the AWGN channel. Fixed thresholds of the comparator and constant false alarm probabilities (CFAR criteria) are both used. The results are similar to those achieved by using coherent structures. The median filter structure has a better performance compared to transversal filter structure, even though the difference between the median filter and an equal length transversal filter is not significant in the CFAR case.

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 this paper, we optimize probability of detection and probability of false alarm in cognitive radio network to minimize probability of error of a particular SU in a centralized cognitive radio network using Genetic algorithm (GA). Our objective is to minimize probability of error and find out optimum values of probability of occupancy detection or probability of detection and probability of false alarm. We use Genetic Algorithm to solve this optimization problem. The result is compared with Differential Evolution algorithm and it is evident from the comparison that DE finds better solution and takes much lesser number of evaluations to find optimum solution.

SUMMARYIn this paper, a novel sensing scheme, uniform quantization for cooperative sensing (UniQCS), that employs a uniform quantizer is proposed. UniQCS is based on energy detection and uses weight vector for global decision function. It performs better than hard decision algorithms such as majority and k‐out‐of‐n in terms of probability of detection and false alarm at the cost of a marginal increase in overhead bits under imperfect reporting channel and false reports. The probability of detection is maximized for a given probability of false alarm constraint by the proposed method. For detailed analysis, the UniQCS is compared with equal gain combiner scheme, which performs far better than hard decision algorithms, via highest bandwidth requirement. The proposed algorithm performs close to equal gain combiner. Moreover, the robustness of UniQCS to sensing error is analyzed when some nodes always report false decisions to the fusion center and the reporting channel is imperfect. For probability of false alarm equal to 0.01, performance gain of UniQCS is at least 45% compared with the other methods when there are two false reporting nodes. UniQCS performance gain is at least 15% compared with other methods for probability of reporting channel error equal to 0.001. Copyright © 2012 John Wiley & Sons, Ltd.

In this letter, we propose a generalized version of the window-based pseudonoise (PN) acquisition scheme verifying one or more best hypotheses rather than the best one per search window. The false alarm probability, detection probability, and mean acquisition time (MAT) of the proposed scheme are derived by using the well-known flow graph approach in additive white Gaussian noise (AWGN) and fading channels. Numerical results demonstrate that the acquisition performances are improved through the proposed generalization, particularly when the pilot energy to total received energy ratio (pilot E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> /I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sub> ) is low.

The statistical analysis of failure detection decisions in terms of the instantaneous probabilities of false alarm and correct detection for a specified failure magnitude at each check-time have previously been performed for several different failure detection techniques that utilize a Kalman filter. By performing a discrete-time specialization of a result of Gallager and Helstrom on a tightened upper bound for continuous-time level-crossing probabilities, upper bounds on the probabilities of false alarm and correct detection over a time interval have been obtained for the specific technique of CR2 tailnre detection (to allow an accounting for the effect of time correlations of the filter estimates). When these upper bounds are optimized to be as tight as possible to the desired probabilities, the resulting optimization problem for discrete-time is a collection of quadratic programming (QP) problems, which may easily be solved exactly without recourse to approximate solutions as were resorted to in the continuous-time formulation. This technique for evaluating tightened upper bounds on the false alarm and correct detection probabilities may be of general interest, since it can be applied to any failure detection technique or signal detection technique that can relate an exceeding of the deterministic decision threshold by the test statistic directly to a deterministic level being exceeded by a scalar Gaussian random process.

Performance of automatic target recognition (ATR) systems depends on numerous factors including the mission description, operating conditions, sensor modality, and ATR algorithm itself. Performance prediction models sensitive to these factors could be applied to ATR algorithm design, mission planning, sensor resource management, and data collection design for algorithm verification. Ideally, such a model would return measures of performance (MOPs) such as probability of detection (Pd), correct classification (Pc), and false alarm (Pfa), all as a function of the relevant predictor variables. Here we discuss the challenges of model-based and data-based approaches to performance prediction, concentrating especially on the synthetic aperture radar (SAR) modality. Our principal conclusion for model-based performance models (predictive models derived from fundamental physics- and statistics-based considerations) is that analytical progress can be made for performance of ATR system components, but that performance prediction for an entire ATR system under realistic conditions will likely require the combined use of Monte Carlo simulations, analytical development, and careful comparison to MOPs from real experiments. The latter are valuable for their high-fidelity, but have a limited range of applicability. Our principal conclusion for data-based performance models (that fit empirically derived MOPs) offer a potentially important means for extending the utility of empirical results. However, great care must be taken in their construction due to the necessarily sparse sampling of operating conditions, the high-dimensionality of the input space, and the diverse character of the predictor variables. Also the applicability of such models for extrapolation is an open question.

The conventional hybrid search algorithm for direct sequence spread spectrum (DSSS) signal acquisition terminates once a decision variable exceeds the detection threshold. Therefore, a spurious “hit” declared before the search reaches the cell where the target signal is located avoids any possibility of a correct detection. This behavior increases the probability of false detection, especially in abnormal scenarios, such as when in the presence of interfering signals. To improve the probability of correct detection under a constant false-alarm rate criterion, this paper proposes an advanced hybrid acquisition scheme, whose search area is extended to the whole Doppler frequency and code phase delay uncertainty region. To reduce the mean acquisition time (MAT) increment resulting from this increased search area, both parameters of a Tong detector and the detection threshold are adaptively customized, according to a signal-to-noise ratio estimate obtained during the search operation. In addition, analytic expressions for the detection probability and false-alarm probability are derived. The most prominent advantage of the proposed method is that it can reduce the false detection probability noticeably, with little increase in resource utilization, and an acceptable MAT. The validity of this algorithm is verified, both theoretically and through simulation. The proposed algorithm is particularly suitable for DSSS signal acquisition when in the presence of either multiple access interference or continuous wave interference.

Ensuring the integrity of subsea process components is one of the primary business objectives of the oil and gas industry. Leak detection system (LDS) is one type of system used to safeguard reliability of a pipeline. Different types of LDS use different technologies for detecting and locating leaks in pipelines. One technology, which is gaining wide acceptance by the industry, is the fiber optic-based LDS. This technology has great potential for subsea pipeline applications. It is the most suited for underwater applications due to the ease of installation and reliable sensing capabilities. Having pipelines underwater in the deep sea presents a great challenge and a potential threat to the environment and operation. Thus, there is a need to have a reliable and effective system to provide the assurances that the monitored subsea pipeline is safe and functioning as per operating conditions. Two important performance parameters that are of concern to operators are the probability of detection and probability of false alarm. This paper presents a probabilistic formulation of the probability of detection and probability of false detection for a fiber optic-based LDS.

Mathematical modeling was carried out based on the experimentally measured fluorescence spectra of oil, vegetation and water to select the most effective spectral registration ranges for the fluorescent method for detecting oil leaks at an excitation wavelength of 355 nm. The results of mathematical modeling show that the probabilities of correct detection and false alarms for the problem of detecting oil leaks significantly depend on the type of oil and, accordingly, on the spectral channels selected for monitoring. For reliable detection of oil spills against the background of vegetation or water bodies, two or three spectral channels must be used. The highest probabilities of correct detection (&gt;0.999) and small probabilities of false alarms (&lt;0.04) can be achieved for oils with intensity maxima of laser-induced fluorescent radiation at wavelengths of ~ 420 and 550 nm (when using two spectral channels) and for oils with fluorescence intensity maximum at a wavelength of ~510 nm (when using three spectral channels). For oils with a maximum fluorescence intensity at a wavelength of about 475 nm (when using three spectral channels), the results are worse, although they remain acceptable (at a measurement noise of 10%, the probability of correct detection and false alarms, respectively, 0.94 and 0.11).