Probability Of Correct Detection Research Articles

Information fusion for efficient target detection in large-scale surveillance Wireless Sensor Networks

In this paper, we consider a surveillance scenario, where nodes of a Wireless Sensor Network (WSN) cooperate to detect an event of interest, e.g., the presence of a mobile target in a monitored region. The considered scenario refers, for example, to ELectronic-signals INTelligence (ELINT), since detection is based on sensing the presence of anomalous electromagnetic signals in the monitored area. Leveraging previous results in the field of cognitive wireless networking, we derive proper decision and fusion strategies. We investigate both clustered (where no direct communication between sensors and the Communication and Control center, C2, is allowed and intermediate data fusion is performed at Cluster Heads, CHs) and unclustered (with direct communications between sensor nodes and the C2). System performance is analyzed in terms of False Alarm (FA)/Correct Detection (CD) probabilities and energy consumption, quantifying inherent tradeoffs between these performance indicators.

Building confidence measures for tourist destination choice

AbstractReviews, social media, web sites and recommendations from trusted inner circle opinions are essential to tourists in the travel planning process, but the large amount of information they now make available can increase the difficulty of choosing a destination. Determining how improvements in data quality and in the variety of tourist information sources will affect the probability of correct situational detection, and help in optimizing the overall decision, is vital. This paper presents a generalized method for estimating levels of individual source reliability and assessing the confidence level in a combined output. Initial reliability levels of tourist information sources are augmented by: (i) combining measures of multiple sources, (ii) incorporating truth reinforcement for related elements and (iii) incorporating the importance of individual elements for determining the truth probability for the whole. The result is a measure of confidence in system output based on establishing links among th...

On the effect of the shape of a flaw on its detectability against noise background

When switching to automatic output-quality testing systems, the aspiration for improving the detectability of insignificant deviations of the output parameters from the statutory ones necessitates the solution of a number of new problems. One of those is assessing the effect of the image-energy spectral density from an axisymmetric flaw on the reliability of its detection against background noise by both human and computer vision systems. Knowing this information is a necessary condition for developing new enhanced testing and evaluating techniques. Results are presented on the probabilities of false alarm and correct detection of axisymmetric circular or rectangular flaws depending on the signal-to-noise ratio (SNR) and the ratio of the flaw radius to the background-fluctuation correlation radius. It has been established that for small SNR, human vision is more effective than machine vision that implements the correlation detector algorithm and the Neyman–Pearson criterion.

Detection of radio pulses in unfiltered signals received by spatially distributed receivers

Two recursive algorithms for detection of radio pulses in unfiltered signals received by spatially distributed sensors, such as antenna array elements, are proposed. The algorithms are based on the stochastic gradient ascent algorithm estimating the parameters of mutual mismatch between signals received by spatially distributed sensors. Stabilization of the estimates of signals’ time alignment is chosen as one of the detection criteria. Another criterion is based on the analysis of the correlation coefficient between the aligned signals. Signal alignment is performed recurrently in real time. It is shown that if correlation coefficient is chosen as the objective function showing the alignment quality we need to estimate only time shift between the received signals. Experiments show the efficiency and the high probability of correct detection of the proposed algorithms. In order to increase detection reliability the algorithms can easily be combined.

Analysis of the features of Earth surface clutter formation in the case of low-flying target detection with the use of a bistatic radar

It has been found that the usage of the bistatic mode makes it possible to reduce the power of clutter due to the decrease of the number and area of elements on the Earth surface that reflect the sounding signal. The power of these reflections to a large extent determines the signal / noise ratio at the input of the air-borne radar receiver and. as a result, the probability of correct detection of targets and accuracy of determining their coordinates. Based on the analysis of the features of the formation of reflecting patches on the Earth surface all possible configurations of these areas and analytical expressions for the calculation of their areas have been obtained. As a result of the study, analytical expressions and graphs of lines of equal distances (izodops) and lines of equal Doppler frequencies (izodals) forming these zones specific for a bistatic radar have been obtained. This has made it possible to develop a universal algorithm for calculating the power of clutter when detecting low-flying targets, which is useful for comparative analysis of the clutter power and detection characteristics for various configurations of the relative position of the receiver, transmitter and targets, as well as for comparing the detection characteristics of a monostatic and a bistatic radars. In accordance with this algorithm calculations have been performed and the graphs of dependency of the clutter power in the Doppler filter bandwidth on the frequency of their adjustment have been obtained as a result of the calculation for monostatic and bistatic radars.

Topology Update Based on Compressed Sensing in WMNs

Routing protocols in wireless mesh networks considering its special topology has been an open field for years. Routing methods which are used now could be classified to two groups. Some methods based on common wireless routing protocols with some changes in metrics and other methods based on opportunistic routing methods. In this paper we use the compressed sensing idea to suggest a new topology update protocol for backbone nodes in WMNs. Stable nodes with low probability of failure beside its capability of processing could support a compressed sensing problem for detection of failed links in predefined topology. In our proposed routing method, link states of backbone nodes should be randomly sampled. Then this sampled link states would be coded. All backbone nodes of WMN would be advertised about these codes by some a few nodes which broadcast these codes. So we would have a compressed sense of link states in all nodes. Our proposed verification based reconstruction method could be used in nodes to detection of failed links. Simulation results show that about 60% overhead decrease of routing protocol would be reached by this method with up to 95% probability of correct detection of failed links.

Whole-Region Hybrid Search Algorithm for DSSS Signal Acquisition

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.

A High-Efficiency Multiplierless Symbol Synchronization Algorithm for IEEE802.11x WLANs

This work presents a novel high-efficiency multiplierless symbol synchronization algorithm for IEEE 802.11x wireless local area networks (WLANs). In our proposed scheme, according to IEEE 802.11x specifications, the element of the local known training sequence in the frame header is approximated and represented by its nearest value of integer power of 2. The multiplication in the cross-correlation for OFDM symbol synchronization can be directly reduced to a shift operation. Therefore, the correlator in the OFDM symbol synchronization for IEEE 802.11x WLANs can be economically implemented by merely utilizing the logic resources in hardware without notable performance loss. Since the multiplers and adders are replaced by the simple shifters, the implementation complexity of the correlator is significantly reduced. Experimental results demonstrate that our proposed method can achieve almost the same performance as the original correlation algorithms with multipliers, and has higher correct detection probability than the existing multiplierless technique at given low SNR.

Energy Detection Algorithm for Spectrum Sensing Using Three Consecutive Sensing Events

An energy detection (ED) algorithm, which takes into consideration three consecutive sensing events, is introduced in this letter. This algorithm, named three-event ED (3EED), takes the decision in one sensing event, considering also the event immediately before and the one immediately after it. We design the proposed algorithm to exploit the knowledge of the primary user (PU) activity duty cycle value for tracking the changes of the PU state and we prove that 3EED becomes independent of the PU model parameters for a low duty cycle. In this letter, we demonstrate that 3EED has better detection performance compared to the conventional ED (CED) algorithm, providing a correct detection probability (CDP) gain of more than 7% for a false-alarm probability (FAP) of 0.1. Also, we compare 3EED with other algorithms in terms of detection performance and complexity.

Superposition network coded cooperation for wireless networks

AbstractCooperative diversity is an innovative approach to improve the reliability of communication. Although this technique is considered in the next‐generation mobile communication standards, it has its own challenges in practice. For example, in simultaneous transmission‐based cooperative protocols, perfect synchronisation of nodes is very hard to realise. The traditional time division multiple access method can simplify the synchronisation problems but leads to long transmission delays. In this paper, a low‐delay cooperative strategy is proposed for a network with multiple sources and a single destination, which requires only a simple type of synchronisation. In this technique, we use a combination of superposition coding and network coding; hence the name superposition network coded cooperation. For this strategy, good bounds are obtained for the probability of correct detection for general M‐PSK and M‐QAM modulation and are compared with simulation results. The results demonstrate improvement in error performance compared with other transmission schemes requiring the same transmission time as superposition network coded cooperation. Copyright © 2016 John Wiley & Sons, Ltd.

Information Fusion of Passive Sensors for Detection of Moving Targets in Dynamic Environments.

This paper addresses the problem of target detection in dynamic environments in a semi-supervised data-driven setting with low-cost passive sensors. A key challenge here is to simultaneously achieve high probabilities of correct detection with low probabilities of false alarm under the constraints of limited computation and communication resources. In general, the changes in a dynamic environment may significantly affect the performance of target detection due to limited training scenarios and the assumptions made on signal behavior under a static environment. To this end, an algorithm of binary hypothesis testing is proposed based on clustering of features extracted from multiple sensors that may observe the target. First, the features are extracted individually from time-series signals of different sensors by using a recently reported feature extraction tool, called symbolic dynamic filtering. Then, these features are grouped as clusters in the feature space to evaluate homogeneity of the sensor responses. Finally, a decision for target detection is made based on the distance measurements between pairs of sensor clusters. The proposed procedure has been experimentally validated in a laboratory setting for mobile target detection. In the experiments, multiple homogeneous infrared sensors have been used with different orientations in the presence of changing ambient illumination intensities. The experimental results show that the proposed target detection procedure with feature-level sensor fusion is robust and that it outperforms those with decision-level and data-level sensor fusion.

Detecting Coppice Legacies from Tree Growth

In coppice-with-standards, once a common type of management in Central European lowland forests, selected trees (standards) were left to grow mature among the regularly harvested coppice stools to obtain construction wood. After the underwood was harvested, the forest canopy opened rapidly, giving standard trees an opportunity to benefit from reduced competition. Although this silvicultural system virtually disappeared after WWII, historical management cycles can still be traced in the tree-rings of remaining standards. Our research aims at answering the question whether tree-ring series of standard trees can be used to reconstruct past management practices. The study was carried out on 117 oak standard trees from five sites situated in formerly coppiced calcareous oak-hornbeam and acidophilous oak forests in the Bohemian Karst Protected Landscape Area, Czech Republic. The evaluation was based on the analysis of growth releases representing the response of the standards to coppicing events, and comparison to the archival records of coppice events. Our results showed that coppicing events can be successfully detected by tree-ring analysis, although there are some limitations. Altogether 241 releases were identified (49% of major releases). Large number of releases could be related to historical records, with the major ones giving better results. The overall probability of correct detection (positive predictive power) was 58%, ranging from 50 to 67%, probability for major releases was 78%, ranging from 63 to 100% for different sites. The ability of individual trees to mirror past coppice events was significantly affected by competition from neighboring trees (their number and the sum of distance-weighted basal areas). A dendro-ecological approach to the study of forest management history can serve as an input for current attempts of coppice reintroduction and for conservation purposes.

Budgeted sensor placement for source localization on trees

We address the problem of choosing a fixed number of sensor vertices in a graph in order to detect the source of a partially-observed diffusion process on the graph itself. Building on the definition of double resolvability we introduce a notion of vertex resolvability. For the case of tree graphs we give polynomial time algorithms for both finding the sensors that maximize the probability of correct detection of the source and for identifying the sensor set that minimizes the expected distance between the real source and the estimated one.

New rank detection methods for reduced-rank MIMO systems

In practical multi-input multi-output (MIMO) systems, the channel matrices often have reduced rank. Reliable detection of the channel rank is essential in achieving the significant gain provided by MIMO configuration. Existing work on MIMO channel rank detection assume a static channel model, so the proposed methods only consider the noise distributions while the distributions of the MIMO channels are not considered. In this paper, we employ a random channel model and propose three threshold-based rank detection methods which take into account the distributions of both the channels and the noises. In our first algorithm, following existing single-threshold rank detection scheme, we rigorously derive an analytical lower bound on the correct rank detection probability and propose a systematic threshold selection method by maximizing the lower bound. Then we propose two new rank detection methods which use multiple thresholds, where each threshold corresponds to one possible rank value. The thresholds are optimized based on the derived lower bounds on the rank detection probability for different channel rank values. The convergence and complexity of the proposed algorithms are analyzed. Simulation results on the correct rank detection probability of the proposed schemes are provided to show their advantage over existing schemes. The mean-squared-error (MSE) and outage probability are also simulated to show the importance of reliable rank detection in MIMO communications.

Performance evaluation of optically-preamplified hybrid QPSK M-ary PPM systems with finite extinction ratios

In this paper, we investigate the impact of finite extinction ratios on the error performance of optically-preamplified homodyne hybrid PDM-QPSK M-ary PPM (PQ-mPPM) systems. The study is carried for symbol sizes M∈{2,4,…,1024} and extinction ratios r∈{10,15,20,25,30}dB for probabilities of bit error down to Pb=3-9, which covers systems with or without forward error correction (FEC). We demonstrate that the probability of slot correct location within a symbol in a PQ-mPPM homodyne system with finite extinction ratios is equal to the probability of symbol correct detection in a direct-detection system with dual polarized noise, with the same extinction ratio and symbol size M. This result is leveraged to compute the probabilities of bit error for the homodyne system using an accurate numerical approach. We show that when the extinction ratio is finite, the probability of slot correct location is decreased which not only increases the probability of bit error in the PPM demodulator subsystem but also leads to an increased probability of bit error in the QPSK demodulator. This results in a situation where systems with higher values of M lose their advantage. For example, at Pb=10-4, M=128 and M=16 require the least signal to noise ratio for the case of r=30dB and 20dB, respectively. The power penalty due to a finite extinction ratio depends on the symbol size M. For instance, at Pb=10-9, M=1024, and r=20dB, the penalty is δ=10.6dB. It increases drastically to 22.1dB for r=10dB. For M=16, it is 0.6dB and 5dB for r=20dB and r=10dB, respectively.

Bounds on Secondary User Connectivity in Cognitive Radio Networks

We consider a large-scale cognitive radio network (CRN) in a Rayleigh fading environment. Errors of spectrum sensing by secondary users (SUs) are considered in the letter. Cumulative inter-network interference (between primary and secondary networks) is calculated and outage probability of primary users (PUs) is derived in the closed-form. The upper and lower bounds on the percolation area are obtained and analyzed, within which the secondary network percolates while the PU's outage probability is kept below a tolerable threshold. The bounds depend on SU's correct detection and false alarm probabilities, and PU's transmit power and protection radius. Further we find that the percolation area that percolates SUs and meanwhile provides protection for PUs does not always exist.

Early detection of network element outages based on customer trouble calls

This paper deals with the issue of early detection of network element outages. Timeliness of outage detection as well as accuracy in finding outages on equipment in a telecommunication network depend on the monitoring system used and its performance. The intent of this paper is to investigate and propose a complementary solution to improve the performance of the existing systems in detecting faults earlier than it was able to do before. In developing our approach two constraints are given. The existing operational environment cannot be changed; threshold tuning and parameter changing cannot be done; furthermore no additional infrastructure investment has been planned. Hence, our approach relies on an alternative method based on a two-stage hybrid statistical and diagnostic detector which we designed in a way that exploits additional available data and avoids alarm monitoring system imperfections. The role of this detector is twofold: early detection of network element outages based on customer trouble calls and rule-based decision making for faulty-element isolation based on knowledge derived from fault and network management data. In this paper we present results of statistical analysis of trouble-reporting data. The analysis showed that the timing of customers' trouble reports and their content have information potential that can be utilized for early detection of outages. The detector is explained in detail and its accuracy and reduction delay is evaluated. The method presented can reduce the outage detection delay time by 2.33h on average observed in relation to the performance of an existing fault management process which was designed to detect outages solely on the basis of an alarm monitoring system, for the “difficulties in work” type of malfunction. We attained an overall probability of correct detection of 95.3%. Out of the total number of outages that hypothetically could be detected, by using this method we were able to detect 77.5% of cases 1h before the alarm was raised in the existing alarm system, while 23% of cases were detected 4h before the actual alarm. The approach has been tested on real telecommunication network data over the period of one year.

Steganalysis of SS Steganography: Hidden Data Identification and Extraction

We consider the problem of steganalysis of multi-carrier spread-spectrum steganography in which secret data are embedded over a wide band in a spectrum (transform) domain of a digital medium. The objective is first to identify the presence or absence of hidden data in a given image (i.e., passive steganalysis). Unlike conventional feature-based passive steganalysis algorithms, we describe an unsupervised (blind) low-complexity approach based on iterative generalized least-squares principles that may enable rapid high-volume image processing. Extensive experiments on image sets and comparisons with existing passive steganalysis techniques demonstrate most satisfactory classification performance measured in probability of correct detection versus induced false alarm rate. If the existence of hidden data is identified, we then aim to detect the number of carriers (messages) and blindly extract the hidden data without the knowledge of the original host nor the embedding carriers. This task is also known as active/forensic steganalysis. Experimental studies on images show that the developed algorithm can achieve recovery probability of error close to what may be attained with known embedding carriers.

Finding optimal measurements with inconclusive results using the problem of minimum error discrimination

We propose an approach for finding an optimal measurement for quantum state discrimination that maximizes the probability of correct detection with a fixed rate of inconclusive results. In our approach, we obtain the optimal measurement by solving the problem of finding a measurement that maximizes the weighted sum of the probability of correct detection and that of inconclusive results. We show that this problem can be reduced to the widely studied problem of finding a minimum error measurement for a certain state set, which maximizes the probability of correct detection without inconclusive results. As an application of our approach, we show how to solve the problem of finding an optimal measurement for qubit states with a fixed rate of inconclusive results.

Detecting long-term growth trends using tree rings: a critical evaluation of methods.

Tree-ring analysis is often used to assess long-term trends in tree growth. A variety of growth-trend detection methods (GDMs) exist to disentangle age/size trends in growth from long-term growth changes. However, these detrending methods strongly differ in approach, with possible implications for their output. Here, we critically evaluate the consistency, sensitivity, reliability and accuracy of four most widely used GDMs: conservative detrending (CD) applies mathematical functions to correct for decreasing ring widths with age; basal area correction (BAC) transforms diameter into basal area growth; regional curve standardization (RCS) detrends individual tree-ring series using average age/size trends; and size class isolation (SCI) calculates growth trends within separate size classes. First, we evaluated whether these GDMs produce consistent results applied to an empirical tree-ring data set of Melia azedarach, a tropical tree species from Thailand. Three GDMs yielded similar results - a growth decline over time - but the widely used CD method did not detect any change. Second, we assessed the sensitivity (probability of correct growth-trend detection), reliability (100% minus probability of detecting false trends) and accuracy (whether the strength of imposed trends is correctly detected) of these GDMs, by applying them to simulated growth trajectories with different imposed trends: no trend, strong trends (-6% and +6% change per decade) and weak trends (-2%, +2%). All methods except CD, showed high sensitivity, reliability and accuracy to detect strong imposed trends. However, these were considerably lower in the weak or no-trend scenarios. BAC showed good sensitivity and accuracy, but low reliability, indicating uncertainty of trend detection using this method. Our study reveals that the choice of GDM influences results of growth-trend studies. We recommend applying multiple methods when analysing trends and encourage performing sensitivity and reliability analysis. Finally, we recommend SCI and RCS, as these methods showed highest reliability to detect long-term growth trends.