Loss In Detection Probability Research Articles

Comparison of closing and passing mode from a line-transect survey of delphinids in the eastern Tropical Pacific Ocean

Line-transect ship surveys are the primary method used to estimate abundance of pelagic cetaceans. However, survey methods are often modified from traditional methods because observers must approach cetacean groups to identify species and estimate group size. Returning to the trackline after approaching a school dramatically reduces the amount of effective survey time, so ships often resume survey effort at the sighting but parallel to the original trackline (closing mode). Survey effort is no longer independent of group locations, and it is unclear how such methodological modifications affect overall abundance estimates. This research presents the results of a study designed to determine the effects of closing mode methods on abundance estimation for cetacean species in the eastern tropical Pacific. Species identification and group size estimation in closing mode are compared with results using survey techniques where the ship does not approach or slow down to investigate a sighting (passing mode). Both empirical data and simulations were used to compare group encounter rates in the two modes and to better understand the mechanisms that might lead to an encounter rate bias in closing mode. As seen in similar studies, observers are able to identify to the species level less frequently in passing mode (81% vs 57% of sightings), and point estimates of delphinid group size were 58% lower in passing mode than closing mode at distances between 1.0 and 5.5km from the trackline. In addition, uncertainty in group size both within and between observers was higher in passing mode. Closing mode delphinid group encounter rates were generally 20–25% lower than passing mode delphinid group encounter rates. Simulations showed the empirically lower encounter rates in closing mode are due to a loss in detection probability caused by the stop-start nature of the survey method. The closing mode encounter rate bias is greater when groups are in fewer and/or tighter clusters and when overall group density is higher. Methodological adjustments and analytical solutions to improve group size estimation and species identification in passing mode and reduce closing mode encounter rate bias are analytically complex and would also result in the loss of important additional life history data. Nevertheless, such avenues should be explored further.

Fast Implementation of Insect Multi-Target Detection Based on Multimodal Optimization

Entomological radars are important for scientific research of insect migration and early warning of migratory pests. However, insects are hard to detect because of their tiny size and highly maneuvering trajectory. Generalized Radon–Fourier transform (GRFT) has been proposed for effective weak maneuvering target detection by long-time coherent detection via jointly motion parameter search, but the heavy computational burden makes it impractical in real signal processing. Particle swarm optimization (PSO) has been used to achieve GRFT detection by fast heuristic parameter search, but it suffers from obvious detection probability loss and is only suitable for single target detection. In this paper, we convert the realization of GRFT into a multimodal optimization problem for insect multi-target detection. A novel niching method without radius parameter is proposed to detect unevenly distributed insect targets. Species reset and boundary constraint strategy are used to improve the detection performance. Simulation analyses of detection performance and computational cost are given to prove the effectiveness of the proposed method. Furthermore, real observation data acquired from a Ku-band entomological radar is used to test this method. The results show that it has better performance on detected target amount and track continuity in insect multi-target detection.

Towards a real-time fully-coherent all-sky search for gravitational waves from compact binary coalescences using particle swarm optimization

While a fully-coherent all-sky search is known to be optimal for detecting gravitational wave signals from compact binary coalescences, its high computational cost has limited current searches to less sensitive coincidence-based schemes. Following up on previous work that has demonstrated the effectiveness of Particle Swarm Optimization in reducing the computational cost of this search, we present an implementation that achieves near real-time computational speed. This is achieved by combining the search efficiency of PSO with a significantly revised and optimized numerical implementation of the underlying mathematical formalism along with additional multi-threaded parallelization layers in a distributed computing framework. For a network of four second-generation detectors with $60$~min data from each, the runtime of the implementation presented here ranges between $\approx 1.4$ to $\approx 0.5$ times the data duration for network signal-to-noise ratios (SNRs) of $\gtrsim 10$ and $\gtrsim 12$, respectively. The reduced runtimes are obtained with small to negligible losses in detection sensitivity: for a false alarm rate of $\simeq 1$~event per year in Gaussian stationary noise, the loss in detection probability is $\leq 5\%$ and $\leq 2\%$ for SNRs of $10$ and $12$, respectively. Using the fast implementation, we are able to quantify frequentist errors in parameter estimation for signals in the double neutron star mass range using a large number of simulated data realizations. A clear dependence of parameter estimation errors and detection sensitivity on the condition number of the network antenna pattern matrix is revealed. Combined with previous work, this paper securely establishes the effectiveness of PSO-based fully-coherent all-sky search across the entire binary inspiral mass range that is relevant to ground-based detectors.

Target-based optimization of advanced gravitational-wave detector network operations

We introduce two novel time-dependent figures of merit for both online and offline optimizations of advanced gravitational-wave (GW) detector network operations with respect to (i) detecting continuous signals from known source locations and (ii) detecting GWs of neutron star binary coalescences from known local galaxies, which thereby have the highest potential for electromagnetic counterpart detection. For each of these scientific goals, we characterize an N-detector network, and all its (N − 1)-detector subnetworks, to identify subnetworks and individual detectors (key contributors) that contribute the most to achieving the scientific goal. Our results show that aLIGO-Hanford is expected to be the key contributor in 2017 to the goal of detecting GWs from the Crab pulsar within the network of LIGO and Virgo detectors. For the same time period and for the same network, both LIGO detectors are key contributors to the goal of detecting GWs from the Vela pulsar, as well as to detecting signals from 10 high interest pulsars. Key contributors to detecting continuous GWs from the Galactic Center can only be identified for finite time intervals within each sidereal day with either the 3-detector network of the LIGO and Virgo detectors in 2017, or the 4-detector network of the LIGO, Virgo, and KAGRA detectors in 2019–2020. Characterization of the LIGO-Virgo detectors with respect to goal (ii) identified the two LIGO detectors as key contributors. Additionally, for all analyses, we identify time periods within a day when lock losses or scheduled service operations could result with the least amount of signal-to-noise or transient detection probability loss for a detector network.

Simulation on Detection Probability Loss of OTHR under Oppressive Active Jamming

Aim to quantificational evaluation of capability of countering oppressive active jamming for Sky-wave Over-The-Horizon Radar (SWOTHR), main oppressive jamming types for SWOTHR and measures of countering oppressive jamming of SWOTHR are analyzed minutely. Based on idea of power countermeasure, evaluation index of detection probability loss is put forward. Based on signal-to-interference ratio equation including the main measures of anti-jamming, simulation models of detection probability loss are established. Simulation results show that SWOTHR has strong capability of countering oppressive active jamming and capability of countering main lobe interference stronger than side lobe interference. And simulation results also confirm feasibility and validity of evaluation index and models.

Analysis of collaborative spectrum sensing without dedicated sensing period

Previous works on spectrum sensing use either samples free of secondary user interference based on dedicated sensing period or samples with partly ‘cleaned’ secondary user interference based on specific characteristics of orthogonal frequency division multiplexing signals or decoded signals. In this article, collaborative spectrum sensing using samples corrupted by secondary user interference without dedicated sensing period or ‘cleaning’ procedure is studied, and its performance is analysed. Two different cases of primary user signals are considered. In the first case, the primary user signal suffers from fast fading, whereas in the second case, the primary user signal suffers from slow fading. New detectors for these two cases are derived, based on which their performances are analysed. Numerical results show that the diversity gain is lost because of the secondary user interference. For fast-fading primary user signal, the sensing performance deteriorates quickly when the number of interfering secondary users increases, whereas for slowly fading primary user signal, an error floor exists when the number of interfering secondary users increases. On the other hand, by choosing appropriate system parameters, high sensing accuracy can still be achieved with acceptable loss in detection probability, but with long transmission time when no dedicated sensing period or ‘cleaning’ procedure are used.

Envelope-Law and Geometric-Mean STAP Detection

Two detectors for space-time adaptive processing (STAP) are proposed here. These are variants that use envelope-law and geometric-mean (GM) (or logarithmic) processing, both being well-known concepts from conventional constant false alarm rate (CFAR) square-law radar detection. The variants are based on normalized adaptive matched filter (NAMF) STAP processing, and their CFAR property is established. Threshold setting for the detectors for specified false alarm probability (FAP) is accomplished using fast simulation based on importance sampling. Performance analyses of these detectors reveal almost indistinguishable loss in detection probability in homogeneous Gaussian interference compared with conventional square-law STAP detector versions. In addition, they exhibit robust detection performance in the presence of interfering targets in the training data for both nonfluctuating as well as fluctuating target models. Comparisons are made with the corresponding envelope-law and GM variants of the adaptive matched filter (AMF) detector previously proposed in a recent paper.

Collusion-Resistant Video Fingerprinting for Large User Group

Digital fingerprinting protects multimedia content from illegal redistribution by uniquely marking copies of the content distributed to users. Most existing multimedia fingerprinting schemes consider a user set on the scale of thousands. However, in such real-world applications as video-on-demand distribution, the number of potential users can be as many as 10-100 million. This large user size demands not only strong collusion resistance but also high efficiency in fingerprint construction, and detection, which makes most existing schemes incapable of being applied to these applications. A recently proposed joint coding and embedding fingerprinting framework provides a promising balance between collusion resistance, efficient construction, and detection, but several issues remain unsolved for applications involving a large group of users. In this paper, we explore how to employ the joint coding and embedding framework and develop practical algorithms to fingerprint video in such challenging settings as to accommodate more than ten million users and resist hundreds of users' collusion. We investigate the proper code structure for large-scale fingerprinting and propose a trimming detection technique that can reduce the decoding computational complexity by more than three orders of magnitude at the cost of less than 0.5% loss in detection probability under moderate to high watermark-to-noise ratios. Both analytic and experimental results show a high potential of joint coding and embedding to meet the needs of real-world large-scale fingerprinting applications.

Reducing the MISR size

Multiple-input signature registers (MISRs) are commonly used in built-in self-test (BIST) applications. The size of the MISR is dictated by the number of signals it has to compress. Normally a MISR includes a stage for every signal that it is sampling. In some applications this leads to very wide MISRs that may include several hundred stages. Large size MISRs pose problems in terms of hardware and wiring overhead. Shorter compressors are, therefore, needed. This paper investigates the problem of reducing the MISR so that it samples multiple signals at every stage. Issues like detection probability loss, test length penalty, fault coverage degradation, are some of the disadvantages that may arise from the MISR shrinkage. This paper analyzes all these issues; shows ways to reduce their negative effect, and compares the results to previously published proposals.

Shrinking wide compressors [BIST

Quite often built-in self-test (BIST) designs make use of multiple-input signature registers (MISR's) to compress the test data. Normally a MISR includes a stage for every signal that it is sampling. In some applications this leads to very wide MISR's that may include several hundred stages. Wide MISR's pose problems in terms of hardware and wiring overhead. Shorter compressors are, therefore, needed. This paper investigates the problem of shrinking an MISR so that it samples multiple signals at every stage. The ultimate shrinkage occurs when only the parity of the sampled signals is compressed. This is the case when a MISR is replaced by a single-input signature register (SISR). Issues like detection probability loss, test length penalty, and fault coverage degradation are some of the disadvantages that may arise from the MISR shrinkage. Minimizing the effect of these issues is a precondition to the success of this method.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>