scholarly journals Analysis of Low Probability of Detection Capability for Chaotic Standard Map-Based FH-OFDMA System

2021 ◽  
Vol 11 (5) ◽  
pp. 2198
Author(s):  
Junwoo Jung ◽  
Jaesung Lim ◽  
Sungyeol Park ◽  
Haengik Kang ◽  
Seungbok Kwon
Keyword(s):  
Detection Probability ◽  
Probability Of Detection ◽  
Lower Probability ◽  
Symbol Sequence ◽  
Ofdma System ◽  
Standard Map ◽  
Detection Probabilities ◽  
Low Probability ◽  
Transmitted Symbol ◽  
Basic Symbol

A frequency hopping orthogonal frequency division multiple access (FH-OFDMA) can provide low probability of detection (LPD) and anti-jamming capabilities to users against adversary detectors. To obtain an extreme LPD capability that cannot be provided by the basic symbol-by-symbol (SBS)-based FH pattern, we proposed two FH patterns, namely chaotic standard map (CSM) and cat map for FH-OFDMA systems. In our previous work, through analysis of complexity to regenerate the transmitted symbol sequence, at the point of adversary detectors, we found that the CSM had a lower probability of intercept than the cat map and SBS. It is possible when a detector already knows symbol and frame structures, and the detector has been synchronized to the FH-OFDMA system. Unlike the previous work, here, we analyze whether the CSM provides greater LPD capability than the cat map and SBS by detection probability using spectrum sensing technique. We analyze the detection probability of the CSM and provide detection probabilities of the cat map and SBS compared to the CSM. Based on our analysis of the detection probability and numerical results, it is evident that the CSM provides greater LPD capability than both the cat map and SBS-based FH-OFDMA systems.

scholarly journals Horizontal or vertical? Camera trap orientations and recording modes for detecting potoroos, bandicoots and pademelons

2014 ◽  
Vol 36 (1) ◽  
pp. 60 ◽  
Author(s):  
Brendan D. Taylor ◽  
Ross L. Goldingay ◽  
John M. Lindsay
Keyword(s):  
Detection Probability ◽  
New South ◽  
Population Monitoring ◽  
Camera Trap ◽  
Probability Of Detection ◽  
Camera Traps ◽  
North East ◽  
South Wales ◽  
Detection Probabilities ◽  
The Stability

Camera traps can detect rare and cryptic species, and may enable description of the stability of populations of threatened species. We investigated the relative performance of cameras oriented horizontally or vertically, and recording mode (still and video) to detect the vulnerable long-nosed potoroo (Potorous tridactylus) as a precursor to population monitoring. We established camera traps for periods of 13–21 days across 21 sites in Richmond Range National Park in north-east New South Wales. Each camera trap set consisted of three KeepGuard KG680V cameras directed at a bait container – one horizontal and one vertical camera in still mode and one horizontal camera in video mode. Potoroos and bandicoots (Perameles nasuta and Isoodon macrourus) were detected at 14 sites and pademelons (Thylogale stigmatica and T. thetis) were detected at 19 sites. We used program Presence to compare detection probabilities for each camera category. The detection probability for all three taxa groups was lowest for the vertical still and similar for the horizontal cameras. The detection probability (horizontal still) was highest for the potoroos (0.43) compared with the bandicoots (0.16) and pademelons (0.25). We estimate that the horizontal stills camera could achieve a 95% probability of detection of a potoroo within 6 days compared with 8 days using a vertical stills camera. This suggests that horizontal cameras in still mode have great potential for monitoring the dynamics of this potoroo population.

Correcting wildlife counts using detection probabilities

2005 ◽  
Vol 32 (3) ◽  
pp. 211 ◽  
Author(s):  
Gary C. White
Keyword(s):  
Detection Probability ◽  
Distance Sampling ◽  
Probability Of Detection ◽  
Mark Recapture ◽  
Additional Information ◽  
Release Studies ◽  
Detection Probabilities ◽  
Minimum Number ◽  
Capture And Release ◽  
Wildlife Populations

One of the most pervasive uses of indices of wildlife populations is uncorrected counts of animals. Two examples are the minimum number known alive from capture and release studies, and aerial surveys where the detection probability is not estimated from a sightability model, marked animals, or distance sampling. Both the mark–recapture and distance-sampling estimators are techniques to estimate the probability of detection of an individual animal (or cluster of animals), which is then used to correct a count of animals. However, often the number of animals in a survey is inadequate to compute an estimate of the detection probability and hence correct the count. Modern methods allow sophisticated modelling to estimate the detection probability, including incorporating covariates to provide additional information about the detection probability. Examples from both distance and mark–recapture sampling are presented to demonstrate the approach.

scholarly journals A Removal Model for Estimating Detection Probabilities From Point-Count Surveys

The Auk ◽  
2002 ◽  
Vol 119 (2) ◽  
pp. 414-425 ◽  
Author(s):  
George L. Farnsworth ◽  
Kenneth H. Pollock ◽  
James D. Nichols ◽  
Theodore R. Simons ◽  
James E. Hines ◽  
...  
Keyword(s):  
Detection Probability ◽  
Time Of Day ◽  
Probability Of Detection ◽  
Point Count ◽  
Great Smoky Mountains ◽  
Point Counts ◽  
Detection Probabilities ◽  
Acadian Flycatcher ◽  
Removal Model ◽  
Count Surveys

AbstractUse of point-count surveys is a popular method for collecting data on abundance and distribution of birds. However, analyses of such data often ignore potential differences in detection probability. We adapted a removal model to directly estimate detection probability during point-count surveys. The model assumes that singing frequency is a major factor influencing probability of detection when birds are surveyed using point counts. This may be appropriate for surveys in which most detections are by sound. The model requires counts to be divided into several time intervals. Point counts are often conducted for 10 min, where the number of birds recorded is divided into those first observed in the first 3 min, the subsequent 2 min, and the last 5 min. We developed a maximum-likelihood estimator for the detectability of birds recorded during counts divided into those intervals. This technique can easily be adapted to point counts divided into intervals of any length. We applied this method to unlimited-radius counts conducted in Great Smoky Mountains National Park. We used model selection criteria to identify whether detection probabilities varied among species, throughout the morning, throughout the season, and among different observers. We found differences in detection probability among species. Species that sing frequently such as Winter Wren (Troglodytes troglodytes) and Acadian Flycatcher (Empidonax virescens) had high detection probabilities (∼90%) and species that call infrequently such as Pileated Woodpecker (Dryocopus pileatus) had low detection probability (36%). We also found detection probabilities varied with the time of day for some species (e.g. thrushes) and between observers for other species. We used the same approach to estimate detection probability and density for a subset of the observations with limited-radius point counts.

Differences in microhabitat selection patterns between a remnant and constructed landscape following management intervention

2017 ◽  
Vol 44 (3) ◽  
pp. 248 ◽  
Author(s):  
Jose W. Valdez ◽  
Kaya Klop-Toker ◽  
Michelle P. Stockwell ◽  
Loren Fardell ◽  
Simon Clulow ◽  
...  
Keyword(s):  
Aquatic Vegetation ◽  
Management Strategies ◽  
Probability Of Detection ◽  
Microhabitat Use ◽  
Microhabitat Selection ◽  
Habitat Creation ◽  
Litoria Aurea ◽  
Use Of Resources ◽  
Detection Probabilities ◽  
Future Management

Context Achieving successful conservation outcomes in habitat creation and reintroductions requires an understanding of how species use their habitat and respond to these interventions. However, few initiatives directly compare microhabitat selection between remnant and managed habitats to measure effectiveness and evaluate outcomes. Probability of detection is also rarely included in studies on microhabitat use, which may lead to erroneous conclusions if detectability varies between variables. Methods In this study, we used the endangered green and golden bell frog (Litoria aurea) to compare differences in microhabitat-use patterns in both a remnant and a constructed habitat. A detectability study was also conducted to determine detection probabilities among microhabitats. Key results Aquatic vegetation was used more than expected in both the remnant and constructed habitats, and rock piles were utilised less than expected in the constructed habitat, despite their recommendation in most habitat templates. We found that detection probabilities altered the outcomes of abundance estimates for nearly all the measured microhabitat variables. Conclusions Future management for this species should focus on providing high proportions of aquatic vegetation. Furthermore, although rock piles have been utilised greatly in past L. aurea habitat creation, placing large rocks on a managed site is expensive and time consuming. Future management initiatives may need to focus on providing smaller proportion of rocks, which would be a more appropriate use of resources. Implications With conservation management projects increasing over the next few decades, understanding habitat use before implementing strategies should be a priority as it will provide important insights and inform decision-making for optimum habitat creation and restoration. Furthermore, accounting for detectability in microhabitat use studies is essential to avoid wrong conclusions that may negatively affect the success of ecological management strategies.

scholarly journals Detection of contaminant plumes released from landfills

2006 ◽  
Vol 3 (3) ◽  
pp. 819-857
Author(s):  
N. B. Yenigül ◽  
A.T. Hendsbergen ◽  
A. M. M. Elfeki ◽  
F. M. Dekking
Keyword(s):  
Simulation Model ◽  
Contaminant Transport ◽  
Analytical Model ◽  
Detection Probability ◽  
Average Concentration ◽  
Contaminant Plume ◽  
Contaminant Plumes ◽  
Heterogeneous Aquifer ◽  
Monitoring Well ◽  
Detection Probabilities

Abstract. Contaminant leaks released from landfills are a significant threat to groundwater quality. The groundwater detection monitoring systems installed in the vicinity of such facilities are vital. In this study the detection probability of a contaminant plume released from a landfill has been investigated by means of both a simulation and an analytical model for both homogeneous and heterogeneous aquifer conditions. The results of the two models are compared for homogeneous aquifer conditions to illustrate the errors that might be encountered with the simulation model. For heterogeneous aquifer conditions contaminant transport is modelled by an analytical model using effective (macro) dispersivities. The results of the analysis show that the simulation model gives the concentration values correctly over most of the plume length for homogeneous aquifer conditions, and that the detection probability of a contaminant plume at given monitoring well locations match quite well. For heterogeneous aquifer conditions the approximating analytical model based on effective (macro) dispersivities yields the average concentration distribution satisfactorily. However, it is insufficient in monitoring system design since the discrepancy between the detection probabilities of contaminant plumes at given monitoring well locations computed by the two models is significant, particularly with high dispersivity and heterogeneity.

Estimating detection probabilities of tagged fish migrating past fixed receiver stations using only local information

2010 ◽  
Vol 67 (4) ◽  
pp. 641-658 ◽  
Author(s):  
Michael C. Melnychuk ◽  
Carl J. Walters
Keyword(s):  
Detection Probability ◽  
Radio Telemetry ◽  
Model Parameters ◽  
Migration Routes ◽  
Mark Recapture ◽  
The Pacific ◽  
Detection Probabilities ◽  
Acoustic Tags ◽  
Attenuation Models ◽  
Local Detection

We developed a method to predict the probability of detecting acoustic tags crossing a receiver station using only detection information at that station. This method is suitable for acoustic or radio telemetry studies in which individually tagged animals migrate past fixed stations (where a station may consist of one or more receivers). It is based on fitting attenuation models to sequences of detections and missed transmissions of individually coded tags in fish migrating past stations of the Pacific Ocean Shelf Tracking Project (POST). We used estimated attenuation model parameters from detected fish at each station to predict the number of fish that crossed the station undetected, which in turn was used to calculate the local detection probability. This estimator was correlated (r = 0.54–0.81 in river and coastal habitats) with mark–recapture estimates of detection probability (pmr) that use nonlocal detection information at stations further along migration routes. This local detection probability estimate can be used as a covariate of pmr in mark–recapture models and can predict approximate values of pmr at final detection stations where pmr is not estimable because of the lack of recaptures further along migration routes.

scholarly journals Estimating Detection Probability and Density from Point-Count Surveys: A Combination of Distance and Double-Observer Sampling

The Auk ◽  
2006 ◽  
Vol 123 (3) ◽  
pp. 735-752 ◽  
Author(s):  
Michelle L. Kissling ◽  
Edward O. Garton
Keyword(s):  
Detection Probability ◽  
Survey Methods ◽  
Effective Area ◽  
Survey Method ◽  
Density Estimates ◽  
Point Count ◽  
Detection Probabilities ◽  
Fixed Radius ◽  
Count Surveys ◽  
Double Observer

Abstract Point counts are the method most commonly used to estimate abundance of birds, but they often fail to account properly for incomplete and variable detection probabilities. We developed a technique that combines distance and double-observer sampling to estimate detection probabilities and effective area surveyed. We applied this paired-observer, variable circular-plot (POVCP) technique to point-count surveys (n = 753) conducted in closed-canopy forests of southeast Alaska. Distance data were analyzed for each species to model a detection probability for each observer and calculate an estimate of density. We then multiplied each observer's density estimates by a correction factor to adjust for detection probabilities <1 at plot center. We compared analytical results from four survey methods: single-observer fixed-radius (50-m) plot; single-observer, variable circular-plot (SOVCP); double-observer fixed-radius (50-m) plot; and POVCP. We examined differences in detection probabilities at plot center, effective area surveyed, and densities for five bird species: Pacific-slope Flycatcher (Empidonax difficilis), Winter Wren (Troglodytes troglodytes), Golden-crowned Kinglet (Regulus satrapa), Hermit Thrush (Catharus guttatus), and Townsend's Warbler (Dendroica townsendi). Average detection probabilities for paired observers increased ≈8% (SE = 2.9) for all species once estimates were corrected for birds missed at plot center. Density estimators of fixed-radius survey methods were likely negatively biased, because the key assumption of perfect detection was not met. Density estimates generated using SOVCP and POVCP were similar, but standard errors were much lower for the POVCP survey method. We recommend using POVCP when study objectives require precise estimates of density. Failure to account for differences in detection probabilities and effective area surveyed results in biased population estimators and, therefore, faulty inferences about the population in question. Estimaciones de la Densidad y de las Probabilidades de Detección a Partir de Muestreos Utilizando Conteos en Puntos: Una Combinación de Muestreos de Distancia y de Doble Observador

scholarly journals Low Probability of Detection Communication: Opportunities and Challenges

2019 ◽  
Vol 26 (5) ◽  
pp. 19-25 ◽  
Author(s):  
Shihao Yan ◽  
Xiangyun Zhou ◽  
Jinsong Hu ◽  
Stephen V. Hanly
Keyword(s):  
Probability Of Detection ◽  
Low Probability
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