Maximum Detection Probability Research Articles

Efficient control of high-precision three-dimensional atom localization via probe absorption in a five-level phase-coherent atomic system

We propose a new scheme for high-precision three-dimensional (3D) atom localization by observing the spatially modulated absorption of a weak probe field operating in a partially closed-loop dependent five-level atomic system. Different spatial structures of localization patterns are presented by controlling the Rabi frequency, detuning, and field-induced collective phase-coherence with a variety of superposed standing wave field configurations. Our results highlight that 100% detection probability of atom is possible in the present model in many ways with high precision measurement of spatial absorption. It has been shown that, in the presence of standing wave fields, position information of the atom with maximum detection probability can be efficiently controlled by employing the travelling-wave field in the system. In the present work, we note that the maximum detection probability of the atom is attainable with the limit of spatial resolution better than λ/50. The efficacy of the present model is to find its application in atom nanolithography and atom-imaging having importance in quantum information processing.

A segmented trajectory planning and guidance method for hypersonic glide vehicles considering target detection performance

This paper proposes a closed-loop detection and guidance method for optimizing the detection effectiveness of Radio Frequency (RF) stealth targets by terminal-guided hypersonic glide vehicles (HGV) during the terminal-guidance phase. This method divides the guidance process into two stages based on the detectability of the target. In the search stage, the Twin Delayed Deep Deterministic Policy Gradient (TD3) coupled with Successive Convex Optimization (SCO) guidance method is employed. This approach separates the detection performance from the numerical solution process of the optimization problem, enabling online adjustment of performance indicator functions to refine trajectory configurations. In the tracking phase, the dynamic characteristics of the target's Radar Cross Section (RCS) are convexified and discretized, incorporated into the objective function of the optimization problem as performance indicators. The Higher-Order Soft-Trust-Region Sequential Convex Programming (HSSCP) method is employed to improve the convergence of the algorithm. Additionally, the online trajectory planning problem is integrated into the Model Predictive Control (MPC) framework to achieve closed-loop guidance. Simulation results indicate that this method can improve the detection performance of RF stealthy targets during the terminal guidance phase, the cumulative detection information for the target increased by approximately 20.6%, and the maximum instantaneous detection probability improved by about 14.3%. Moreover, it can achieve convergence of complex multi-constraint problems within a limited number of iterations.

Dynamic Spectrum Sensing For 5G Cognitive Radio Networks Using Optimization Technique

With increasing development of 5G technology, the rapid growth of various technologies and the growth of various wireless devices, demand for wireless spectrum becomes more urgent. Wireless communication technologies have been advancing rapidly, leading to the emergence of 5G communication systems .Spectrum sensing is the key model utilized to access the spectrum dynamically in CRN. Various researchers are done in spectrum sensing scenario and different methods are designed to perform the task of spectrum resource sharing. Most of the methods design a decision statistics for identifying the signal by analyzing the features of noise and signals. One promising approach to address the challenges of spectrum sensing in cognitive radio networks for 5G communication is the integration of deep learning with hybrid optimization techniques. By combining the power of deep learning algorithms with optimization methods, it becomes possible to improve the efficiency and accuracy of spectrum sensing in dynamic and diverse communication scenarios. One of the key advantages of MIMO-based spectrum sensing is its ability to exploit the spatial diversity inherent in the environment. By using multiple antennas at both the transmitter and receiver, MIMO systems can distinguish between signals arriving from different directions, thereby improving the accuracy of spectrum sensing. Moreover, MIMO technology also enables the cognitive radio network to utilize the available spectrum more efficiently. With the ability to establish multiple parallel communication links, MIMO-based cognitive radio networks can achieve higher data rates and improved spectral efficiency, especially in dynamic and challenging radio environments. Research in this field is focused on further enhancing the performance of MIMO-based spectrum sensing in cognitive radio networks through advanced signal processing algorithms and machine learning technique Spectrum sensing detect existence of primary users (PUs) and it becomes a main research topic of CRN in industry and academic domain. This research developed a new framework based on algorithm to progress the mechanism of spectrum sensing in the CRN by detecting the availability of free channel. The signal components are extracted from the received signal and thereby spectrum availability of detected through fusion center using proposed Feedback Artificial Optimization Algorithm-based Deep Q network. However, Simulation results show that the proposed multiple-input-multiple-output(MIMO) spectrum sensing method achieves good performance Cognitive Radion Network attains maximum probability of detection and minimum probability of false alarm as 70%, and 38% for Rayleigh channel.

Discrete search stability for a hidden target on M-intervals with a known probabilistic distributed effort

We consider a discrete search problem to detect a hidden target on one of the [Formula: see text]-intersected real lines at the origin. The target position is a vector of independent random variables with a known multivariate probability distribution that is symmetric around the origin. From the available information about the probability of the target, we can consider one bounded interval as a search space on each line. To maximize the detection probability, we divide each interval into a number of small subintervals. Some subintervals have low target probabilities; thus, we delete them and distribute their probabilities among the searched subintervals. The problem has a discrete version where each line contains a set of independent search subintervals. The search effort is bounded by a normal distribution. More than obtaining the maximum detection probability, we also need to get the minimum search effort to detect the target. After studying the stability of the minimum search effort, we present an illustrative example to show the effectiveness of our model.

The essential habitat role of a unique coastal inlet for a widely distributed apex predator.

Essential habitats support specific functions for species, such as reproduction, feeding or refuge. For highly mobile aquatic species, identifying essential habitats within the wider distribution range is central to understanding species ecology, and underpinning effective management plans. This study examined the movement and space use patterns of sevengill sharks (Notorynchus cepedianus) in Caleta Valdés (CV), a unique coastal habitat in northern Patagonia, Argentina. Seasonal residency patterns of sharks were evident, with higher detectability in late spring and early summer and lower during autumn and winter. The overlap between the residency patterns of sharks and their prey, elephant seals, suggests that CV functions as a seasonal feeding aggregation site for N. cepedianus. The study also found sexual differences in movement behaviour, with males performing abrupt departures from CV and showing increased roaming with the presence of more sharks, and maximum detection probability at high tide. These movements could be related to different feeding strategies between sexes or mate-searching behaviour, suggesting that CV may also be essential for reproduction. Overall, this study highlights the importance of coastal sites as essential habitats for N. cepedianus and deepens our understanding of the ecological role of this apex predator in marine ecosystems.

An Efficient Cooperative Spectrum Sensing Method Using Renyi Entropy Weighted Optimal Likelihood Ratio for CRN

The main concept behind employing cognitive radio is to enable secondary users (SUs) or unlicensed users to utilize the available spectrum. Spectrum sensing methods detect the existence of primary users (PUs) and have become the main topic of research in the CRN industry and in academia. This paper proposes a new framework based on the Adam gradient descent (Adam GD) algorithm to develop a spectrum sensing mechanism used in CRNs and detecting the availability of free channels. The signal's components are extracted from the received signal and the spectrum is searched for availability which is detected through a fusion center using the proposed algorithm. The proposed Adam GD algorithm attains the maximum detection probability rate and the minimum false alarm probability of 0.71 and 0.39, respectively, for a Rayleigh channel.

Propagation behavior of orbital angular momentum in vector anomalous vortex beams under maritime atmospheric turbulence

This study explores the propagation properties of orbital angular momentum (OAM) carried by a vector anomalous vortex beam (VAVB) in maritime atmospheric turbulence, utilizing the Rytov approximation. A comparative analysis is conducted between the VAVB and Laguerre-Gaussian beam, revealing that the VAVB exhibits a higher detection probability under specific circumstances. This suggests that the VAVB is more suitable for scenarios where maximizing detection probability is critical. The detection probability of the signal OAM mode is affected by the characteristics of maritime atmospheric turbulence and propagation distance, but can be significantly improved by manipulating beam parameters such as wavelength, beam order, beam waist, and quantum number, while considering the characteristics of maritime atmospheric turbulence. Hence, the use of VAVB has the potential to facilitate reliable optical communication in challenging maritime environments.

The maximum entropy principle in search theory

The paper considers the relationship between search theory and information theory. The traditional problem of search theory is to develop a search plan for a physical object in the sea or on land. The search plan has to develop the distribution of available search resources in such a way that the probability of detection the search object is to be maximum. The optimal solution is traditionally considered as so-called "uniformly optimal search plan", which provides a uniform distribution of the posterior probability of the location of the object as the search is conducted. At the same time, optimality simultaneously according to the criteria of maximum detection probability and equality of a posteriori probability is possible only for the exponential detection function, which is used most often in search theory. For other kinds of detection functions, the optimal solutions according to the specified criteria do not match. In this paper, the approach to this problem is considered on the basis of the maximum entropy principle. For a situation of discrete distribution, it is shown that, within the framework of information theory, the search problem has a simpler solution that does not depend on the kind of the detection function.

Cyclostationary Algorithm for Signal Analysis in Cognitive 4G Networks with Spectral Sensing and Resource Allocation

Cognitive Radio (CR) effectively involved in the management of spectrum to perform improved data transmission. CR system actively engaged in the data sensing, learning and dynamic adjustment of radio spectrum parameters with management of unused spectrum in the signal. The spectrum sensing is indispensable in the CR for the management of Primary Users (PUs) and Secondary users (SUs) without any interference. Spectrum sensing is considered as the effective adaptive signal processing model to evaluate the computational complexity model for the signal transmission through Matched filtering, Waveform and Cyclostationary based Energy sensing model. Cyclostationary based model is effective for the energy based sensing model based on unique characteristics with estimation of available channel in the spectrum to extract the received signal in the PU signal. Cyclostationary based model uses the spectrum availability without any periodic property to extract the noise features. This paper developed a Adaptive Cross Score Cyclostationary (ACSCS) to evaluate the spectrum sensing in the CR network. The developed ACSCS model uses the computational complexity with estimation of Signal-to-Interference-and-Noise Ratio (SINR) elimination of cost function. ACSCS model uses the Adaptive Least square Spectral Self-Coherence Restoral (SCORE) with the Adaptive Cross Score (ACS) to overcome the issues in CR. With the derived ACSCS algorithm minimizes the computational complexity based on cost function compared with the ACS algorithm. To minimize the computational complexity pipeline triangular array based Gram-Schmidt Orthogonalization (GSO) structure for the optimization of network. The simulation performance analysis with the ACSCS scheme uses the Rician Multipath Fading channel to estimate detection probability to sense the Receiver Operating Characteristics, detection probability and probability of false alarm using Maximum Likelihood (ML) detector. The ACSC model uses the Square-law combining (SLC) with the moment generation function in the multipath fading channel for the channel sensing with reduced computational complexity. The simulation analysis expressed that ACSC scheme achieves the maximal detection probability value of 1. The analysis expressed that proposed ACSC scheme achieves the improved channel estimation in the 4G communication environment.

Data-driven transmit weight matrix design method for target detection of the TS-FDA-MIMO radar

Recently, reinforcement learning (RL) has attracted more and more attention in research, and this method is implemented in different fields as an optimizer. In this paper, we apply RL to design the transmit weight matrix so as to obtain the optimal target detection performance, i.e., maximum detection probability. Firstly, the data model of transmit subaperturing frequency diverse array multiple-input multiple-output (TS-FDA-MIMO) radar is proposed. Next, we utilize the Neyman-Pearson (NP) criterion to deduce the detection probability of the TS-FDA-MIMO radar, and the transmit weight matrix design optimization problem is proposed. In addition, we construct an adaptive transmit weight matrix design framework based on the maximum detection probability and introduce the deep deterministic policy gradient (DDPG) algorithm of RL to the optimization controller in the proposed framework. The memory bank is used to store position information of the motive target, and the prior information is used in off-line training of the actor and critic networks. After network training, the current target state information is input into the RL network to obtain the output transmit weight matrix, and the optimization of the transmit weight matrix is completed. Finally, the simulation results verify the effectiveness of the proposed adaptive transmit weight matrix design framework based on RL.

A search method for a hypersonic gliding vehicle based on early warning information guidance

The high speed and high manoeuvrability of a hypersonic gliding vehicle (HGV) pose a severe challenge to the existing early warning detection system. Optimising the search resources of a phased array radar to improve the HGV detecting efficiency has become a practical problem. In this study, a HGV search method based on a hybrid optimisation algorithm is proposed with the early warning information guidance as a priori condition. Firstly, the HGV priority judgement model is established, and the priority quantification equations for height, velocity and distance are designed. Secondly, the search parameters of the radar are optimised with the maximum cumulative detection probability, the shortest average discovery time and the highest priority level as the objective functions, and a radar search model is established. Finally, to overcome the problem that particle swarm optimisation (PSO) is easy to fall into local optimal solution, a hybrid optimisation algorithm based on differential evolution (DE) and PSO is proposed. The performance of the proposed method is verified in two simulation scenarios, and the results show that the proposed method outperforms existing mainstream search methods and can reasonably allocate search resources according to the priority of HGV.

Maximum Probability of Detection of a Broadband Phase-Shift Keyed Signal with Allowance for Distortions in the Single-Loop Input Circuit of a Receiver

Maximum Probability of Detection of a Broadband Phase-Shift Keyed Signal with Allowance for Distortions in the Single-Loop Input Circuit of a Receiver

On Optimality of Deterministic Rules in Adversarial Bayesian Detection

We consider a Bayesian binary detection problem in the presence of an adversary. The adversary is assumed to be capable of falsifying the observation, under a norm constraint, such that a detector will make a decision based on the falsified observation. We consider a sequential game between the adversary and the defender. The defender first determines the detection rule. Then, given the knowledge of the detection rule, the adversary determines the attack strategy to falsify the observation such that the probability of detection based on the falsified observation can be minimized. We prove that there exists a deterministic, optimal detection rule that the defender can use to achieve the maximum detection probability. We further prove that the maximum detection probability can be achieved by employing a deterministic detection rule where the decision is made based on proximity of the observation to a certain set.

A Complete Mission Concept Design and Analysis of the Student-Led CubeSat Project: Light-1

Terrestrial gamma ray flashes (TGF) are intense and prompt bursts of X- and gamma-rays of up to 100 MeV of energy. Typically associated with thunderstorm activity, TGFs are produced by bremsstrahlung effects of electrons accelerated in strong electric fields generated by lightning. TGFs can be effectively targeted by gamma detectors with enhanced time stamping capabilities onboard of satellites operating at near-Earth low obits (LEO). Light-1 is a miniature satellite, a 3U CubeSat designed to detect, monitor and study terrestrial gamma ray flashes in low Earth orbit. The two payload detectors are composed of a photomultiplier tube and silicon photomultipliers. The two detectors are mounted at two ends of the CubeSat and the proposed orientation of the CubeSat will ensure maximum TGF detection probability. To allow an increased frequency of data downlink, Khalifa University has collaborated with NanoAvionics Corp, and hence Light-1 has access to three ground stations situated across the map, Abu Dhabi in United Arab Emirates, Vilnius in Lithuania, and Aalborg in Denmark. The satellite expected to launch in late-2021 is currently in its assembly and integration phase. This paper describes mission, concept, objectives, success criteria, design, analysis, status, and the future plans of Light-1 satellite.

Cooperative spectrum sensing optimization for cognitive radio in 6 G networks

The upcoming sixth generation (6 G) systems can meet the high user demands. The existing communication systems are becoming inefficient in meeting the user demands. The multifold growth in the usage of high-definition multimedia applications requires new capabilities. The users are looking forward to high throughput and low latency. The shift from 5 G to 6 G networks is since the 6 G networks are expected to combine the terrestrial, aerial, and maritime communications into a robust network. This will provide the users a faster network with high reliability, accommodation to a larger number of users, and ultra-low latency. However, the limited availability of spectrum is a bottleneck in enhancing the user experience. Therefore, advanced techniques like cognitive radios and cooperative spectrum sensing are critical in the design of future network. The optimal usage and management of the available spectrum is significant for the performance of the network. In this paper, a cooperative spectrum sensing technique using Manta Ray Foraging Algorithm (MRFO) is proposed. The weighting vector at the fusion center is optimized using MRFO. The allocation of the spectrum is done using the optimal weight vector for secondary users. The proposed work aims at finding the maximum probability of detection. Probability of detection is significant in spectrum sensing. The channel needs to be sensed for the presence or absence of primary users. If the detection probability is maximized, then the channel usage efficiency will increase. The proposed method is compared with other state of the art methods. The results show that MRFO can be used efficiently for spectrum sharing by cognitive radios.

The state, transport, and fate of aboveground terrestrial arthropod eDNA

AbstractEnvironmental DNA (eDNA) analyses have become invaluable for detecting and monitoring aquatic and terrestrial species and assessing site biodiversity within aquatic environments or soil. Recent studies have extended these techniques by using eDNA to identify the presence of aboveground terrestrial arthropods directly from vegetative surfaces. However, while the dynamics of eDNA state, transport, and fate (its “ecology”) have been explored within aquatic environments and soil, they have yet to be explored within aboveground terrestrial systems. Here, we explore the ecology of terrestrial eDNA deposited by fluid‐feeding arthropods on leaf surfaces. We carried out a series of experiments to evaluate the optimal filter pore size for intracellular eDNA collection, how eDNA is affected by rain events, and its degradation rate under different solar radiation conditions. We found that the captured concentration of intracellular eDNA was not significantly affected by an increase in filter pore size, suggesting a wide range of viable pore size options exist for targeting intracellular eDNA. We also found extracellular eDNA from fluid excrement degrades more rapidly than intracellular when exposed to solar radiation, indicating the latter is a more viable target for collection. Finally, we identified that rainfall or mist will remove most terrestrial eDNA present on vegetation surfaces. We provide researchers and environmental managers key insights into successfully designing and carrying out aboveground terrestrial arthropod eDNA surveys that maximize detection probability.

Evaluation and Hydrological Application of a Data Fusing Method of Multi-Source Precipitation Products-A Case Study over Tuojiang River Basin

Precipitation is an essential driving factor of hydrological models. Its temporal and spatial resolution and reliability directly affect the accuracy of hydrological modeling. Acquiring accurate areal precipitation needs substantial ground rainfall stations in space. In many basins, ground rainfall stations are sparse and uneven, so real-time satellite precipitation products (SPPs) have become an important supplement to ground-gauged precipitation (GGP). A multi-source precipitation fusion method suitable for the Soil and Water Assessment Tool (SWAT) model has been proposed in this paper. First, the multivariate inverse distance similarity method (MIDSM) was proposed to search for the optimal representative precipitation points of GGP and SPPs in sub-basins. Subsequently, the correlation-coefficient-based weighted average method (CCBWA) was presented and applied to calculate the fused multi-source precipitation product (FMSPP), which combined GGP and multiple satellite precipitation products. The effectiveness of the FMSPP was proven over the Tuojiang River Basin. In the case study, three SPPs were chosen as the satellite precipitation sources, namely the Climate Forecast System Reanalysis (CFSR), Tropical Rainfall Measuring Mission Project (TRMM), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network Climate Data Record (PERSIANN-CDR). The evaluation indicators illustrated that FMSPP could capture the occurrence of rainfall events very well, with a maximum Probability of Detection (POD) and Critical Success Index (CSI) of 0.92 and 0.83, respectively. Furthermore, its correlation with GGP, changing in the range of 0.84–0.96, was higher in most sub-basins on the monthly scale than the other three SPPs. These results demonstrated that the performance of FMSPP was the best compared with the original SPPs. Finally, FMSPP was applied in the SWAT model and was found to effectively drive the SWAT model in contrast with a single precipitation source. The FMSPP manifested the highest accuracy in hydrological modeling, with the Coefficient of Determination (R2) of 0.84, Nash Sutcliff (NS) of 0.83, and Percent Bias (PBIAS) of only −1.9%.

Effects of camera‐trap placement and number on detection of members of a mammalian assemblage

AbstractA central goal in camera‐trapping (CT) studies is to maximize detection probability and precision of occupancy estimates while minimizing the number of CTs to reduce equipment and labor costs. Few studies, however, have examined the effect of CT number on detection probability. Moreover, historically, most studies focused on a specific species and the design could be tailored toward maximizing detection of this target species. Increasingly, however, such studies use data for all captured, non‐target, species (by‐catch data) for animal community‐level analyses. It remains unclear if, and how, the targeting of CTs toward one species affects the detection of non‐target species. We paired CTs from a permanent camera‐trapping grid (with 38 CTs) targeted at monitoring Eurasian lynx (Lynx lynx) in Innlandet County, Norway, with additional randomly placed CTs at two spatial scales (38 CTs within the same habitat patch and 38 CTs within the same 50‐km2 grid cell as the lynx‐targeted CTs) for three months. We combined multi‐scale occupancy models that enable the separation of large‐scale occupancy, CT‐scale site use, and detection probability with single‐scale occupancy models. This allowed us to study the effects of targeted placement and CT number on the detection probability of the target species (lynx) and seven non‐target mammal species (four carnivores, three herbivores, and one rodent). We found that all species, except moose (Alces alces), had the highest detection probability at lynx‐targeted CTs. Moose had equal detection probabilities at all three placement types. Adding extra CTs generally increased detection probabilities. Consequently, for all species, combining a lynx‐targeted CT with one or more randomly placed CTs, increased the accuracy and precision of occupancy estimates for 50‐km2 grid cells compared to single CT estimates. The placement of single CTs underestimated grid‐cell occupancy compared to known minimum occupancy and were similar to site‐use probability estimates of multi‐scale models. It is, however, uncertain to which spatial extent these site‐use probabilities refer. We therefore recommend the use of multiple (targeted) CTs to estimate occupancy in large grid cells and to interpret occupancy estimates from single CTs as site use of an, as of yet undefined, area surrounding the CT.

On Accurate Source Enumeration: A New Bayesian Information Criterion

This work addresses the issue of source number detection in the general asymptotic regime where the numbers of antennas and samples both tend to infinity but their ratio converges to a constant. Among the information criteria for source enumeration, Bayesian information criterion (BIC) is able to provide an elegant link between detection probability and a posterior probability. That is, maximizing detection probability amounts to maximizing a posterior probability which consists of the likelihood function (LF), a prior probability function (PPF) and penalty function (PF). Unfortunately, the PPF does not converge to a constant in the general regime, which is ignored in the existing information criteria, leading to considerable performance degradation in source enumeration. On the other hand, the PF relies on free parameters, which, however, cannot be accurately determined in the state-of-the-art approaches. To fill the performance gaps, this work calculates the PPF by utilizing random matrix theory, and derives the PF by re-organizing the parameter vector, leading to an accurate BIC. Moreover, it is proven that the proposed BIC is able to correctly detect the source number with probability one in the general asymptotic regime. Simulation results confirm our theoretical computations and validate the superiority of the proposed detection approach over the state-of-the-art schemes.

Optimization of Radar Parameters for Maximum Detection Probability Under Generalized Discrete Clutter Conditions Using Stochastic Geometry

We propose an analytical framework based on stochastic geometry (SG) formulations to estimate a radar's detection performance under generalized discrete clutter conditions. We model the spatial distribution of discrete clutter scatterers as a homogeneous Poisson point process and the radar cross-section of each extended scatterer as a random variable of the Weibull distribution. Using this framework, we derive a metric called the radar detection coverage probability as a function of radar parameters such as transmitted power, system noise temperature and radar bandwidth; and clutter parameters such as clutter density and mean clutter cross-section. We derive the optimum radar bandwidth for maximizing this metric under noisy and cluttered conditions. We also derive the peak transmitted power beyond which there will be no discernible improvement in radar detection performance due to clutter limited conditions. When both transmitted power and bandwidth are fixed, we show how the detection threshold can be optimized for best performance. We experimentally validate the SG results with a hybrid of Monte Carlo and full wave electromagnetic solver based simulations using finite difference time domain (FDTD) techniques.