Low Probability Of Detection Research Articles

Imperfect detection in plant populations can cause misestimates of demographic rates and missed population trends: The case for Astragalus microcymbus Barneby

Abstract Stage‐structured population models are widely used for plant demographic studies to assess population dynamics. Over the last several decades, there have been advancements in mark‐recapture methods in animal systems, but little to no use in plants because of the assumption that all individuals available for capture are seen. We examine population trends and demographic rates of a long‐lived forb derived from conventional matrix population models compared with mark‐recapture methods. We found a low probability of detection, primarily due to vegetative dormancy. While both models account for dormancy, only the mark‐recapture method detected a population trend. Elasticity describes the effects of changes in vital rates on the population growth rate. Similar elasticity values are thought to buffer populations from environmental stochasticity. Vital rate estimates differed between methods, resulting in a higher growth rate with conventional methods but greater evenness among vital rate importance for the mark‐recapture method. Synthesis and applications. This study highlights that long‐term demographic viability studies that have not addressed detectability can be revisited with minimal additional data to correct for imperfect detection. Plant studies have traditionally assumed perfect detection and may have missed trends, risk indicators, and conservation opportunities for rare plant populations.

Quantum Frequency Combs with Path Identity for Quantum Remote Sensing

Quantum sensing promises to revolutionize sensing applications by employing quantum states of light or matter as sensing probes. Photons are the clear choice as quantum probes for remote sensing because they can travel to and interact with a distant target. Existing schemes are mainly based on the quantum illumination framework, which requires quantum memory to store a single photon of an initially entangled pair until its twin reflects off a target and returns for final correlation measurements. Existing demonstrations are limited to tabletop experiments, and expanding the sensing range faces various roadblocks, including long-time quantum storage and photon loss and noise when transmitting quantum signals over long distances. We propose a novel quantum sensing framework that addresses these challenges using quantum frequency combs with path identity for remote sensing of signatures (“qCOMBPASS”). The combination of one key quantum phenomenon and two quantum resources—namely, quantum-induced coherence by path identity, quantum frequency combs, and two-mode squeezed light—allows for quantum remote sensing without requiring quantum memory. The proposed scheme is akin to a quantum radar based on entangled frequency-comb pairs that uses path identity to detect, range, or sense a remote target of interest by measuring pulses of one comb in the pair that never traveled to the target but that contains target information “teleported” by quantum-induced coherence by path identity from the other comb in the pair that traveled to the target but is not detected. We develop the basic qCOMBPASS theory, analyze the properties of the qCOMBPASS transceiver, and introduce the qCOMBPASS equation—a quantum analog of the well-known LIDAR equation in classical remote sensing. We also describe an experimental scheme to demonstrate the concept using two-mode squeezed quantum combs. qCOMBPASS can strongly impact various applications in remote quantum sensing, imaging, metrology, and communications. These applications include detection and ranging of low-reflectivity objects, measurement of small displacements of a remote target with precision beyond the standard quantum limit (SQL), standoff hyperspectral quantum imaging, discreet surveillance from space with low detection probability (detect without being detected), very-long-baseline interferometry, quantum Doppler sensing, quantum clock synchronization, and networks of distributed quantum sensors. Published by the American Physical Society 2024

Effectiveness of canine‐assisted surveillance and human searches for early detection of invasive spotted lanternfly

AbstractPrevention and early detection of invasive species are championed as the most cost‐effective and efficient strategies for reducing or preventing negative impacts on ecosystems. Spotted lanternfly (SLF), Lycorma delicatula, is a recently introduced invasive insect whose range in the United States has been expanding rapidly since it was first discovered in Pennsylvania in 2014. Feeding by this planthopper can cause severe impacts on agricultural production, particularly grapes (Vitis spp.). Human visual surveys are the most common search method employed for detection but can be ineffective due to the insect's cryptic egg masses and low density during early stages of infestation. Therefore, finding alternative early detection methods has become a priority for agencies tasked with addressing SLF management. This study experimentally tested whether trained detector dogs could improve the probability of detecting SLF in both agricultural and forest settings. We surveyed transects in 20 vineyards and their adjacent wooded areas in Pennsylvania and New Jersey, USA, and used a multiscale occupancy model to estimate detection probability achieved by human observers and detection dogs as a function of SLF infestation level, weather, and habitat covariates. We modeled transect‐level occupancy of SLF as a function of infestation level, habitat type, topographic position index, and distance to forests. Occupancy probability of SLF was higher on vines within vineyards than in forests, and occupancy declined with increasing distance from forests, which is informative for future search efforts. Detection probability of SLF was lower at forested sites but was higher at high infestation sites. Detection dogs had a lower detection probability than humans in the vineyards, but the detection probability of dogs was >3× greater than that of humans in forested sites. Our study suggests that detection dogs are more effective than human visual searches as an early detection method for SLF in forested areas, and utilizing detector dogs could strengthen SLF early detection efforts. This study demonstrates the potential applicability of using canine‐assisted search strategies combined with occupancy models to enhance the surveillance and prevention of other difficult‐to‐detect invasive species.

Leveraging local wildlife surveys for robust occupancy trend estimation

Natural resource agencies are frequently tasked with monitoring populations of at-risk species to ensure management activities do not negatively affect the viability of wildlife populations. Typically, these monitoring efforts evaluate trends in a population’s abundance, occupancy, or geographic distribution. Often, surveys provide local information, but results are generally not incorporated into broad-scale monitoring efforts that focus on range-wide population changes due to their variable nature in both spatial extent and effort. We investigated whether aggregating these local (hereafter “variable”) surveys can generate enough statistical power to estimate broad-scale population trends using simulations of declining populations of fishers (Pekaniapennati) over a 10-year time horizon. Our simulations included three population sizes which we refer to as abundant, common, and rare (N0 = 700, 350, and 100 individuals, respectively) with each declining at a rapid and moderate pace (λ = 0.933, and 0.977, respectively). For each population, we simulated variable surveys using an occupancy framework to subsample the population with parameters that mimic combining multiple independent monitoring efforts which vary annually in location, and effort. Regardless of spatial consistency of annual sampling, there was minimal variation in statistical power under both high and low detection probability simulations. However, when sampling effort varied each year, statistical power was lower for most populations and sampling scenarios when compared to consistent sampling effort unless some baseline level of sampling effort was reliably achieved in all years. In many cases, adding low-level consistent baseline sampling to variable surveys resulted in statistical power close to that of consistent sampling efforts. Our results suggest statistical power is driven by annual consistency in the proportion of landscape sampled rather than spatial consistency in sampling locations. This result indicates that current variable surveys could be leveraged and combined to detect population declines for at-risk species at broad-scales if a baseline proportion of landscape is robustly sampled. The level of baseline sampling is highly dependent on population size and magnitudes of population change. In simulations with a common or abundant population experiencing a rapid decline, a baseline survey effort of at least 5% of the landscape in combination with variable surveys resulted in statistical power consistently above the standard threshold of 0.80 for occupancy monitoring. Leveraging existing local efforts to achieve high detection probability and baseline sampling would reduce financial and logistical burdens of broad-scale wildlife monitoring efforts.

Identification of theft in a power distribution network: A tree-based approach

Electricity is an essential commodity, yet it is not freely available to everyone in the country due to widespread electricity theft. This theft results in significant financial losses for Distribution Companies (DISCOMS), hindering their ability to provide reliable services to consumers. Although there are methods to curb electricity theft, DISCOMS have struggled to control it effectively. The most successful current method is the manual investigation of nodes; however, it has its drawbacks, such as a low probability of theft detection when nodes are randomly sampled and excessive time consumption when all nodes are investigated. To address these issues, a systematic approach utilizing the capture, storage, and analysis of data is essential. This paper presents a coloring-based tree approach to identify power theft without altering the existing network infrastructure. The proposed approach aims to reduce the dataset for manual inspection, thereby increasing the probability of theft detection while substantially reducing investigation time. By discouraging blind checking of nodes and providing a basis for targeted node inspections, this method benefits both DISCOMS and general consumers. In this paper, the existing methods of theft detection have been compared with the proposed approach of tree-based theft detection. The application of the proposed approach removed certain nodes from the set of nodes to be investigated for theft detection.

Adaptive stochastic resonance enhanced weak linear frequency modulated signal perception in low signal-to-noise ratio environments

Abstract The linear frequency modulated (LFM) signal has gained extensive utilization in radar, sonar and covert communication system due to its large time-bandwidth product, high-precision distance measurement and low detection probability. The perception of weak LMF signals holds significant importance yet encounters substantial challenges within the increasing complexity of the electromagnetic environments. Consequently, this paper proposes an adaptive stochastic resonance (ASR) enhanced approach for perceiving weak LFM signals in low signal-to-noise ratio (SNR) conditions. This approach initially commences a pioneering study on the quantitative synergistic resonance mechanism among LFM signals, random noise, and nonlinear stochastic resonance (SR) systems. Subsequently, the ASR system’s implementation becomes straightforward through the adaptive adjustment of SR system parameters according to the LFM signal and noise characteristics. This implementation leverages the inherent property of noise energy transfer to signal energy, facilitating the enhancement of ordered weak signals via random noise. Following the enhancement of the output signal by the ASR system, the Wigner–Ville distribution (WVD) transform’s time-frequency concentration property is employed to extract the time-frequency characteristics. Building on the WVD transform, the Radon transform with linear integral projection is applied to further harness the time-frequency domain energy concentration, thereby achieving the effective perception of weak LFM signals. Finally, the effectiveness of the proposed method in improving the perception performance of weak LFM signal in very low SNR conditions is verified through numerical simulations. It is anticipated that this method will have potential application value in fields such as radar, sonar, and communication under complex electromagnetic environments.

Concept of economic crimes in legal doctrine and legislation, their differentiation

Ukraine’s transition to a market economy, the development of entrepreneurship, the promotion of competition, and the expansion of foreign trade contributed to the creation of new opportunities for criminal activity in the economic sphere. At the same time, the low probability of detection and the high profits associated with economic crime make the economic sphere very attractive to criminals. In turn, the huge losses associated with economic crime undermine social protection systems and destabilize economic systems, thus clearly indicating a failure of self-regulation. The legislator, having in mind economic crimes, operates with the concept of “CRIMINAL OFFENSES IN THE SPHERE OF ECONOMIC ACTIVITY” (Chapter VII of the Criminal Code of Ukraine) and is limited only to their list. According to the legislative approach, an economic crime can be defined only as a socially dangerous culpable act (action or inaction) committed by the subject of the criminal offense provided for by the Criminal Code of Ukraine and provided for by the provisions of Chapter VII of the Special Part of the Criminal Code. At the same time, on the basis of the generalized data of the legislative and doctrinal approach, it is proposed to define an economic crime as a socially dangerous culpable act (action or inaction) committed by the subject of a criminal offense, which consists in the implementation of illegal economic activity (economic processes, relations, operations) or violation of requirements for legal economic activity to satisfy the selfish interests of a person or a group of persons, encroaches on the sphere of economic relations and poses a threat to the economic security of the state, leads to an imbalance of the state’s economic system. At the same time, the so-called economic crimes (Chapter VII of the Criminal Code of Ukraine - criminal offenses in the sphere of economic activity) should be separated from crimes due to which law enforcement agencies can interfere in the sphere of economic relations. Economic crimes can be differentiated according to various criteria: according to the spheres of social relations that regulate one or another type of economic activity; according to the content of the committed crime; by immediate main and additional objects; by subject characteristics; by criminal law characteristics.

European eel (Anguilla anguilla) survival modeling based on a 22-year capture-mark-recapture survey of a Mediterranean subpopulation.

Since the 1980s, the European eel (Anguilla anguilla) has declined by over 90% in recruitment across its European and North African distribution area. This diadromous fish spawns at sea and migrates into continental waters, where it grows for three to more than 30 years, depending on habitat conditions and location. During their growth, different habitat use tactics can locally influence the life-history traits of eels, including their survival rates. Thus, the spatio-temporal dimension of this species is crucial for management. Based on a rare Mediterranean long-term survey of more than 20 years (2001-2022) in an artificial drainage canal connected to a vast brackish lagoon (the Vaccarès lagoon), we aimed to study the dynamics of one subpopulation's life-history traits. We used Bayesian multistate capture-mark-recapture (CMR) models to assess the temporal variability in survival and abundance at both seasonal and inter-annual scales, considering life-stage structure. High survival rates and low detection probabilities were found for the undifferentiated and female yellow stages. In contrast, female silver eels exhibited lower survival rates and higher capture probabilities. Estimating detection probabilities and survival rates enabled accurate assessment of relative abundance across different life stages and time periods. Our findings indicated a substantial decrease in the abundance of undifferentiated and female yellow eels in the early 2000s, whereas the abundance of female silver eels remained consistently low yet stable throughout the study period. Considering the life stage seemed essential to study the dynamics of the eel during its continental growing period. The present results will provide key elements to propose and implement suitable sustainable environmental management strategies for eel conservation.

High-redshift Merger Model for Low-frequency Gravitational Wave Background

In 2023, the Pulsar Timing Array Collaborations announced the discovery of a gravitational wave background (GWB), predominantly attributed to supermassive black hole binary (SMBHB) mergers. However, the detected GWB is several times stronger than the default value expected from galactic observations at low and moderate redshifts. Recent findings by the James Webb Space Telescope have unveiled a substantial number of massive, high-redshift galaxies, suggesting more massive SMBHB mergers at these early epochs. Motivated by these findings, we propose an “early merger” model that complements the standard merger statistics by incorporating these early, massive galaxies. We compare the early and standard “late merger” models, which assume peak merger rates in the local Universe, and match both merger models to the currently detected GWB. Our analysis shows that the early merger model has a significantly lower detection probability for single binaries and predicts a ∼30% likelihood that the first detectable single source will be highly redshifted and remarkably massive with rapid frequency evolution. In contrast, the late merger model predicts a nearly monochromatic first source at low redshift. The future confirmation of an enhanced population of massive high-redshift galaxies and the detection of fast-evolving binaries would strongly support the early merger model, offering significant insights into the evolution of galaxies and SMBHs.

Density of a cryptic Australian small mammal: The threatened Julia Creek dunnart (Sminthopsis douglasi).

Globally, hundreds of mammal species face the threat of extinction in the coming decades, and in many cases, their ecology remains poorly understood. Fundamental ecological knowledge is crucial for effective conservation management of these species, but it is particularly lacking for small, cryptic mammals. The Julia Creek dunnart (Sminthopsis douglasi), a threatened, cryptic carnivorous marsupial that occurs in scattered populations in the central west of Queensland, Australia, was once so poorly studied that it was believed extinct. Sporadic research since its rediscovery in the early 1990s has revealed that S. douglasi is distributed across land at risk from many threats. Fundamental knowledge of S. douglasi population density is urgently required to inform conservation management at key sites, yet the species has historically proven hard to detect. Indeed, the status of the largest known population of S. douglasi, in Bladensburg National Park, is unknown. Here, we conducted a population study on S. douglasi at two sites within Bladensburg National Park via live mark-recapture surveys during 2022 and 2023. From likelihood-based spatially explicit capture-recapture (SECR) modelling we provide the first estimates of density and population size for S. douglasi. Live trapping resulted in captures of 49 individual S. douglasi (with 83 captures total, including recaptures). We estimated S. douglasi to occur at a density of 0.38 individuals ha-1 (0.25-0.58) at one site and 0.16 individuals ha-1 (0.09-0.27) at another site, with an estimated mean population size in suitable habitat at Bladensburg National Park of 1211 individuals (776-1646). Our S. douglasi density estimates were similar to that reported for other threatened small mammals in Australia. We also found evidence of extreme S. douglasi population fluctuations over time at Bladensburg National Park, which is of concern for its future conservation. Our study has provided the first estimate of density for S. douglasi, a threatened dasyurid species from the Mitchell Grass Downs of central western Queensland, Australia. Our research provides crucial population data to assist the management of this poorly studied species. We demonstrate a method that can be applied to species with low detection probability to ultimately help address the mammal extinction crisis faced by Australia and the rest of the world.

Improved YOLOv5 Network for Aviation Plug Defect Detection

Ensuring the integrity of aviation plug components is crucial for maintaining the safety and functionality of the aerospace industry. Traditional methods for detecting surface defects often show low detection probabilities, highlighting the need for more advanced automated detection systems. This paper enhances the YOLOv5 model by integrating the Generalized Efficient Layer Aggregation Network (GELAN), which optimizes feature aggregation and boosts model robustness, replacing the conventional Convolutional Block Attention Module (CBAM). The upgraded YOLOv5 architecture, incorporating GELAN, effectively aggregates multi-scale and multi-layer features, thus preserving essential information across the network’s depth. This capability is vital for maintaining high-fidelity feature representations, critical for detecting minute and complex defects. Additionally, the Focal EIOU loss function effectively tackles class imbalance and concentrates the model’s attention on difficult detection areas, thus significantly improving its sensitivity and overall accuracy in identifying defects. Replacing the traditional coupled head with a lightweight decoupled head improves the separation of localization and classification tasks, enhancing both accuracy and convergence speed. The lightweight decoupled head also reduces computational load without compromising detection efficiency. Experimental results demonstrate that the enhanced YOLOv5 architecture significantly improves detection probability, achieving a detection rate of 78.5%. This improvement occurs with only a minor increase in inference time per image, underscoring the efficiency of the proposed model. The optimized YOLOv5 model with GELAN proves highly effective, offering significant benefits for the precision and reliability required in aviation component inspections.

Multi‐Task Learning for Tornado Identification Using Doppler Radar Data

AbstractTornadoes, as highly destructive weather events, require accurate detection for effective decision‐making. Traditional radar‐based tornado detection algorithms (TDA) face challenges with limited tornado feature extraction capabilities, leading to high false alarm rates and low detection probabilities. This study introduces the Multi‐Task Identification Network (MTI‐Net), leveraging Doppler radar data to enhance tornado recognition. MTI‐Net integrates tornado detection and estimation tasks to acquire comprehensive spatial and locational information. As part of MTI‐Net, we introduce a novel backbone network of Multi‐Head Convolutional Block (MHCB), which incorporates Spatial and Channel Attention Units (SAU and CAU). SAU optimizes local tornado feature extraction, while CAU reduces false alarms by enhancing dependencies among input variables. Experiments demonstrate the superiority of MTI‐Net over TDA, with a decrease in false alarm rates from 0.94 to 0.46 and an increase in hit rates from 0.23 to 0.81, highlighting the effectiveness of MTI‐Net in handling small‐scale tornado events.

Characterising galaxy clusters’ completeness function in Planck with hydrodynamical simulations

Galaxy cluster number counts are an important probe with which to constrain cosmological parameters. One of the main ingredients of the analysis, along with accurate estimates of cluster masses, is the selection function, and in particular the completeness associated with the cluster sample under consideration. Incorrectly characterising this function can lead to biases in cosmological constraints. In this work, we want to study the completeness of the Planck cluster catalogue, estimating the probability of cluster detection in a realistic setting using hydrodynamical simulations. In particular, we probe the case in which the cluster model assumed in the detection method differs from the shapes and profiles of true galaxy clusters. We created around 9000 images of the Sunyaev–Zel’dovich effect from galaxy clusters from the IllustrisTNG simulation, and used a Monte Carlo injection method to estimate the completeness function. We studied the impact of having different cluster pressure profiles and complex cluster morphologies on the detection process. We find that the cluster profile has a significant effect on completeness, with clusters with steeper profiles producing a higher completeness than ones with flatter profiles. We also show that cluster morphology has a small impact on completeness, finding that elliptical clusters have a slightly lower probability of detection with respect to spherically symmetric ones. Finally, we investigate the impact of a different completeness function on a cosmological analysis with cluster number counts, showing a shift in the constraints on Ωm and σ8 that lies in the same direction as the shift driven by the mass bias.

On the anti‐intercept features of noise radars

AbstractRobustness against Electronic Warfare/Electronic Defence attacks represents an important advantage of Noise Radar Technology (NRT). An evaluation of the related Low Probability of Detection (LPD) and of Intercept (LPI) is presented for Continuous Emission Noise Radar (CE‐NR) waveforms with different operational parameters, that is, “tailored”, and with various “degrees of randomness”. In this frame, three different noise radar waveforms, a phase Noise (APCN) and two “tailored” noise waveforms (FMeth and COSPAR), are compared by time–frequency analysis. Using a correlator (i.e. a two antennas) receiver, assuming a complete knowledge of the band (B) and duration (T) of the coherent emission of these waveforms, it will be shown that the LPD features of a CE‐NR do not significantly differ from those of any CE radar transmitting deterministic waveforms. However, in real operations, B and T are unknown; hence, assuming an instantaneous bandwidth estimation will show that the duration T can be estimated only for some specific “tailored” waveforms (of course, not to be operationally used). The effect of “tailoring” is analysed with prospects for future work. Finally, some limitations in the classification of these radar signals are analysed.

Passive acoustic monitoring and convolutional neural networks facilitate high-resolution and broadscale monitoring of a threatened species

Population monitoring is an essential component of biodiversity conservation and management, but low detection probabilities for rare and/or cryptic species makes estimating abundance and occupancy challenging. Passive acoustic monitoring combined with machine learning algorithms represents a potential path forward to effectively and efficiently monitor the occurrence of rare vocalizing species across entire forest landscapes. Our objectives were to develop and implement a convolutional neural network (PNW-Cnet) to identify vocalizations of a rare and threatened forest nesting bird species – the marbled murrelet (Brachyramphus marmoratus) – in the Pacific Northwest, U.S.A., 2018–2021. We used PNW-Cnet predictions from broadscale passive acoustic monitoring data to examine spatiotemporal patterns in the distribution of murrelets. PNW-Cnet showed sufficiently high prediction accuracy (overall precision > 0.9) to enable broadscale population monitoring. Spatiotemporal analysis showed that annual peak murrelet call abundance occurs in ordinal weeks 28–32 (late July–Mid August) but this varied by study area. The greatest number of detections typically occurred in the Olympic Peninsula and Oregon Coast Range where late-successional forest dominates and nearer to ocean habitats. We demonstrate that passive acoustic monitoring can be used to understand intensity of use across broad scales for a rare and cryptic species in addition to the typical detection/non-detection data that are often collected. Passive acoustic monitoring combined with PNW-Cnet offers considerable promise for species distribution modeling and long-term population monitoring for rare species.

СПОСОБ ОБЪЕКТНО-ОРИЕНТИРОВАННОГО РАСПОЗНАВАНИЯ ОБЛАЧНОСТИ ГЛУБОКОЙ КОНВЕКЦИИ НА ОСНОВЕ ДАННЫХ ГЕОСТАЦИОНАРНОГО МЕТЕОРОЛОГИЧЕСКОГО ИСКУССТВЕННОГО CПУТНИКА ЗЕМЛИ С ПРИМЕНЕНИЕМ МАШИННОГО ОБУЧЕНИЯ

Due to high spatial and temporal resolution, geostationary meteorological satellite imagery is a valuable source of information on the development of deep convective clouds and related severe weather events. Some methods for automatic deep convection detection from satellite data provide a satisfactory probability of detection for independent datasets, but are characterized by a high false alarm rate. The paper gives a description of an algorithm for automatic detection of deep convective clouds with satellite imagery using gradient boosting, logistic regression, and artificial neural network models. The results of validation of the proposed method using dependent and independent data of ground-based observations for the period 2013-2020 are presented. A low false alarm rate and high probability of detection suggest that the algorithm can be used in the operational mode.

Directional Antenna-Based Routing Protocol With Low Detection Probability in Airborne Networks

Directional Antenna-Based Routing Protocol With Low Detection Probability in Airborne Networks

Area Surveillance With Low Detection Probability Using UAV Swarms

Area Surveillance With Low Detection Probability Using UAV Swarms

The role of combining clinical skills and chest X-ray in detecting pediatric congenital heart diseases

Background: Congenital heart disease is one of the most common congenital defects and the leading cause of death in the first year of life. Echocardiography plays an essential role in diagnosing; however, not all physicians can access this technique. Therefore, a chest X-ray is necessary for evaluating heart disease in infants. Objectives: To describe the role of clinical examniation and chest X-ray in detecting common congenital heart problems in children. Subjects and methods: 71 patients were diagnosed with congenital heart disease by clinical examination and confirmed by Doppler echocardiography at the Pediatric Center, Hue Central Hospital, from April 2020 to March 2021. Set up a cross-sectional study. Results: On a chest X-ray, the detection rate of left and right atrial enlargement was 4.2% and 21.1%, respectively. On chest X-rays and clinical examinations, the probability of detecting cardiac, left, and right ventricular enlargement were, respectively, 54.9% and 12.7%; 29.6% and 49.3%; and 16.9% and 19.7%. Conclusion: A chest X-ray has a low detection probability for atrial enlargement. The ability to detect cardiac enlargement on chest X-ray is better than on clinical examination, whereas with left and right ventricular enlargement, the ability to detect on clinical examination is better. The possibility of detecting symptoms is increased when clinical and chest X-rays are combined. Key words: chest X-rays, congenital heart diseases.

Stealth Aircraft Penetration Trajectory Planning in 3D Complex Dynamic Environment Based on Sparse A* Algorithm

To find a trajectory with low radar detection probability for stealth aircraft under the assumption of 2D space, performing a rapid turning maneuver is a useful way to reduce the radar detection probability of an aircraft by changing the azimuth angle rapidly to reduce the time of high radar cross-section (RCS) exposure to radar. However, in real flight, not only does the azimuth angle to the radar change rapidly but the elevation angle also changes rapidly, and the change in the radar cross-section is also significant in this process. Based on this premise, this paper established a trajectory planning method based on the sparse A* algorithm in a 3D complex, dynamic environment, called the 3D sparse A* method, based on a log-normal radar model (the 3D-SASLRM method), which considers the RCS statistical uncertainty and the statistical characteristics of the radar signals. Simulations were performed in both simple and complex scenarios. It was concluded that the established 3D-SASLRM method can significantly reduce the radar detection probability. And the essence of reducing under the assumption of 3D space is also to reduce the time of high radar cross-section exposure to radar.