Cumulative Detection Probability Research Articles

Detection range of a passing submerged emitter of sound in ambient noisea).

An underwater acoustic detection problem is studied in which the ambient noise present at a receiver is calculated, given information describing environmental conditions, including windspeed, and the positions of nearby ships which act as sources of background noise. The signal, whose detection is sought, is narrowband and transmitted from a source that passes by the receiver along a straight track. Cumulative Probability of Detection (CPoD) is calculated along a series of tracks with increasing closest-point-of-approach distances to the receiver. Two detection ranges are analyzed, a so-called "defender" detection range and an "intruder" detection range. Both are conservative measures associated with CPoD equaling 0.5 during the transit of the submerged vessel. Predictions of the detection range are compared across independent attempts to solve the same problem with different modelling approaches. The spread of results (i.e., the "reproducibility" of the predictions) is discussed and reasons for differences are highlighted. Environmental conditions that strongly affect detection performance are discussed, as is the use of CPoD as a single-valued metric to describe detection performance.

Performance of unmarked abundance models with data from machine‐learning classification of passive acoustic recordings

AbstractThe ability to conduct cost‐effective wildlife monitoring at scale is rapidly increasing due to the availability of inexpensive autonomous recording units (ARUs) and automated species recognition, presenting a variety of advantages over human‐based surveys. However, estimating abundance with such data collection techniques remains challenging because most abundance models require data that are difficult for low‐cost monoaural ARUs to gather (e.g., counts of individuals, distance to individuals), especially when using the output of automated species recognition. Statistical models that do not require counting or measuring distances to target individuals in combination with low‐cost ARUs provide a promising way of obtaining abundance estimates for large‐scale wildlife monitoring projects but remain untested. We present a case study using avian field data collected in the forests of Pennsylvania during the spring of 2020 and 2021 using both traditional point counts and passive acoustic monitoring at the same locations. We tested the ability of the Royle–Nichols and time‐to‐detection models to estimate the abundance of two species from detection histories generated by applying a machine‐learning classifier to ARU‐gathered data. We compared abundance estimates from these models with estimates from the same models fit using point‐count data and to two additional models appropriate for point counts, the N‐mixture model and distance models. We found that the Royle–Nichols and time‐to‐detection models can be used with ARU data to produce abundance estimates similar to those generated by a point‐count‐based study but with greater precision. ARU‐based models produced confidence or credible intervals that were on average 31.9% (±11.9 SE) smaller than their point‐count counterpart. Our findings were consistent across two species with differing relative abundance and habitat use patterns. The higher precision of models fit using ARU data is likely due to higher cumulative detection probability, which itself may be the result of greater survey effort using ARUs and machine‐learning classifiers to sample significantly more time for focal species at any given point. Our results provide preliminary support for the use of ARUs in abundance‐based study applications, and thus may afford researchers a better understanding of habitat quality and population trends, while allowing them to make more informed conservation recommendations and actions.

Applications of Underwater Video for Imperiled Fish Species Population Monitoring

Abstract Common freshwater fish sampling methods (e.g., seining and electrofishing) are inherently invasive and often not appropriate for imperiled species. Visual observation methods provide a noninvasive alternative for population monitoring. Despite increasing popularity, the use of underwater video to monitor imperiled fishes is relatively unexplored. We evaluated the efficacy of underwater video to estimate occurrence and abundance of the imperiled Comanche Springs Pupfish Cyprinodon elegans using both point observations and time intervals (surveys). We deployed camera traps at sites within major habitat types (pool, canal, and ciénaga) of Balmorhea State Park, Texas, United States in March and October 2019 (seasons). We detected Comanche Springs at all occupied sites in both seasons when viewing ~30 min of video. The species was detected at 80% of occupied sites when viewing ~10 min and ~5 min of video in March and October, respectively. Comanche Springs Pupfish detection probability was higher in October, with no variability among habitat types. On average, cumulative species detection probability was >0.9 with fifteen 60-s surveys. However, species detection probability of a single survey ranged from 0.02 to 0.62 (mean = 0.14). Although there was no variation between seasons or among habitats, variation was high in the detection of the maximum Comanche Springs Pupfish count among sites even with observations every 5 s. Individual capture probability from a repeated-count abundance model was less variable than species detection probability (0.01-0.33) and generally low (mean = 0.06). Site absolute abundance was generally comparable among major habitats, but with higher uncertainty with increasing maximum count. Our study provides a comprehensive assessment of underwater video for imperiled fish species population monitoring. The findings show a trade-off between processing effort and information loss and limitations associated with imperfect detection and individual capture common to any fish sampling method.

A Study on Design and Analysis of an Alert-Confirm Detection Method

Active electronically scanning antennas are faster and more flexible in beam-scheduling than mechanical antennas. Thus, they require an advanced resource management or detection methods to operate efficiently. In a surveillance radar performing periodic detection, alert-confirm detection is an excellent method to improve the cumulative detection probability by reducing the period while maintaining the detection probability. This paper proposes a design method for alert-confirm detection based on the parameters of the conventional design. We developed a simulator based on simulink@matworks and verified the result through Monte Carlo simulation.

Research on Dynamic Target Search for Multi-UAV Based on Cooperative Coevolution Motion-Encoded Particle Swarm Optimization

Effectively strategizing the trajectories of multiple Unmanned Aerial Vehicles (UAVs) within a dynamic environment to optimize the search for and tracking of mobile targets presents a formidable challenge. In this study, a cooperative coevolution motion-encoded particle swarm optimization algorithm called the CC-MPSO search algorithm is designed to tackle the moving target search issue effectively. Firstly, a Markov process-based target motion model considering the uncertainty of target motion is investigated. Secondly, Bayesian theory is used to formulate the moving target search as an optimization problem where the objective function is defined as maximizing the cumulative probability of detection of the target in finite time. Finally, the problem is solved based on the CC-MPSO algorithm to obtain the optimal search path nodes. The motion encoding mechanism converts the search path nodes into a set of motion paths, which enables more flexible handling of UAV trajectories and improves the efficiency of dynamic path planning. Meanwhile, the cooperative coevolution optimization framework enables collaboration between different UAVs to improve global search performance through multiple swarm information sharing, which helps avoid falling into local optimal solutions. The simulation results show that the CC-MPSO algorithm demonstrates efficacy, reliability, and superior overall performance when compared to the five commonly used swarm intelligence algorithms.

Integral Imaging Display System Based on Human Visual Distance Perception Model

In an integral imaging (II) display system, the self-adjustment ability of the human eye can result in blurry observations when viewing 3D targets outside the focal plane within a specific range. This can impact the overall imaging quality of the II system. This research examines the visual characteristics of the human eye and analyzes the path of light from a point source to the eye in the process of capturing and reconstructing the light field. Then, an overall depth of field (DOF) model of II is derived based on the human visual system (HVS). On this basis, an II system based on the human visual distance (HVD) perception model is proposed, and an interactive II display system is constructed. The experimental results confirm the effectiveness of the proposed method. The display system improves the viewing distance range, enhances spatial resolution and provides better stereoscopic display effects. When comparing our method with three other methods, it is clear that our approach produces better results in optical experiments and objective evaluations: the cumulative probability of blur detection (CPBD) value is 38.73%, the structural similarity index (SSIM) value is 86.56%, and the peak signal-to-noise ratio (PSNR) value is 31.12. These values align with subjective evaluations based on the characteristics of the human visual system.

Chaotic heuristic assisted method for the search path planning of the multi-BWBUG cooperative system

Blended-wing-body underwater gliders (BWBUGs) are low cost and have high endurance. As such, they are highly suitable for underwater search missions. However, the search path planning of BWBUGs presents significant challenges due to the complexities in designing the planning model and solution algorithm. In this study, we propose a novel chaotic heuristic assisted method (CHAM) for search missions involving multi-BWBUG cooperative systems. First, our approach addresses the challenges by introducing a probabilistic sensing model that accurately describes the detection performance of the BWBUG. Then, a search path planning model of the multi-BWBUG cooperative system is developed on the basis of the cumulative detection probability and communication energy consumption ratio. Second, we propose the sine-cosine chaos strategy, which exhibits earlier chaotic state entry and higher ergodicity compared to the sine chaos strategy. Leveraging this, CHAM integrates the sine-cosine chaos strategy with a well-designed hierarchical collaboration mechanism. The proposed algorithm adapts different position update strategies at various stages to enhance convergence speed and avoid local optima. Finally, the simulation experiments and statistical analysis results provide compelling evidence that CHAM excels in performing search path planning for the multi-BWBUG cooperative system, and the comprehensive performance of CHAM is better than that of comparison algorithms.

Rare but not lost: Endemic mountain lizard occupancy following megafire and grazing disturbances

AbstractWildfire and grazing by invasive herbivores can influence habitat suitability for ground‐dwelling fauna, such as reptiles. Australia has a large and diverse reptile fauna, with the Australian Alps bioregion in the southeast of the continent supporting a disproportionately high number of threatened species. In this bioregion, many species are threatened by fire, habitat loss or modification, and invasive species. The range of one such threatened endemic lizard, Cyclodomorphus praealtus (family Scincidae), was impacted by the 2019–20 megafires and is also subject to widespread grazing by invasive species. We investigated the relationship between C. praealtus site occupancy and fire and grazing. We completed 2045 surveys across 120 sites over 4 years, detecting the species at 43% of sites and increasing the species' known geographic range. Using single season detection occupancy models, we found C. praealtus occupancy was not associated with elevation, vegetation height or whether the site was burnt, but was positively associated with grazing activity. Our results indicate that C. praealtus has the capacity to persist following a single fire in some cases, and that habitats with high occupancy probabilities are subject to high grazing pressure. However, our results do not rule out more nuanced impacts associated with these disturbances, which affect a large proportion of C. praealtus' habitat. Our cumulative detection probability calculations revealed that considerable survey effort is often required to determine C. praealtus site occupancy. We therefore recommend that impact assessments assume species presence within areas of suitable habitat within the species' range. Our study improves our understanding of disturbance impacts on C. praealtus' occupancy, while demonstrating the need for sufficiently resourced impact assessments for cryptic and threatened species.

Quantitative Analysis on the Influence of the Oceanic Front on Underwater Acoustic Detection with Investigated Marine Data

At present, some shortcomings of the research on coupling modeling of the oceanic front–sound field may need attention: (1) Most of the acoustic propagation simulation is based on ideal front models, but the application of investigated marine data is lacking. (2) Most studies focus on the acoustic field characteristics, with the influence of fronts on acoustic propagation, but few studies aim at the direct quantitative analysis of the performance of underwater acoustic detection in oceanic fronts. To deal with the above problems, based on the measured data in the northwest Pacific Ocean, here, we first design different sound source layout schemes and calculate sound field characteristics in the sub-Arctic front using the ray theory. Then, the cumulative detection probability model is built based on the active sonar equation to evaluate the efficiency of underwater detection. Finally, the detection probability is calculated and expressed by regionalization, and the influence of the sub-Arctic front on underwater detection is quantitatively analyzed. The results show that the sub-Arctic front can significantly affect the underwater acoustic detection. The sound source located in the front in the cold-water mass has a better detection performance, especially detecting towards cold water (horizontal detection range > 60 km). In contrast, the sound source located in the warm-water mass has a poor detection performance (horizontal detection range in shallow sea < 10 km).

De‐noising method of aquatic product image with foreign matters based on improved wavelet transform and bilateral filter

AbstractAiming at the strong spot noise caused by the reflection of meat slices in fish image with foreign matters that collected by ordinary camera or Single Lens Reflex (SLR) camera, we proposed an improved wavelet transform and bilateral filtering algorithm to reduce the noise of image. In this paper, our algorithm absorbed the advantages of wavelet threshold de‐noising algorithm and bilateral filtering algorithm, the wavelet threshold de‐noising algorithm being used for high frequency components and bilateral filtering being used for low frequency components, which not only achieved the effect of eliminating noise and weakening light spot, but also protected the details of the image. In the experiment, in order to verify the effectiveness of the proposed algorithm, we used the traditional bilateral filtering, Gaussian filtering and median filtering algorithms to process 500 salmon fish images which had residual fishbone, respectively. Since the original image itself was a noisy image, it was impossible to evaluate the noise reduction effect by using an algorithm that requires a noiseless image as a reference image, so we used five methods to evaluate the quality of the image processed by each algorithm, which were Blind/Referenceless Image Spatial QUality Evaluator (BRISQE), Cumulative Probability of Blur Detection (CPBD), variance, image entropy, and operation speed. Among them, BRISQE and CPBD could quantitatively reflect the overall quality of the image, the lower the BRISQE score or the higher the CPBD score, the better the quality of the image. Compared with other algorithms, the BRISQE score and CPBD score of the image processed by our algorithm were the best, with the average scores reaching 20.65 and .8421, respectively. Experimental results showed that the improved algorithm in this paper can effectively eliminate this kind of strong spot noise. The processed image has achieved better visual effect in both subjective and objective level.

Development and validation of an environmental DNA assay to detect federally threatened groundwater salamanders in central Texas.

The molecular detection of DNA fragments that are shed into the environment (eDNA) has become an increasingly applied tool used to inventory biological communities and to perform targeted species surveys. This method is particularly useful in habitats where it is difficult or not practical to visually detect or trap the target organisms. Central Texas Eurycea salamanders inhabit both surface and subterranean aquatic environments. Subterranean surveys are challenging or infeasible, and the detection of salamander eDNA in water samples is an appealing survey technique for these situations. Here, we develop and validate an eDNA assay using quantitative PCR for E. chisholmensis, E. naufragia, and E. tonkawae. These three species are federally threatened and constitute the Septentriomolge clade that occurs in the northern segment of the Edwards Aquifer. First, we validated the specificity of the assay in silico and with DNA extracted from tissue samples of both target Septentriomolge and non-target amphibians that overlap in distribution. Then, we evaluated the sensitivity of the assay in two controls, one with salamander-positive water and one at field sites known to be occupied by Septentriomolge. For the salamander-positive control, the estimated probability of eDNA occurrence (ψ) was 0.981 (SE = 0.019), and the estimated probability of detecting eDNA in a qPCR replicate (p) was 0.981 (SE = 0.011). For the field control, the estimated probability of eDNA occurring at a site (ψ) was 0.938 (95% CRI: 0.714-0.998). The estimated probability of collecting eDNA in a water sample (θ) was positively correlated with salamander relative density and ranged from 0.371 (95% CRI: 0.201-0.561) to 0.999 (95% CRI: 0.850- > 0.999) among sampled sites. Therefore, sites with low salamander density require more water samples for eDNA evaluation, and we determined that our site with the lowest estimated θ would require seven water samples for the cumulative collection probability to exceed 0.95. The estimated probability of detecting eDNA in a qPCR replicate (p) was 0.882 (95% CRI: 0.807-0.936), and our assay required two qPCR replicates for the cumulative detection probability to exceed 0.95. In complementary visual encounter surveys, the estimated probability of salamanders occurring at a known-occupied site was 0.905 (SE = 0.096), and the estimated probability of detecting salamanders in a visual encounter survey was 0.925 (SE = 0.052). We additionally discuss future research needed to refine this method and understand its limitations before practical application and incorporation into formal survey protocols for these taxa.

Increasing sampling efficiency of Bombus communities with rare and endangered species by optimizing detection probabilities: A multi-species occupancy modelling approach using roadsides as a case study

Nearly one-quarter of bumble bee species assessed by the International Union for the Conservation of Nature are considered to be in decline, necessitating monitoring to support recovery planning. A key element of robust wildlife monitoring is accounting for imperfect detection in the observation process and efforts to understand sources of detection uncertainty in bumble bee surveys began recently in response to the listing of the rusty patched bumble bee, Bombus affinis, as federally endangered in the U.S. and Canada. We investigated within-community variation in detection probabilities among eight species that vary in occupancy, including B. affinis. We sampled bumble bees along roadsides in the greater metropolitan area of Minneapolis-St. Paul, Minnesota in the United States in 2018. We generated estimates of occupancy, detection probability, and cumulative detection probability from a multispecies occupancy model using a Bayesian framework that included covariates on occupancy and detection. Covariates selected a priori included floral cover and impervious surface effects on occupancy, and date, time of day, and observer effects on detection. We found that Bombus species with low occurrence are not necessarily less detectable than common species with higher occurrence, and that species reach their peak detection probability at different dates. Detection was higher in the afternoon and varied among observers. Well-designed Bombus monitoring programs can reduce sampling effort and optimize detection by sampling on dates near species detection peaks, sampling in the afternoon, and using cumulative detection probabilities to determine optimum sampling effort.

Convert path planning for underwater vehicle based on sonar detection probability

Underwater vehicle is essential for many tasks such as underwater exploration, equipment deployment and covert reconnaissance. Many underwater missions must be carried out with stealth to avoid being detected by enemy sonars, and safety and security are required in such high-risk situations. In this paper, a covert path planning algorithm for underwater vehicle is proposed based on the sonar detection probability analysis. First the sonar performance prediction model is constructed with sonar parameters, marine environment and underwater vehicle acoustic properties. Afterwards an improved ant colony algorithm is established by minimizing the cumulative detection probability. The evaluation indicate that the optimal covert path obtained for underwater vehicle is effective for reducing the cumulative detection probability.

Fast trajectory planning for Dubins vehicles under cumulative probability of radar detection

This paper studies a minimum-time trajectory planning problem under radar detection, where a Dubins vehicle aims to approach a target under a limited probability of being detected. Since the probability is accumulated along the vehicle’s trajectory in an integral form, we have to address a non-convex constrained functional optimization problem. To this end, Pontryagin’s minimum principle is adopted to derive the optimality conditions, based on which we obtain a set of parameterized trajectories that contain all optimal ones. By leveraging the design of intermediate points, fast algorithms are proposed to approximately compute a minimum-time trajectory among the set. Simulations are performed to validate the effectiveness and efficiency of the proposed algorithms.

Beam Scheduling for Early Warning With Distributed MIMO Radars

Distributed multiple-input multiple-output (MIMO) radars achieve long-range detection by steering and focusing the narrow beams of all transmit and receive antennas on a single point. This paper proposes a beam scheduling scheme based on the joint optimization of three components: arrangement of focal points, beam dwell time allocation of each focal point, and scanning order of focal points. The purpose is to enable distributed MIMO radars to provide early warning of incursions entering a broad surveillance region. To this end, a mathematical model is first developed to evaluate radar detection capability in a cell under test (CUT) over a scheduling period. By integrating all the CUTs within the region, the detection time required to reach a specified performance in terms of cumulative detection probability is then derived. On this basis, a beam scheduling strategy is devised, considering the maximum speed and minimum average radar cross-section constraints of targets to be detected. The main mechanism of the strategy is to minimize the detection time without sacrificing performance. To address the optimization problem of beam scheduling, we propose a two-stage method using a detection capability threshold as an extra optimization variable. In our method, the first stage is used to obtain optimal schemes for different given thresholds, and in the next stage, the optimal threshold is determined from these schemes. The simulation results show that the proposed algorithm is effective.

Addressing Detection Uncertainty in Bombus affinis (Hymenoptera: Apidae) Surveys Can Improve Inferences Made From Monitoring.

The U.S. Fish and Wildlife Service developed national guidelines to track species recovery of the endangered rusty patched bumble bee [Bombus affinis Cresson (Hymenoptera: Apidae)] and to investigate changes in species occupancy across space and time. As with other native bee monitoring efforts, managers have specifically acknowledged the need to address species detection uncertainty and determine the sampling effort required to infer species absence within sites. We used single-season, single-species occupancy models fit to field data collected in four states to estimate imperfect detection of B. affinis and to determine the survey effort required to achieve high confidence of species detection. Our analysis revealed a precipitous, seasonal, decline in B. affinis detection probability throughout the July through September sampling window in 2021. We estimated that six, 30-min surveys conducted in early July are required to achieve a 95% cumulative detection probability, whereas >10 surveys would be required in early August to achieve the same level of confidence. Our analysis also showed B. affinis was less likely to be detected during hot and humid days and at patches of reduced habitat quality. Bombus affinis was frequently observed on Monarda fistulosa (Lamiales: Lamiaceae), followed by [Pycnanthemum virginianum Rob. and Fernald (Lamiales: Lamiaceae)], Eutrochium maculatum Lamont (Asterales: Asteraceae), and Veronicastrum virginicum Farw. (Lamiales: Plantaginaceae). Although our research is focused on B. affinis, it is relevant for monitoring other bumble bees of conservation concern, such as B. occidentalis Greene (Hymenoptera: Apidae) and B. terricola Kirby (Hymenoptera: Apidae) for which monitoring efforts have been recently initiated and occupancy is a variable of conservation interest.

Single underwater image enhancement using integrated variational model

Underwater images often suffer from various degradations such as blurring, fog, low contrast, and color distortion because the light is absorbed and scattered when traveling through water. To solve critical issues, we establish a novel framework combining variational methods and pyramid technology to improve image quality in the frequency domain. Two novel variational models, the adaptive variational contrast enhancement (AVCE) model and the total Laplacian model, are designed with the aim of enhancing the contrast of foreground and preserving texture features at different scales. In order to solve these two models efficiently, we also exploit two optimal algorithms based on gradient descent method (GDM) and alternating direction method of multipliers (ADMM). In addition, fast Fourier transform (FFT) is applied for further accelerating the calculation procedure. Extensive experiments demonstrate that our approach achieves good performance on contrast enhancement, color correction, and texture enlargement for underwater images. Qualitative and quantitative comparisons further validate the superiority of our proposed method. In the quantitative comparisons, the proposed method achieves 1.6170, 0.6484, 0.6333, 0.0332, 4.0355, and 1.6843 scores in terms of underwater image quality measures (UIQM), underwater color image quality evaluation (UCIQE), cumulative probability of blur detection (CPBD), Energy, Entropy, and Contrast metrics, and obtains an average of 10% improvement compared with several state-of-the-art methods. The code is available online at: https://github.com/Hou-Guojia/UIE-IVM.

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.

Monitoring methods influence native predator detectability and inferred occupancy responses to introduced carnivore management

Context Management actions that suppress introduced predator densities can benefit the population recovery of native species. Nevertheless, ensuring that predator management produces measurable population-level benefits can be influenced by multiple factors affecting species detection. Monitoring designs using multiple survey methods may perform better than increasing sampling effort with single-method protocols. Aims This study aimed to estimate individual and cumulative detection probabilities and site occupancy estimates from the use of five different monitoring methods to survey a native mesopredator, the lace monitor (Varanus varius). Second, we assessed the effect of lethal red fox (Vulpes vulpes) baiting on lace monitor detection probabilities and site occupancy estimates collected from each monitoring method. Methods Multi-method sampling for Varanus varius occurred at 76 sites across lethal fox baited and non-baited habitats in East Gippsland, Victoria. Bayesian site occupancy models were used to estimate the effects of detection method and fox-baiting treatments on Varanus varius detection probability and site occupancy. Key results Method-specific detection probabilities (P = 0.00–0.12) and site occupancy estimates (Ψ = 0–0.53) varied considerably among methods, but combinations of multi-method monitoring improved lace monitor detection probability (P = 0.11–0.18) and site occupancy (Ψ = 0.87 ± [0.66–0.93]−0.91 ± [0.76–0.97] mean ± [95% credible intervals]) above any single method. However, there was extreme heterogeneity in the size and direction of the introduced predator baiting effect on method-specific lace monitor detection. Three methods (box traps and two different visual search surveys) all indicated lace monitor detection probabilities increased in fox-baited sites. However, sand pads reported a decrease in lace monitor detection at fox-baited sites, whereas pipe traps obtained no detections. Conclusions Combining detection data from all methods led to the inference of a positive fox-baiting effect, albeit with a smaller magnitude and better certainty than that estimated using a reduced method monitoring design, which had fewer detection data after excluding biased detection from sand pads. Implications Using a multi-method monitoring approach improved lace monitor detection and reduced sampling effort. However, depending on sampling methodology, the management effects on lace monitors can change.

Fast Trajectory Planning for Dubins Vehicles Under Cumulative Probability of Radar Detection

Fast Trajectory Planning for Dubins Vehicles Under Cumulative Probability of Radar Detection