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The measurements from multistatic radar systems are typically subjected to complicated data association, noise corruption, missed detection, and false alarms. Moreover, most of the current multistatic Doppler radar-based approaches in multitarget tracking are based on the assumption of known detection probability. This assumption can lead to biased or even complete corruption of estimation results. This paper proposes a method for tracking multiple targets from multistatic Doppler radar with unknown detection probability. A closed form labeled multitarget Bayes filter was used to track unknown and time-varying targets with unknown probability of detection in the presence of clutter, misdetection, and association uncertainty. The efficiency of the proposed algorithm was illustrated via numerical simulation examples.

The formerly endangered Kirtland's warbler (Setophaga kirtlandii) is among a growing number of conservation-reliant species that depend on active management to avoid reverting to endangered status. Because the Kirtland's warbler is a habitat specialist of young, even-aged jack pine (Pinus banksiana), managers of the recovery effort stressed creating new jack pine stands and monitoring numbers of singing males through an annual census using single visits to individual stands. Kirtland's warbler will occupy and breed in red pine (P. resinosa), but red pine has not been surveyed for Kirtland's warblers in the annual population census. Furthermore, the current monitoring approach cannot determine their species detection probability or individual detection probability, which is essential to evaluate both red pine use and the accuracy of the census. From 2016–2018 we estimated density and detection probabilities in jack pine and red pine stands through repeated visits to a limited number of stands rather than single visits to many stands. Estimates of species detection probability indicated that ≥1 male Kirtland's warbler would be detected on most sites when any were present, but individual detection probabilities were less and varied by stand type, indicating that single visits to sites would underestimate numbers and that accurate estimation of detection probability was important for estimation of density in different stand types. We offer quantitative estimates of detection probabilities for determination of Kirtland's warbler population size in jack pine versus red pine stands in the same areas and breeding seasons. Managers of Kirtland's warblers should incorporate detection probabilities into population surveys to achieve more accurate estimates of population size.

Occupancy estimation is an effective analytic framework, but requires repeated surveys of a sample unit to estimate the probability of detection. Detection rates can be estimated from spatially replicated rather than temporally replicated surveys, but this may violate the closure assumption and result in biased estimates of occupancy. We present a new application of a multi-scale occupancy model that permits the simultaneous use of presence–absence data collected at 2 spatial scales and uses a removal design to estimate the probability of detection. Occupancy at the small scale corresponds to local territory occupancy, whereas occupancy at the large scale corresponds to regional occupancy of the sample units. Small-scale occupancy also corresponds to a spatial availability or coverage parameter where a species may be unavailable for sampling at a fraction of the survey stations. We applied the multi-scale occupancy model to a hierarchical sample design for 2 bird species in the Black Hills National Forest: brown creeper (Certhia americana) and lark sparrow (Chondestes grammacus). Our application of the multi-scale occupancy model is particularly well suited for hierarchical sample designs, such as spatially replicated survey stations within sample units that are typical of avian monitoring programs. The model appropriately accounts for the non-independence of the spatially replicated survey stations, addresses the closure assumption for the spatially replicated survey stations, and is useful for decomposing the observation process into detection and availability parameters. This analytic approach is likely to be useful for monitoring at local and regional scales, modeling multi-scale habitat relationships, and estimating population state variables for rare species of conservation concern.

Covert communications have arisen as an effective communications security measure that overcomes some of the limitations of cryptography and physical layer security. The main objective is to completely conceal from external devices the very existence of the link for exchanging confidential messages. In this paper, we take a step further and consider a scenario in which a covert communications node disguises itself as another functional entity for even more covertness. To be specific, we study a system where a source node communicates with a seemingly receive-only destination node which, in fact, is full-duplex (FD) and covertly delivers critical messages to another hidden receiver while evading the surveillance. Our aim is to identify the achievable covert rate at the hidden receiver by optimizing the public data rate and the transmit power of the FD destination node subject to the worst-case detection error probability (DEP) of the warden. Closed-form solutions are provided, and we investigate the effects of various system parameters on the covert rate through numerical results, one of which reveals that applying more (less) destination transmit power achieves a higher covert rate when the source transmit power is low (high). Since our work provides a performance guideline from the information-theoretic point of view, we conclude this paper with a discussion on possible future research such as analyses with practical modulations and imperfect channel state information.

Due to the importance of energy production, it is critical to reduce unnecessary or unpredicted power generation equipment outages. As a major power generation company, Hydro-Québec uses models to estimate the service life of turbine runners in order to avoid such outages. The influential inputs for these models include the characteristics of flaws present in the runners. Since Non-destructive testing (NDT) methods are used to detect and characterize these flaws, this study aims to evaluate the reliability of NDT methods for inspection of weld joints in hydraulic turbine runners. Conventional ultrasonic testing (UT), radiography testing (RT), phased array ultrasonic testing (PAUT), and PAUT using total focusing method (TFM) were compared. The NDT tests were conducted on a T-joint specimen manufactured with embedded flaws. This specimen was used to determine the probability of detection (POD) curves for volumetric flaw sizes. The results showed that array technology, PAUT or TFM, has demonstrated a detection capability (a90) around 2.5 mm, which is similar to the commonly used equivalent critical flaw size for such structure.

The probability of defects detection (POD) is developed as an efficient tool to evaluate the detection capacity of non-destructive testing methods. An experimental study has been carried out applying the magnetic particle test method with an electromagnetic yoke on welded steel joints, which contained surface defects previously characterized in shape, size and location. The test conditions were varied, such as the type of magnetization current, and the type of magnetic particle. The probability of detecting defects in welded joints evaluated by the Hit/miss method increased with the size of the defect, independent of its shape factor. Smaller defects were likely to be detected with dry magnetic particles compared to wet fluorescent ones, a50 (2,118 mm ˂ 2,469 mm), a90 (6,395 mm ˂ 6,77 mm) and a90/95 (12,12 mm ˂ 12,19 mm).

The capability of Active Thermography (AT) techniques in detecting shallow defects has been proved by many works in the last years, both on metals and composites. However, there are few works in which these techniques have been used adopting simulated defects more representative of the real ones. The aim of this work is to investigate the capability of Pulsed Thermography of detecting shallow spherical defects in metal specimens produced with laser powder bed fusion (L-PBF) process and characterized by a thermal behaviour very far from the flat bottom hole and so near to the real one. In particular, the quantitative characterization of defects has been carried out to obtain the Probability of Detection (PoD) curves. In fact, it is very common in non-destructive controls to define the limits of defect detectability by referring to PoD curves based on the analysis of flat bottom holes with a more generous estimation and therefore not true to real defect conditions. For this purpose, a series of specimens, made by means of Laser-Powder Bed Fusion technology (L-PBF) in AISI 316L, were inspected using Pulsed Thermography (PT), adopting two flash lamps and a cooled infrared sensor. To improve the quality of the raw thermal data, different post-processing algorithms were adopted. The results provide indications about the advantages and limitations of Active Thermography (AT) for the non-destructive offline controls of the structural integrity of metallic components.

X-ray imaging can be efficiently used for high-throughput in-line inspection of industrial products. However, designing a system that satisfies industrial requirements and achieves high accuracy is a challenging problem. The effect of many system settings is application-specific and difficult to predict in advance. Consequently, the system is often configured using empirical rules and visual observations. The performance of the resulting system is characterized by extensive experimental testing. We propose to use computational methods to substitute real measurements with generated images corresponding to the same experimental settings. With this approach, it is possible to observe the influence of experimental settings on a large amount of data and to make a prediction of the system performance faster than with conventional methods. We argue that a high accuracy of the image generator may be unnecessary for an accurate performance prediction. We propose a quantitative methodology to characterize the quality of the generation model using Probability of Detection curves. The proposed approach can be adapted to various applications and we demonstrate it on the poultry inspection problem. We show how a calibrated image generation model can be used to quantitatively evaluate the effect of the X-ray exposure time on the performance of the inspection system.

N-mixture models provide an appealing alternative to mark–recapture models, in that they allow for estimation of detection probability and population size from count data, without requiring that individual animals be identified. There is, however, a cost to using the N-mixture models: inference is very sensitive to the model’s assumptions. We consider the effects of three violations of assumptions that might reasonably be expected in practice: double counting, unmodeled variation in population size over time, and unmodeled variation in detection probability over time. These three examples show that small violations of assumptions can lead to large biases in estimation. The violations of assumptions we consider are not only small qualitatively, but are also small in the sense that they are unlikely to be detected using goodness-of-fit tests. In cases where reliable estimates of population size are needed, we encourage investigators to allocate resources to acquiring additional data, such as recaptures of marked individuals, for estimation of detection probabilities.

Applying life estimation approaches to determine in-service life of structures and plan the inspection schedules accordingly are becoming acceptable safety design procedures in aerospace. However, these design systems shall be fed with reliable parameters related to material properties, loading conditions and defect characteristics. In this context, the role of non-destructive (NDT) testing reliability is of high importance in detecting and sizing defects. Eddy current test (ECT) is an electromagnetic NDT method frequently used to inspect tiny surface fatigue cracks in sensitive industries. Owing to the new advances in robotic technologies, there is a trend to integrate the ECT into automated systems to perform NDT inspections more efficiently. In fact, ECT can be effectively automated as to increase the coverage, repeatability and scanning speed. The reliability of ECT scanning, however, should be thoroughly investigated and compared to conventional modes of applications to obtain a better understanding of the advantages and shortcomings related to this technique. In this contribution, a series of manual and automated ECT tests are carried out on a set of samples using a split-D reflection differential surface probe. The study investigates the level of noise recorded in each technique and discuss its dependency on different parameters, such as surface roughness and frequency. Afterwards, a description of the effect of crack orientation on ECT signal amplitude is provided through experimental tests and finite element simulations. Finally, the reliability of each ECT technique is investigated by means of probability of detection (POD) curves. POD parameters are then extracted and compared to examine the effect of scanning index, frequency and automation on detection reliability.