Detection Of Radiation Sources Research Articles

Data synthesis improves detection of radiation sources in urban environments

Radiation detectors in and around airports and other ports of entry play a crucial role in preventing nuclear terrorism. While these systems aim to detect and intercept nuclear materials before they enter a country, they are not foolproof. To address this, distributed sensors have been proposed to detect nuclear materials if they enter urban areas. Past work on small detector systems has shown that data fusion can improve detection. Here, we show how this could be done for a large detector network using Pearson's Method. We evaluate how a sensor network would perform in New York City using a combination of radiation transport and geographic information systems. We use OpenStreetMap data to construct a grid over the streets and analyze vehicle paths using pickup and drop off data from the New York State Department of Transportation. The results show that data synthesis in a large network (40 k detectors) not only improves the time to the first detection but reduces the number of missed sources by up to 99 %.

Image-Like Situational Difference Mapping (ILSDM)-Based Method for Detecting Anomalies in Electromagnetic Environment

Electromagnetic environment anomaly radiation source detection (EMEARD) can provide a basis for electromagnetic (EM) equipment behavior cognition and battlefield threat assessment. Existing methods mainly focus on generating EM space situations but ignore the research on anomaly signal detection. In this paper, the connections between information geometry and performance of sensor networks for anomaly detection are explored. Nonlinear estimation methods based on information geometry can be directly used for anomaly detection. However, Riemann distance as a metric function faces the disadvantages of extensive computation and high complexity. To tackle this issue here, we propose a method based on image-like situation difference mapping, namely ILSDM. which takes the data-driven strategy to obtain the analytic expression of situation difference mapping distance function (DMDF), and anomaly detection is equivalent to the mapping reconstruction of information geometry. Firstly, the EM space situation is acquired through the distributed sensor network. In addition, to solve the perceptual blind area problem, the sensors’ data is estimated by spatial autocovariance optimal interpolation algorithm (Kriging). Secondly, we apply the Gaussian mixture model (GMM) to fit the statistical features of each sensing node into probability density function (PDF), and adjacent sensors with similar PDF are fused by the neighbor average fusion method (NAF). Then, based on the information geometry, the fused PDFs are mapped to the Riemannian manifold space as a series of coordinates, and we further propose to use image-like situation (ILS) to represented the EM space situation. Finally, DMDF measures the information distance between different ILSs to achieve anomaly detection. Numerical simulations and real experiments were conducted to prove the superiority and feasibility of our method for anomaly detection in EM space.

Contextually aware roadside radiation measurement testbed

We demonstrate a contextually aware multimodal roadside radiation measurement detection testbed for traffic monitoring applications in nuclear nonproliferation. Many variables in traffic such as vehicle or cargo size, mass, speed, shape, and distance of closest approach can have significant impacts on the radiation measured from a vehicle-transported radiation source. These factors can lead to uncertainties in the analysis of the radiation source, especially for lower-strength radiation sources of interest. Our testbed, known as the Multimodal Measurement System (MMS) uses non-radiation sensors including magnetometers, geophones, radiofrequency receivers, cameras, and LiDAR to extract contextual information about vehicles passing by the system. These contextual data can then be fused with data from radiation measurements to increase the system’s sensitivity and accuracy in nuclear threat detection applications. This work describes the instrumentation of the MMS and its data acquisition pipeline. Furthermore, we describe the pre-analysis performed on the raw multimodal data streams for data fusion, and the high-level machine learning analyses for detection and characterization. The variety of sensors within the MMS provides a valuable testbed that can be used to identify the combinations of contextual sensors that provide the greatest improvements to radiation source detection and characterization within the restrictions for various proliferation detection applications. The MMS is also modular so that additional combinations of sensors can be explored in the future.

Development and Applications of Compton Camera-A Review.

The history of Compton cameras began with the detection of radiation sources originally for applications in astronomy. A Compton camera is a promising γ-ray detector that operates in the wide energy range of a few tens of keV to MeV. The γ-ray detection method of a Compton camera is based on Compton scattering kinematics, which is used to determine the direction and energy of the γ-rays without using a mechanical collimator. Although the Compton camera was originally designed for astrophysical applications, it was later applied in medical imaging as well. Moreover, its application in environmental radiation measurements is also under study. Although a few review papers regarding Compton cameras have been published, they either focus very specifically on the detectors used in such cameras or the particular applications of Compton cameras. Thus, the aim of this paper is to review the features and types of Compton cameras and introduce their applications, associated imaging algorithms, improvement scopes, and their future aspects.

An Intelligent Robot Detection System of Uncontrolled Radioactive Sources.

In recent years, radioactive sources have been widely used in various fields (e.g., nuclear industry, agriculture, medical industry, environmental protection, and scientific research) and successfully applied to develop scientific projects, such as nuclear power generation, sewage treatment, medical diagnosis, and new material development. However, radiation sources continuously got out of control and even lost. Manual search for uncontrolled radiation sources is inefficient and prone to radiation injuries. Therefore, it is practically significant to design a radiation source detection robot. Against this backdrop, this study designs an intelligent robot detection system of uncontrolled radiation sources and develops an intelligent robot for detecting and disposing of uncontrolled radiation sources. The research results help to realize the autonomous search and disposal of radiation sources.

An autonomous radiation source detection policy based on deep reinforcement learning with generalized ability in unknown environments

Autonomous radiation source detection has long been studied for radiation emergencies. Compared to conventional data-driven or path planning methods, deep reinforcement learning shows a strong capacity in source detection while still lacking the generalized ability to the geometry in unknown environments. In this work, the detection task is decomposed into two subtasks: exploration and localization. A hierarchical control policy (HC) is proposed to perform the subtasks at different stages. The low-level controller learns how to execute the individual subtasks by deep reinforcement learning, and the high-level controller determines which subtasks should be executed at the current stage. In experimental tests under different geometrical conditions, HC achieves the best performance among the autonomous decision policies. The robustness and generalized ability of the hierarchy have been demonstrated.

Rapid compton camera imaging for source terms investigation in the nuclear decommissioning with a subset-driven origin ensemble algorithm

As the nuclear facilities (e.g. nuclear power plants) built in the last century gradually reached their life cycle, some nuclear facilities entered the decommissioning stage. Radioactivity measurement runs through the entire process of nuclear decommissioning, and source terms investigation is particularly important. Compton camera is a new technical method in nuclear facility decommissioning, and has the unique advantage of direct visualization of radiation pollution in nuclear facilities. This paper proposed a subset-driven origin ensemble algorithm with resolution recovery, which is suitable for rapid reconstruction of large space via Compton cameras. The proposed algorithm can correct the projection data deviation caused by the inherent spatial and energy resolution of the Compton camera as well as the Doppler broadening effect, and realize the rapid positioning of the spatial radiation source within a few seconds. We used both the Monte Carlo simulation based on Geant4 and the experiments of the double-layer Compton camera prototype to verify the feasibility of this new algorithm in real-time radiation source detection. This research can provide a promising method for the spatial radiation field reconstruction of nuclear facilities based on Compton cameras and the refined management of nuclear facilities decommissioning.

Design and implementation of a low cost, high performance ionizing radiation source detection and source direction finding system

Design and implementation of a low cost, high performance ionizing radiation source detection and source direction finding system

Development of Image Reconstruction Algorithm in Realtime for Compton Camera Systems

Compton Camera is a imaging technique that uses the principle of Compton scattering interaction to reproduce the distribution image of the radiation source. Compton Camera is a 3-D imaging technique, which has a high sensitivity, wide viewing angle and a range of shooting energy that is much higher than the transmissive imaging technique. In addition, the Compton Camera imaging technique does not use a mechanical collimation system, so it is compact in size, suitable for mobile applications, security control, radiation source detection and medical applications. In this paper, the authors built an image reconstruction algorithm for Compton Camera imaging equipment using data from Monte Carlo simulation. The image quality and the parameters of the algorithm will be evaluated in detail to determine the practical applicability.

Combating Illegal Movement of Radioactive Materials and Objects Across the State Border of Ukraine

This article, by using general scientific methods, presents the main issues and ways to improve the appropriate technical equipment of state border crossing points, legal responsibilities of the State Border Guard Service of Ukraine and the main results of operational activities to combat the illegal movement of radioactive materials and objects across the state border. The article shows the results of international cooperation and the implementation of international technical assistance projects by the State Border Guard Service of Ukraine on improving the system of prevention of illicit trafficking of radioactive materials and objects.
 Furthermore, the article examines the main responsibilities of the State Border Guard Service of Ukraine on ensuring radiation safety, which affects the real needs of equipping the state border guard bodies with the necessary technical tools. It is indicated that the detection of suspicious objects at state border crossing points may be limited to detection of exceeding the permissible radiation level without details about its kind with their further transfer to specially authorised state authorities of Ukraine for detailed examination.
 It is essential to note the need to improve the legal regulation of illegal trafficking of materials and objects that are hazardous radioactive sources across the state border considering negative economic and social consequences to the person, society, state and international community. There is an urgent need for well-defined regulation regarding the radiation materials and objects to prevent their movement across the state border without obtaining a special permit.
 Taking into account the need to reduce the budget expenditure of the border agency, this article provides recommendations to determine the purpose of the state re-equipment program of state border crossing points with modern tools for detection of radiation sources.

Surrogate-Based Robust Design for a Non-Smooth Radiation Source Detection Problem

In this paper, we develop and numerically illustrate a robust sensor network design to optimally detect a radiation source in an urban environment. This problem exhibits several challenges: penalty functionals are non-smooth due to the presence of buildings, radiation transport models are often computationally expensive, sensor locations are not limited to a discrete number of points, and source intensity and location responses, based on a fixed number of sensors, are not unique. We consider a radiation source located in a prototypical 250 m × 180 m urban setting. To address the non-smooth properties of the model and computationally expensive simulation codes, we employ a verified surrogate model based on radial basis functions. Using this surrogate, we formulate and solve a robust design problem that is optimal in an average sense for detecting source location and intensity with minimized uncertainty.

Network Detection of Radiation Sources Using Localization-Based Approaches

Radiation source detection is an important problem in homeland security-related applications. Deploying a network of detectors is expected to provide improved detection due to the combined, albeit dispersed, capture area of multiple detectors. Recently, localization-based detection algorithms provided performance gains beyond the simple “aggregated” area as a result of localization being enabled by the networked detectors. We propose the following three localization-based detection approaches: 1) source-attractor radiation detection (SRD); 2) triangulation-based radiation source detection (TriRSD); and 3) the ratio of square distance-based radiation source detection (ROSD-RSD). We use canonical datasets from Domestic Nuclear Detection Office's intelligence radiation sensors systems tests to assess the performance of these methods. Extensive results illustrate that SRD outperforms TriRSD and ROSD-RSD, and other existing detection algorithms based on the sequential probability ratio test and maximum likelihood estimation in terms of both false alarm and detection rates.

Double Q-Learning for Radiation Source Detection.

Anomalous radiation source detection in urban environments is challenging due to the complex nature of background radiation. When a suspicious area is determined, a radiation survey is usually carried out to search for anomalous radiation sources. To locate the source with high accuracy and in a short time, different survey approaches have been studied such as scanning the area with fixed survey paths and data-driven approaches that update the survey path on the fly with newly acquired measurements. In this work, we propose reinforcement learning as a data-driven approach to conduct radiation detection tasks with no human intervention. A simulated radiation environment is constructed, and a convolutional neural network-based double Q-learning algorithm is built and tested for radiation source detection tasks. Simulation results show that the double Q-learning algorithm can reliably navigate the detector and reduce the searching time by at least 44% compared with traditional uniform search methods and gradient search methods.

Improving image reconstruction algorithm for rotating modulation collimators using a variance stabilizing transform

A rotating modulation collimator (RMC) is an indirect imaging technique that has proven useful for remote radiation source detection. While it was initially invented for detecting sources in a far field, a recent development by Kowash has shown the feasibility of the RMC for detecting mid-range sources. However, their image reconstruction algorithm often produces spurious source estimates in pixels where no source exists. In this paper, we propose to improve the RMC image quality using a variance stabilizing transform. The transform reduces the inhomogeneous Poisson noise in the RMC data. In our simulation study, the proposed algorithm could reconstruct RMC images without misleading artifacts.

Network algorithms for detection of radiation sources

Networks of radiation counters have been recently developed for detecting low-level, hazardous radiation sources, and they have been utilized in indoor and outdoor characterization tests. Subsequently, the test measurements have been “replayed” using multiple sub-networks, which enabled the analysis of various scenarios beyond the tests. We present a particle filter algorithm that combines measurements from gamma counters across the network to detect radiation sources. Using replays from an outdoor test, we construct a border monitoring scenario that consists of twelve 2in.×2in. NaI detectors or counters deployed on the periphery to monitor a 42×42 m2 region. A 137Cs source is moved across this region, starting several meters outside and finally moving away from it. The measurements from individual, pairs and boundary detectors are replayed using the particle filter algorithm. The algorithm outputs demonstrate, both quantitatively and qualitatively, the benefits of networking all boundary counters: the source is detected meters before it enters the region, while being inside, and until moving several meters away. On the other hand, when counters are used individually or in pairs, the source is detected for much shorter durations, and sometimes not detected at all while inside the region.