Localization Of Radioactive Sources Research Articles

Gaussian process-based online sensor selection for source localization

This paper addresses the sensor selection problem for source localization within cyber–physical systems (CPSs). While recent machine learning and reinforcement learning approaches aim to optimize sensor selection and placement within the Area of Interest (AoI), their need for intensive data collection and training precludes online operation. Furthermore, these methods often require prior knowledge of the unknown source’s characteristics and lack adaptability to the dynamic nature of CPSs, leading to inefficiencies in unseen environments. This paper addresses these shortcomings using Gaussian process Optimization coupled with an active sensor selection mechanism to locate the unknown source within the AoI. The proposed approach first builds a probabilistic model of the environment, which is discretized into a grid, without prior training using a Gaussian Process surrogate model. Next, the model iteratively and systematically learns the underlying spatial phenomenon using Gaussian Process optimization. Concurrently, the approach selects a subset of sensors by optimizing a fitness function that advocates selecting informative and energy-efficient sensors. Next, the probabilistic model, having accurately learned the environment, directs the algorithm to the unknown source by identifying the cell with the highest likelihood of containing it. Finally, a peak refinement step is performed, which computes the exact location of the source within the designated cell. The proposed method’s efficacy is validated through experiments in radioactive source localization, validation studies, and adaptability assessments across various environments. In terms of quality of localization (QoL), it outperforms recent localization benchmarks, such as a reinforcement learning-based approach and DANS, by around 18% and 100%, respectively.

Radioactive source localization employing resistive electrode array (REA) detector.

Objective.In this feasibility study, we explore an application of a Resistive Electrode Array (REA) for localization of a radioactive point source. The inverse problem posed by multichannel REA detection is studied from mathematical perspective and involves the questions of the minimal configuration of the conductive leads that can achieve this goal. The basic configuration consists of a circularly shaped REA with four opposite electrical lead-pairs at its perimeter.Approach.A robust mathematical reconstruction method for a 3D radioactive source relative to the REA is presented. The characteristic empirical Green's function for the detector response of the REA is determined by numerically solving Laplace equations with appropriate boundary conditions. Based on this model, Monte Carlo simulations of the inverse problem with Gaussian noise are performed and the overall accuracy of the localization is investigated.Main results.The results show a 3D error distribution of localization which is uniform in the (x,y)-plane of the REA and strongly correlated in the orthogonalz-axis. The overall accuracy decreases with higher distance of the source to the detector which is intuitive due to approximate flux dependence following the inverse square law. Further, a saturation in accuracy regarding the number of electrical leads and a linear dependence of the reconstruction error on the measurement noise level are observed.Significance.A broad range of REA detector configurations and their characteristics are investigated by this study for radioactive source localization allowing diverse practical applications with detector diameters ranging from millimeters to meters.

Localization of radioactive sources by multimodal imaging using a single gamma camera

Localization of radioactive sources by multimodal imaging using a single gamma camera

Radioactive source localization using a mobile radiation detection system featuring informed path-based decisions

Radioactive source localization using a mobile radiation detection system featuring informed path-based decisions

Dual-stage planner for autonomous radioactive source localization in unknown environments

This paper describes a novel approach for the autonomous localization of radioactive sources in complex unknown environments. The approach contains two stages: a source tracking stage for detailed source term estimation (STE) with recent measurements, and a relocation stage for transiting the robot to another sub-area after the current one is ruled out. The STE algorithm in conjunction with the convex polyhedrons extracted from the environment enables our method to be deployed in realistic scenarios with complex obstacles, which are often unfeasible for existing methods. The source confirmation algorithm in the relocation stage considerably accelerates the overall processing, improving the time efficiency in finding the radioactive sources. In both simulations and experiments, this approach is able to precisely localize at the success rate of 90% within an average of less than 13 measurements in 7 different environments, which is impressive when compared to the traditional STE which can hardly achieve an accurate estimation in most cases.

Autonomous exploration for radioactive sources localization based on radiation field reconstruction

In recent years, unmanned ground vehicles (UGVs) have been used to search for lost or stolen radioactive sources to avoid radiation exposure for operators. To achieve autonomous localization of radioactive sources, the UGVs must have the ability to automatically determine the next radiation measurement location instead of following a predefined path. Also, the radiation field of radioactive sources has to be reconstructed or inverted utilizing discrete measurements to obtain the radiation intensity distribution in the area of interest. In this study, we propose an effective source localization framework and method, in which UGVs are able to autonomously explore in the radiation area to determine the location of radioactive sources through an iterative process: path planning, radiation field reconstruction and estimation of source location. In the search process, the next radiation measurement point of the UGVs is fully predicted by the design path planning algorithm. After obtaining the measurement points and their radiation measurements, the radiation field of radioactive sources is reconstructed by the Gaussian process regression (GPR) model based on machine learning method. Based on the reconstructed radiation field, the locations of radioactive sources can be determined by the peak analysis method. The proposed method is verified through extensive simulation experiments, and the real source localization experiment on a Cs-137 point source shows that the proposed method can accurately locate the radioactive source with an error of approximately 0.30 m. The experimental results reveal the important practicality of our proposed method for source autonomous localization tasks.

The search for orphan radioactive source based on GPR-PRM method

The loss of radioactive sources poses potential risks to public safety, in order to safely recover radioactive sources, a method for orphan radioactive source localization is proposed. The method is divided into two parts: constructing the radiation field by Gaussian process regression (GPR) and path planning for the mobile robot using the probabilistic roadmap (PRM) method. A mobile robot collects data from grid cells along its path, alternating between the two parts until the source localization condition is met. The path planning is determined by the PRM method combining with the improved A* algorithm, which can also help the robot jump out of local optima. The feasibility of the proposed method is verified through two simulation experiments. The first experiment comprehensively demonstrated the search process of the proposed method. In the second experiment, a comparison of different initial parameter pa was added to show that the initial search preference of the robot has an impact on the efficiency. Overall, this method enables high-precision localization of indoor radioactive source in the presence of radiation shielding at low equipment costs.

Heuristic techniques for maximum likelihood localization of radioactive sources via a sensor network

Maximum likelihood estimation (MLE) is an effective method for localizing radioactive sources in a given area. However, it requires an exhaustive search for parameter estimation, which is time-consuming. In this study, heuristic techniques were employed to search for radiation source parameters that provide the maximum likelihood by using a network of sensors. Hence, the time consumption of MLE would be effectively reduced. First, the radiation source was detected using the k-sigma method. Subsequently, the MLE was applied for parameter estimation using the readings and positions of the detectors that have detected the radiation source. A comparative study was performed in which the estimation accuracy and time consumption of the MLE were evaluated for traditional methods and heuristic techniques. The traditional MLE was performed via a grid search method using fixed and multiple resolutions. Additionally, four commonly used heuristic algorithms were applied: the firefly algorithm (FFA), particle swarm optimization (PSO), ant colony optimization (ACO), and artificial bee colony (ABC). The experiment was conducted using real data collected by the Low Scatter Irradiator facility at the Savannah River National Laboratory as part of the Intelligent Radiation Sensing System program. The comparative study showed that the estimation time was 3.27 s using fixed resolution MLE and 0.59 s using multi-resolution MLE. The time consumption for the heuristic-based MLE was 0.75, 0.03, 0.02, and 0.059 s for FFA, PSO, ACO, and ABC, respectively. The location estimation error was approximately 0.4 m using either the grid search-based MLE or the heuristic-based MLE. Hence, heuristic-based MLE can provide comparable estimation accuracy through a less time-consuming process than traditional MLE.

Machine learning for localization of radioactive sources via a distributed sensor network

In this paper, we focus on the detection and localization of radioactive sources by exploiting supervised machine learning. Machine learning is utilized in a wide variety of applications due to its effectiveness in prediction and autonomous decision-making. However, applying machine learning would only be effective when representative features for the application can be acquired, through which learning algorithms can be trained. Hence, first, we present a feature extraction technique for radioactive source localization, and then propose a parameter estimation method via machine learning. A distributed sensor network is employed to assist in estimating the radioactive source’s location and intensity. We propose a feature extraction method that evaluates a feature vector using the reading and position of each sensor located in a region where a radiation source is detected. The feature extraction is based on a data fusion process, where a single feature value is provided to represent both the reading value and position coordinates corresponding to a given sensor. After the feature extraction, we apply the decision tree machine learning method for regression to localize a radioactive source. To examine the effectiveness of the proposed work, a performance comparison is carried out with recent existing methods in terms of the estimation accuracy and the execution time. Experimental results show that the proposed algorithm provides accurate source intensity estimation and achieves a good compromise between localization accuracy and execution time.

Optimization Strategies for Bayesian Source Localization Algorithms

Target search via Bayesian estimation is a commonly studied problem in which the goal is to locate and possibly characterize a lost “target” within a given space using noisy sensors. Example applications include locating gas leaks, radio sources, ships at sea, etc, using appropriate sensors for the specific phenomena. The motivating application for this effort is detecting and locating nearby radioactive material in nuclear facilities. Past work has produced several implementations for addressing this problem, with varying levels of accuracy and sophistication. Here we present both new and previously developed solution methods to the radioactive source localization problem. The solutions have diverse strategies for selecting advantageous positions at which to collect measurements. The objective is to minimize the number of measurements needed to complete the target localization. We empirically compare the effectiveness of various strategies, three of which are novel and prove superior over prior methods of measurement selection. The relative advantages of the novel strategies are discussed as well as their broader applicability to a general class of target search problems where measurement intensity correlates with target proximity. Note to Practitioners—This paper was motivated by the desire to automate tasks in radiation work spaces of nuclear facilities, particularly laboratory facilities such as those at Los Alamos National Laboratory. Prior work by the authors studied the problem of autonomously locating and characterizing radiation sources in a survey region. This paper presents enhancements to the prior method, which reduce the number of radiation measurements needed to produce the solution. This increases the efficiency and practicality of the method for end users.

Real-Time Gamma Radioactive Source Localization by Data Fusion of 3D-LiDAR Terrain Scan and Radiation Data from Semi-Autonomous UAV Flights.

Rapid and accurate reconnaissance in the event of radiological and nuclear (RN) incidents or attacks is vital to launch an appropriate response. This need is made stronger by the increasing threat of RN attacks on soft targets and critical infrastructure in densely populated areas. In such an event, even small radioactive sources can cause major disruption to the general population. In this work, we present a real-time radiological source localization method based on an optimization problem considering a background and radiation model. Supported by extensive real-world experiments, we show that an airborne system using this method is capable for reliably locating category 3-4 radioactive sources according to IAEA safety standards in real time from altitudes up to 150 m. A sensor bundle including a LiDAR sensor, a Gamma probe as well as a communication module was mounted on a UAV that served as a carrier platform. The method was evaluated on a comprehensive set of test flights, including 28 flight scenarios over 316 min using three different radiation sources. All additional gamma sources were correctly detected, multiple sources were detected if they were sufficiently separated from each other, with the distance between the true source position and the estimated source averaging 17.1 m. We also discuss the limitations of the system in terms of detection limit and source separation.

A Machine Learning Approach to a Multidetector Array Response Function for Nuclear Search

During nuclear search operations, the localization of radioactive sources can be a time-consuming process that requires mapping relative radiation intensity in a large area to determine the position of a source. This article introduces the use of machine learning, specifically a temporal convolutional network (TCN), to estimate the direction between a detector array and a static <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">137</sup> Cs source. This application of machine learning provides a directional vector in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\pi $ </tex-math></inline-formula> with a 90% confidence of 5.6° and a 99% confidence within 11.2°. With the use of low-cost NaI(Tl) detectors, the effects of self-shielding within the array creates gamma-ray shadows depending on the orientation to the source. Using the convolved detector array response function, we apply supervised machine learning with a neural network to predict a unit vector that points toward the observed source. The directional vector is expected to reduce search times once implemented in future work.

High-sensitivity stereo coded aperture gamma camera for three-dimensional localization of radioactive hotspots

This work presents a novel stereo coded aperture gamma camera based on four detection modules placed within four quadrants. Each module includes 48 × 48 strips of Bi4Ge3O12 scintillators, 24 × 24 pieces of silicon photomultipliers (SiPMs), and a set of data acquisition circuits. All signals from the SiPMs are individually extracted and finally simplified into 144 outputs to calculate the positions and energies of gamma-rays. Two masks of modified uniformly redundant arrays are fixed in the front of the top-right and bottom-left modules and two anti-masks are fixed in the top-left and bottom-right modules to achieve simultaneous imaging without dual measure. Once the projections of radioactive sources are determined, their source-to-detector distances can be estimated by our new multocular ranging algorithm. A series of lab and field experiments, including flood histogram and energy spectrum testing, field-of-view and angular resolution testing, real-time imaging, long-distance imaging, and ranging ability testing, with different radioactive sources, such as 137Cs, 22Na, 241Am, 60Co, 133Ba, and 75Se, have been completed to evaluate the comprehensive ability of the stereo gamma camera. The results show that the stereo gamma camera has a strong ability for the three-dimensional localization of radioactive sources over long distances with a high image sensitivity. So far, the stereo gamma camera has been successfully used in practical nuclear security and safety tasks although the angular resolution and the ranging algorithm robustness should be more optimized in the future.

Rapid localization of radioactive leaks based on hybrid adaptive grey wolf algorithm

Radioactive source localization algorithms have been widely used in the detection of nuclear accident areas. But some shortcomings, such as complex algorithm structure, slow localization speed and poor accuracy, were obviously performed to affect mobile robot locating autonomously. In this paper, a potential alternative method was investigated to be a new usage of locating leaks, just via specifying the change of exposure rate. In this model, several key factors, such as gamma ray attenuation, scattering factor, travel angle guide, spatial discretization, etc., were taken into consideration, to demonstrate the effectiveness of the algorithm, which is appropriated in unknown areas of the radioactive waste repository. Since there are three factors with different contribution, such as position, quantity of the source and gamma ray energy, which considered to demonstrate its impact on success. So, a hybrid adaptive grey wolf algorithm (HAGWO) has been adopted and implemented to develop a novel rapid method of radioactive leak location. Three aspects, including the good point set initialization in population size, balanced convergence function, and self-adaptive greedy strategy for population update, were optimized and merged into the locating model. To investigate the effectiveness of the algorithm, results of HAGWO are compared with grey wolf algorithm (GWO), good point set initialization strategy GWO(GGWO) and adaptive head wolf strategy GWO (ALGWO) in convergence speed, accuracy, stability and positioning error of single and double leak points. It is observed that convergence speed is increased by 37.93 ± 2% at the highest; the convergence accuracy is increased by 92.42 ± 2% at the most; the stability is improved by 30% ∼ 50%. The positioning error of single leak point is within 1.08%, and the positioning error of double leak point is less than 8.90%. Besides, compared with GWO, GGWO and ALGWO, the single-point accuracy is improved by 1.36 percentage points (to GWO), and the double-point accuracy is improved by 40.35 percentage points (to ALGWO) at most. It is observed that HAGWO performs the best in locating leaks, with a faster convergence, stronger stability and more accuracy.

Particle Filter Based Range Search Approach for Localization of Radioactive Materials

Wireless Sensor Networks (WSNs) have recently become crucial in monitoring operations. The development of a Data Fusion Algorithm for radioactive source localization utilizing WSN based on the Particle Filter (PF) technique is presented. The localization of an unknown-intensity point radioactive source using sensor nodes measured intensities in Count per Minute (CPM) is considered. The surveillance area is covered by several sensors (radiation detectors) n. Instead of using four sensors, as described in previous research, two consecutive sensors are used in sequence (S1, S2), (S2, S3)……, (Sn-1, Sn) till reaching the last sensor available Sn. Apollonius circle calculated range guides a particle filter for estimating the source location using actual measurements. Compared with other approaches such as the Iterative Pruning Clustering algorithm, more accurate estimates in terms of the error between the estimated source position and ground truth are obtained. The comparison is conducted using the same real measurements data. The Particle Filter based algorithm is implemented in a Xilinx FPGA chip. The architecture is a two sequential steps implementation, where particle generation, weight calculation, and normalization are carried out in parallel during the first step, followed by a sequential or parallelized resampling in the second step. This architecture targets a balance between hardware resources and speed of operation. The future work plan includes security-related studies and complete WSN implementation using μC/FPGA devices.

Robust radioactive sources research method using possibility particle filter

Of growing concern for the security of many nations are numerous incidents of lost or stolen radioactive sources or materials. The detection of and search for these abnormal radioactive sources plays an important role in monitoring nuclear safety and disposal of nuclear waste. In this paper, a method for autonomously searching for radioactive sources in a flat open rectangular-shaped field through mobile platforms was proposed. In this method, by using the possibility particle filter, the search for radioactive sources was realized according to a series of radiation information measured by the mobile platform carrying a Geiger–Müller counter. According to the inverse square law and the radiation counting governed by Poisson distribution, a radioactive source localization model was constructed. Then, a mobile platform controlled by an information entropy strategy constantly moved within the search area and detected radiation at specific points. The possibility filter algorithm, implemented via the sequential Monte Carlo method, is used to update posterior probability distributions of the source parameters. The performance of the proposed search algorithm, including a comparison with a standard particle filter algorithm, is studied by simulations. The simulation experiment proves that the possibility particle filter algorithm has good robustness. The successful application of the experimental dataset collected in the simulations verifies the measurement model and theoretical consideration.

Neural network method for localization of radioactive sources within a partially coded field-of-view in coded-aperture imaging

Coded-aperture imagers typically have a smaller field-of-view (FOV) than in un-collimated gamma imaging systems. However, sources out of the fully coded field-of-view (FCFOV) can cause pseudo hotspots on the wrong side of an image reconstructed using the cross-correlation method. In this work, we propose a neural network method to identify and localize the sources within the partially coded field-of-view (PCFOV). The model was trained using Monte Carlo simulation data and evaluated with both simulation and experimental data. The results showed that the proposed model can identify and localize sources with good classification accuracy, low positioning error, and strong robustness to the statistical noise.

A Directional Gamma-Ray Spectrometer With Microcontroller-Embedded Machine Learning

The enhancement of a compact gamma-ray detection module for spectroscopy and imaging with machine learning for directional sensitivity is presented. In particular this development is targeted towards drone-based localization of radioactive sources in the environment. The unit is composed of a cylindrical monolithic scintillator crystal (3” <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 3” LaBr <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> (Ce <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3+</sup> +Sr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2+</sup> )), read by an array of 144 solid-state SiPM detectors whose signals are conditioned by an integrated front-end. In addition to state-of-the-art energy resolution (2.6% at 662keV) and sub-centimeter spatial resolution in the reconstruction of the photon interaction point projected on the base, this portable unit enables the 2D angular localization of gamma sources on a plane parallel to the detectors array as a function of the reconstructed interaction point distribution, thanks to a decision tree. The classifier is compared with other techniques (k-NN, PCA) and optimized with 1000 splits. It runs in a 32-bit ARM micro-controller for real-time operation with a processing time of 2.75 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> per event, compatible with high gamma-ray counting rate (100kcps) operation. Despite the absence of a collimator, classification is correct within ±30° for a single photon. Angular resolution of 0.5° and accuracy better than 2° are experimentally demonstrated (along with 0.8W power consumption and 3kg weight), showing potential for identification of sources in the field.

Localization of radioactive source using a network of small form factor CZT sensors

We present a small factor (0.5 cm3) CZT static sensor network consisted of a number of Non-Directional Detectors (NDD) capable to localize bare and lightly shielded radiation sources. The localization is performed with a fusion algorithm based on a fast analytical technique. The algorithm has been tested using simulated data and verified with experimental data from a static 137Cs source of 7 MBq. The localization accuracy of the order of 15 cm has been archived in 3D for bare sources and of the order of 20 cm in 2D for light shielded sources within a monitored volume of 5 m × 2.8 m × 2 m.

Monte Carlo and experimental analysis of a novel directional rotating scatter mask gamma detection system

Abstract For such diverse applications as on-site radiation monitoring, treaty or material verification, cargo monitoring during transport, and orphan source location a radioactive source localization system is of great use. The Rotating Scatter Mask (RSM) detection system, a rapid scatter mask retrofit to a conventional radiation detector, provides a low-cost, large field of view and energy range solution to this problem. This work demonstrates successful experimental operation of a prototype system to identify source direction which was modeled using a library of signals simulated using GEANT and a novel algorithm, validated by comparing results with MCNP and experiment.