True Location Research Articles

BeeAR: Augmented Reality Beeline Navigation for Spatial Knowledge Acquisition

Navigation assistance systems have become integral to our daily routines, helping us to find our way through unfamiliar environments. However, their use may come at a price, as empirical evidence suggests a potentially harmful impact of these systems on our spatial abilities, including the acquisition of spatial knowledge. This could be remedied by giving users more freedom and involving them in the decision-making process. Therefore, we present a navigation system that combines augmented reality and Beeline Navigation (BeeAR). Here, the location of the destination is overlaid with a digital landmark and permanently displayed to the user via a visual, translucent AR display (without a map). Since the digital content is integrated into the real world, no mapping between the device and reality is required, potentially lowering the workload. Making one's own decisions along the route is expected to increase engagement with the environment, leading to increased acquisition of spatial knowledge. We compare BeeAR with findings from a previous study comparing Free Choice Navigation (FCN) and Turn-by-Turn (TBT) navigation conducted along the same routes on the outskirts of Vienna, Austria. Although BeeAR and FCN do not provide users with a map, BeeAR users could better retrace the walked route and remembered more points of interest along the route than FCN users. Participants of all three navigation conditions achieved a high configuration similarity between drawn points of interest and their true locations, albeit only one navigation condition included a map.

Spike-timing dependent plasticity partially compensates for neural delays in a multi-layered network of motion-sensitive neurons.

The ability of the brain to represent the external world in real-time is impacted by the fact that neural processing takes time. Because neural delays accumulate as information progresses through the visual system, representations encoded at each hierarchical level are based upon input that is progressively outdated with respect to the external world. This 'representational lag' is particularly relevant to the task of localizing a moving object-because the object's location changes with time, neural representations of its location potentially lag behind its true location. Converging evidence suggests that the brain has evolved mechanisms that allow it to compensate for its inherent delays by extrapolating the position of moving objects along their trajectory. We have previously shown how spike-timing dependent plasticity (STDP) can achieve motion extrapolation in a two-layer, feedforward network of velocity-tuned neurons, by shifting the receptive fields of second layer neurons in the opposite direction to a moving stimulus. The current study extends this work by implementing two important changes to the network to bring it more into line with biology: we expanded the network to multiple layers to reflect the depth of the visual hierarchy, and we implemented more realistic synaptic time-courses. We investigate the accumulation of STDP-driven receptive field shifts across several layers, observing a velocity-dependent reduction in representational lag. These results highlight the role of STDP, operating purely along the feedforward pathway, as a developmental strategy for delay compensation.

Resolution Limits of Non-Adaptive 20 Questions Search for a Moving Target

Using the 20 questions estimation framework with query-dependent noise, we study non-adaptive search strategies for a moving target over the unit cube with unknown initial location and velocities under a piecewise constant velocity model. In this search problem, there is an oracle who knows the instantaneous location of the target at any time. Our task is to query the oracle as few times as possible to accurately estimate the location of the target at any specified time. We first study the case where the oracle’s answer to each query is corrupted by discrete noise and then generalize our results to the case of additive white Gaussian noise. In our formulation, the performance criterion is the resolution, which is defined as the maximal L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> distance between the true locations and estimated locations. We characterize the minimal resolution of an optimal non-adaptive query procedure with a finite number of queries by deriving non-asymptotic and asymptotic bounds. Our bounds are tight in the first-order asymptotic sense when the number of queries satisfies a certain condition and our bounds are tight in the stronger second-order asymptotic sense when the target moves with a constant velocity. To prove our results, we relate the current problem to channel coding, borrow ideas from finite blocklength information theory and construct bounds on the number of possible quantized target trajectories.

Evaluating Avatar Designs for On-Person Screening in Aviation Security

In aviation security, on-person screening quality is dependent on security screeners accurately translating the location of a digital 2D alarm to a 3D passenger using mental spatial scaling. Currently, security screeners are shown an alarm on a generic human figure (avatar) designed to protect passenger privacy. The problem is that the avatar may be overly generic and it can be challenging for security screeners to quickly and confidently locate the threat. To compensate, security screeners may increase their search area to guarantee that the threat is found. In prior work, an avatar design with relevant landmarks improved location accuracy. For this study, we compared the redesigned avatar to three other avatar designs that are used by on-person screening vendors. The results of the study showed that screeners had significantly smaller search areas (30% smaller) and were closer to the true target location (15% closer) when using the redesigned avatar.

Multi-Object Tracking Based on Re-Identification Enhancement and Associated Correction

During the Multi-Object Tracking (MOT) process, the effectiveness of Re-Identification (Re-ID) is typically contingent upon the performance of detection. In particular, detection errors may result in the blurring of Re-ID features, thereby diminishing the efficacy of Re-ID. In particular, Re-ID exhibits poor robustness in intricate scenarios such as distance and scale alterations, occlusion, and crowding. Meanwhile, the frequent occurrence of object occlusion can result in the interruption of object tracking, thereby impacting the tracking efficiency. To address these issues, a novel MOT method approach that leverages Re-ID enhancement and association correction is proposed. Through an unanchored, one-stage backbone network with CenterNet as the detector, the object position is detected by predicting the heatmap of the object center point. A constraint on the Predicted Distance Difference Loss (PDDL) is then designed for the response value of the predicted heatmap, effectively enhancing the detection accuracy and reduce the detection bias. To address the issue of Re-ID reliance on detection accuracy and enhance its robustness, an Effective Feature Graph Convolutional Neural network (EFGCN) is proposed. This approach expands learnable effective features by constructing a graph model that enhances them through a GCN model of heatmap true value locations and surrounding response value locations. Additionally, a target trajectory Association Correction Module (ACM) is introduced in the association phase to handle interruptions and trajectory fragments caused by occlusion. By improving data association for tracking judgment when dealing with occluded targets and considering the relationship between trajectory fragmentation and the detection of low-scoring targets, attempts are made to continue with tracking occluded targets to form complete object associations. The proposed method is evaluated on the MOT16 and MOT17 datasets and compared with other algorithms. The results demonstrate that it is highly effective.

FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction.

Near-infrared diffuse optical tomography (DOT) is a promising functional modality for breast cancer imaging; however, the clinical translation of DOT is hampered by technical limitations. Specifically, conventional finite element method (FEM)-based optical image reconstruction approaches are time-consuming and ineffective in recovering full lesion contrast. To address this, we developed a deep learning-based reconstruction model (FDU-Net) comprised of a Fully connected subnet, followed by a convolutional encoder-Decoder subnet, and a U-Net for fast, end-to-end 3D DOT image reconstruction. The FDU-Net was trained on digital phantoms that include randomly located singular spherical inclusions of various sizes and contrasts. Reconstruction performance was evaluated in 400 simulated cases with realistic noise profiles for the FDU-Net and conventional FEM approaches. Our results show that the overall quality of images reconstructed by FDU-Net is significantly improved compared to FEM-based methods and a previously proposed deep-learning network. Importantly, once trained, FDU-Net demonstrates substantially better capability to recover true inclusion contrast and location without using any inclusion information during reconstruction. The model was also generalizable to multi-focal and irregularly shaped inclusions unseen during training. Finally, FDU-Net, trained on simulated data, could successfully reconstruct a breast tumor from a real patient measurement. Overall, our deep learning-based approach demonstrates marked superiority over the conventional DOT image reconstruction methods while also offering over four orders of magnitude acceleration in computational time. Once adapted to the clinical breast imaging workflow, FDU-Net has the potential to provide real-time accurate lesion characterization by DOT to assist the clinical diagnosis and management of breast cancer.

Diffusion MRI of the facial-vestibulocochlear nerve complex: a prospective clinical validation study

ObjectivesSurgical planning of vestibular schwannoma surgery would benefit greatly from a robust method of delineating the facial-vestibulocochlear nerve complex with respect to the tumour. This study aimed to optimise a multi-shell readout-segmented diffusion-weighted imaging (rs-DWI) protocol and develop a novel post-processing pipeline to delineate the facial-vestibulocochlear complex within the skull base region, evaluating its accuracy intraoperatively using neuronavigation and tracked electrophysiological recordings.MethodsIn a prospective study of five healthy volunteers and five patients who underwent vestibular schwannoma surgery, rs-DWI was performed and colour tissue maps (CTM) and probabilistic tractography of the cranial nerves were generated. In patients, the average symmetric surface distance (ASSD) and 95% Hausdorff distance (HD-95) were calculated with reference to the neuroradiologist-approved facial nerve segmentation. The accuracy of patient results was assessed intraoperatively using neuronavigation and tracked electrophysiological recordings.ResultsUsing CTM alone, the facial-vestibulocochlear complex of healthy volunteer subjects was visualised on 9/10 sides. CTM were generated in all 5 patients with vestibular schwannoma enabling the facial nerve to be accurately identified preoperatively. The mean ASSD between the annotators’ two segmentations was 1.11 mm (SD 0.40) and the mean HD-95 was 4.62 mm (SD 1.78). The median distance from the nerve segmentation to a positive stimulation point was 1.21 mm (IQR 0.81–3.27 mm) and 2.03 mm (IQR 0.99–3.84 mm) for the two annotators, respectively.Conclusionsrs-DWI may be used to acquire dMRI data of the cranial nerves within the posterior fossa.Clinical relevance statementReadout-segmented diffusion-weighted imaging and colour tissue mapping provide 1–2 mm spatially accurate imaging of the facial-vestibulocochlear nerve complex, enabling accurate preoperative localisation of the facial nerve. This study evaluated the technique in 5 healthy volunteers and 5 patients with vestibular schwannoma.Key Points• Readout-segmented diffusion-weighted imaging (rs-DWI) with colour tissue mapping (CTM) visualised the facial-vestibulocochlear nerve complex on 9/10 sides in 5 healthy volunteer subjects.• Using rs-DWI and CTM, the facial nerve was visualised in all 5 patients with vestibular schwannoma and within 1.21–2.03 mm of the nerve’s true intraoperative location.• Reproducible results were obtained on different scanners.

Using Dummy Locations to Conceal Whereabouts of Mobile Users in Location-Based Services

Location-based services are extremely popular in these days. Mobile users use the services such as GPS and Google Maps almost every day to assist their daily activities like finding a restaurant or looking for an apartment. However, in order to use the services, users have to share their location information with the service providers. This requirement may hold back the service adaptation since users may not like to share their locations with others. One common method to preserve user privacy is to send a couple of dummy locations along with the true location to the service providers, so the provider would not be able to tell which location is true. This method is simple and effective, but it also has some drawbacks that make the privacy safeguarding fragile. If the dummy locations are not carefully planned, they may land on wild fields or water and could be easily perceived as fake. This research investigates the flaws of using dummy locations to uphold user privacy from both the users’ and service providers’ points of view, and proposes innovative methods to close the loopholes, so more users will be willing to use location-based services.

Task Allocation Under Geo-Indistinguishability via Group-Based Noise Addition

Locations are usually necessary for task allocation in spatial crowdsourcing, which may put individual privacy in jeopardy without proper protection. Although existing studies have well explored the problem of location privacy protection in task allocation under geo-indistinguishability, they potentially assume the workers could perform any tasks, which might not be practical in reality. Moreover, they usually adopt planar laplacian mechanism to achieve geo-indistinguishability, which will introduce excessive noise due to its randomness and boundlessness. To this end, we propose a task allo <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CA</b> tio <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> approach via gr <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> up-based nois <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> addition under Geo-I, referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CANOE</i> . Its main idea is that each worker uploads the noisy distances between his true location and the obfuscated locations of his preferred tasks instead of uploading his obfuscated location. In particular, to alleviate the total noise when conducting grouping, we put forward an optimized global grouping with adaptive local adjustment method <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OGAL</i> with convergence guarantee. To collect the noisy distances which are required for subsequent task allocation, we develop a utility-aware obfuscated distance collection method <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UODC</i> with solid privacy and utility guarantees. We further theoretically analyze the privacy, utility and complexity guarantees of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CANOE</i> . Extensive analyses and experiments over two real-world datasets confirm the effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CANOE</i> .

Releasing survey microdata with exact cluster locations and additional privacy safeguards

Household survey programs around the world publish fine-granular georeferenced microdata to support research on the interdependence of human livelihoods and their surrounding environment. To safeguard the respondents’ privacy, micro-level survey data is usually (pseudo)-anonymized through deletion or perturbation procedures such as obfuscating the true location of data collection. This, however, poses a challenge to emerging approaches that augment survey data with auxiliary information on a local level. Here, we propose an alternative microdata dissemination strategy that leverages the utility of the original microdata with additional privacy safeguards through synthetically generated data using generative models. We back our proposal with experiments using data from the 2011 Costa Rican census and satellite-derived auxiliary information. Our strategy reduces the respondents’ re-identification risk for any number of disclosed attributes by 60–80% even under re-identification attempts.

Late integration of vision and proprioception during perturbed reaches.

The motor system corrects rapidly, but selectively, for perturbations to ongoing reaching movements, depending on the constraints of the task. To account for such sophistication, it has been postulated that corrections are based on an estimated limb state that integrates all sensory changes caused by the perturbation, taking into account their processing delays. Here, we asked if information from different sensory modalities is integrated immediately or processed separately in the early phase of a response. We perturbed the estimated state of the limb with both unimodal and bimodal visual and proprioceptive perturbations without changing the actual limb state. For visual perturbations, a cursor representing the hand was shifted to the left or the right relative to the true hand location. For proprioceptive perturbations, the biceps or triceps muscles were vibrated, which induced illusory limb-state changes to the right or the left. In the bimodal condition, the perturbations to vision and proprioception were either congruent or incongruent in their directions. Response latencies show that it takes ∼100 ms longer to respond to unimodal visual perturbations than to unimodal proprioceptive perturbations. Responses to bimodal perturbations show that it takes an additional ∼100 ms beyond the response to unimodal visual perturbations for intermodal consistency to impact the response. These results suggest that visual and proprioceptive signals are initially processed separately for state estimation and only combined at the level of the limb's motor output, instead of being immediately integrated into a single state estimate of the limb.NEW & NOTEWORTHY Both visual and proprioceptive signals provide information about arm state during reaching. By perturbing the perceived, but not the actual, position of the hand in both modalities using visual disturbances and muscle vibration, we examined multimodal integration and state estimation during reaching. Our results suggest that the early reach corrections are based on separate state estimates from the two sensory modalities and only later are based on a combined state estimate.

Active seismic surveys based on distributed acoustic sensing in near-surface active fault exploration: A test in Datong Basin, North China

The exploration of active faults provides a basis for earthquake disaster prevention and migration. Active seismic survey is generally considered to be one of the most effective active fault exploration techniques because of its high resolution and deep penetration. However, this technique always yields low-resolution seismic data of a near-surface active fault. This limitation can be improved with the increase of sensor density with conventional nodal seismometers, but this is an expensive approach for active fault exploration. Distributed Acoustic Sensing (DAS) seems to be an ideal technique to replace traditional nodal seismometer surveys because it has the advantages of high-density sensors, many channels, and low cost. However, the applicability of active seismic surveys via DAS in the exploration of active faults remains unknown. Here, we designed an active seismic survey test in the Datong Basin, North China, focusing on a graben marked by well-developed normal faults in and around the basin. We selected a buried active fault identified in our previous active seismic profile, and DAS and nodal seismometers were deployed over a possible site of the active fault to conduct a comparative active seismic survey using a tamping machine. The comparisons with data recorded by three-component nodal seismometers demonstrate that DAS can also provide effective near-surface active fault information, and the imaging results also showed that the combination of conventional telecommunication fibre-optic cable and active seismic data processing techniques can be effectively used for near-surface active fault exploration, which is helpful in determining the true location of an active fault.

Mapping subsurface tile lines on a research farm using aerial photography, paper maps, and expert knowledge

AbstractAccurate maps of subsurface tile drainage lines are needed for agronomic and environmental research studies and the maintenance of current tile drainage systems. In this study, tile lines at the Purdue University Agronomy Center for Research and Education near West Lafayette, Indiana were located using a combination of visual aerial photo interpretation, expert knowledge, and paper construction drawings. The mapping accuracy was assessed at 27 locations where tile lines were located physically using a tile probe. Tile lines were correctly predicted 89% of the time with an average spatial accuracy of ±1.23 m of the true tile locations. This approach was better than a previous tile line location map prepared using an automated remote sensing method, which had an average spatial accuracy of ±2.12 m.

Simulations of drifting fish aggregating device (dFAD) trajectories in the Atlantic and Indian Oceans

Tropical tuna purse-seine fisheries deploy thousands of human-made drifting fish aggregating devices (dFADs) annually, raising a number of concerns regarding ecosystem impacts. In this study, we explored the use of a Lagrangian particle-tracking model to simulate the drift of dFADs in the Atlantic and Indian Oceans. We simulated more than 100,000 dFADs trajectories using the Lagrangian tool Ichthyop forced with velocity fields from an ocean model output (GLORYS12V1) and two satellite-derived ocean currents products (OSCAR and GEKCO). Importantly, through a collaborative agreement with the French frozen tuna producers’ organization we had access to the true locations of all dFADs along their drift and could therefore evaluate the accuracy of our simulations. The accuracy was assessed by comparing the observed and simulated trajectories in terms of spatial distribution, separation distance, and a non-dimensional skill score (an index based on separation distances normalized by net displacements of dFADs). In the two oceans, simulations forced with GLORYS12V1 were more accurate than with OSCAR and GEKCO, probably due to the differences in the spatio-temporal resolution of the forcing products. When we compared multiple depths for GLORYS12V1, the model performed better at 0 m in the Indian Ocean and at 5 m in the Atlantic Ocean, which could be related to the longer vertical structure of dFADs in the Atlantic Ocean. We showed that including a windage factor did not improve the accuracy of modeled dFADs trajectories. We found that mean model-data separation distances were similar in both oceans, exceeding 100 km after 6–8 days of drift. While separation distances between simulated and observed trajectories show that model errors were similar in the two oceans, the generally longer distances traveled by dFADs in the Indian Ocean than in the Atlantic Ocean lead to considerably higher skill scores in the former than in the latter. This explains the relatively good predictive ability of the model to represent mean dFAD densities at the basin scale in both oceans, while at the same time indicates higher prediction skills for the movements of individual dFADs in the Indian Ocean than in the Atlantic Ocean.

Water distribution network leak localization with histogram-based gradient boosting

Abstract Accurate and rapid leak localization in water distribution networks is extremely important as it prevents further loss of water and reduces water scarcity. A framework for identifying relevant leak event parameters such as the leak location, leakage area, and start time is presented in this paper. Firstly, the proposed data-driven methodology consists of acquiring pressure data at nodes in the network through hydraulic simulations by randomly changing the leak event initial conditions (leak location, area, and start time). Pressure uncertainties are added to the sensor measurements in order to make the problem more realistic. Secondly, the acquired data are then used to train, test, and validate a machine learning model in order to predict the relevant parameters. The random forest and the histogram-based gradient boosting machine learning algorithms are investigated and compared for the leak detection problem. The proposed approach with the histogram-based gradient boosting algorithm shows high accuracy in predicting the true leak location.

Inaccuracies in Low Income Housing Geocodes: When and Why They Matter

Scholars across disciplines frequently employ data on housing developments subsidized by the National Low Income Housing Tax Credit (LIHTC). We find that the geographic coordinates for these developments, generated by the U.S. Department of Housing and Urban Development (HUD), are frequently inaccurate. Using both the population of data from California and a national sample, we find that HUD-provided geocodes are inaccurate nearly half the time while Google-generated geocodes are almost always more accurate. However, while Google’s geolocation is more likely to be accurate, when it is inaccurate, it deviates from the true location by a much greater distance than HUD. We therefore recommend that scholars use Google-generated geocodes for most research applications where the localized environment matters; however, in studies where observations are aggregated to a larger area, researchers may prefer to use HUD geocodes, which are more frequently inaccurate but typically by smaller distances.

Click, click, buy: The market for novel synthetic peptide hormones on mainstream e-commerce platforms in the UK

In recent years there has been an increasing focus on the potential public health risks attached to human enhancement drugs (HEDs). While much work has explored the use and supply of HEDs, the market for these compounds is constantly evolving, with new substances being created and sold, and access becoming increasingly simplified through digital platforms. However, one area that has yet to receive scholarly attention is the availability of ‘grey market’ novel synthetic peptide hormones (SPH) through sellers on the mainstream e-commerce platforms Amazon, eBay, and Alibaba. This commentary piece therefore sets out to uncover this burgeoning corner of the broader HEDs trade in the UK market.Findings indicate that SPH are readily accessible over these e-commerce sites, with sellers evading restrictions by claiming that the substances are ‘not for human consumption’ or ‘for research only’, while simultaneously advertising their products as wellbeing supplements. Labelling practices are further identified as a point of concern, since packaging is not necessarily reflective of product quality or true location of origin. Finally, discussion centres on the enhanced legitimacy offered by selling through mainstream sites like Amazon, the capacity for counterfeiting and misrepresentation, and the public health implications of this substantial grey market.

Imaging in lossy media

We study the effects of absorption in the medium on synthetic aperture imaging. We model absorption using the loss tangent, which is the imaginary part of the relative dielectric permittivity, and study two cases: (i) the loss tangent is known and (ii) the loss tangent is unknown. When the loss tangent is known and used in Kirchhoff migration (KM), we find that images of targets are range-shifted by approximately a central wavelength so that their predicted locations are closer to the synthetic aperture than they actually are. In contrast, we find that when the medium is unknown, the KM image does not exhibit this range-shift. Hence, we determine that it is better to not make use of any knowledge of the absorption when imaging. Using a recently developed transformation of KM images, which we call reciprocal-KM (rKM), we achieve tunably high-resolution images of targets through adjusting the value of a user-defined parameter ε. When rKM is applied to an imaging region containing two targets, we find that their predicted locations shift, especially in range, but within a fraction of central wavelength of their true locations.

Honey bees infer source location from the dances of returning foragers

Honeybees (Apis mellifera carnica) communicate the direction and distance to a food source by means of a waggle dance. We ask whether bees recruited by the dance use it only as a flying instruction, with the technical form of a polar vector, or also translate it into a location vector that enables them to set courses directed toward the food source from arbitrary locations within their familiar territory. The flights of recruits captured on exiting the hive and released at distant sites were tracked by radar. The recruits performed first a straight flight in approximately the compass direction indicated by the dance. However, this "vector" portion of their flights and the ensuing tortuous "search" portion were strongly and differentially affected by the release site. Searches were biased toward the true location of the food and away from the location specified by translating the origin for the danced polar vector to the release site. We conclude that by following the dance recruits get two messages, a polar flying instruction (bearing and range from the hive) and a location vector that enables them to approach the source from anywhere in their familiar territory. The dance communication is much richer than thought so far.

Localisation of atmospheric release of radioisotopes using inverse methods and footprints of receptors as sources

Releases of radionuclides to the atmosphere occasionally occur with no warning and with first observation at radioactivity monitoring stations. The Chernobyl accident of 1986 was first detected at Forsmark, Sweden, long before the official announcement by the Soviet Union, and the release of Ruthenium 106 detected across Europe in 2017 still has no official release location. The current study details a method based on footprint analysis of an atmospheric dispersion model to locate the source of an atmospheric release. The method was applied to the European Tracer EXperiment of 1994 to validate the method and to the Ruthenium observations of autumn 2017 to determine likely release locations and time characteristics of this release. The method can readily utilise an ensemble of numerical weather prediction data which improves the localisation results by taking into account meteorological uncertainties compared to only using deterministic weather data. In applying the method to the ETEX scenario, the most likely release location improved from a distance of 113 km from the true release location when using deterministic meteorology, to a distance of 63 km when using ensemble meteorology data, although such improvements may be scenario dependent. The method was constructed to be robust with respect to the choices of model parameters and measurement uncertainties. The localisation method can be useful for decision makers to enact countermeasures to protect the environment against the effects of radioactivity when observations are available from environmental radioactivity monitoring networks.