Ajith Gunatilaka

Detection and parameter estimation of multiple radioactive sources

Given an area where an unknown number of unaccounted radioactive sources potentially exist, and using gamma- radiation count measurements collected at known locations within this area, the problem is to estimate the number of sources as well as their locations and intensities. Two approaches are investigated. The first is based on the maximum likelihood estimation and the generalised maximum likelihood rule for multiple hypothesis testing. The second approach estimates the parameters and the number of sources in the Bayesian framework via Monte Carlo integration. Numerical analysis and the performance comparison of both approaches against the Cramer-Rao bound are carried out.

On Localisation of a Radiological Point Source

The problem is to localise a source of gamma radiation using dose rate measurements taken with a gamma probe at various points in space. A statistical model of dose-rate counts is developed using experimental data recorded in a laboratory. The problem is then studied using the theoretical Cramer-Rao bound (CRB) analysis, which quantifies the accuracy with which it is possible to localise the source and estimate its intensity. Three estimation algorithms are implemented and their performance investigated by simulations. The algorithms are then applied to field trial radiological survey data. The maximum likelihood algorithm performs the best of the considered algorithms.

Information driven search for point sources of gamma radiation

The problem is to estimate the number of radioactive point sources in a specified area and to estimate their parameters (locations and magnitudes), using measurements collected by a low-cost Geiger-Muller counter. The measurements are Poisson distributed with the mean proportional to the radiation field intensity. The radiation field represents a superposition of background radiation and the source contributions subjected to the inverse distance squared attenuation. The solution is based on an information gain driven search which comprises a sequential Bayesian estimator coupled with a sensor/observer control unit. The control unit directs the observer(s) to move to new locations and acquire measurements that maximise the information gain in the Renyi divergence sense. The performance of the proposed information driven search, including a comparison with a unform search along a predefined path, is studied by simulations. A successful application of the proposed technique to experimental datasets, recently collected in the field trials, verifies the measurement model and the theoretical considerations.

Internet Host Geolocation Using Maximum Likelihood Estimation Technique

Accurately locating the geographical position of Internet hosts has many useful applications. Existing approaches for host geolocation use Internet latency measurements, IP-to-location mapping and also geographical and demographical hints. In this paper, we investigate the applicability of the Maximum Likelihood Estimation (MLE) technique for Internet host geolocation. Our approach is based on a probability model for latency measurements that we developed by analyzing a large set of data collected on the PlanetLab network test bed. This approach uses latency measurements from multiple hosts of known location to the host to be geolocated, to estimate the target location. Using both simulated and real data, we analyze the accuracy of our approach. Our results for geolocating Internet hosts in North America confirms the validity of using MLE with certainty as its accuracy is found to be better in comparison to existing techniques that are based on Internet latency.

Information driven localisation of a radiological point source

The paper presents an algorithm for detection and a subsequent information gain driven control of the observer for the purpose of parameter estimation of an unaccounted point source of relatively low-level gamma radiation. The source parameters to be estimated are its location and intensity. Source detection and parameter estimation are carried out jointly in the Bayesian framework using a particle filter. The observer motion and the radiation exposure time are controlled by the algorithm. Initially the observer control vectors take predefined values until the source is positively detected. After detection, the control vectors are selected sequentially for the purpose of reduction in the observation time and consequently the radiation exposure. The selection of control vectors is carried out via a multiple-step ahead maximisation of the Fisher information gain.

Bayesian likelihood-free localisation of a biochemical source using multiple dispersion models

Localisation of a source of a toxic release of biochemical aerosols in the atmosphere is a problem of great importance for public safety. Two main practical difficulties are encountered in this problem: the lack of knowledge of the likelihood function of measurements collected by biochemical sensors, and the plethora of candidate dispersion models, developed under various assumptions (e.g. meteorological conditions, terrain). Aiming to overcome these two difficulties, the paper proposes a likelihood-free approximate Bayesian computation method, which simultaneously uses a set of candidate dispersion models, to localise the source. This estimation framework is implemented via the Monte Carlo method and tested using two experimental datasets. HighlightsWe develop a statistical method for adaptive likelihood free Bayesian estimation and model selection.The method is developed in the context of localisation of an emitting source of toxic material in the atmosphere.Three atmospheric dispersion models are considered.Two real datasets are used to assess the performance of the proposed method.

High-Resolution Monitoring of Atmospheric Pollutants Using a System of Low-Cost Sensors

Increased levels of particulate matter (PM) in the atmosphere have contributed to an increase in mortality and morbidity in communities and are the main contributing factor for respiratory health problems in the population. Currently, PM concentrations are sparsely monitored; for instance, a region of over 2200 square kilometers surrounding Melbourne in Victoria, Australia, is monitored using ten sensor stations. This paper proposes to improve the estimation of PM concentration by complementing the existing high-precision but expensive PM devices with low-cost lower precision PM sensor nodes. Our evaluation reveals that local PM estimation accuracies improve with higher densities of low-precision sensor nodes. Our analysis examines the impact of the precision of the lost-cost sensors on the overall estimation accuracy.

Achievable accuracy in Gaussian plume parameter estimation using a network of binary sensors

Theoretical Cramer-Rao bounds derived for parameter estimation of an atmospheric dispersion model.The work important for hazard modelling in the context of toxic source localisation.Bounds derived for the binary sensor network, as well as for the non-quantised (analogue measurement) sensor network.Theoretical bounds examined as a function of the binary threshold, sensor placement, and prior uncertainty.A comparison of the theoretical bound with empirical RMS errors of an MCMC estimation technique provided as a validation. The Gaussian plume model is the core of most regulatory atmospheric dispersion models. The parameters of the model include the source characteristics (e.g. location, strength) and environmental parameters (wind speed, direction, atmospheric stability conditions). The paper presents a theoretical analysis of the best achievable accuracy in estimation of Gaussian plume parameters in the context of a continuous point-source release and using a binary sensor network for acquisition of measurements. The problem is relevant for automatic localisation of atmospheric pollutants with applications in public health and defence. The theoretical bounds of achievable accuracy provide a guideline for sensor network deployment and its performance under various environmental conditions. The bounds are compared with empirical errors obtained using a Markov chain Monte Carlo (MCMC) parameter estimation technique.

A study of cognitive strategies for an autonomous search

A mobile autonomous agent searching for an emitting source of unknown strength.Emitted substance transported by diffusion and advection.Three cognitive search strategies compared by simulations and using real data.Under persistent sensory cues, the distribution of search time is inverse Gaussian.Search time depends on the ratio between the search area and the sensing area. Cognitive search is a collective term for search strategies based on information theoretic rewards required in sequential decision making under uncertainty. The paper presents a comparative study of cognitive search strategies for finding an emitting source of unknown strength using sparse sensing cues in the form of occasional non-zero sensor measurements. The study is cast in the context of an emitting source of particles transported by turbulent flow. The search algorithm, which sequentially estimates the source parameters and the reward function for motion control, has been implemented using the sequential Monte Carlo method. The distribution of the search time has been explained by the inverse Gaussian distribution.

Achievable accuracy in parameter estimation of a Gaussian plume dispersion model

The Gaussian plume model is the core of most regulatory atmospheric dispersion models. The parameters of the model include the source characteristics (e.g. location, strength, size) and environmental parameters (wind speed, direction, atmospheric stability conditions). A sensor network is at disposal to measure the concentration of biological pathogen or chemical substance within the plume. This paper presents a theoretical analysis of the best achievable accuracy in estimation of Gaussian plume model parameters. Numerical results illustrate how parameter estimation accuracy depends on sensor measurement accuracy, the density of sensors and the quality of (prior) meteorological advice. The theoretical bounds are compared with empirical errors obtained using an importance sampling parameter estimation technique.