This paper considers localisation of point sources of gamma radiation using dose rate measurements. Binary and continuous genetic algorithms (GA) were used to implement maximum likelihood estimation (MLE) of position and strength of point radiation sources. MLE was achieved by minimising the objective function which computes the negative log likelihood. Real experimental data collected during a DSTO-conducted field trial were used to test and verify the performance of the algorithms. The performance of GA-based implementation was compared to an implementation that uses MATLAB builtin routine fminsearch. Source parameters estimated by the algorithms were also compared to the theoretical bounds obtained using Cramer-Rao bound (CRB) analysis which quantifies the accuracy with which it is possible to localise the source and estimate its strength. All three implementations localised a single point source well, nearly approaching the CRB. Reasonable position estimates were achieved for two and three source cases, but the source strength estimates were found to have much larger RMS errors than what is predicted by the CRB. While the GA-based implementations took longer to converge compared to the fminsearch algorithm, they encountered fewer divergent runs than the latter algorithm.
Chemical sensor networks are used to detect the presence of hazardous chemicals released intentionally or accidentally into the atmosphere. Although many performance attributes of chemical sensor networks, such as energy utilization, detection delay, and false alarm characteristics, have been studied in the literature, the effect of spatial correlation of sensor readings on the network performance has not hitherto been studied. Because chemical tracers dispersing in the turbulent atmosphere are inherently spatially correlated, understanding how such correlations impact on the network performance is vital for chemical sensor network optimization. In this paper we investigate the impact of spatial correlation on the performance of chemical sensor networks.
Due to the significant amount of energy consumed by chemical sensors for sensing, reducing sensing activity is critical for improving the lifespan of chemical sensor networks. In this paper, we consider a simple decentralized dynamic sensor activation protocol that aims to maintain a majority of sensors in the inactive (passive) state in the absence of a chemical attack, and rapidly activate the sensors when an attack is detected. This paper proposes two analytical models to study the behavior of the sensor network under the proposed protocol. Our first analytical model employs the known analogy between the information spread in a sensor network and the propagation of epidemics across a population. The second model describes the protocol by using a framework of graph theory.
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