Estimating the location of a Nuclear Source with Multiple Drones in an Urban Environment

The problem of locating the source of radioactive emissions using a network of sensors is considered. Estimating the three-dimensional location of a nuclear source is especially difficult in environments in which no sensor can be placed in close proximity to the source. In this dissertation, maximum-likelihood (ML) estimation is applied to a Poisson process model for radiation received at sensors that is proportional to the inverse square of the distance between the source and the sensor. The joint multivariate density for the sensors is then maximized in order to estimate the location and strength of the radioactive source. Additionally, a limited number of sensors is used to implement a two-stage adaptive algorithm. In the first stage the drones sit at the center of a building\u27s faces and an approximate location of the radiation source is obtained. Based on the results of the first stage, in the second stage the drones move to additional locations to collect more data. The data from both stages is utilized to obtain a more accurate estimate of the location of the radiation source. A third topic involves the effects of spatially non-homogeneous attenuation due to highly absorbing materials such as concrete. A novel metric is presented for identifying situations in which non-homogeneity significantly skews estimation results. This metric is used to drive a multiple iteration multi-stage estimation algorithm utilizing multiple applications of ML estimation. The algorithm is analyzed in realistic situations such as highly absorbing walls and a central shaft. Finally, a hybrid algorithm is proposed that first determines with a high degree of reliability whether non-homogeneous attenuation is present. If non-homogeneous attenuation is declared absent, the sensors move according to the adaptive algorithm. If non-homogeneous attenuation is declared present, the multiple-iteration algorithm is employed. This hybrid algorithm performs extremely well whether non-homogeneous is present or absent