Greedy Methods in Plume Detection, Localization and Tracking

Greedy method, as an efficient computing tool, can be applied to various combinatorial or nonlinear optimization problems where finding the global optimum is difficult, if not computationally infeasible. A greedy algorithm has the nature of making the locally optimal choice at each stage and then solving the subproblems that arise later. It iteratively make

Improvements to MLE Algorithm for Localizing Radiation Sources with a Distributed Detector Network

Maximum Likelihood Estimation (MLE) is a widely used method for the localization of radiation sources using distributed detector networks. While robust, MLE is computationally intensive, requiring an exhaustive search over parameter space. To mitigate the computational load of MLE, many techniques have been presented, including iterative and multi-resolution methods. In this work, we present two ways to improve the MLE localization of radiation sources. First, we present a method to mitigate the pitfalls of a standard multi-resolution algorithm. Our method expands the search region of each layer before performing the MLE search. Doing so allows the multi-resolution algorithm to correct an incorrect selection made in a prior layer. We test our proposed method against single-resolution MLE and standard multi-resolution MLE algorithms, and find that the use of grid expansion incurs a general decrease in localization error and a negligible increase in computation time over the standard multi-resolution algorithm. Second, we present a method to perform the MLE localization without prior knowledge of the background radiation intensity. We estimate the source and background intensities using linear regression (LR) and then use these estimates to initialize the intensity parameter search for MLE. We test this method using single-resolution, multi-resolution, and multi-resolution with grid expansion MLE algorithms and compare performance to MLE algorithms that don\u27t use the LR initialization method. We found that using the LR estimates to initialize the intensity parameter search caused a marginal increase in both localization error and computation time for the tested algorithms. The technique is only beneficial in the case of an unknown background intensity

Single Pixel Neutron Camera Using Compressive Sensing

The ability to quickly and cheaply localize radiation sources is incredibly important in the national security field. The ever increasing amount of intercontinental shipping makes cargo containers a likely vector for the transfer of prohibited material. Current methodology for detection of neutron sources suffers from size and expense limitations. The single pixel neutron camera offers a simple design that is inexpensive to implement allowing for an increase in detection points. The compressive sensing framework provides fast and accurate localization of sources among surrounding shielded material. Additionally, this design frees the detector from volume restrictions allowing for increased detection efficiency. This makes finding weak sources more likely. Finally, the focus is shifted from detector to collimator design, which is generally simpler and has fewer restrictions. Using MCNP numerical simulations, various source geometries were imaged and localized. The collimator featured a simple, multiplex type design that allows taking measurements at each individual pixel location. The ability of a compressive sensing based device was proven to achieve the objectives outlined above. Additionally, the simulations of the design show that sources can be localized using ~ 5% sampling rate. This means fast and accurate identification of sources even in heavily shielded containers. Single pixel neutron detection devices are shown to be ideal for cheap and durable course spatial detection

Single Pixel Neutron Camera Using Compressive Sensing

The ability to quickly and cheaply localize radiation sources is incredibly important in the national security field. The ever increasing amount of intercontinental shipping makes cargo containers a likely vector for the transfer of prohibited material. Current methodology for detection of neutron sources suffers from size and expense limitations. The single pixel neutron camera offers a simple design that is inexpensive to implement allowing for an increase in detection points. The compressive sensing framework provides fast and accurate localization of sources among surrounding shielded material. Additionally, this design frees the detector from volume restrictions allowing for increased detection efficiency. This makes finding weak sources more likely. Finally, the focus is shifted from detector to collimator design, which is generally simpler and has fewer restrictions. Using MCNP numerical simulations, various source geometries were imaged and localized. The collimator featured a simple, multiplex type design that allows taking measurements at each individual pixel location. The ability of a compressive sensing based device was proven to achieve the objectives outlined above. Additionally, the simulations of the design show that sources can be localized using ~ 5% sampling rate. This means fast and accurate identification of sources even in heavily shielded containers. Single pixel neutron detection devices are shown to be ideal for cheap and durable course spatial detection

Real Time Fusion of Radioisotope Direction Estimation and Visual Object Tracking

Research into discovering prohibited nuclear material plays an integral role in providing security from terrorism. Although many diverse methods contribute to defense, there exists a capability gap in localizing moving sources. This thesis introduces a real time radioisotope tracking algorithm assisted by visual object tracking methods to fill the capability gap. The proposed algorithm can estimate carrier likelihood for objects in its field of view, and is designed to assist a pedestrian agent wearing a backpack detector. The complex, crowd-filled, urban environments where this algorithm must function combined with the size and weight limitations of a pedestrian system makes designing a functioning algorithm challenging.The contribution of this thesis is threefold. First, a generalized directional estimator is proposed. Second, two state-of-the-art visual object detection and visual object tracking methods are combined into a single tracking algorithm. Third, those outputs are fused to produce a real time radioisotope tracking algorithm. This algorithm is designed for use with the backpack detector built by the IDEAS for WIND research group. This setup takes advantage of recent advances in detector, camera, and computer technologies to meet the challenging physical limitations.The directional estimator operates via gradient boosting regression to predict radioisotope direction with a variance of 50 degrees when trained on a simple laboratory dataset. Under conditions similar to other state-of-the-art methods, the accuracy is comparable. YOLOv3 and SiamFC are chosen by evaluating advanced visual tracking methods in terms of speed and efficiency across multiple architectures, and in terms of accuracy on datasets like the Visual Object Tracking (VOT) Challenge and Common Objects in Context (COCO). The resultant tracking algorithm operates in real time. The outputs of direction estimation and visual tracking are fused using sequential Bayesian inference to predict carrier likelihood. Using lab trials evaluated by hand on visual and nuclear data, and a synthesized challenge dataset using visual data from the Boston Marathon attack, it can be observed that this prototype system advances the state-of-the-art towards localization of a moving source