{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/33375"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/33375","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Simultaneous localization and tracking in wireless ad-hoc sensor networks","abstract":"In this thesis we present LaSLAT, a sensor network algorithm that uses range measurements between sensors and a moving target to simultaneously localize the sensors, calibrate sensing hardware, and recover the target's trajectory. LaSLAT is based on a Bayesian filter that updates a probability distribution over the parameters of interest as measurements arrive. The algorithm is distributable and requires a fixed amount of storage space with respect to the number of measurements it has incorporated. LaSLAT is easy to adapt to new types of hardware and new physical environments due to its use of intuitive probability distributions: one adaptation demonstrated in this thesis uses a mixture measurement model to detect and compensate for bad acoustic range measurements due to echoes. We present results from a centralized implementation of LaSLAT using a network of Cricket sensors. In both 2D and 3D networks, LaSLAT is able to localize sensors to within several centimeters of their ground truth positions while recovering a range measurement bias for each sensor and the complete trajectory of the mobile.","abstract_html":"In this thesis we present LaSLAT, a sensor network algorithm that uses range measurements between sensors and a moving target to simultaneously localize the sensors, calibrate sensing hardware, and recover the target's trajectory. LaSLAT is based on a Bayesian filter that updates a probability distribution over the parameters of interest as measurements arrive. The algorithm is distributable and requires a fixed amount of storage space with respect to the number of measurements it has incorporated. LaSLAT is easy to adapt to new types of hardware and new physical environments due to its use of intuitive probability distributions: one adaptation demonstrated in this thesis uses a mixture measurement model to detect and compensate for bad acoustic range measurements due to echoes. We present results from a centralized implementation of LaSLAT using a network of Cricket sensors. In both 2D and 3D networks, LaSLAT is able to localize sensors to within several centimeters of their ground truth positions while recovering a range measurement bias for each sensor and the complete trajectory of the mobile.","abstract_has_math":false,"creators":["Taylor, Christopher J. 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