Reference number : GC-2017-Q2-00460, amendment number : , agreement type : G, recipient legal name : Kirubarajan, Thia (McMaster University), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Department of National Defence / NSERC Research Partnership - Project, program name french : Partenariat de recherche du ministère de la Défense nationale et du CRSNG - projet, program purpose english : Sensing or inference using data from heterogeneous and geographically-distributed sensors has many civilian as well as defense applications. For example, in defense-oriented surveillance systems, multiple unidentified targets are tracked using noisy data from sensors such as radar, sonar, electro-optical or infrared cameras to identify their locations and courses and classify their types. In civilian urban-monitoring or smart-city systems, multiple cameras are used to monitor traffic and to ensure the safety and security of people in an area. Advances in sensor technologies have resulted in affordable high-quality sensors (e.g., video cameras, acoustic devices, short-range radars) that are ubiquitous around us. Also, unlike before when sensors were usually deployed by those interested in surveillance, now data from ad-hoc sensors-of-opportunity are also available. Admittedly, computing technology has also improved along with advances in sensor technology. However, in order to achieve real-time sensing capability, it is necessary to develop efficient algorithms to process the vast amounts of data from a multitude of sensors (e.g., 4K video data at high framerates, sonar data with extremely high false alarm rates) with a time-varying sensor architecture or configuration. That is, we need algorithms that can adapt to ever-changing target characteristics and sensor configurations at design-time as well as at run-time. We propose to develop multi-level (e.g., at sensor, platform, system and system-of-systems levels) adaptation algorithms to process data from a time-varying set of sensors mounted on platforms that may evolve over time with the objective of accurate tracking, classification and situational awareness. In addition to training highly qualified personnel and advancing the state-of-the-art in sensing technology, the_x000D_
proposed work will have significant economic and societal impacts. The algorithms to be developed in this project can be applied, with some modifications, to defense as well as civilian sensing systems., program purpose french : Sensing or inference using data from heterogeneous and geographically-distributed sensors has many civilian as well as defense applications. For example, in defense-oriented surveillance systems, multiple unidentified targets are tracked using noisy data from sensors such as radar, sonar, electro-optical or infrared cameras to identify their locations and courses and classify their types. In civilian urban-monitoring or smart-city systems, multiple cameras are used to monitor traffic and to ensure the safety and security of people in an area. Advances in sensor technologies have resulted in affordable high-quality sensors (e.g., video cameras, acoustic devices, short-range radars) that are ubiquitous around us. Also, unlike before when sensors were usually deployed by those interested in surveillance, now data from ad-hoc sensors-of-opportunity are also available. Admittedly, computing technology has also improved along with advances in sensor technology. However, in order to achieve real-time sensing capability, it is necessary to develop efficient algorithms to process the vast amounts of data from a multitude of sensors (e.g., 4K video data at high framerates, sonar data with extremely high false alarm rates) with a time-varying sensor architecture or configuration. That is, we need algorithms that can adapt to ever-changing target characteristics and sensor configurations at design-time as well as at run-time. We propose to develop multi-level (e.g., at sensor, platform, system and system-of-systems levels) adaptation algorithms to process data from a time-varying set of sensors mounted on platforms that may evolve over time with the objective of accurate tracking, classification and situational awareness. In addition to training highly qualified personnel and advancing the state-of-the-art in sensing technology, the_x000D_
proposed work will have significant economic and societal impacts. The algorithms to be developed in this project can be applied, with some modifications, to defense as well as civilian sensing systems., agreement title english : Multi-level adaptive systems and algorithms for agile and opportunistic sensing, agreement title french : Multi-level adaptive systems and algorithms for agile and opportunistic sensing, agreement number : DNDPJ, agreement value : 730000.0, agreement start date : 2017-08-23, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2021-2022)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2021-2022)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada