Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

Sensor systems are becoming ubiquitous as we strive to improve our understanding of our surroundings and ourselves. Extensive sensor networks are being deployed to monitor the environment. People have started to wear bio-sensing devices to track their health status and physical activity. Self-driving cars are becoming a reality. In many sensor systems, we are faced with a deluge of measurements and must estimate a high-dimensional state that summarizes the pertinent information. For example, a self-driving car needs to locate and track all other vehicles and pedestrians, and must identify and recognize road conditions such as traffic lights.
Although there have been important advances, we lack algorithms and computing infrastructure that can efficiently process a high-volume stream of measurements and perform high-dimensional state estimation. Modern sensor networks can involve hundreds of sensor nodes, each generating hundreds of measurements per second. To describe the underlying state we may need hundreds of dimensions. Consider a "traffic state vector" describing the evolution of hundreds of traffic flows in a city, where the measurements are the counts of cars traversing all major intersections every second. The challenge intensifies when we add the requirement of real-time processing, which is critical for acquiring an evolving understanding of the status of the environment or system so that we can react accordingly. Many of the current algorithms cannot achieve sufficient accuracy; others require too much computation and cannot operate in real-time.
I will develop novel algorithms that can estimate and predict the state of a system, in real time, when the state dimension is in the hundreds and we must process thousands of measurements per second. The algorithms will be based on sequential Monte Carlo methods and particle flow. Concepts from island particle filters will be used to parallelize the algorithms, making it feasible to employ them in real-time tracking applications. I will extend these algorithms to the case of multi-sensor, multi-object tracking, where we must also determine how many objects are present in a scene.
I will apply the algorithms in two application domains: breast cancer detection using radio-frequency (RF) measurements and environmental perception for self-driving vehicles. We have developed a prototype radio-frequency breast cancer detection system and the research in this program will provide the detection algorithms needed to process the data obtained from a scan to decide if a tumour is present. For self-driving vehicles, the multi-sensor, multi-object tracking algorithms will allow us to process the measurements from multiple high-resolution radar sensors to determine in real-time the presence, locations, and shapes of other vehicles. These tracking algorithms will be provided to collaborators and integrated into autonomous driving systems.