Data Exploitation and processing for multi-sensor radar big data

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Intelligence, Surveillance, and Reconnaissance (ISR) systems comprise of several sensors, such as radar, sonar, electro-optical, hyperspectral, and infrared. Improved sensor technologies generate sensors with enhanced capabilities such as increased resolution and sensitivity that can achieve the improved surveillance capability stated above. However, they also result in increasing amount of sensor data at a rate beyond using traditional tools to analyze. This project proposes to develop signal and image processing for multi-sensor and radar big data. Big data has the potential to stretch the horizon of traditional monitoring and surveillance systems by fully exploiting the data available. Conventional signal processing techniques are mostly developed on optimality theory which are not practical for big data. There are two general approaches in big data analytic that can be adopted by signal and image processing to process massive amount of data. The first one uses distributed processing to decompose a task on distributed computing units to reduce the load of individual computing units and to enhance computing speed. The Hadoop-based MapReduce framework is a widely used approach for big data storage and analytics. The second one uses the concept of approximation. It uses random projection usually based on hashing to reduce the dimensionality and tries to obtain results close to those based on the complete data set. In this project, new signal processing and machine learning techniques will be developed based on approximation, sparse representation, distributed processing and ensemble approach that can fully exploit the information in the sensory big data to identify change of patterns for surveillance purpose. These techniques will be applied to the airborne radar, electro-optical (EO) and infrared (IR) data provided by Defence Research and Development Canada (DRDC) and the industrial partners.