GMTI, Machine Learning, Maritime Radar, WAMI, Wide Area Surveillance
A key building block underpinning intelligence, surveillance, target acquisition, and reconnaissance (ISTAR) is the provision of a wide area surveillance (WAS) capability. A fundamental sensor technology for achieving WAS remains the radar sensor which has traditionally employed real aperture radar (RAR) scanning modes to rapidly survey large areas. More recently, the WAS capability has been supplemented and greatly enhanced by the development of wide area motion imagery (WAMI) based on electro-optical (EO) sensors which provide the basis for a detection based surveillance capability across regions of tens of square kilometers. Despite these developments in sensor technology the continued evolution of the problem space raises significant new challenges to the achievement of robust WAS. For example, the migration of sensors to high altitude platforms, such as Remotely Piloted Aircraft System (RPAS), leads to greatly increased surface clutter interference resulting in severe degradation of radar performance. Furthermore, the increased importance of surveillance of urban areas coupled with the desire to detect and track small maneuverable targets in support of activity based intelligence (ABI) results in an extremely challenging detection and tracking problem. Legacy detection and tracking approaches tend to perform poorly under these new clutter environments. This failure is strongly linked to the inability to accurately describe and model the statistical processes associated with the clutter and target signatures which have been observed to be complex nonlinear functions of time, space, environment and target class. This is the type of challenge for which machine learning (ML) has been shown to be highly applicable in other fields. While the transference of civilian applications, such as object recognition or speech recognition techniques, have been investigated for application to military problems, such as SAR image analysis, there has been little investigation of the application of ML techniques to the WAS problem.
The exploratory team (ET) will define the scope and goals of a follow-on RTG to develop ML structures to support WAS sensor processing. More precisely the ET will accomplish the following objectives:
1. Identify sensor technologies of interest. A follow-on RTG may be recommended to focus on a single sensor or a subset of sensors.
2. Identify existing data sets and required data collects to support follow-on RTG.
3. Examine, refine and shortlist promising ML strategies for investigation under follow-on RTG such as supervised, unsupervised and semi-supervised learning. Traditional approaches such as Support Vector Machine (SVM) through to more recent approaches in deep learning will be considered.
4. Develop preliminary recommendations for scope, work plan and schedule for follow-on RTG.