Object Recognition and Identification Using ESM Data
This addresses target identification challenges in surveillance and security, particularly for maritime and air-to-ground applications, but appears incremental as it builds on existing sensor fusion methods.
The paper tackled the problem of target recognition and identification in surveillance systems by developing a new fusion architecture for non-homogeneous Electronic Support Measures (ESM) and kinematic reports, with simulations showing benefits from utilizing different ESM data types.
Recognition and identification of unknown targets is a crucial task in surveillance and security systems. Electronic Support Measures (ESM) are one of the most effective sensors for identification, especially for maritime and air--to--ground applications. In typical surveillance systems multiple ESM sensors are usually deployed along with kinematic sensors like radar. Different ESM sensors may produce different types of reports ready to be sent to the fusion center. The focus of this paper is to develop a new architecture for target recognition and identification when non--homogeneous ESM and possibly kinematic reports are received at the fusion center. The new fusion architecture is evaluated using simulations to show the benefit of utilizing different ESM reports such as attributes and signal level ESM data.