Object Recognition and Identification Using ESM Data

TL;DR

This paper proposes a new fusion architecture for recognizing and identifying targets using diverse ESM and kinematic reports, enhancing surveillance capabilities in maritime and air-to-ground scenarios.

Contribution

It introduces a novel architecture for integrating heterogeneous ESM and kinematic data for improved target recognition and identification.

Findings

Simulation results demonstrate improved recognition accuracy.

Utilizing diverse ESM report types enhances identification performance.

The architecture effectively handles non-homogeneous sensor data.

Abstract

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.