Sequential data fusion applied to a distributed radar, acoustic and visual sensor network

Abstract

Surveillance tasks, both in the public security and in the military domains, exploit sensors and sensor networks with complementary functionalities. Systems with an integrated multisensory suite benefit from the complementarity of the sensed data types. Capability gaps can indeed be reduced and an enhanced profile of the Area of Interest (AoI) can be retrieved. Although a diversified selection of surveillance devices is a mandatory requirement, an operational advantage is only achieved when the outcomes produced by all the sensors are effectively merged and exploited. This research paper addresses an innovative sensor integration approach for a highly-diversified sensor suite. The network is aimed at maximizing the information sharing and combining the data analysis, supported by a sequential data fusion engine. The parameter that will be highlighted is the relative positioning of the sensors in different combinations. The validity of the proposed solution is verified in a public security environment, where multiple actors are present. First results based on simulation are shown.