Next Generation Autonomous Tracking

Autonomous tracking is a critical capability necessary for unmanned platforms to effectively operate in contested environments with constrained communications.  Passive sensing is frequently utilized in these environments to perform tracking, provide situational understanding, and address the Find-Fix-Track portion of the kill chain.  However, passive sensing modalities present challenges, even in permissive environments.  The construction of false or “ghost” tracks are a common phenomenon and introduce significant ambiguity in the situational awareness picture.  Venator proposes the development of multiple algorithms to form accurate tracks from passive sensor data.  The primary focus will be on algorithms that improve detection-to-track association, reducing the likelihood of ghost track formation.  An additional focus area will be on novel algorithms that can improve kinematic state estimation, leading to increased track accuracies.  Venator proposes to utilize its distributed data fusion system, ACCESS, as the framework within which these capabilities will be implemented and integrated. ACCESS was purpose-built to perform distributed fusion on autonomous vehicles in contested environments and is robust to platform attrition and communications loss or degradation. Additional improvements are proposed to increase the effectiveness of ACCESS in conditions of low bandwidth availability and protracted periods of limited communications.