Orchestrated management of heterogeneous sensors incorporating feedback from intelligence assets

Abstract

We develop a method for autonomous management of multiple heterogeneous sensors mounted on unmanned aerial vehicles (UAVs) for multitarget tracking. The main contribution of the paper is incorporation of feedback received from intelligence assets (humans) on priorities assigned to specific targets. We formulate the problem as a partially observable Markov decision processes (POMDP) where information received from assets is captured as a penalty on the cost function. The resulting constrained optimization problem is solved using an augmented Lagrangian method. Information obtained from sensors and assets is fused centrally for guiding the UAVs to track these targets.

Controlled tracking in urban terrain: Closing the loop

“…This work was further expanded by Ragi and Chong to accommodate track swap avoidance, target evasion, and threat‐motion model for UAVs; the more general framework of decentralised POMDP (Dec‐POMDP) was also studied in . A method to incorporate information received from human intelligence assets into the UAV decision making for target tracking was demonstrated by Sarkale and Chong using the POMDP framework. Allocation of other sensor resources such as target revisit interval and radar steering angle were examined by Kirubarajan et al .…”