This paper documents initial work into the development of a novel framework for sensor resource management: the Adaptive Horizon Sensor Management Framework (AHSMF). The concept at the core of AHSMF is that the optimal length of the planning horizon is dependent on the accuracy with which one can predict actual future performance, which is itself dependent on the level of uncertainty in the system (e.g. target state uncertainty). In the simplest case, in which there is no uncertainty (e.g. the target state and behavior are precisely known), a Dynamic Programming approach allows the planning horizon to extend far into the future as it is known precisely what the long-term impact of actions will be. However, we argue that in highly uncertain environments, the planning horizon should remain relatively short as the implications of actions on medium (and longer) term performance are hard to quantify. The basis of this paper is to validate this concept. We present two examples. The first is a simple toy problem in which we must plan over two time steps. We show that one step-ahead planning can perform better than two step-ahead planning if (i): the future impact of actions is highly variable, and (ii): the system controller has only limited information that does not capture this variability. The second example considers the problem of tracking a highly manoeuvring target using unmanned air vehicles (UAVs) that perform passive sensing. In this case, even more complex mechanisms influence the optimal length of the planning horizon. Two step-ahead planning outperformed one step-ahead planning (in terms of tracking accuracy) in many scenarios. However, in the most difficult, challenging and uncertain problems, with just one UAV tracking a target that frequently manoeuvred, one step-ahead planning was shown to perform significantly better. Future work will aim to identify the exact mechanisms responsible for the sub-optimality of multi-step-ahead planning in this, and other, pertinent applications. This will then provide a framework for adjusting the planning horizon online, in order to avoid unnecessary over-planning and maximize performance.

Date Published: 21 September 2007
PDF: 12 pages
Proc. SPIE 6699, Signal and Data Processing of Small Targets 2007, 66990V (21 September 2007); doi: 10.1117/12.734664

Published in SPIE Proceedings Vol. 6699:
Signal and Data Processing of Small Targets 2007
Oliver E. Drummond; Richard D. Teichgraeber, Editor(s)