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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.
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