The goal of sensor resource management (SRM) is to allocate resources appropriately in order to gain as much information as possible about a system. In our previous paper, we introduced a centralized non-myopic planning algorithm, C-SPLAN, that uses sparse sampling to estimate the value of resource assignments. Sparse sampling is related to Monte Carlo simulation. In the SRM problem we consider, our network of sensors observes a set of tracks; each sensor can be set to operate in one of several modes and/or viewing geometries. Each mode incurs a different cost and provides different information about the tracks. Each track has a kinematic state and is of a certain class; the sensors can observe either or both of these, depending on their mode of operation. The goal is to maximize the overall rate of information gain, i.e. rate of improvement in kinematic tracking and classification accuracy of all tracks in the Area of Interest. We compared C-SPLAN's performance on several tracking and target identification problems to that of other algorithms. In this paper we extend our approach to a distributed framework and present the D-SPLAN algorithm. We compare the performance as well as computational and communications costs of C-SPLAN and D-SPLAN as well as near-term planners.

Date Published: 7 May 2007
PDF: 16 pages
Proc. SPIE 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI, 65670B (7 May 2007); doi: 10.1117/12.719929

Published in SPIE Proceedings Vol. 6567:
Signal Processing, Sensor Fusion, and Target Recognition XVI
Ivan Kadar, Editor(s)