Particle filter approximations for general open loop and open loop feedback sensor management


Aoki, Edson Hiroshi and Bagchi, Arunabha and Mandal, Pranab and Boers, Yvo (2011) Particle filter approximations for general open loop and open loop feedback sensor management. [Report]

open access
Abstract:Sensor management is a stochastic control problem where the control mechanism is directed at the generation of observations. Typically, sensor management attempts to optimize a certain statistic derived from the posterior distribution of the state, such as covariance or entropy. However, these statistics often depend on future measurements which are not available at the moment the control decision is taken, making it necessary to consider their expectation over the entire measurement space. Though the idea of computing such expectations using a particle filter is not new, so far it has been applied only to specific sensor management problems and criterions. In this memorandum, for a considerably broad class of problems, we explicitly show how particle filters can be used to approximate general sensor management criterions in the open loop and open loop feedback cases. As examples, we apply these approximations to selected sensor management criterions. As an additional contribution of this memorandum, we show that every performance metric can be used to define a corresponding estimate and a corresponding task-driven sensor management criterion, and both of them can be approximated using particle filters. This is used to propose an approximate sensor management scheme based on the OSPA metric for multi-target tracking, which is included among our examples.
Item Type:Report
Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:
Link to this item:
Publisher URL:
Export this item as:BibTeX
HTML Citation
Reference Manager


Repository Staff Only: item control page

Metis ID: 279187