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

E.H. Aoki, Arunabha Bagchi, Pranab K. Mandal, Y. Boers

Research output: Book/ReportReport

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.
LanguageUndefined
Place of PublicationEnschede
PublisherUniversity of Twente, Department of Applied Mathematics
Number of pages8
StatePublished - Sep 2011

Publication series

NameMemorandum / Department of Applied Mathematics
PublisherUniversity of Twente, Department of Applied Mathematics
No.1952
ISSN (Print)1874-4850
ISSN (Electronic)1874-4850

Keywords

  • Kullback-Leibler divergence
  • METIS-279187
  • IR-78132
  • EWI-20563
  • EC Grant Agreement nr.: FP7/238710
  • OSPA metric
  • Particle filter
  • Sensor management
  • Rényi divergence
  • Entropy

Cite this

Aoki, E. H., Bagchi, A., Mandal, P. K., & Boers, Y. (2011). Particle filter approximations for general open loop and open loop feedback sensor management. (Memorandum / Department of Applied Mathematics; No. 1952). Enschede: University of Twente, Department of Applied Mathematics.
Aoki, E.H. ; Bagchi, Arunabha ; Mandal, Pranab K. ; Boers, Y./ Particle filter approximations for general open loop and open loop feedback sensor management. Enschede : University of Twente, Department of Applied Mathematics, 2011. 8 p. (Memorandum / Department of Applied Mathematics; 1952).
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Aoki, EH, Bagchi, A, Mandal, PK & Boers, Y 2011, Particle filter approximations for general open loop and open loop feedback sensor management. Memorandum / Department of Applied Mathematics, no. 1952, University of Twente, Department of Applied Mathematics, Enschede.

Particle filter approximations for general open loop and open loop feedback sensor management. / Aoki, E.H.; Bagchi, Arunabha; Mandal, Pranab K.; Boers, Y.

Enschede : University of Twente, Department of Applied Mathematics, 2011. 8 p. (Memorandum / Department of Applied Mathematics; No. 1952).

Research output: Book/ReportReport

TY - BOOK

T1 - Particle filter approximations for general open loop and open loop feedback sensor management

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AU - Mandal,Pranab K.

AU - Boers,Y.

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N2 - 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.

AB - 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.

KW - Kullback-Leibler divergence

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Aoki EH, Bagchi A, Mandal PK, Boers Y. Particle filter approximations for general open loop and open loop feedback sensor management. Enschede: University of Twente, Department of Applied Mathematics, 2011. 8 p. (Memorandum / Department of Applied Mathematics; 1952).