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Objective Functions for Bayesian Control-Theoretic Sensor Management, II: MНC-Like Approximation

  • Ronald Mahler
Part of the Cooperative Systems book series (COSY, volume 3)

Abstract

Multisensor-multitarget sensor management is at root a problem in nonlinear control theory. Single-sensor, single-target control typically employs a Kalman filter to predict future target states, in conjunction with a core objective function (usually, a Mahalanobis distance) that dynamically positions the sensor Field of View (FoV) over predicted target position. An earlier (1996) paper proposed a foundation for sensor management based on the Bayes recursive filter for the entire multisensor-multitarget system, used in conjunction with a multi-target Kullback-Leibler objective function. This chapter proposes a potentially computationally tractable approximation of this approach. We analyze possible single-step and multistep objective functions: general multitarget Csiszár information-theoretic functionals and “geometric” functionals, used with various optimization strategies (maxi-min, maxi-mean, and “maxi-null”). We show that some of these objective functions lead to potentially tractable sensor management algorithms when used in conjunction with МНС (multi-hypothesis correlator) algorithms.

Keywords

Objective Function Data Fusion Cooperative Control Sensor Management Multitarget Tracking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Ronald Mahler
    • 1
  1. 1.Lockheed Martin NE&SS Tactical SystemsEaganUSA

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