Optimization-based dynamic sensor management for distributed multitarget tracking

  • Authors:

  • Ratnasingham Tharmarasa;Thiagalingam Kirubarajan;Jiming Peng;Thomas Lang

  • Affiliations:

  • Estimation, Tracking, and Fusion Laboratory, Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada;Estimation, Tracking, and Fusion Laboratory, Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada;Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Urbana, IL;General Dynamics Canada, Air and Naval System, Ottawa, ON, Canada

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, the general problem of dynamic assignment of sensors to local fusion centers (LFCs) in a distributed tracking framework is considered. With recent technological advances, a large number of sensors can be deployed for multitarget tracking purposes. However, due to physical limitations such as frequency, power, bandwidth, and fusion center capacity, only a limited number of them can be used by each LFC. The transmission power of future sensors is anticipated to be software controllable within certain lower and upper limits. Thus, the frequency reusability and the sensor reachability can be improved by controlling transmission powers. Then, the problem is to select the sensor subsets that should be used by each LFC and to find their transmission frequencies and powers in order to maximize the tracking accuracies and minimize the total power consumption. The frequency channel limitation and the advantage of variable transmitting power have not been discussed in the literature. In this paper, the optimal formulation for the aforementioned sensor management problem is provided based on the posterior Cramér-Rao lower bound. Finding the optimal solution to the aforementioned NP-hard multiobjective mixed-integer optimization problem in real time is difficult in large-scale scenarios. An algorithm is presented to find a suboptimal solution in real time by decomposing the original problem into subproblems, which are easier to solve, without using simplistic clustering algorithms that are typically used. Simulation results illustrating the performance of sensor array manager are also presented.