Analysis of an Information-Based Sensor Manager Applicable to Landmine Detection

Previously, a framework for sensor management has been developed for the detection of static targets such as landmines. The sensor manager functions by tasking the available sensors to greedily maximize the expected information gain obtained with each new sensor observation. This paper examines several of the key assumptions and decisions that were made in the formulation of this sensor manager to assess both the validity and the performance effects of these decisions. Specifically, this paper examines which unmanaged sensing technique is best used to make performance comparisons with the sensor manager, whether multiple sensors should be optimized independently or jointly, and finally whether the Kullback-Leibler divergence or Renyi divergence is the best choice of information measure to use. This paper demonstrates that in all three cases, the choices made in the original formulation of the sensor manager are the most effective and appropriate.