We consider the problem of scheduling an agile sensor for performing optimal search for a target.
A probability density function is created for representing our knowledge about where the target might be and it is utilized by the proposed sensor management criteria for finding optimal search strategies.
The proposed criteria are: an information-driven criterion based on the Kullback-Leibler divergence and a criterion with practical meaning, i.e. performing the sensing action that will yield the maximum probability of detecting the target.
It is shown that using the aforementioned criteria results in the same sensing actions when searching for a target and this result establishes a practical operational justification for using information-driven sensor management for performing search.
|Name||Memorandum / Department of Applied Mathematics|
|Publisher||University of Twente, Department of Applied Mathematics|
- Sensor management
- Optimal search
- Particle filter
- Kullback-Leibler divergence
- Probability of detection