Sensor management for landmine detection using correlated sensor observations

Previous research has developed an information-theoretic sensor management framework for improving static target detection performance. This framework has been successfully applied to a large dataset of real landmine data; performance using the sensor manager on this dataset was demonstrated to be superior to performance using a direct search technique in which sensors blindly sweep through the gridded region of interest. In previous work, the sensor manager has modeled the observations made in each grid cell as being independent from the other observations made in that cell by the same sensor and also as being independent from observations made in that cell by other sensors. Such a modeling approach fails to account for the correlations that will result between observations made both by the same and different sensors. This paper alters the modeling framework that has been used previously to incorporate observation correlation, which will more realistically model the interrelationships between sensor observations. After introducing the new modeling approach, results are then presented that compare the performance of the sensor manager to the performance of an unmanaged direct search procedure. The sensor manager is again demonstrated to outperform direct search. Furthermore, the performance effects of modeling and failing to model correlation are examined through simulation. Failing to model correlation that is present in the data is demonstrated to substantially degrade performance and cause direct search to outperform the sensor manager. However, when correlated modeling is used to model correlated data, the sensor manager is again demonstrated to outperform direct search.