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In practice, environmental information about an ocean bottom area to be searched using SONAR is often known a priori to some coarse level of resolution. The SONAR search sensor then typically has a different performance characterization function for each environmental classification. Large ocean bottom surveys using search SONAR can pose some difficulties when the environmental conditions vary significantly over the search area because search planning tools cannot adequately segment the area into sub-regions of homogeneous search sensor performance. Such segmentation is critically important to unmanned search vehicles; homogenous bottom segmentation will result in more accurate predictions of search performance and area coverage rate. The Naval Surface Warfare Center, Panama City Division (NSWC PCD) has developed an automated area segmentation algorithm that subdivides the mission area under the constraint that the variation of the search sensor’s performance within each sub-mission area cannot exceed a specified threshold, thereby creating sub-regions of homogeneous sensor performance. The algorithm also calculates a new, composite sensor performance function for each sub-mission area. The technique accounts for practical constraints such as enforcing a minimum sub-mission area size and requiring sub-mission areas to be rectangular. Segmentation occurs both across the rows and down the columns of the mission area. Ideally, mission planning should consider both segmentation directions and choose the one with the more favorable result. The Automated Area Segmentation Algorithm was tested using two a priori bottom segmentations: rectangular and triangular; and two search sensor configurations: a set of three bi-modal curves and a set of three uni-modal curves. For each of these four scenarios, the Automated Area Segmentation Algorithm automatically partitioned the mission area across rows and down columns to create regions with homogeneous sensor performance. The testing results indicated that the algorithm correctly segmented the rectangular a priori regions. For the triangular a priori segmentation, the algorithm created reasonable rectangular sub-areas.
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