A Multi-Objective Genetic Algorithm Method for Active Sonar Clutter Reduction


  1. Gammon, M.A.
Corporate Authors
Defence Research and Development Canada, Atlantic Research Centre, Halifax NS (CAN)
Target-like echoes from the use of active sonar, commonly known as 'clutter', is a particularly difficult problem when trying to separate real from false contacts. A method for reducing the amount of clutter in tracking underwater targets and providing a likely target area is accomplished by using an iterative Multi-Objective Genetic Algorithm (MOGA). The MOGA developed requires the automatic selection of a Pareto optimal result of the likely target position. The optimization minimizes the position of the genetic population with the last given contact positions, as one objective, while using an average position based on a history of optimal solutions as a second objective. In order to concentrate on a particular area, the algorithm is applied iteratively, taking into account the size of the area being examined and other constraints. In each subsequent iteration, a smaller area is used to limit the amount of clutter being examined. After a nominal number of iterations, the area of the probable target location and the optimal target result from the algorithm are examined to determine whether the MOGA has determined a good position estimate. A simulation of the performance of the algorithm in a clutter environment is used to investigate the robustness of this particular method. The application of this approach may be of significant benefit in future configurations of acoustic processing systems developed from the DRDC Atlantic Research Centre's System Test Bed (STB).

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multistatic active sonar;clutter;multiobjective genetic algorithm
Report Number
DRDC-RDDC-2015-R197 — Scientific Report
Date of publication
01 Sep 2015
Number of Pages
Electronic Document(PDF)

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