Modifying a Multi-Objective Genetic Algorithm Method for Active Sonar Clutter Reduction using Real-World Data

Target-like echoes from the use of active sonar, known as 'clutter', pose a problem to separate real from false contacts. A method for reducing the amount of clutter in tracking underwater targets is accomplished by using an iterative Multi-Objective Genetic Algorithm (MOGA). 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. 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 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 random clutter environment was first used to investigate the robustness of this particular method. Real world data was then used to determine the effectiveness of this approach. Modifications to the objectives were made and a third objective added to reflect the acoustic properties of the target.