This dissertation presents the method to solve the problem of path planning for unmanned aerial vehicle (UAV) in adversarial environments including radar-guided surface-to-air missiles (SAMs) and unknown threats. SAM lethal envelope and radar detection for SAM threats and visibility calculation for unknown threats are considered to compute the cost for path planning. In particular, dynamic SAM lethal envelope is taken into account for path planning in that SAM lethal envelope does change its direction according to the flight direction of UAV. In addition, terrain masking, nonisotropic radar cross section (RCS) and dynamic constraints of UAV are considered to determine the cost of the path. Improved particle swarm optimization (PSO) algorithm is proposed for finding an optimal path. 3D environments including the altitude are considered for calculating the cost and determining the path. The proposed algorithm is composed of pre-processing steps, multi-swarm PSO algorithm and post-processingsteps. Voronoi diagram and Dijkstra algorithm as pre-processing steps provide the initial path for multi-swarm PSO algorithm which uses multiple swarms with sub-swarms for the balance between exploration and exploitation. Post-processing steps include waypoint insertion and 3D path smoothing. The computation time is reduced by using the map generation, the coordinate transformation and the graphic processing unit (GPU)
implementation of the algorithm. Various simulations are carried out to compare the performance of the proposed method according to the number of iterations, the number of swarms and the number of cost evaluation points. The t-test results show that suggested method is statistically better than existing methods. In addition, we propose the method to represent a complex SAM lethal envelope rather than a simplified mathematical model such as an elliptical shape.