Target Forecasting and Path Planning Algorithm Based on PCA

In the cooperative sea wargame scenario, the key problem of battle decision is target forecasting and path planning, which presents high dimensional features with redundant data and low correlation between most data and tasks. In this paper, a decision-making method based on Principal Component Analysis (PCA) algorithm is proposed, which can effectively reduce the dimension and extract the features of high-dimensional spatial data in a short time. At the same time, the target forecasting and path planning algorithm based on PCA algorithm is established, the fire distribution method is designed, and the simulation experiment is carried out on the wargame deduction platform. The experimental results show that the intelligent algorithm based on PCA can forecast the target position with high precision, and complete the path planning according to the forecasted target position, which improves the winning rate of the battle.