Ship Navigation Perception Data Fusion Algorithm Based on Improved Grey Wolf Optimization and BP Neural Network

BP neural network has shown strong ability in data fusion and prediction tasks, but it still has some inherent shortcomings. This article proposes a data fusion method for BP neural network (LMBPNN) based on improved grey wolf optimization (LDGWO) algorithm, and applies it to the fusion of ship navigation perception data. It optimizes the initial weights and biases of the neural network, significantly improving its convergence speed and accuracy in handling multi-source data fusion tasks. Through actual ship navigation perception data fusion experiments, it has been verified that this method has significant advantages in fusion accuracy, real-time performance, and stability.