Simulation implementation of intelligent track-to-track association algorithm for multi-sensor network

Multi-sensor network joint detection is a newly emerging interdisciplinary detection method that has been developing rapidly in recent years. Compared with traditional single sensor detection, it can enhance the robustness and reliability of the entire system by using multi sensor network track-to-track association technology in solving problems such as targeting, detection, and positioning. It can also improve target accuracy, expand system time, and improve sensor coverage Advantages such as improving the information utilization rate of the system. This article proposes a multi sensor track association algorithm based on Convolutional Neural Network(CNN). Through three steps of constructing, initializing, and training the neural network, a multi sensor track association model based on CNN is established, which solves the problem of automatic track association under the background of multi-sensor network detection. Simulation experiments on multiple sets of data are conducted, through data proof, the intelligent method of using neural network algorithms can effectively improve the track association of multiple sensor stations.