A Data Correlation Algorithm Based on Resnet for Unsteady Data

A data correlation algorithm based on Resnet for unsteady data was designed in this paper. Traditional data correlation algorithms, such as joint probabilistic data association algorithm (JPDA) and k-nearest neighbor correlation algorithm (KNN), require to satisfy the conditions that data error distribution is approximately stable or stable, and have a significant performance degradation under unsteady data conditions. Resnet was used to extract multi-level and heterogeneous features to solve the optimal decision function in correlation algorithm problem. First, the global correlation problem was decomposed into the basic correlation problem with fixed problem space size. Then, the deep network was designed to extract the invariant features of the unsteady data and find the classification model of the solution of the basic correlation problem. In this way, the correlation robustness and accuracy can be improved when the error distribution changes and the parameters cannot be accurately estimated. The simulation results show that the algorithm designed in this paper is effective. When the error distribution changes within a certain range and under unknown conditions, it is better than JPDA and k-nearest neighbor algorithm.