Track Fusion Algorithm Based on Maximum Likelihood Estimation

To address system biases and non-stationary noise in distributed radar networks, this paper proposes a robust track fusion algorithm based on a hybrid weighting mechanism. Drawing on the Interactive Multiple Model (IMM) framework, individual radar nodes are treated as parallel sub-filters. The algorithm integrates statistical maximum likelihood estimation with spatial geometric support to dynamically update fusion weights. A weight transition matrix is employed to describe the evolution of node credibility, while a Gaussian kernel-based similarity model evaluates geometric consistency to effectively suppress measurement outliers. By incorporating a mixing factor, the system adaptively balances statistical reliability and spatial support. Results indicate that this approach significantly enhances tracking accuracy and environmental adaptability, providing superior resilience against complex electromagnetic interference compared to traditional likelihood-based methods.