GCINet: neural network enhanced weight design for GCI fusion

Allocating the weight to each local density is essential in generalized covariance intersection (GCI) fusion. However, such a problem has not been fully addressed in the existing literature and still remains an open issue. In this paper, we propose a deep learning enhanced framework that dynamically optimizes GCI fusion weights by leveraging sensor node dependent local variables, resulting in the GCINet for fusion of probability density functions (PDFs). The key innovation lies in the employment of contextual based variables (e.g., measurement noise) as input to a neural network, which is trained by minimizing a suitably defined cost function. The proposed approach eliminates the need for manual weight tuning and overcomes the limitations of traditional optimization-based methods reliant on, e.g., Shannon entropy or Chernoff information. Application of proposed GCINet to distributed extended object tracking (EOT) application is discussed. Simulation results show that the proposed GCINet achieves superior accuracy compared to GCI fusion under equal as well as heuristically designed fusion weights.