Distributed State Estimation Method Based on Heterogeneous Filter Collaboration

In target state estimation using sensor networks, conventional methods often face difficulties. These include nonlinear target dynamics, high communication overhead, and weak robustness against disturbances. This paper introduces a distributed estimation framework with heterogeneous filters. It combines the strengths of different filters to suit various operating conditions. The framework builds a multi-layered cluster of nodes. These nodes include standard Kalman filters (KF), extended Kalman filters (EKF), and particle filters (PF). Within each homogeneous filter group, the system applies a covariance-based fusion strategy. Between heterogeneous groups, it uses an adaptive fusion method driven by residuals. To reduce communication load, the framework avoids transmitting raw data. It also introduces a node activation mechanism based on trajectory nonlinearity. This mechanism decides which filter groups should run at each time. Simulation results show clear improvements. The method enhances accuracy and robustness. It also lowers computation and communication costs. These results show promise for real-world use and large-scale deployment.