A Hopfield neural tracker for phased array antenna

摘要

A state estimator based on neural network is applied to phased array tracking. The state estimation is formulated as a dynamic optimization problem, and solved using a Hopfield neural network. This neural tracker has the flexibility for adaptively varying the target-track update rate as a function of target maneuvering. The value of the update time is dependent on the magnitude of the residual error of the state estimator. Simulation results show improvement of the new approach over the standard variable update time /spl alpha/-/spl beta/ filter for phased array tracking.