Online time-varying navigation ratio identification and state estimation of cooperative attack

Abstract

An online navigation ratio identification model based on the gated recurrent unit (GRU) and a state estimation extended Kalman filter (EKF) are proposed under the scenario in which multiple enemy missiles attack a stationary target using a time-cooperative guidance law. The navigation ratio identification is solved as a dynamic problem, and the time-varying navigation ratios of each missile, instead of the effective navigation constants and cooperative gains, are identified in this paper. In other words, the simplified assumption that the true value is within a known finite set, which is generally adopted in a conventional identification-estimation scheme such as multiple-model adaptive estimators (MMAEs) or interacting multiple-models (IMMs), is discarded. To increase the training speed and identification accuracy, the improved multiple-model mechanism (IMMM) is adopted, and a multiple-model layer, in which regimes representing different values are set, is connected behind a conventional neural network. Since the navigation ratios are identified online, the connections between missiles are decoupled, and only one filter is required for each missile. This could greatly reduce the computational burden of onboard computers. The effectiveness of the proposed online identification model and the performance of the state estimation filter are demonstrated through numerical simulations.