Novel Deep-Learning-Aided Multimodal Target Tracking

Existing interacting multiple models (IMMs) are limited by the time delay in responding to system model jumps due to the nature of the soft hand-off algorithm that interacts among subfilters. To address this issue, a novel method for deep-learning-aided localization of a multimodel system is proposed in this paper. The main contribution of the proposed algorithm is that a mode estimation network based on a bidirectional long short-term memory network (BiLSTM) is newly proposed to quickly and accurately estimate the multimodal system mode, which minimizes the delay. In addition, a federated Kalman filter with a selective reinitialization algorithm from the proposed BiLSTM is proposed for better estimation of multimodal systems. Simulation and flight test results of a UAV demonstrate that the proposed algorithm yields better localization performance than the conventional IMM algorithm because the proposed mode estimation network has fast and accurate mode detection.