Novel Deep-Learning-Aided Multimodal Target Tracking
SungTae Moon, Wonkeun Youn, Hyochoong Bang
Abstract
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.