Deep Uncertainty-Aware Tracking for Maneuvering Targets

Traditional target tracking approaches typically presuppose a target motion model. Once the target maneuvers, the model may get mismatched and is incapable of depicting the complex motion characteristics of the target. Most data-driven methods are designed based on a regression-only framework, which increases the difficulty of network training. Moreover, these methods are typically designed under the assumption of a fixed measurement noise distribution. However, the measurement noise distribution of actual radar is influenced by fluctuations in the target's radar cross section. Such fluctuations may lead to a degradation in tracking performance. In this paper, we propose a representation method for radar measurements, referred to as the measurement uncertainty projection operator (MUPO). It projects the measurement onto a target state space to capture the measurement noise distribution. Additionally, we propose a framework that reformulates maneuvering target tracking as a hybrid task of classification and regression. To realize the functionality of this framework, we establish a MUPO-based target tracking network. The rationality of the proposed network structure is supported by the validation experiment. Numerical results demonstrate that the proposed method achieves superior estimation precision compared with existing data-driven methods during target maneuvers. Furthermore, the proposed algorithm retains its superior tracking performance when extended to 3D maneuvering target tracking and multi-target tracking scenarios.