3-D Target Tracking for Distributed Heterogeneous 2-D–3-D Passive Radar Network
Yueyang Hu, Jianxin Yi, Feng Cheng, Xianrong Wan, Shibo Hu · IEEE Sensors Journal · 2023
In air target tracking with a distributed passive radar network, each local node will track the target separately. For two-dimensional (2-D) passive radar, the target’s altitude is often ignored due to the lack of elevation measurement, leading to an estimation deviation of the target state and thereby reducing the subsequent track fusion accuracy. This article proposes a three-dimensional (3-D) target tracking and fusion method for a distributed heterogeneous network with 2-D and 3-D passive radars. The proposed method solves two problems: associating 2-D tracks with 3-D tracks and fusing them. First, we transform the 2-D and 3-D tracks into a common bistatic coordinate and then perform the track association. Second, we obtain equivalent measurements by recalculating Jacobian matrices and measurement predictions of associated local tracks. Then, the global tracks are updated using the equivalent measurements, which have equal state estimation accuracy as centralized fusion. Monte Carlo simulations also demonstrate the equivalence of the estimation accuracy. Moreover, we verify the effectiveness of the proposed algorithm using the field experiment data of a 2-D–3-D passive radar network.