  Active tracking of space noncooperative object that merely relies on vision
camera is greatly significant for autonomous rendezvous and debris removal.
Considering its Partial Observable Markov Decision Process (POMDP) property,
this paper proposes a novel tracker based on deep recurrent reinforcement
learning, named as RAMAVT which drives the chasing spacecraft to follow
arbitrary space noncooperative object with high-frequency and near-optimal
velocity control commands. To further improve the active tracking performance,
we introduce Multi-Head Attention (MHA) module and Squeeze-and-Excitation (SE)
layer into RAMAVT, which remarkably improve the representative ability of
neural network with almost no extra computational cost. Extensive experiments
and ablation study implemented on SNCOAT benchmark show the effectiveness and
robustness of our method compared with other state-of-the-art algorithm. The
source codes are available on https://github.com/Dongzhou-1996/RAMAVT.
