  Target identification of ship-radiated noise is a crucial area in underwater
target recognition. However, there is currently a lack of multi-target ship
datasets that accurately represent real-world underwater acoustic conditions.
To tackle this issue, we conducted experimental data acquisition, resulting in
the release of QiandaoEar22 \textemdash a comprehensive underwater acoustic
multi-target dataset. This dataset encompasses 9 hours and 28 minutes of
real-world ship-radiated noise data and 21 hours and 58 minutes of background
noise data. To demonstrate the availability of QiandaoEar22, we executed two
experimental tasks. The first task focuses on assessing the presence of
ship-radiated noise, while the second task involves identifying specific ships
within the recognized targets in the multi-ship mixed data. In the latter task,
we extracted eight features from the data and employed six deep learning
networks for classification, aiming to evaluate and compare the performance of
various features and networks. The experimental results reveal that
ship-radiated noise can be successfully identified from background noise in
over 99\% of cases. Additionally, for the specific identification of individual
ships, the optimal recognition accuracy achieves 99.56\%. Finally, based on our
findings, we provide advice on selecting appropriate features and deep learning
networks, which may offer valuable insights for related research. Our work not
only establishes a benchmark for algorithm evaluation but also inspires the
development of innovative methods to enhance UATD and UATR systems.
