  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 ntackle this issue, we release QiandaoEar22 \textemdash an underwater
acoustic multi-target dataset, which can be download on
https://ieee-dataport.org/documents/qiandaoear22. 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. We demonstrate the availability of
QiandaoEar22 by conducting an experiment of identifying specific ship from the
multiple targets. Taking different features as the input and six deep learning
networks as classifier, we evaluate the baseline performance of different
methods. The experimental results reveal that identifying the specific target
of UUV from others can achieve the optimal recognition accuracy of 97.78\%, and
we find using spectrum and MFCC as feature inputs and DenseNet as the
classifier can achieve better recognition performance. Our work not only
establishes a benchmark for the dataset but helps the further development of
innovative methods for the tasks of underwater acoustic target detection (UATD)
and underwater acoustic target recognition(UATR).
