Construction of Marine Target Detection
Dataset for Intelligent Radar Application
Hao Ding
1 , Ningbo Liu
1,2(&) , Wei Zhou
1 , Yonghua Xue
1 ,
and Jian Guan
1
1 Naval Aviation University, YanTai, People’s Republic of China
hao3431@tom.com, lnb198300@163.com
2 No. 23 Institute of the Second Academy, CASIC, Beijing,
People’s Republic of China
Abstract. With the development of artificial intelligence technology, intelligent
processing technology, which is represented by deep learning theory, has been
widely used in radar community. The precondition of its application is the
support of a large number of training dataset. However, for the technical
direction of marine target detection, open dataset is insufficient to meet application requirements. To this end, framework of marine target detection dataset
for intelligent radar application is constructed in this paper. It consists of three
core modules, namely the detection equipment and data acquisition system, data
management and processing, and integrated display. At present, sea clutter
property cognition dataset, clutter suppression and target detection dataset,
target characteristics dataset and detection performance evaluation dataset are
preliminarily obtained. Dataset management and property analysis software are
also developed, so as to serve applications more conveniently.
Keywords: Sea clutter Á Target detection Á Intelligent radar Á Dataset
1 Introduction
For marine radars operating in high sea states and complex meteorological conditions,
it is very difficult to detect targets in the presence of complex sea clutter, especially for
targets with low grazing angles, slow speed and small RCS. Such target signals are
embedded in sea clutter in both time domain and Doppler domain, resulting in a
significant detection performance degradation [1–6].
In recent years, with the development and application of artificial intelligence
technology, such as machine learning and deep learning theory, radar signal processing
is also expected to break through the original theoretical framework and steps into the
era of intelligence. In fact, in the past two years, intelligent processing technology,
which is represented by deep learning theory, has been widely applied in radar. It is a
more efficient intelligent signal processing method with the advantages of automatic
extraction of deep features with higher accuracy [7]. However, the precondition for
deep learning application is the support of sufficient training dataset. At present, open
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 245–253, 2020.
https://doi.org/10.1007/978-981-15-0187-6_28
Dataset for Intelligent Radar Application
Hao Ding
1 , Ningbo Liu
1,2(&) , Wei Zhou
1 , Yonghua Xue
1 ,
and Jian Guan
1
1 Naval Aviation University, YanTai, People’s Republic of China
hao3431@tom.com, lnb198300@163.com
2 No. 23 Institute of the Second Academy, CASIC, Beijing,
People’s Republic of China
Abstract. With the development of artificial intelligence technology, intelligent
processing technology, which is represented by deep learning theory, has been
widely used in radar community. The precondition of its application is the
support of a large number of training dataset. However, for the technical
direction of marine target detection, open dataset is insufficient to meet application requirements. To this end, framework of marine target detection dataset
for intelligent radar application is constructed in this paper. It consists of three
core modules, namely the detection equipment and data acquisition system, data
management and processing, and integrated display. At present, sea clutter
property cognition dataset, clutter suppression and target detection dataset,
target characteristics dataset and detection performance evaluation dataset are
preliminarily obtained. Dataset management and property analysis software are
also developed, so as to serve applications more conveniently.
Keywords: Sea clutter Á Target detection Á Intelligent radar Á Dataset
1 Introduction
For marine radars operating in high sea states and complex meteorological conditions,
it is very difficult to detect targets in the presence of complex sea clutter, especially for
targets with low grazing angles, slow speed and small RCS. Such target signals are
embedded in sea clutter in both time domain and Doppler domain, resulting in a
significant detection performance degradation [1–6].
In recent years, with the development and application of artificial intelligence
technology, such as machine learning and deep learning theory, radar signal processing
is also expected to break through the original theoretical framework and steps into the
era of intelligence. In fact, in the past two years, intelligent processing technology,
which is represented by deep learning theory, has been widely applied in radar. It is a
more efficient intelligent signal processing method with the advantages of automatic
extraction of deep features with higher accuracy [7]. However, the precondition for
deep learning application is the support of sufficient training dataset. At present, open
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 245–253, 2020.
https://doi.org/10.1007/978-981-15-0187-6_28