TY  - JOUR
TI  - Maritime automatic target recognition for ground-based scanning radars by using
sequential range profiles
AB  - Classification of marine targets using radar data products has become an important area for modern research
society. However, due to several reasons such as the similarity between ship structures and spatial specifications,
classification of marine targets constitutes a challenging problem. In almost all of the studies, this problem has been
handled by focusing on a single instance of range profiles or synthetic aperture radar data. However, this approach
is seen to achieve only a particular success. This study introduces a novel classification approach that is shown to
provide additional classification enhancements by exploiting the extra information extracted from sequential range
profiles generated by ground-based marine surveillance radars. With this purpose, both synthetic and measuremental
range profiles are taken into consideration. Synthetic profile data are generated for seven marine targets by using an
electromagnetic scattering simulation tool (RASES)1. On the other hand, a total of 2387 range profile data of 171
different target tracks are collected for five different marine target class types by using an X-band marine surveillance
radar. Each target tracked for a long period of time to gather sequential HRRP data subsets. HRRP data subsets are
used to generate HMM based transition matrix probabilities and sequential classification results by evaluating proposed
method. Probabilistic neural network (PNN) and convolutional neural network (CNN) classification algorithms applied
to gather classification results. The proposed method results are compared with both single value classification and
majority voting rule (MVR) method results. According to the examination results, the proposed classification approach
provides remarkable enhancements in the correct classification rates when compared to the case of single profile data
approach.
AU  - DURU, NEVCIHAN
AU  - BATI, Baki
DO  - 10.3906/elk-2004-143
PY  - 2021
JO  - Turkish Journal of Electrical Engineering and Computer Sciences
VL  - 29
IS  - 2
SN  - 1300-0632
SP  - 929
EP  - 943
DB  - TRDizin
UR  - http://search/yayin/detay/514950
ER  -