=Paper=
{{Paper
|id=Vol-2416/paper67
|storemode=property
|title=High performance radar images modelling and recognition of real objects
|pdfUrl=https://ceur-ws.org/Vol-2416/paper67.pdf
|volume=Vol-2416
|authors=Denis Zherdev,Vladimir Prokudin
}}
==High performance radar images modelling and recognition of real objects ==
https://ceur-ws.org/Vol-2416/paper67.pdf
High performance radar images modelling and recognition of
real objects
D A Zherdev1,2, V V Prokudin1
1
Samara National Research University, Moskovskoe Shosse 34А, Samara, Russia, 443086
2
Image Processing Systems Institute of RAS - Branch of the FSRC "Crystallography and
Photonics" RAS, Molodogvardejskaya street 151, Samara, Russia, 443001
e-mail: t_treasure@mail.ru
Abstract. In the work there is a modernization of the parallel algorithm for the radar images
formation of 3D models with the synthesis of the antenna aperture. In the formation of the
scene description, the various structures are used in which it is possible to use more efficient
and derived calculations. In addition, it is the topical task to recognize objects on radar images.
Thus, on the basis of the implemented parallel program for modelling, the high performance
required for simulating multiple radar images can be achieved.
1. Introduction
This research is a continuation of ideas and methods used in [1], where high-performance radar images
modeling approach was considered. The goal of this work is to obtain a greater acceleration of the
parallel program for synthetic aperture radar modeling by building a kd tree describing a three-
dimensional scene [2-4]. We used CUDA to perform a high-performance computing on a graphic card
and achieve this goal. It is the main difficulty to form a trajectory signal along with the radar travel
and then compute a radar image. In this study, an algorithm for obtaining radar characteristics was
implemented with the construction of the kd tree structure, that allow to describe any three-
dimensional scene.
2. Modelling and recognition
The main computationally expensive part of the radar images modelling algorithm is in the processing
of radiated and reflected radar signals. In this work, a modification of the CUDA algorithm was
implemented, which difference at the stage of the trajectory signal constructing is in a previously
calculated kd-tree for any three-dimensional scene. The subsequent execution of the synthesis of the
radar aperture was performed the same way as discussed in [1]. This approach of computable
operations reducing allowed us to achieve a three-fold acceleration compared with the previous
algorithm implementation.
In addition, during the research, we carried out the experiments of object recognition in radar
images. There are many methods and approaches of object recognition, among which the popular
methods are convolutional neural networks [5], support vectors, nearest neighbours, etc. [6]. In this
work, the object recognition was performed using the method of support subspaces [7]. We used three-
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D A Zherdev, V V Prokudin
dimensional models of the tank, BMP, BTR to construct the radar images. All sizes of the models
were matched to the corresponding sizes of their real prototypes at the three-dimensional coordinate
system. We used the modelling parameters presented in Table 1 when generated the training set. The
bearing angle was 17°, which corresponds to the conditions of the real obtained SAR images training
dataset. We modelled 100 images for each object. The step of rotation was equal the 3.6° in the
observation plane. Figure 3 shows the real and modeled SAR images of BTR.
There are the results of research shown below which related to the construction of a training model
sample and the subsequent recognition of real images using model images at the training stage. Figure
1, a), b) shows the real radar images of a tank from the widely known MSTAR database, and figure 2,
a), b) shows radar images obtained by modelling using the synthetic aperture radar method, with
angles the bearing angle of 17° and 15° and the aspect angles are 17 ° and 100 °, respectively.
Table 1. Modelling parameters.
# Parameter Value
1 Radar start point (x,y,z), m (-4335.5,1325.5,-50)
2 Radar observation mode spotlight
3 Synthesis lenght, m 100
4 Wave length (chirp), m 0.029 – 0.033
5 Azimuth resolution, m 0.3
6 Range resolution, m 0.3
7 Impulse duration, mcs 0.5
8 Min range, m 4510
9 Max range, m 4630
10 Azimuth step, m 0.25
11 Range step, ns 0.5
a) b)
Figure 1. MSTAR dataset SAR images: a) bearing angle 17°, elevation angle 15°; b) bearing
angle 17°, elevation angle 100°.
a) b)
Figure 2. Modelled SAR images: a) bearing angle 17°, elevation angle 15°; b) bearing angle 17°,
elevation angle 100°.
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The recognition results for three classes obtained using the recognition contingency index based
algorithm [7] (without subclassing) are listed in Table 2. Note that the result of 62.78% correct
recognition of the three classes was obtained on a relatively small training set (300 images). The
MSTAR training sample contains 587 of image samples.
a) b)
Figure 3. SAR images: a) model and b) real BTR target.
Table 2. Recognition results with modelled dataset training.
Evaluated class
BMP2 BTR70 T72
BMP2 402 124 61 68.48%
BTR70 134 43 19 21.93%
T72 119 51 412 70.79%
At the binary classification: the BMP and tank using the same algorithm, the result of 80.24% was
achieved. The recognition results of the two classes without division into subclasses are shown in
Table 3.
Table 3. Results of two objects recognition.
Evaluated class
BMP2 T72
BMP2 496 91 84.50%
T72 140 442 75.94%
In addition, we carried out the experiment of the real images recognition by training on model
images, which obtained using the ray tracing approach [8]. Table 4 shows the recognition results. The
total percentage of correct recognition was 27.6%. These results show that images modelled via
raytracing is not quite suitable for recognition real images. Perhaps they can be used to create a
simplified model of a three-dimensional scene.
Table 3. Results of objects recognition (dataset modelled via raytracing).
Evaluated class
BMP2 BTR70 T72
BMP2 207 270 221 29.66%
BTR70 78 67 51 34.18%
T72 331 146 105 18.04%
It should be noted that in the MSTAR database, the training sample has 587 images. In contrast to
presented model images dataset, the rotation of an object in the MSTAR images was performed with
an irregular step and with rather large positioning errors.
3. Conclusion
The paper shows that the results of objects recognition using real images has the ability of the
effective usage at the developed software. The images were obtained by modeling can form the
training dataset at the proposed algorithm. In addition, the parallel algorithm acceleration is obtained
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by kd-tree construction let us help to perform high-computing and effective scattering calculation on
the various object surfaces.
4. References
[1] Zherdev D A, Prokudin V V and Minaev E Y 2018 HPC implementation of radar images
modelling method using CUDA Journal of Physics: Conference Series 1096 012083
[2] Horn D R, Sugerman J, Houston M and Hanrahan P 2007 Interactive kd tree GPU raytracing
Proceedings of the symposium on Interactive 3D graphics and games 167-174
[3] Wehr D, Radkowski R 2018 Parallel kd-tree construction on the gpu with an adaptive split and
sort strategy International Journal of Parallel Programming 46(6) 1139-1156
[4] Vinkler M, Havran V and Bittner J 2016 Performance Comparison of Bounding Volume
Hierarchies and Kd‐Trees for GPU Ray Tracing Computer Graphics Forum 35(8) 68-79
[5] Savchenko A V 2018 Trigonometric series in orthogonal expansions for density estimates of
deep image features Computer Optics 42(1) 149-158 DOI: 10.18287/2412-6179-2018-42-1-
149-158
[6] Borodinov A A, Myasnikov V V 2018 Classification of radar images with different methods of
image preprocessing CEUR Proceedings 2210 6-13
[7] Fursov V, Zherdev D and Kazanskiy N 2016 Support subspaces method for synthetic aperture
radar automatic target recognition International Journal of Advanced Robotic Systems 13(5)
DOI: 10.1177/1729881416664848
[8] Zherdev D A, Fursov V A 2015 Support plane method applied to ground objects recognition
using modelled SAR images Applications of Digital Image Processing XXXVIII International
Society for Optics and Photonics 9599
Acknowledgments
The work was funded by the Russian Federation Ministry of Education and Science (agreement 007-
GZ/Ch3363/26) and RFBR (project # 17-29-03112 ofi_m).
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