Aerospace and Electronic Systems Magazine April 2018 - 53

Miralles et al.

Figure 10.

each azimuth sector separately. To point the camera, the two-axis
gimbal can be used, in combination with the already implemented
tracking algorithm. The servos of the camera gimbal are linked to
the radar processing and receive the azimuth and elevation angles
of the most significant target. This could be the one with the highest amplitude or the one that is closest to the radar. The gimbal
then moves in the target's direction and offers a more confined
area for image processing. The zoom increases the resolution of
the target. Object detectors in image processing need a certain
minimum number of pixels on which they can operate. When using a wide-angle objective, the resolution of a target in medium
distance would be too low. With a zoom objective, the resolution
is increased, the probability of detection is higher, and better classification can be performed.

3D reconstruction of the scene with radar data. The three targets are
spaced in range and have different positions in azimuth and elevation.

CONCLUSION

A compact, portable, and multifunctional system, which coma medium range is a computationally intensive task for pure image
bines an FMCW MIMO radar and a camera, has been introduced.
processing algorithms.
The device has been shown to be suitable for the presented apThe results of the 3D-FFT beamforming radar processing proplication (change detection in wide zones such as nuclear plants).
posed in this article are shown in Figure 10. As can be seen, a
Detailed hardware and software architecture descriptions have
correct estimation of the range, azimuth, and elevation for all three
been presented, together with a description of the DOA estargets used in the measurement is achieved.
timation and the MIMO cycle timing. The performance of the
An additional and more intuitively understandable image, fumultifunctional system has been verified by measurements in a
sion of the camera image and the radar data, is presented in Figmultitarget environment showing synergies among the two funcure 11. Here, the camera image is superimposed with the radar
tionalities. The system is able to perform a 3D reconstruction of
data. To still see the scene, the amplitude of the reflection is used
to define the transparency level and the
color of the radar cell. With this method,
strong reflections occur as opaque areas
in the image. In this case, the evaluation
is done for the three most significant targets in amplitude. According to the distance, a red, a yellow, and a green tag are
placed close to the targets, where the red
tag shows the closest, and usually most
important, target. This way of displaying the information is intuitive, because
a strong target within the monitored
scene is highlighted in the camera image. Hence, a higher degree of automation is achieved. Another aspect is that
the system is compact and portable and
thus can be easily integrated into existing infrastructures.
Another application is detection and
classification based on image processing. In this case, the radar can be combined with a zoom camera pointing to
the objects previously detected by the
radar. Therefore, a different camera
objective is needed, e.g., with a focal
length of f = 75 mm, which leads to an
Figure 11.
FOV of FOVh = 5.0°. This is compara- Camera and radar image overlaid. The color and the level of opaqueness are proportional to the amble to the azimuth resolution of the ra- plitude of the reflection. The targets' ranges are displayed in boxes with different colors. The FOV is
dar, which makes it possible to observe limited by the camera.
APRIL 2018

IEEE A&E SYSTEMS MAGAZINE

53



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