IEEE - Aerospace and Electronic Systems - March 2022 - 38

Deep Learning Approach for Fixed and Rotary-Wing Target Detection and Classification in Radars
Figure 8.
Different types of range-Doppler map images. (a) Pure noise and hence has very high mean noise levels. (b) and (c) Single targets with low
and high SNR, respectively. Signals with higher SNR have lower mean background noise, which manifests as the increased target to background
contrast. (d) Doppler signature ofa helicopter, with Doppler spread caused by rotation of its blades.
because this is the first time that a single neural network is
being used for airborne target detection and classification,
that too, on real radar data. Figure 10 illustrates two correctly
classified images by the noise filtration step.
RESULTS OF TARGET DETECTION
A total of 83,740 range-Doppler images are manually
labeled, consisting of more than 100,000 fixed-wing targets
and 21,000 rotary-wing targets. The data is divided
into training, validation, and testing data in a ratio of
14:3:3. Given the sizeable amount of data already available,
no data augmentation is done. To counter the data
dependency, we ensured that no target reception of the
same dwell is included twice in our data. Even if the echo
of the same target was used more than once, it was certified
that they differed by at least one scan-time. The difference
manifested in target position due to difference in
detection time and, hence, resulting in a subtle change in
target SNR.
To the best of our knowledge, ours is the first work to
Figure 9.
Efficacy of neural network for rejecting incoming data for being
too noisy.
38
demonstrate the detection and classification of airborne
targets in real-world radar data. Each of the range-Doppler
maps is made from an echo of an air traffic control (ATC)
radar operating at a frequency of 3 GHz. Transmitted
pulses are linear frequency modulated signals with bandwidth
500 KHz and range resolution of 300 m. Pulse repetition
frequency is kept as 2 KHz, with a sampling
frequency of1 MHz. Operations such as matched filtering,
Doppler processing, and MTI filtering are applied before
use by the CNN detector. Only detections with Intersection
over Union (IoU) > 50% are considered as true positives.
This ensures that the center of the label box (peak
aircraft return) always lies in our detected bounding box.
Other detections accumulate the false positives count and
contributed towards increased Pfa. Thereafter, coordinates
of the peak pixel value in the detection region are used to
ascertain the range and velocity of the target. Figure 11
shows some sample results of range-Doppler images.
Comparison of the proposed techniques with contemporary
techniques is considered incoherent primarily
IEEE A&E SYSTEMS MAGAZINE
MARCH 2022

IEEE - Aerospace and Electronic Systems - March 2022

Table of Contents for the Digital Edition of IEEE - Aerospace and Electronic Systems - March 2022

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