Classification and Discrimination of Birds and Small Drones Using Radar Micro-Doppler Spectrogram Images

Abstract

This paper investigates the use of micro-Doppler spectrogram signatures of flying targets, such as drones and birds, to aid in their remote classification. Using a custom-designed 10-GHz continuous wave (CW) radar system, measurements from different scenarios on a variety of targets were recorded to create datasets for image classification. Time/velocity spectrograms generated for micro-Doppler analysis of multiple drones and birds were used for target identification and movement classification using TensorFlow. Using support vector machines (SVMs), the results showed an accuracy of about 90% for drone size classification, about 96% for drone vs. bird classification, and about 85% for individual drone and bird distinction between five classes. Different characteristics of target detection were explored, including the landscape and behavior of the target.

Use of Artificial Intelligence in Design, Development, Additive Manufacturing, and Certification of Multifunctional Composites for Aircraft, Drones, and Spacecraft

“…While the test results showed high accuracies for the random forest models and linear regression in the prediction of the impact damage locations, this study did not address the use of ML in the design, development, manufacturing, testing, certification and sustainment of aerospace vehicles [20]. A type of supervised learning algorithm known as a support vector machine was used in ML to conduct binary classification problems of the image by categorizing digital data into two groups [21]. While TensorFlow was used for target identification and movement classification, this paper did not address the use of ANN in the design, development, testing and certification of multifunctional composites for drones, spacecraft and aircraft [21].…”

“…A type of supervised learning algorithm known as a support vector machine was used in ML to conduct binary classification problems of the image by categorizing digital data into two groups [21]. While TensorFlow was used for target identification and movement classification, this paper did not address the use of ANN in the design, development, testing and certification of multifunctional composites for drones, spacecraft and aircraft [21]. Twelve ML techniques were used in data-driven predictions of the compressive strength of the fiber-reinforced polymer-confined concrete test coupons [22].…”

Micro-Doppler radar characterization of vehicle vibrations

“…This method is very useful in imaging motion such as helicopter blades [14]. [15], acoustic stimulation from loudspeakers [16], [17], human gait [18], [19] and drone vs. bird detection [20]. In our research, the radar is attempting to capture the vibration from various surfaces on a vehicle's chassis.…”

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

“…In recent years, many research works have been published to address UAV detection, tracking, and classification problems. The main drone detection technologies are: radar sensors [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], RF sensors [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], audio sensors [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ], and camera sensors using visual UAV characteristics [ 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ]. Based on the above-mentioned sources, the advantages and disadvantages of each drone detection technology are compared in Table 2 .…”

“…Experimental measurements showed 99.4% recognition accuracy with various short-range radar sensors. In [ 35 ], the authors examined micro-Doppler data obtained from a custom-built 10 GHz continuous wave (CW) radar system that was specifically designed for use with a range of targets, including UAVs and birds, in various scenarios. Support vector machines (SVMs) were used for performing different classification types, such as drone size classification, drone and bird binary classification, as well as multi-class-specific classification among the five classes.…”