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Human and bird detection and classification based on Doppler radar spectrograms and vision images using convolutional neural networks
(2021-01-01)
Gokaraju, Jnana Sai Abhishek Varma
;
Song, Weon Keun
;
Ka, Min Ho
;
Kaitwanidvilai, Somyot
The study investigated object detection and classification based on both Doppler radar spectrograms and vision images using two deep convolutional neural networks. The kinematic models for a walking human and a bird flapping its wings were incorporated into MATLAB simulations to create data sets. The dynamic simulator identified the final position of each ellipsoidal body segment taking its rotational motion into consideration in addition to its bulk motion at each sampling point to describe its specific motion naturally. The total motion induced a micro-Doppler effect and created a micro-Doppler signature that varied in response to changes in the input parameters, such as varying body segment size, velocity, and radar location. Micro-Doppler signature identification of the radar signals returned from the target objects that were animated by the simulator required kinematic modeling based on a short-time Fourier transform analysis of the signals. Both You Only Look Once V3 and Inception V3 were used for the detection and classification of the objects with different red, green, blue colors on black or white backgrounds. The results suggested that clear micro-Doppler signature image-based object recognition could be achieved in low-visibility conditions. This feasibility study demonstrated the application possibility of Doppler radar to autonomous vehicle driving as a backup sensor for cameras in darkness. In this study, the first successful attempt of animated kinematic models and their synchronized radar spectrograms to object recognition was made.
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Doppler radar for dynamic hand gesture recognition based on signal image processing
(2019-07-01)
Arthamanolap, Kongphum
;
Gabbualoy, Somprasong
;
Phasukkit, Pattarapong
From previous researches, Doppler radar was used to detect signal for implement with many applications. Nevertheless, it is difficult to analyze for recognize object. At present, technique of deep learning in terms of signal processing and image processing are using in many researches to classify categories of data. In this paper, signal image was used by deep learning to classify hand gesture by receiving signals from 24GHz transceiver: BGT24MTR11. We transformed the signals to images for 3 categories including Spectrogram, Time domain from original signal and feature MFCC graph. After that those of converted image will be trained by Deep learning for classify the hand gesture types. From the result of this experiment has been shown that signal image can be used to recognize hand gesture and Spectrogram graph makes the highest accuracy as 94%.
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A sensor for fruit classification using doppler radar
(2018-11-09)
Leekul, P.
;
Krairiksh, M.
This paper presents a microwave sensor that can detect defected fruits. In this work, mangosteen is used as an example. Translucent is detected by measuring Doppler frequency as mangosteen is rotated. From the simulated scattered waves at different directions around the fruits, the different in scattered wave results in Doppler signal from the defected fruit. The different D.C. voltage from the mixer identified whether there is translucent for the whole fruit. This cost effective sensor is a good candidate for fruit classification.
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