Abstract
Artificial intelligence is making a huge impact in terms of accuracy, robustness, and real-time capabilities in advanced driver assist systems. The conventional RaDaR systems using routine signal processing (SP) techniques are not able to meet such high requirements when it comes to the classification of the target’s intent. Here a strategy that combines SP and machine learning (ML) techniques to detect dynamic changes in target morphologies with the help of micro-Doppler (MD) signatures is proposed. The target’s MD signatures are used to create target libraries, which will be further used to train the ML model for accurate classification of the target’s intent. Finally, the overall raw data generated is implemented on an edge computing platform aimed at a future deployment of the ML model in field-deployed computing devices.
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References
B.R. Suraj et al., Digital implementation of radar receiver and signal processing algorithms. IEEE ICSCAN 22, 1–6 (2021)
P. Kittisak et al., Wide-band FMCW radar for high-resolution object detections, in 36th International Technical Conference on Circuits/Systems, Computers, and Communication (ITC-CSCC), vol. 24 (IEEE, 2021), pp. 1–2
C. Anjana, G.A. Shanmugha Sundaram, Simulating the impact of wind turbine on RADAR signals in L and S band using XGtd, in 2015 International Conference on Communication and Signal Processing (ICCSP) (IEEE, 2015), pp. 200–204
S. Sundaresan, G.A. Shanmugha Sundaram, Simulation study on modeling the effects of wind turbine on communication signals (C and X bands) using Xgtd, in International Conference on Communication and Signal Processing (ICCSP) (IEEE, 2015), pp. 195–199
C.C. Victor et al., Micro-Doppler effect in radar: phenomenon, model, and simulation study. IEEE Trans. Aerosp. Electron. Syst. 42, 1–20 (2006)
N. Jiyoon et al., Adaptive-sliding—window-based detection for noncooperative spectrum sensing in radar band. IEEE Syst. J. 1–4 (2018)
N. Mahvish, Automotive radar target detection using ambiguity function. University of Birmingham. Ph.D. Thesis (2016)
S. Srivatsa, G.A. Shanmugha Sundaram, PAM4-based RADAR counter-measures in hostile environments, in Advances in Intelligent Systems and Computing: ISTA’17, vol. 683 (Springer, Cham, 2018), pp. 390–400
W. Longhui, W. Jian, Z. Xudong, Discrete random-Fourier transform and its approximation algorithm in short range ubiquitous radar. IEEE Sens. J. 14 (2021)
J. Gray, The effect of non-uniform motion on the Doppler spectrum of scattered continuous wave waveform. IEEE Proc. Radar Sonar Navig. 150(4) (2003). IEEE
M.S. Srinikethan, S. Aravind, N. Avinash, G.A. Shanmugha Sundaram, Two dimensional array of layered metal-dielectric sandwich structures for terahertz applications, in Proceedings of International Conference on Advances in Computing, Communications and Informatics (ICACCI) (IEEE, 2015), pp. 684–688
V. Vaishnavi, V.G. Priya, A. Sharmila Devi, S. Venkatesh, G.A. Shanmugha Sundaram, Simulation of helical modulation in a focal plane array, in Proceedings of International Conference on Communication and Signal Processing (ICCSP) (IEEE, 2007), pp. 1414–1418
Y. Yang et al., Person identification using micro-Doppler signatures of human motions and UWB radar. IEEE Microwave Wirel. Compon. Lett. 29(5), 366–368 (2019)
G. Zhang, H. Li, F. Wenger, Object detection and 3D estimation via an FMCW radar using a fully convolutional network, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2020), pp. 4487–4491
D.E. Barrick, FM/CW radar signals and digital processing. National Oceanic and atmospheric administration boulder co wave propagation lab. NOAA Technical Report ERL, vol. 283. DTIC (1973)
L. Ping et al., A comprehensive survey of machine learning applied to radar signal processing, in IEEE Statistical Signal Processing Workshop (SSP), vol. 39, no. 49 (IEEE, 2020)
D. Yu, L. Deng, Deep learning and its applications to signal and information processing [Exploratory DSP]. IEEE Sig. Process. Mag. 28(1), 145–154 (2011). IEEE
C.J. Burges, A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, vol. 2, no. 2 (Springer, 2000), pp. 121–167
T. Zhang, An introduction to support vector machines and other kernel-based learning methods. AI Mag. 22(2), 103–104 (2001). PKP Publishing Services Network
S.A. Ramakrishna et al., Deep learning based range and DOA estimation using low-resolution FMCW radars, in Proceedings of IEEE Statistical Signal Processing Workshop (SSP), vol. 34 (IEEE, 2021), pp. 1–5
K. Cao et al., An overview on edge computing research. IEEE Access 8, 85714–85728 (2020)
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Abhishek Neelakandan, K.V., Shanmugha Sundaram, G.A. (2023). EDGE-Based ML in W-Band Target Micro-Doppler Feature Extraction. In: Thampi, S.M., Mukhopadhyay, J., Paprzycki, M., Li, KC. (eds) International Symposium on Intelligent Informatics. ISI 2022. Smart Innovation, Systems and Technologies, vol 333. Springer, Singapore. https://doi.org/10.1007/978-981-19-8094-7_30
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DOI: https://doi.org/10.1007/978-981-19-8094-7_30
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