volume | PIER Journals

Abstract

In this paper, an artificial neural network (ANN) based approach is proposed for the estimation of the target angle using Multiple Input Multiple Output (MIMO) radars operating in Frequency Modulated Continuous Wave(FMCW). The proposed technique operates in two stages, with the first stage being the formation of the range profile at each MIMO element via Discrete Fourier Transform (DFT) and the second stage being the estimation of the target azimuth angle via an artificial neural network. The range profile formed in the first stage is fed to the second stage as a single snapshot angle measurement. The performance of the proposed technique is apprised with other existing methods under different Signal-to-Noise Ratio (SNR) conditions and measurement model uncertainties. The simulations performed show that the learning capability of the model strongly hinges on SNR conditions, and the learning process is ameliorated as SNR in training data increases as anticipated. Under low SNR conditions, the proposed technique performs better than other techniques in terms of Mean Square Error (MSE). We have also shown that our solution remains unaffected by the model uncertainties as it fully relies on the calibration data, while the performance of the model-based angle estimation techniques dramatically degrades as the uncertainty in the underlying model grows.

1. Wang, S. and R. Herschel, "Fast 3D-CFAR for drone detection with MIMO radars," 2021 18th European Radar Conference (EuRAD), 209-212, 2022.
doi:10.23919/EuRAD50154.2022.9784486

2. Akcapinar, K. and S. Baykut, "CM-CFAR parameter learning based square-law detector for foreign object debris radar," 2018 15th European Radar Conference (EuRAD), 421-424, 2018.
doi:10.23919/EuRAD.2018.8546514

3. Zankl, D., S. Schuster, R. Feger, and A. Stelzer, "What a Blast!: A massive MIMO radar system for monitoring the surface in steel industry blast furnaces," IEEE Microwave Magazine, Vol. 18, No. 6, 52-69, Sept.-Oct. 2017.
doi:10.1109/MMM.2017.2711998

4. Li, J. and P. Stoica, "MIMO radar with colocated antennas," IEEE Signal Processing Magazine, Vol. 24, No. 5, 106-114, Sept. 2007.
doi:10.1109/MSP.2007.904812

5. Hassanien, A., M. G. Amin, Y. D. Zhang, and F. Ahmad, "High-resolution single-snapshot DOA estimation in MIMO radar with colocated antennas," 2015 IEEE Radar Conference (RadarCon), 1134-1138, 2015.
doi:10.1109/RADAR.2015.7131164

6. Roy, R. and T. Kailath, "ESPRIT-estimation of signal parameters via rotational invariance techniques," IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 37, No. 7, 984-995, Jul. 1989.
doi:10.1109/29.32276

7. Schmidt, R., "Multiple emitter location and signal parameter estimation," IEEE Transactions on Antennas and Propagation, Vol. 34, No. 3, 276-280, Mar. 1986.
doi:10.1109/TAP.1986.1143830

8. Oumar, O. A., M. F. Siyau, and T. P. Sattar, "Comparison between MUSIC and ESPRIT direction of arrival estimation algorithms for wireless communication systems," The First International Conference on Future Generation Communication Technologies, 99-103, 2012.
doi:10.1109/FGCT.2012.6476563

9. Yu, Y., A. P. Petropulu, and H. V. Poor, "MIMO radar using compressive sampling," IEEE Journal of Selected Topics in Signal Processing, Vol. 4, No. 1, 146-163, Feb. 2010.
doi:10.1109/JSTSP.2009.2038973

10. Ni, Z., B. Huang, and M. Cao, "Angular positions estimation of spatially extended targets for MIMO radar using complex spatiotemporal sparse Bayesian learning," IEEE Access, Vol. 7, 94473-94480, 2019.
doi:10.1109/ACCESS.2019.2926442

11. Liu, Z. M., C. Zhang, and P. S. Yu, "Direction-of-arrival estimation based on deep neural networks with robustness to array imperfections," IEEE Transactions on Antennas and Propagation, Vol. 66, No. 12, 7315-7327, Dec. 2018.
doi:10.1109/TAP.2018.2874430

12. Huang, H., J. Yang, H. Huang, Y. Song, and G. Gui, "Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system," IEEE Transactions on Vehicular Technology, Vol. 67, No. 9, 8549-8560, Sept. 2018.
doi:10.1109/TVT.2018.2851783

13. Bialer, O., N. Garnett, and T. Tirer, "Performance advantages of deep neural networks for angle of arrival estimation," ICASSP 2019 --- 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3907-3911, 2019.
doi:10.1109/ICASSP.2019.8682604

14. Zhu, W., M. Zhang, P. Li, and C. Wu, "Two-dimensional DOA estimation via deep ensemble learning," IEEE Access, Vol. 8, 124544-124552, 2020.
doi:10.1109/ACCESS.2020.3005221

15. Ma, Y., Y. Zeng, and S. Sun, "A deep learning based super resolution DoA estimator with single snapshot MIMO radar data," IEEE Transactions on Vehicular Technology, Vol. 71, No. 4, 4142-4155, Apr. 2022.
doi:10.1109/TVT.2022.3151674

16. Li, X., X. Wang, Q. Yang, and S. Fu, "Signal processing for TDM MIMO FMCW millimeter-wave radar sensors," IEEE Access, Vol. 9, 167959-167971, 2021.
doi:10.1109/ACCESS.2021.3137387

17. Al-Sadoon, M. A. G., N. T. Ali, Y. Dama, A. Zuid, S. M. R. Jones, R. A. Abd-Alhameed, and J. M. Noras, "A new low complexity angle of arrival algorithm for 1D and 2D direction estimation in MIMO smart antenna systems," Sensors (Basel), Vol. 17, No. 11, 2631, Nov. 15, 2017.
doi:10.3390/s17112631

Mr. Kudret Akçapınar

Dr. Naime Özben Önhon

Dr. Özgür Gürbüz