Web
Analytics

Parameter Estimation and Target Detection of Phased-MIMO Radar Using Capon Estimator

  Syahfrizal Tahcfulloh (1*), Muttaqin Hardiwansyah (2)

(1) Department of Electrical Engineering, Universitas Borneo Tarakan - Indonesia orcid
(2) Department of Electrical Engineering, Universitas Trunojoyo Madura - Indonesia
(*) Corresponding Author

Received: July 27, 2020; Revised: December 02, 2020
Accepted: December 13, 2020; Published: December 31, 2020


How to cite (IEEE): S. Tahcfulloh,  and M. Hardiwansyah, "Parameter Estimation and Target Detection of Phased-MIMO Radar Using Capon Estimator," Jurnal Elektronika dan Telekomunikasi, vol. 20, no. 2, pp. 60-69, Dec. 2020. doi: 10.14203/jet.v20.60-69

Abstract

Phased-Multiple Input Multiple Output (PMIMO) radar is multi-antenna radar that combines the main advantages of the phased array (PA) and the MIMO radars. The advantage of the PA radar is that it has a high directional coherent gain making it suitable for detecting distant and small radar cross-section (RCS) targets. Meanwhile, the main advantage of the MIMO radar is its high waveform diversity gain which makes it suitable for detecting multiple targets. The combination of these advantages is manifested by the use of overlapping subarrays in the transmit (Tx) array to improve the performance of parameters such as angle resolution and detection accuracy at amplitude and phase proportional to the maximum number of detectable targets. This paper derives a parameter estimation formula with Capon's adaptive estimator and evaluates it for the performance of these parameters. Likewise, derivation for expressions of detection performance such as the probability of false alarm and the probability of detection is also given. The effectiveness and validation of its performance are compared to conventional estimator for other types of radars in terms of the effect of the number of target angles, the RCS of targets, and variations in the number of subarrays at Tx of this radar. Meanwhile, the detection performance is evaluated based on the effect of Signal to Noise Ratio (SNR) and the number of subarrays at Tx. The evaluation results of the estimator show that it is superior to the conventional estimator for estimating the parameters of this radar as well as the detection performance. Having no sidelobe makes this estimator strong against the influence of interference and jamming so that it is suitable and attractive for the design of radar systems. Root mean square error (RMSE) on magnitude detection from LS and Capon estimators were 0.033 and 0.062, respectively. Meanwhile, the detection performance for this radar has the probability of false alarm above 10-4 and the probability of detection of more than 99%.


  http://dx.doi.org/10.14203/jet.v20.60-69

Keywords


Capon estimator; MIMO radar; phased-array antenna; subarrays; target detection

Full Text:

  PDF

References


I. Bilik, O. Longman, S. Villeval, and J. Tabrikian, “The rise of radar for autonomous vehicles: signal processing solutions and future research directions,” IEEE Signal Process. Mag., vol. 36, no. 5, pp. 20–31, Sep. 2019. Crossref

S. Tahcfulloh and G. Hendrantoro, “FPMIMO: a general MIMO structure with overlapping subarrays for various radar applications,” IEEE Access, vol. 8, pp. 11248–11267, Jan. 2020. Crossref

H. Pratiwi, M. R. Hidayat, A. A. Pramudita, and F. Y. Suratman, “Improved FMCW radar system for multi-target detection of human respiration vital sign,” Jurnal Elektronika Telekomunikasi, vol. 19, no. 2, pp. 38–44, Dec. 2019. Crossref

A. Hassanien and S. A. Vorobyov, “Phased-MIMO radar: a tradeoff between phased-array and MIMO radars,” IEEE Trans. Signal Process., vol. 58, no. 6, pp. 3137–3151, Jun. 2010. Crossref

M. Hardiwansyah, S. Tahcfulloh, and G. Hendrantoro, “Parameter identifiability of phased-MIMO radar,” in Proc. Int. Conf. Artificial Intell. Inform. Technol., Yogyakarta, Indonesia, Mar. 2019, pp. 192–195. Crossref

L. Xu, J. Li, and P. Stoica, “Target detection and parameter estimation for MIMO radar systems”, IEEE Trans. Aerosp. Electron. Syst., vol. 44, no. 3, pp. 927–939, Jul. 2008. Crossref

M. R. Widyantara, Sugihartono, F. Y. Suratman, S. Widodo, and P. Daud, “Analysis of non linear frequency modulation (NLFM) waveforms for pulse compression radar,” Jurnal Elektronika Telekomunikasi, vol. 18, no. 1, pp. 27–34, Aug. 2018. Crossref

C. Gao, H. Zhou, R. Wu, X. Xu, F. Shen, and Z. Guo, “Parameter estimation and multi-pulse target detection of MIMO radar,” in Proc. 2016 IEEE Region 10 Conf., Singapore, Nov. 2016, pp. 909–914. Crossref

J. Li and P. Stoica, MIMO Radar Signal Processing. Hoboken, NJ: John Wiley & Sons, Inc., 2009.

A. K. M. T. Rahman, S. M. M. H. Mahmud, T. K. Biswas, and S. Naznin, “Target detection performance of coherent MIMO radar using space time adaptive processing,” in Proc. 2014 Int. Conf. Informatics Electron. Vision, Dhaka, Bangladesh, 2014, pp. 1–5. Crossref

E. Fishler, A. Haimovich, R. S. Blum, L. J. Cimini, D. Chizhik, and R. A. Valenzuela, “Spatial diversity in radars-models and detection performance”, IEEE Trans. Signal Process., vol. 54, no. 3, pp. 823–838, Mar. 2006. Crossref


Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM

Refbacks

  • There are currently no refbacks.




Copyright (c) 2020 National Research and Innovation Agency

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.