access icon openaccess MIMO radar signals modulation recognition based on partial instantaneous autocorrelation spectrum

This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Received 16/01/2019, Accepted 20/05/2019, Revised 08/05/2019, Published 30/05/2019

Aiming at the problem of large amount of calculation and long recognition time of traditional method of modulation identification of centralised multiple-input multiple-output (MIMO) radar signal, partial zero-delay instantaneous autocorrelation spectrum is proposed to identify the modulation of centralised MIMO radar here. In this method, the modulation identification of the centralised MIMO radar is performed by extracting the ratio of the sub-peak to the highest peak in the zero-delay transient autocorrelation spectrum of the part signal. The computational complexity of this method is low, and it identifies without additional features for coded MIMO radar signals. Here, the definition of partial instantaneous correlation spectrum is given; the feasibility of using this method to identify the modulation type of the centralised MIMO radar is analysed, and the identification process is discussed. It is showed that the proposed method can shorten the recognition time while guaranteeing the detection performance under a certain SNR in the simulation results.

Inspec keywords: correlation methods; computational complexity; radar signal processing; modulation; MIMO communication; MIMO radar; MIMO systems

Other keywords: multiple-input multiple-output radar signal; coded MIMO radar signals; zero-delay transient autocorrelation spectrum; long recognition time; centralised MIMO radar; partial instantaneous correlation spectrum; MIMO radar signals modulation recognition; modulation type; modulation identification; zero-delay instantaneous autocorrelation spectrum; partial instantaneous autocorrelation spectrum; part signal

Subjects: Radar equipment, systems and applications; Multivariable control systems; Signal processing and detection; Other topics in statistics; Radio links and equipment

References

    1. 1)
      • 7. Roe, A.L.: ‘Artificial neural networks for ESM emitter identification-an initial study [C]’. IEE Colloquium on Neural Networks for Systems: Principles and Applications Proc., London, UK, 1991, pp. 1–3.
    2. 2)
      • 1. Li, J., Stoica, P.: ‘MIMO radar signal processing[M]’ (Hohn Wiley & Sons, Inc., Hoboken, New Jersey, 2008).
    3. 3)
      • 3. Haimovich, A.M., Blum, R.S., Cimini, L.J.: ‘MIMO radar with widely separated antennas [J]’, Signal Process. Mag., 2008, 25, (1), pp. 116–129.
    4. 4)
      • 4. Liedtke, F.F.: ‘‘Computer simulation of an automatic classification procedure for digitally modulated communication signals with unknown parameters [J]’, Signal Process., 1984, 6, (4), pp. 311–323.
    5. 5)
      • 6. Roe, J.: ‘A review of applications of artificial intelligence techniques to naval ESM signal processing [C]’. IEE Colloquium on Application of Artificial Intelligence Techniques to Signal Processing Proc., London, UK, 1989, pp. 1–5.
    6. 6)
      • 8. Wang, X.: ‘Research on MIMO radar signal recognition and sorting method [D]’ (Cheng Du University of Electronic Science and technology of China, Chengdu, China, 2013).
    7. 7)
      • 2. Li, J., Stoica, P.: ‘MIMO radar with colocated antennas [J]’, Signal Process. Mag., 2007, 24, (5), pp. 106–114.
    8. 8)
      • 5. Nong, Y., Lianan, P.: ‘Signal recognition of radar warning system [J]’, Aerosp. Electron. Countermeas., 1991, 2, (2), pp. 22–26.
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