access icon free Combined optimisation of waveform and quantisation thresholds for multistatic radar systems

The problem of designing waveform and quantisation thresholds is studied in a multistatic radar setting, where distributed receivers are connected to a fusion centre via capacity constraints. Different from the previous cloud radio-multistatic radar system which utilises an additive quantisation Gaussian noise model, a real quantisation system is designed. The authors first optimise the waveform without quantisation. Then they compress the received signal at receivers into a scalar without any performance degradation. Furthermore, the scalar quantiser is adopted and quantisation thresholds are designed. Numerical simulations are performed and the effectiveness of combined waveform and thresholds optimisation strategy is demonstrated.

Inspec keywords: radar signal processing; numerical analysis; data compression; quantisation (signal); Gaussian noise; optimisation

Other keywords: cloud radio-multistatic radar system; fusion centre; received signal compression; scalar quantiser; capacity constraints; additive quantisation Gaussian noise model; combined waveform-quantisation threshold optimisation strategy; real quantisation system; distributed receivers; numerical simulations

Subjects: Optimisation techniques; Other numerical methods; Signal processing and detection; Radar equipment, systems and applications

References

    1. 1)
      • 2. Rihaczek, A.W.: ‘Principles of high-resolution radar’ (Artech House Publishers, Boston, 1996).
    2. 2)
      • 7. Khalili, S., Simeone, O., Haimovich, A.M.: ‘Cloud radio-multistatic radar: combined optimization of code vector and backhaul quantization’, IEEE Signal Process. Lett., 2014, 22, (4), pp. 494498.
    3. 3)
      • 5. Naghsh, M.M., Modarres-Hashemi, M., ShahbazPanahi, S., et al: ‘Unified optimization framework for multi-static radar code design using information-theoretic criterion’, IEEE Trans. Signal Process., 2013, 61, (21), pp. 54015416.
    4. 4)
      • 3. Kay, S.M.: ‘Optimal signal design for detection of Gaussian point targets in stationary Gaussian clutter/reverbation’, IEEE Trans. Signal Process., 2007, 1, (1), pp. 3141.
    5. 5)
      • 6. China Mobile.: ‘C-RAN: the road towards green RAN’, White Paper, ver. 2.5, China mobile Research Institute, Oct. 2011.
    6. 6)
      • 9. Srinivasan, R.: ‘Distributed radar detection theory’, IEE Proc., 1986, 133, (1), pp. 5560.
    7. 7)
      • 14. Rihaczek, A.W., Mitchell, R.L.: ‘Radar waveforms for suppression of extended clutter’, IEEE Trans. Aerosp. Electron. Syst., 1967, AES-3, (3), pp. 510517.
    8. 8)
      • 19. Kay, S.M.: ‘Fundamentals of statistical signal processing-volume II: detection theory’ (Prentice-Hall, Englewood Cliffs, NJ, USA, 1998, 1st edn.).
    9. 9)
      • 12. Aubry, A., Maio, A.D., Naghsh, M.M.: ‘Optimizing radar waveform and Doppler filter bank via generalized fractional programming’, IEEE J. Sel. Topics Signal Process., 2015, 9, (8), pp. 13871399.
    10. 10)
      • 10. Longo, M., Lookabaugh, T.D., Gray, R.M.: ‘Quantization for decentralized hypothesis testing under communication constraints’, IEEE Trans. Inf. Theory, 1990, 36, (2), pp. 241255.
    11. 11)
      • 18. Tang, B., Naghsh, M.M., Tang, J.: ‘Relative entropy-based waveform design for MIMO radar detection in the presence of clutter and interference’, IEEE Trans. Signal Process., 2015, 63, (14), pp. 37833796.
    12. 12)
      • 21. Bhattacharyya, A.: ‘On a measure of divergence between two statistical populations defined by their probability distributions’, Bull. Calcutta Math. Soc., 1943, 35, pp. 99109.
    13. 13)
      • 23. Bertsekas, D.P., Tsitsiklis, J.N: ‘Parallel and distributed computation: numerical methods’ (Athenan Scientific, Massachusetts, 1997).
    14. 14)
      • 24. Lindgren, B.W.: ‘Statistical theory’ (MacMillan, New York, 1976).
    15. 15)
      • 13. Kirk, B.H., Owen, J.W., Narayanan, R.M., et al: ‘Cognitive software defined radar: waveform design for clutter and interference suppression’. Proc. SPIE SPIE 10188 Radar Sensor Technology XXI, 2017.
    16. 16)
      • 17. Song, J., Babu, P., Palomar, D.P.: ‘Sequence design to minimize the weighted integrated and peak sidelobe levels’, IEEE Trans. Signal Process., 2016, 64, (8), pp. 20512064.
    17. 17)
      • 16. Setlur, P., Rangaswamy, M.: ‘Waveform design for radar STAP in signal dependent interference’, IEEE Trans. Signal Process., 2016, 64, (1), pp. 1934.
    18. 18)
      • 15. Romero, R.A., Goodman, N.A.: ‘Waveform design in signal-dependent interference and application to target recognition with multiple transmissions’, IET Radar Sonar Navig.., 2009, 3, (4), pp. 328340.
    19. 19)
      • 22. Kailath, T.: ‘The divergence and Bhattacharyya distance measures in signal selection’, IEEE Trans. Commun., 1967, 15, (2), pp. 5260.
    20. 20)
      • 4. Kay, S.M.: ‘Waveform design for multistatic radar detection’, IEEE Trans. Aerosp. Electron. Syst., 2009, 45, (3), pp. 11531166.
    21. 21)
      • 20. Naghsh, M.M., Modarres-Hashemi, M.: ‘Exact theoretical performance analysis of optimum detector in statistical multi-input multi-output radars’, IET Radar Sonar Navig.., 2012, 6, (2), pp. 99111.
    22. 22)
      • 11. Ho, S.: ‘Data fusion in a relay network’. IEEE Int. Symp. Information Theory, Toronto, July. 2008, pp. 16071611.
    23. 23)
      • 8. Jeong, S., Simeone, O., Haimovich, A.M., et al: ‘Optimization of multistatic cloud radar with multiple-access wireless backhaul’. IEEE Radar Conf., 2015, pp. 16501655.
    24. 24)
      • 1. Cook, C.E., Bernfeld, M.: ‘Radar signals: an introduction to theory and application’ (Artech House Publishers, 1993).
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