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Weichieh Huang - One of the best experts on this subject based on the ideXlab platform.

T I Laakso - One of the best experts on this subject based on the ideXlab platform.

Xiaojing Zhang - One of the best experts on this subject based on the ideXlab platform.

Martin Makundi - One of the best experts on this subject based on the ideXlab platform.

Mohammad Mahdi Nayebi - One of the best experts on this subject based on the ideXlab platform.

  • Invariant activity detection of a Constant Magnitude signal with unknown parameters in white Gaussian noise
    IET Communications, 2009
    Co-Authors: Mostafa Derakhtian, Aliakbar Tadaion, Saeed Gazor, Mohammad Mahdi Nayebi
    Abstract:

    The authors propose invariant tests for the detection of a complex signal with unknown Constant amplitude and unknown phase variation in additive white Gaussian noise (AWGN). The authors show that in this problem, the uniformly most powerful invariant (UMPI) detector does exist only if the number of samples N is two. For more than two samples N≥3, the authors derive the most powerful invariant (MPI) detector in known signal-to-noise ratio (SNR) and use its performance as the upper bound benchmark for any invariant test. In addition, the authors derive the generalised likelihood ratio (GLR) detector and evaluate its performance against the MPI performance bound. This detector is very simple and represents the ratio of the L1-norm to the L2-norm of the data. Simulation results illustrate the close performances of the two detectors even at low SNRs, whereas in contrast to the MPI test the SNR is not required in the proposed GLR test. In order to understand why the knowledge of SNR is not so important in this detection problem, the authors also derive the GLR test for the case of known SNR. Interestingly, the resulting GLR detector (derived for the case of known SNR) turns out to be equivalent to the one derived for unknown SNR, i.e. a knowledge of the SNR is not used in any of the GLR tests. This reveals why the knowledge of the SNR is not so useful in this detection problem.

  • Invariant Detection of a Constant Magnitude Signal with Unknown Parameters in White Gaussian Noise
    2007 IEEE International Conference on Signal Processing and Communications, 2007
    Co-Authors: Aliakbar Tadaion, Mostafa Derakhtian, Saeed Gazor, Mohammad Mahdi Nayebi
    Abstract:

    In this paper, we use invariant tests for the detection of a complex signal with unknown phase variation and unknown amplitude in additive white Gaussian noise (AWGN). We show that in this problem, the uniformly most powerful invariant (UMPI) detector exists only if the signal-to-noise-ratio (SNR) is known. We derive the UMPI detector in known SNR and use it as the upper performance bound for any invariant test. In addition, we derive the generalized likelihood ratio (GLR) detector and evaluate its performance against the UMPI performance bound. We show that the GLR detector asymptotically approaches the UMPI test in large SNRs. Simulation results illustrate the close performances of the two detectors even at low SNRs, while in contrast of the UMPI test the SNR is unknown in the proposed GLR test. In order to understand, why the knowledge of SNR is not so important in this detection problem, we also derive the GLR test for the case of known SNR. Interestingly, the resulting GLR detector (derived for the case of known SNR) turns out equivalent with the one derived for unknown SNR, i.e., the knowledge of the SNR is not used in any of GLR tests. This reveals why the knowledge of the SNR is not so useful in this detection problem.