A special-purpose adaptive machine is described which carries out estimation in real time of an unknown binary waveform which is perturbed with additive Gaussian noise. Unknown waveforms of over 103 samples in duration can be recovered. The unknown waveforms are of unknown epoch and can reappear at either random or periodic time intervals. The observed signal is received at moderate or low signal-to-noise ratios so that a single observation of the received data (even if one knew the precise signal arrival time) is not sufficient to provide a good estimate of the signal waveshape. Experimental results are described which show transient behavior waveform estimate. The transient behavior is expressed as the number of errors in the current estimate of the signal plotted vs. time. In a noisy environment, each ``learning'' transient is a random time function. These learning transients are shown for several different signal-to-noise ratios and indicate the threshold noise levels for various types of initial states of the machine memory.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Experiments in Adaptive Estimation of Unknown Binary Waveforms


    Beteiligte:


    Erscheinungsdatum :

    1967-01-01


    Format / Umfang :

    3362426 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Doppler compensation for binary phase-coded waveforms

    Yuping Cheng, / Zheng Bao, / Fufu Zhao, et al. | IEEE | 2002



    Parameter Estimation using Numerical Merger Waveforms

    J. I. Thorpe / S. McWilliams / B. Kelly et al. | NTIS | 2008


    Robust Adaptive Unknown Input Estimation with Uncertain System Realization

    Griffith, Tristan / Gehlot, Vinod P. / Balas, Mark J. | AIAA | 2022


    Robust Adaptive Unknown Input Estimation with Uncertain System Realization

    Griffith, Tristan / Gehlot, Vinod P. / Balas, Mark J. | TIBKAT | 2022