Using Gaussian-Uniform Mixture Models for Robust Time-Interval Measurement


Time-interval measurement systems using threshold detectors experience severe performance degradation in the presence of noise and interference. This paper describes an approach to robust measurement of your time intervals in the presence of interference. This approach is based on modeling the distribution of the measurement results as a Gaussian–uniform mixture. A batch maximum-chance and a recursive particle filtering estimator are implemented, that incorporate the on top of model. The accuracy and robustness of the approach are evaluated by numerical simulations and by comparison with the Cramér–Rao lower sure. Finally, as a case study, the approach is applied to the experimental data obtained from an in-house developed ultrawideband time-interval measurement system.

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