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 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-likelihood and a recursive particle filtering estimator are implemented, which incorporate the above model. The accuracy and robustness of the approach are evaluated by numerical simulations and by comparison with the Cramer–Rao lower bound. Finally, as a case study, the approach is applied to the experimental data obtained from an in-house developed ultrawideband time-interval measurement system.