Real-Time Maximum Time Interval Error Estimation

The sampling period is divided into a finite number of sub-intervals, and the data samples are processed in each sub-interval to generate intermediate results. Only these intermediate results are stored, not the entire plurality of data samples. This segmentation approach significantly reduces the storage capacity required while still enabling accurate maximum time interval error estimation for the complete sampling period.

The method processes data samples as they are received and generates intermediate results for each sub-interval during the sampling period, rather than waiting until all data is collected. This preliminary processing enables real-time estimation of the maximum time interval error, as the intermediate results are progressively available and can be used immediately without requiring the entire dataset to be gathered first.

The method dynamically adapts its processing approach based on the duration of the sampling period. A time threshold is defined, and if the sampling period duration is less than or equal to this threshold, the plurality of data samples is directly processed in real-time. If the sampling period exceeds the threshold, the sampling period is divided into sub-intervals and intermediate results are stored. This dynamic adaptation enables the system to handle both short and long sampling periods effectively while optimizing storage and computing resource usage.