On-line multiscale filtering

The existing nonlinear filtering techniques described in the previous section do perform better than linear filters, such as FIR and HR filters, for a broad 

In contrast, if the number of measurements is odd, the last point cannot be decomposed without a time delay. For example, when three points are 

---

On-line multiscale filtering of data with Gaussian errors... 

On-line multiscale filtering is based on multiscale filtering of data in a moving window of dyadic length as shown in Fig. 11. 

As in the case of any filtering or compression method, multiscale filtering relies on some information about the data and the nature of the errors to tune its filter parameters, which include the threshold, decomposition depth, the wavelet filter, and the size of the median filter for the robust techniques. Hints for selecting these tuning parameters in off-line and on-line modes are discussed below. 

---