Abstract: We have conducted the optimal synthesis of rootmean-
squared objective filter to estimate the state vector in the case if
within the observation channel with memory the anomalous noises
with unknown mathematical expectation are complement in the
function of the regular noises. The synthesis has been carried out for
linear stochastic systems of continuous - time.
Abstract: For optimal unbiased filter as mean-square and in the
case of functioning anomalous noises in the observation memory
channel, we have proved insensitivity of filter to inaccurate
knowledge of the anomalous noise intensity matrix and its
equivalence to truncated filter plotted only by non anomalous
components of an observation vector.
Abstract: In this paper an alternative analysis in the time
domain is described and the results of the interpolation process are
presented by means of functions that are based on the rule of
conditional mathematical expectation and the covariance function. A
comparison between the interpolation error caused by low order
filters and the classic sinc(t) truncated function is also presented.
When fewer samples are used, low-order filters have less error. If the
number of samples increases, the sinc(t) type functions are a better
alternative. Generally speaking there is an optimal filter for each
input signal which depends on the filter length and covariance
function of the signal. A novel scheme of work for adaptive
interpolation filters is also presented.