초록 ▼

The present invention comprises a method for filling in missing data intervals in a quantized time-dependent data signal that is generated by, e.g., an underwater acoustic sensing device. In accordance with one embodiment of the invention, this quantized time-dependent data signal is analyzed to det

The present invention comprises a method for filling in missing data intervals in a quantized time-dependent data signal that is generated by, e.g., an underwater acoustic sensing device. In accordance with one embodiment of the invention, this quantized time-dependent data signal is analyzed to determine the number and location of any intervals of missing data, i.e., gaps in the time series data signal caused by noise in the sensing equipment or the local environment. The quantized time-dependent data signal is also modified by a low pass filter to remove any undesirable high frequency noise components within the signal. A plurality of mathematical models are then individually tested to derive an optimum regression curve for that model, relative to a selected portion of the signal data immediately preceding each previously identified data gap. The aforesaid selected portion is empirically determined on the basis of a data base of signal values compiled from actual undersea propagated signals received in cases of known target motion scenarios. An optimum regression curve is that regression curve, linear or nonlinear, for which a mathematical convergence of the model is achieved. Convergence of the model is determined by application of a smallest root-mean-square analysis to each of the plurality of models tested. Once a model possessing the smallest root-mean-square value is derived from among the plurality of models tested, that optimum model is then selected, recorded, and stored for use in filling the data gap. This process is then repeated for each subsequent data gap until all of the identified data gaps are filled.

대표청구항 ▼

A method for processing a time series of quantized passive acoustic target transmitted signal values so as to compensate for missing intervals of data within said time series, said method comprising the following steps: a first step of determining empirically at least three data window intervals cor

A method for processing a time series of quantized passive acoustic target transmitted signal values so as to compensate for missing intervals of data within said time series, said method comprising the following steps: a first step of determining empirically at least three data window intervals corresponding respectively to high, moderate and low signal-to-noise levels within said quantized passive acoustic target transmitted signal values wherein said data window intervals provide a basis for the desired degree of fit of a regressively derived curve of substitute data; a second step of low pass filtering said quantized passive acoustic target transmitted signal values to eliminate undesirable transitory signals; a third step of detecting sequentially with respect to said quantized passive acoustic target transmitted signal values the number of missing intervals of data; a fourth step of determining an initial reference curve of point values spanning a first interval of missing data adjacent to a first selected data window based on at least one of said window intervals for a desirable signal-to-noise-ratio; and a fifth step of regressively deriving from said initial reference curve a convergent solution curve yielding the aforesaid desired degree of fit of substitute data.