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Исследование статистических характеристик ошибок предсказания значений интерферометрического сигнала в алгоритме нелинейной фильтрации Калмана

Исследование статистических характеристик ошибок предсказания значений интерферометрического сигнала в алгоритме нелинейной фильтрации Калмана

Abstract

Рассмотрены основные особенности алгоритма нелинейной фильтрации Калмана применительно к задаче динамической обработки интерферометрических сигналов. Получены аналитические оценки, определяющие статистические характеристики ошибок предсказания значений сигнала, проведен анализ гистограмм ошибок с учетом изменения различных параметров интерферометрического сигнала. Выполнено моделирование операции предсказания сигнала при известных фиксированных параметрах и изменяющихся параметрах сигналов в алгоритме нелинейной фильтрации Калмана. Получены количественные оценки ошибок предсказания значений интерферометрического сигнала путем построения и анализа гистограмм ошибок при воздействии аддитивного шума и при случайных изменениях амплитуды и частоты интерферометрического сигнала. Показано, что нелинейный фильтр Калмана обеспечивает обработку сигналов со случайно изменяющимися параметрами, однако непосредственно не учитывает погрешность линеаризации гармонической функции, представляющей интерферометрический сигнал, что является источником погрешностей фильтрации. Основной недостаток линейного предсказания состоит в негауссовской статистике ошибок предсказания, в том числе при случайных отклонениях амплитуды и (или) частоты сигнала. При стохаcтической фильтрации интерферометрических сигналов целесообразно использовать процедуры предсказания, основанные на учете локальных статистик сигнала и его параметров.

Basic peculiarities of nonlinear Kalman filtering algorithm applied to processing of interferometric signals are considered. Analytical estimates determining statistical characteristics of signal values prediction errors were obtained and analysis of errors histograms taking into account variations of different parameters of interferometric signal was carried out. Modeling of the signal prediction procedure with known fixed parameters and variable parameters of signal in the algorithm of nonlinear Kalman filtering was performed. Numerical estimates of prediction errors for interferometric signal values were obtained by formation and analysis of the errors histograms under the influence of additive noise and random variations of amplitude and frequency of interferometric signal. Nonlinear Kalman filter is shown to provide processing of signals with randomly variable parameters, however, it does not take into account directly the linearization error of harmonic function representing interferometric signal that is a filtering error source. The main drawback of the linear prediction consists in non-Gaussian statistics of prediction errors including cases of random deviations of signal amplitude and/or frequency. When implementing stochastic filtering of interferometric signals, it is reasonable to use prediction procedures based on local statistics of a signal and its parameters taken into account.

Keywords

ИНТЕРФЕРОМЕТРИЧЕСКИЙ СИГНАЛ,INTERFEROMETRIC SIGNAL,НЕЛИНЕЙНЫЙ ФИЛЬТР КАЛМАНА,NONLINEAR KALMAN FILTERING,ОШИБКА ПРЕДСКАЗАНИЯ,PREDICTION ERROR,ГАУССОВСКИЙ ШУМ,GAUSSIAN NOISE,ГИСТОГРАММА,HISTOGRAM

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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