The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este artigo concentra-se no modelo útil para analisar o desempenho do erro de estimadores M de um único parâmetro de sinal desconhecido: este é o modelo de intensidade de erro. Desenvolvemos a representação do processo pontual para o erro de estimativa, a distribuição condicional do estimador e a distribuição do processo pontual candidato ao erro. Em seguida, a função de intensidade de erro é definida como a densidade de probabilidade da estimativa e a forma geral da função de intensidade de erro é derivada. Calculamos a forma explícita das funções de intensidade com base no modelo de máximos locais do processo de ponto gerador de erro. Embora os métodos descritos neste artigo sejam aplicáveis a qualquer problema de estimativa com parâmetros contínuos, nossa principal aplicação será a estimativa de atraso de tempo. Especificamente, consideraremos o caso em que está envolvida interferência impulsiva coerente além do ruído gaussiano. Com base nos resultados da simulação numérica, comparamos cada modelo de intensidade de erro em termos da precisão das previsões de probabilidade de erro e erro quadrático médio (MSE), e a questão da extensibilidade à estimativa de múltiplos parâmetros também é discutida.
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Joong-Kyu KIM, "Local Maxima Error Intensity Functions and Its Application to Time Delay Estimator in the Presence of Shot Noise Interference" in IEICE TRANSACTIONS on Fundamentals,
vol. E83-A, no. 9, pp. 1844-1852, September 2000, doi: .
Abstract: This paper concentrates on the model useful for analyzing the error performance of M-estimators of a single unknown signal parameter: that is the error intensity model. We develop the point process representation for the estimation error, the conditional distribution of the estimator, and the distribution of error candidate point process. Then the error intensity function is defined as the probability density of the estimate and the general form of the error intensity function is derived. We compute the explicit form of the intensity functions based on the local maxima model of the error generating point process. While the methods described in this paper are applicable to any estimation problem with continuous parameters, our main application will be time delay estimation. Specifically, we will consider the case where coherent impulsive interference is involved in addition to the Gaussian noise. Based on numerical simulation results, we compare each of the error intensity model in terms of the accuracy of both error probability and mean squared error (MSE) predictions, and the issue of extendibility to multiple parameter estimation is also discussed.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e83-a_9_1844/_p
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@ARTICLE{e83-a_9_1844,
author={Joong-Kyu KIM, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Local Maxima Error Intensity Functions and Its Application to Time Delay Estimator in the Presence of Shot Noise Interference},
year={2000},
volume={E83-A},
number={9},
pages={1844-1852},
abstract={This paper concentrates on the model useful for analyzing the error performance of M-estimators of a single unknown signal parameter: that is the error intensity model. We develop the point process representation for the estimation error, the conditional distribution of the estimator, and the distribution of error candidate point process. Then the error intensity function is defined as the probability density of the estimate and the general form of the error intensity function is derived. We compute the explicit form of the intensity functions based on the local maxima model of the error generating point process. While the methods described in this paper are applicable to any estimation problem with continuous parameters, our main application will be time delay estimation. Specifically, we will consider the case where coherent impulsive interference is involved in addition to the Gaussian noise. Based on numerical simulation results, we compare each of the error intensity model in terms of the accuracy of both error probability and mean squared error (MSE) predictions, and the issue of extendibility to multiple parameter estimation is also discussed.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Local Maxima Error Intensity Functions and Its Application to Time Delay Estimator in the Presence of Shot Noise Interference
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1844
EP - 1852
AU - Joong-Kyu KIM
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E83-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2000
AB - This paper concentrates on the model useful for analyzing the error performance of M-estimators of a single unknown signal parameter: that is the error intensity model. We develop the point process representation for the estimation error, the conditional distribution of the estimator, and the distribution of error candidate point process. Then the error intensity function is defined as the probability density of the estimate and the general form of the error intensity function is derived. We compute the explicit form of the intensity functions based on the local maxima model of the error generating point process. While the methods described in this paper are applicable to any estimation problem with continuous parameters, our main application will be time delay estimation. Specifically, we will consider the case where coherent impulsive interference is involved in addition to the Gaussian noise. Based on numerical simulation results, we compare each of the error intensity model in terms of the accuracy of both error probability and mean squared error (MSE) predictions, and the issue of extendibility to multiple parameter estimation is also discussed.
ER -