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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
레이더 추적 시스템에서는 고정되지 않은 글린트 노이즈가 종종 관찰됩니다. 글린트 노이즈의 분포는 가우시안이 아니며 꼬리가 두껍습니다. 기존의 재귀 식별 알고리즘은 확률론적 근사(SA) 방법을 사용합니다. 그러나 SA 방법은 천천히 수렴하므로 비정상 잡음에는 유효하지 않습니다. 본 논문에서는 이러한 문제를 극복하기 위해 SGD(Stochastic Gradient Descent) 방법을 사용하는 적응형 알고리즘을 제안합니다. SGD 방식은 SA 방식의 간단한 구조를 유지하고 있어 실제 구현에 적합합니다. SGD 방법의 수렴 동작을 분석하고 충분한 단계 크기 범위에 대한 폐쇄형 표현을 도출합니다. 실제로 노이즈 데이터는 일반적으로 사용할 수 없으므로 노이즈 추출 기법을 제안합니다. SGD 방법을 결합하면 레이더 측정에서 직접 온라인 적응형 소음 식별을 수행할 수 있습니다. 시뮬레이션 결과는 SGD 방법의 성능이 최대 가능성(ML) 방법의 성능과 비슷하다는 것을 보여줍니다. 또한, 잡음 추출 방식은 레이더 측정의 식별 결과가 순수한 글린트 잡음 데이터의 식별 결과에 가깝기 때문에 효과적입니다.
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부
Wen-Rong WU, Kuo-Guan WU, "Adaptive Identification of Non-Gaussian/Non-stationary Glint Noise" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 12, pp. 2783-2792, December 1999, doi: .
Abstract: Non-stationary glint noise is often observed in a radar tracking system. The distribution of glint noise is non-Gaussian and heavy-tailed. Conventional recursive identification algorithms use the stochastic approximation (SA) method. However, the SA method converges slowly and is invalid for non-stationary noise. This paper proposes an adaptive algorithm, which uses the stochastic gradient descent (SGD) method, to overcome these problems. The SGD method retains the simple structure of the SA method and is suitable for real-world implementation. Convergence behavior of the SGD method is analyzed and closed-form expressions for sufficient step size bounds are derived. Since noise data are usually not available in practice, we then propose a noise extraction scheme. Combining the SGD method, we can perform on-line adaptive noise identification directly from radar measurements. Simulation results show that the performance of the SGD method is comparable to that of the maximum-likelihood (ML) method. Also, the noise extraction scheme is effective that the identification results from the radar measurements are close to those from pure glint noise data.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_12_2783/_p
부
@ARTICLE{e82-a_12_2783,
author={Wen-Rong WU, Kuo-Guan WU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Adaptive Identification of Non-Gaussian/Non-stationary Glint Noise},
year={1999},
volume={E82-A},
number={12},
pages={2783-2792},
abstract={Non-stationary glint noise is often observed in a radar tracking system. The distribution of glint noise is non-Gaussian and heavy-tailed. Conventional recursive identification algorithms use the stochastic approximation (SA) method. However, the SA method converges slowly and is invalid for non-stationary noise. This paper proposes an adaptive algorithm, which uses the stochastic gradient descent (SGD) method, to overcome these problems. The SGD method retains the simple structure of the SA method and is suitable for real-world implementation. Convergence behavior of the SGD method is analyzed and closed-form expressions for sufficient step size bounds are derived. Since noise data are usually not available in practice, we then propose a noise extraction scheme. Combining the SGD method, we can perform on-line adaptive noise identification directly from radar measurements. Simulation results show that the performance of the SGD method is comparable to that of the maximum-likelihood (ML) method. Also, the noise extraction scheme is effective that the identification results from the radar measurements are close to those from pure glint noise data.},
keywords={},
doi={},
ISSN={},
month={December},}
부
TY - JOUR
TI - Adaptive Identification of Non-Gaussian/Non-stationary Glint Noise
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2783
EP - 2792
AU - Wen-Rong WU
AU - Kuo-Guan WU
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 1999
AB - Non-stationary glint noise is often observed in a radar tracking system. The distribution of glint noise is non-Gaussian and heavy-tailed. Conventional recursive identification algorithms use the stochastic approximation (SA) method. However, the SA method converges slowly and is invalid for non-stationary noise. This paper proposes an adaptive algorithm, which uses the stochastic gradient descent (SGD) method, to overcome these problems. The SGD method retains the simple structure of the SA method and is suitable for real-world implementation. Convergence behavior of the SGD method is analyzed and closed-form expressions for sufficient step size bounds are derived. Since noise data are usually not available in practice, we then propose a noise extraction scheme. Combining the SGD method, we can perform on-line adaptive noise identification directly from radar measurements. Simulation results show that the performance of the SGD method is comparable to that of the maximum-likelihood (ML) method. Also, the noise extraction scheme is effective that the identification results from the radar measurements are close to those from pure glint noise data.
ER -