JOURNAL ARTICLE

Time Delay Estimation Using LASSO (Least Absolute Selection and Shrinkage Operator)

Jun-Seok LimYong-Guk PyeonSeok-Im Choi

Year: 2014 Journal:   The Journal of Korean Institute of Communications and Information Sciences Vol: 39B (10)Pages: 715-721   Publisher: THE KOREAN INSTITUTE OF COMMUNICATIONS AND INFORMATION SCIENCES (KICS)

Abstract

두 개 센서에 도래하는 신호 간의 시간 지연을 추정 방법에는 여러 가지가 존재한다. 그 중에서 채널 추정 기법을 기반으로 한 방법의 경우는 두 센서에 입력되는 서로 다른 신호간의 상대적인 지연을 채널의 임펄스 응답처럼 추정하도록 되어 있다. 이 경우에는 해당 채널의 특성이 희박 채널의 특성을 가지고 있다. 기존의 방법들은 채널의 희박성을 이용하지 못하고 있는 방법이 대부분이다. 본 논문에서는 채널의 희박성을 이용하기 위하여 희박신호 최적화 방법의 하나인 LASSO 최적화를 사용한 시간 지연 추정 방법을 제안한다. 제안한 방법을 기존의 방법과 비교하여, 백색 가우시안 신호원에서는 약 10dB 이상의 성능 개선 결과를 보이고, 유색 신호원에서도 갑자기 추정성능이 열하되는 현상이 없음을 보인다. In decades, many researchers have studied the time delay estimation (TDE) method for the signals in the two different receivers. The channel estimation based TDE is one of the typical TDE methods. The channel estimation based TDE models the time delay between two receiving signals as an impulse response in a channel between two receivers. In general the impulse response becomes sparse. However, most conventional TDE algorithms cannot have utilized the sparsity. In this paper, we propose a TDE method taking the sparsity into consideration. The performance comparison shows that the proposed algorithm improves the estimation accuracy by 10 dB in the white gaussian source. In addition, even in the colored source, the proposed algorithm doesn't show the estimation threshold effect.

Keywords:
Lasso (programming language) Algorithm Channel (broadcasting) Impulse (physics) Gaussian Impulse response Computer science Mathematics Telecommunications

Metrics

1
Cited By
0.27
FWCI (Field Weighted Citation Impact)
27
Refs
0.58
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Speech and Audio Processing
Physical Sciences →  Computer Science →  Signal Processing
Advanced Adaptive Filtering Techniques
Physical Sciences →  Engineering →  Computational Mechanics
Blind Source Separation Techniques
Physical Sciences →  Computer Science →  Signal Processing

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