IEICE TRANSACTIONS on Fundamentals
Nonlinear Least-Squares Time-Difference Estimation from Sub-Nyquist-Rate Samples
Summary :
In this paper, time-difference estimation of filtered random signals passed through multipath channels is discussed. First, we reformulate the approach based on innovation-rate sampling (IRS) to fit our random signal model, then use the IRS results to drive the nonlinear least-squares (NLS) minimization algorithm. This hybrid approach (referred to as the IRS-NLS method) provides consistent estimates even for cases with sub-Nyquist sampling assuming the use of compactly-supported sampling kernels that satisfies the recently-developed nonaliasing condition in the frequency domain. Numerical simulations show that the proposed NLS-IRS method can improve performance over the straight-forward IRS method, and provides approximately the same performance as the NLS method with reduced sampling rate, even for closely-spaced time delays. This enables, given a fixed observation time, significant reduction in the required number of samples, while maintaining the same level of estimation performance.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E95-A No.7 pp.1117-1124
- Publication Date
- 2012/07/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E95.A.1117
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
Authors
Keyword
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Koji HARADA, Hideaki SAKAI, "Nonlinear Least-Squares Time-Difference Estimation from Sub-Nyquist-Rate Samples" in IEICE TRANSACTIONS on Fundamentals,
vol. E95-A, no. 7, pp. 1117-1124, July 2012, doi: 10.1587/transfun.E95.A.1117.
Abstract: In this paper, time-difference estimation of filtered random signals passed through multipath channels is discussed. First, we reformulate the approach based on innovation-rate sampling (IRS) to fit our random signal model, then use the IRS results to drive the nonlinear least-squares (NLS) minimization algorithm. This hybrid approach (referred to as the IRS-NLS method) provides consistent estimates even for cases with sub-Nyquist sampling assuming the use of compactly-supported sampling kernels that satisfies the recently-developed nonaliasing condition in the frequency domain. Numerical simulations show that the proposed NLS-IRS method can improve performance over the straight-forward IRS method, and provides approximately the same performance as the NLS method with reduced sampling rate, even for closely-spaced time delays. This enables, given a fixed observation time, significant reduction in the required number of samples, while maintaining the same level of estimation performance.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E95.A.1117/_p
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@ARTICLE{e95-a_7_1117,
author={Koji HARADA, Hideaki SAKAI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Nonlinear Least-Squares Time-Difference Estimation from Sub-Nyquist-Rate Samples},
year={2012},
volume={E95-A},
number={7},
pages={1117-1124},
abstract={In this paper, time-difference estimation of filtered random signals passed through multipath channels is discussed. First, we reformulate the approach based on innovation-rate sampling (IRS) to fit our random signal model, then use the IRS results to drive the nonlinear least-squares (NLS) minimization algorithm. This hybrid approach (referred to as the IRS-NLS method) provides consistent estimates even for cases with sub-Nyquist sampling assuming the use of compactly-supported sampling kernels that satisfies the recently-developed nonaliasing condition in the frequency domain. Numerical simulations show that the proposed NLS-IRS method can improve performance over the straight-forward IRS method, and provides approximately the same performance as the NLS method with reduced sampling rate, even for closely-spaced time delays. This enables, given a fixed observation time, significant reduction in the required number of samples, while maintaining the same level of estimation performance.},
keywords={},
doi={10.1587/transfun.E95.A.1117},
ISSN={1745-1337},
month={July},}
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TY - JOUR
TI - Nonlinear Least-Squares Time-Difference Estimation from Sub-Nyquist-Rate Samples
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1117
EP - 1124
AU - Koji HARADA
AU - Hideaki SAKAI
PY - 2012
DO - 10.1587/transfun.E95.A.1117
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E95-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2012
AB - In this paper, time-difference estimation of filtered random signals passed through multipath channels is discussed. First, we reformulate the approach based on innovation-rate sampling (IRS) to fit our random signal model, then use the IRS results to drive the nonlinear least-squares (NLS) minimization algorithm. This hybrid approach (referred to as the IRS-NLS method) provides consistent estimates even for cases with sub-Nyquist sampling assuming the use of compactly-supported sampling kernels that satisfies the recently-developed nonaliasing condition in the frequency domain. Numerical simulations show that the proposed NLS-IRS method can improve performance over the straight-forward IRS method, and provides approximately the same performance as the NLS method with reduced sampling rate, even for closely-spaced time delays. This enables, given a fixed observation time, significant reduction in the required number of samples, while maintaining the same level of estimation performance.
ER -
English
