Bounds on Passive Tdoa Estimation in Mixtures

Amir Weiss; Arie Yeredor

doi:10.1109/SSP.2018.8450810

Bounds on Passive Tdoa Estimation in Mixtures

Amir Weiss, Arie Yeredor

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We consider the problem of Time Difference of Arrival (TDOA) estimation in mixtures, namely when several sources are received by several receivers, possibly with different delays and attenuations. Under the assumption that the sources are stationary Gaussian with known spectra (a semi-blind scenario), we derive the Cramér-Rao Lower Bound on the Mean Squared Error (MSE) in unbiased joint estimation of the delays and of the mixing coefficients. We then analyze the results, drawing conclusions on the effects of the different model parameters (mixing coefficients, delay differences, signal to noise ratio) on the resulting bound, pointing out essential differences from the classical cases of static mixtures (with no delays) on one hand, and of single-source TDOA estimation on the other hand.

Original language	English
Title of host publication	2018 IEEE Statistical Signal Processing Workshop, SSP 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	50-54
Number of pages	5
ISBN (Print)	9781538615706
DOIs	https://doi.org/10.1109/SSP.2018.8450810
State	Published - 29 Aug 2018
Event	20th IEEE Statistical Signal Processing Workshop, SSP 2018 - Freiburg im Breisgau, Germany Duration: 10 Jun 2018 → 13 Jun 2018

Publication series

Name	2018 IEEE Statistical Signal Processing Workshop, SSP 2018

Conference

Conference	20th IEEE Statistical Signal Processing Workshop, SSP 2018
Country/Territory	Germany
City	Freiburg im Breisgau
Period	10/06/18 → 13/06/18

Keywords

Cramér-Rao lower bound
TDOA
maximum likelihood
source separation

Access to Document

10.1109/SSP.2018.8450810

Cite this

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abstract = "We consider the problem of Time Difference of Arrival (TDOA) estimation in mixtures, namely when several sources are received by several receivers, possibly with different delays and attenuations. Under the assumption that the sources are stationary Gaussian with known spectra (a semi-blind scenario), we derive the Cram{\'e}r-Rao Lower Bound on the Mean Squared Error (MSE) in unbiased joint estimation of the delays and of the mixing coefficients. We then analyze the results, drawing conclusions on the effects of the different model parameters (mixing coefficients, delay differences, signal to noise ratio) on the resulting bound, pointing out essential differences from the classical cases of static mixtures (with no delays) on one hand, and of single-source TDOA estimation on the other hand.",

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author = "Amir Weiss and Arie Yeredor",

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Weiss, A & Yeredor, A 2018, Bounds on Passive Tdoa Estimation in Mixtures. in 2018 IEEE Statistical Signal Processing Workshop, SSP 2018., 8450810, 2018 IEEE Statistical Signal Processing Workshop, SSP 2018, Institute of Electrical and Electronics Engineers Inc., pp. 50-54, 20th IEEE Statistical Signal Processing Workshop, SSP 2018, Freiburg im Breisgau, Germany, 10/06/18. https://doi.org/10.1109/SSP.2018.8450810

Bounds on Passive Tdoa Estimation in Mixtures. / Weiss, Amir; Yeredor, Arie.

2018 IEEE Statistical Signal Processing Workshop, SSP 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 50-54 8450810 (2018 IEEE Statistical Signal Processing Workshop, SSP 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Weiss, Amir

AU - Yeredor, Arie

PY - 2018/8/29

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N2 - We consider the problem of Time Difference of Arrival (TDOA) estimation in mixtures, namely when several sources are received by several receivers, possibly with different delays and attenuations. Under the assumption that the sources are stationary Gaussian with known spectra (a semi-blind scenario), we derive the Cramér-Rao Lower Bound on the Mean Squared Error (MSE) in unbiased joint estimation of the delays and of the mixing coefficients. We then analyze the results, drawing conclusions on the effects of the different model parameters (mixing coefficients, delay differences, signal to noise ratio) on the resulting bound, pointing out essential differences from the classical cases of static mixtures (with no delays) on one hand, and of single-source TDOA estimation on the other hand.

AB - We consider the problem of Time Difference of Arrival (TDOA) estimation in mixtures, namely when several sources are received by several receivers, possibly with different delays and attenuations. Under the assumption that the sources are stationary Gaussian with known spectra (a semi-blind scenario), we derive the Cramér-Rao Lower Bound on the Mean Squared Error (MSE) in unbiased joint estimation of the delays and of the mixing coefficients. We then analyze the results, drawing conclusions on the effects of the different model parameters (mixing coefficients, delay differences, signal to noise ratio) on the resulting bound, pointing out essential differences from the classical cases of static mixtures (with no delays) on one hand, and of single-source TDOA estimation on the other hand.

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Bounds on Passive Tdoa Estimation in Mixtures

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Keywords

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