Model-based learning of underwater acoustic communication performance for marine robots

Accurate prediction of acoustic communication performance is an important capability for marine robots. In this paper, we propose a model-based learning methodology for the prediction of underwater acoustic communication performance. The learning algorithm consists of two steps: (i) estimation of th...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Kontoudis, George P. [verfasserIn] Krauss, Stephen [verfasserIn] Stilwell, Daniel J. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Model-based learning Autonomous underwater vehicles Wireless communications Spatial statistics Kriging

Übergeordnetes Werk:	Enthalten in: Robotics and autonomous systems - Amsterdam [u.a.] : Elsevier, 1988, 142
Übergeordnetes Werk:	volume:142

DOI / URN:	10.1016/j.robot.2021.103811

Katalog-ID:	ELV006161634

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520			\|a Accurate prediction of acoustic communication performance is an important capability for marine robots. In this paper, we propose a model-based learning methodology for the prediction of underwater acoustic communication performance. The learning algorithm consists of two steps: (i) estimation of the covariance matrix by evaluating candidate functions with estimated parameters using detrended measurements;and (ii) prediction of communication performance. Covariance estimation is addressed with a multi-stage iterative training method that produces unbiased and robust results with nested models. The efficiency of the framework is validated with simulations and experimental data from field trials. The field trials involved a manned surface vehicle and an autonomous underwater vehicle.
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700	1		\|a Stilwell, Daniel J. \|e verfasserin \|4 aut
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allfields	10.1016/j.robot.2021.103811 doi (DE-627)ELV006161634 (ELSEVIER)S0921-8890(21)00096-8 DE-627 ger DE-627 rda eng 620 DE-600 50.25 bkl 54.72 bkl Kontoudis, George P. verfasserin (orcid)0000-0003-2193-7700 aut Model-based learning of underwater acoustic communication performance for marine robots 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate prediction of acoustic communication performance is an important capability for marine robots. In this paper, we propose a model-based learning methodology for the prediction of underwater acoustic communication performance. The learning algorithm consists of two steps: (i) estimation of the covariance matrix by evaluating candidate functions with estimated parameters using detrended measurements;and (ii) prediction of communication performance. Covariance estimation is addressed with a multi-stage iterative training method that produces unbiased and robust results with nested models. The efficiency of the framework is validated with simulations and experimental data from field trials. The field trials involved a manned surface vehicle and an autonomous underwater vehicle. Model-based learning Autonomous underwater vehicles Wireless communications Spatial statistics Kriging Krauss, Stephen verfasserin aut Stilwell, Daniel J. verfasserin aut Enthalten in Robotics and autonomous systems Amsterdam [u.a.] : Elsevier, 1988 142 Online-Ressource (DE-627)271597615 (DE-600)1480750-6 (DE-576)078708168 1872-793X nnns volume:142 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.25 Robotertechnik 54.72 Künstliche Intelligenz AR 142
spelling	10.1016/j.robot.2021.103811 doi (DE-627)ELV006161634 (ELSEVIER)S0921-8890(21)00096-8 DE-627 ger DE-627 rda eng 620 DE-600 50.25 bkl 54.72 bkl Kontoudis, George P. verfasserin (orcid)0000-0003-2193-7700 aut Model-based learning of underwater acoustic communication performance for marine robots 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate prediction of acoustic communication performance is an important capability for marine robots. In this paper, we propose a model-based learning methodology for the prediction of underwater acoustic communication performance. The learning algorithm consists of two steps: (i) estimation of the covariance matrix by evaluating candidate functions with estimated parameters using detrended measurements;and (ii) prediction of communication performance. Covariance estimation is addressed with a multi-stage iterative training method that produces unbiased and robust results with nested models. The efficiency of the framework is validated with simulations and experimental data from field trials. The field trials involved a manned surface vehicle and an autonomous underwater vehicle. Model-based learning Autonomous underwater vehicles Wireless communications Spatial statistics Kriging Krauss, Stephen verfasserin aut Stilwell, Daniel J. verfasserin aut Enthalten in Robotics and autonomous systems Amsterdam [u.a.] : Elsevier, 1988 142 Online-Ressource (DE-627)271597615 (DE-600)1480750-6 (DE-576)078708168 1872-793X nnns volume:142 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.25 Robotertechnik 54.72 Künstliche Intelligenz AR 142
allfields_unstemmed	10.1016/j.robot.2021.103811 doi (DE-627)ELV006161634 (ELSEVIER)S0921-8890(21)00096-8 DE-627 ger DE-627 rda eng 620 DE-600 50.25 bkl 54.72 bkl Kontoudis, George P. verfasserin (orcid)0000-0003-2193-7700 aut Model-based learning of underwater acoustic communication performance for marine robots 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate prediction of acoustic communication performance is an important capability for marine robots. In this paper, we propose a model-based learning methodology for the prediction of underwater acoustic communication performance. The learning algorithm consists of two steps: (i) estimation of the covariance matrix by evaluating candidate functions with estimated parameters using detrended measurements;and (ii) prediction of communication performance. Covariance estimation is addressed with a multi-stage iterative training method that produces unbiased and robust results with nested models. The efficiency of the framework is validated with simulations and experimental data from field trials. The field trials involved a manned surface vehicle and an autonomous underwater vehicle. Model-based learning Autonomous underwater vehicles Wireless communications Spatial statistics Kriging Krauss, Stephen verfasserin aut Stilwell, Daniel J. verfasserin aut Enthalten in Robotics and autonomous systems Amsterdam [u.a.] : Elsevier, 1988 142 Online-Ressource (DE-627)271597615 (DE-600)1480750-6 (DE-576)078708168 1872-793X nnns volume:142 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.25 Robotertechnik 54.72 Künstliche Intelligenz AR 142
allfieldsGer	10.1016/j.robot.2021.103811 doi (DE-627)ELV006161634 (ELSEVIER)S0921-8890(21)00096-8 DE-627 ger DE-627 rda eng 620 DE-600 50.25 bkl 54.72 bkl Kontoudis, George P. verfasserin (orcid)0000-0003-2193-7700 aut Model-based learning of underwater acoustic communication performance for marine robots 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate prediction of acoustic communication performance is an important capability for marine robots. In this paper, we propose a model-based learning methodology for the prediction of underwater acoustic communication performance. The learning algorithm consists of two steps: (i) estimation of the covariance matrix by evaluating candidate functions with estimated parameters using detrended measurements;and (ii) prediction of communication performance. Covariance estimation is addressed with a multi-stage iterative training method that produces unbiased and robust results with nested models. The efficiency of the framework is validated with simulations and experimental data from field trials. The field trials involved a manned surface vehicle and an autonomous underwater vehicle. Model-based learning Autonomous underwater vehicles Wireless communications Spatial statistics Kriging Krauss, Stephen verfasserin aut Stilwell, Daniel J. verfasserin aut Enthalten in Robotics and autonomous systems Amsterdam [u.a.] : Elsevier, 1988 142 Online-Ressource (DE-627)271597615 (DE-600)1480750-6 (DE-576)078708168 1872-793X nnns volume:142 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.25 Robotertechnik 54.72 Künstliche Intelligenz AR 142
allfieldsSound	10.1016/j.robot.2021.103811 doi (DE-627)ELV006161634 (ELSEVIER)S0921-8890(21)00096-8 DE-627 ger DE-627 rda eng 620 DE-600 50.25 bkl 54.72 bkl Kontoudis, George P. verfasserin (orcid)0000-0003-2193-7700 aut Model-based learning of underwater acoustic communication performance for marine robots 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate prediction of acoustic communication performance is an important capability for marine robots. In this paper, we propose a model-based learning methodology for the prediction of underwater acoustic communication performance. The learning algorithm consists of two steps: (i) estimation of the covariance matrix by evaluating candidate functions with estimated parameters using detrended measurements;and (ii) prediction of communication performance. Covariance estimation is addressed with a multi-stage iterative training method that produces unbiased and robust results with nested models. The efficiency of the framework is validated with simulations and experimental data from field trials. The field trials involved a manned surface vehicle and an autonomous underwater vehicle. Model-based learning Autonomous underwater vehicles Wireless communications Spatial statistics Kriging Krauss, Stephen verfasserin aut Stilwell, Daniel J. verfasserin aut Enthalten in Robotics and autonomous systems Amsterdam [u.a.] : Elsevier, 1988 142 Online-Ressource (DE-627)271597615 (DE-600)1480750-6 (DE-576)078708168 1872-793X nnns volume:142 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.25 Robotertechnik 54.72 Künstliche Intelligenz AR 142
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author	Kontoudis, George P.
spellingShingle	Kontoudis, George P. ddc 620 bkl 50.25 bkl 54.72 misc Model-based learning misc Autonomous underwater vehicles misc Wireless communications misc Spatial statistics misc Kriging Model-based learning of underwater acoustic communication performance for marine robots
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topic_title	620 DE-600 50.25 bkl 54.72 bkl Model-based learning of underwater acoustic communication performance for marine robots Model-based learning Autonomous underwater vehicles Wireless communications Spatial statistics Kriging
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title	Model-based learning of underwater acoustic communication performance for marine robots
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title_full	Model-based learning of underwater acoustic communication performance for marine robots
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author_browse	Kontoudis, George P. Krauss, Stephen Stilwell, Daniel J.
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title_sort	model-based learning of underwater acoustic communication performance for marine robots
title_auth	Model-based learning of underwater acoustic communication performance for marine robots
abstract	Accurate prediction of acoustic communication performance is an important capability for marine robots. In this paper, we propose a model-based learning methodology for the prediction of underwater acoustic communication performance. The learning algorithm consists of two steps: (i) estimation of the covariance matrix by evaluating candidate functions with estimated parameters using detrended measurements;and (ii) prediction of communication performance. Covariance estimation is addressed with a multi-stage iterative training method that produces unbiased and robust results with nested models. The efficiency of the framework is validated with simulations and experimental data from field trials. The field trials involved a manned surface vehicle and an autonomous underwater vehicle.
abstractGer	Accurate prediction of acoustic communication performance is an important capability for marine robots. In this paper, we propose a model-based learning methodology for the prediction of underwater acoustic communication performance. The learning algorithm consists of two steps: (i) estimation of the covariance matrix by evaluating candidate functions with estimated parameters using detrended measurements;and (ii) prediction of communication performance. Covariance estimation is addressed with a multi-stage iterative training method that produces unbiased and robust results with nested models. The efficiency of the framework is validated with simulations and experimental data from field trials. The field trials involved a manned surface vehicle and an autonomous underwater vehicle.
abstract_unstemmed	Accurate prediction of acoustic communication performance is an important capability for marine robots. In this paper, we propose a model-based learning methodology for the prediction of underwater acoustic communication performance. The learning algorithm consists of two steps: (i) estimation of the covariance matrix by evaluating candidate functions with estimated parameters using detrended measurements;and (ii) prediction of communication performance. Covariance estimation is addressed with a multi-stage iterative training method that produces unbiased and robust results with nested models. The efficiency of the framework is validated with simulations and experimental data from field trials. The field trials involved a manned surface vehicle and an autonomous underwater vehicle.
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title_short	Model-based learning of underwater acoustic communication performance for marine robots
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doi_str	10.1016/j.robot.2021.103811
up_date	2024-07-06T20:26:03.962Z
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In this paper, we propose a model-based learning methodology for the prediction of underwater acoustic communication performance. The learning algorithm consists of two steps: (i) estimation of the covariance matrix by evaluating candidate functions with estimated parameters using detrended measurements;and (ii) prediction of communication performance. Covariance estimation is addressed with a multi-stage iterative training method that produces unbiased and robust results with nested models. The efficiency of the framework is validated with simulations and experimental data from field trials. 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