Gaussian processes based extended target tracking in polar coordinates with input uncertainty

Abstract Gaussian processes (GP) based extended target tracking (ETT) technique has attracted the attention of scientists since it can accurately estimate the kinematic states as well as the contour states of irregular-shape targets. Most traditional GP methods consider the ETT problem with a linear...
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Autor*in:	Guo, Yunfei [verfasserIn] Yang, Dongsheng Ren, Lei Yan, Lei

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Extended target tracking Gaussian processes Input uncertainty Nonlinear measurement Measurement origin uncertainty

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: EURASIP journal on advances in signal processing - Heidelberg : Springer, 2007, 2022(2022), 1 vom: 29. Okt.
Übergeordnetes Werk:	volume:2022 ; year:2022 ; number:1 ; day:29 ; month:10

Links:	Volltext

DOI / URN:	10.1186/s13634-022-00940-w

Katalog-ID:	SPR048496944

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520			\|a Abstract Gaussian processes (GP) based extended target tracking (ETT) technique has attracted the attention of scientists since it can accurately estimate the kinematic states as well as the contour states of irregular-shape targets. Most traditional GP methods consider the ETT problem with a linear measurement model in Cartesian coordinates and ignore the input uncertainty of GP for simplicity. In many applications, however, measurements are generated with high-resolution sensors in polar coordinates. More importantly, the ignorance of input uncertainty in GP will exacerbate the tracking performance in these cases. In order to track an irregular-shape extended target in polar coordinates with input uncertainty, an improved GP-based probabilistic data association (IGP-PDA) algorithm is developed that includes the following enhancements: Firstly, a nonlinear measurement model is used and the unbiased converted measurement technique is invoked for the ETT problem. Secondly, the analytical form of statistical properties of the input uncertainty due to measurements noise and predicted error is derived. Thirdly, an IGP-PDA taking into account measurement origin uncertainty as well as input uncertainty is proposed, and three sub-optimal implementations are provided. Last, the posterior Cramer-Rao lower bound of ETT with input uncertainty is derived. Simulation results verify the effectiveness of the proposed method.
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allfields	10.1186/s13634-022-00940-w doi (DE-627)SPR048496944 (SPR)s13634-022-00940-w-e DE-627 ger DE-627 rakwb eng Guo, Yunfei verfasserin (orcid)0000-0001-7887-4312 aut Gaussian processes based extended target tracking in polar coordinates with input uncertainty 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Gaussian processes (GP) based extended target tracking (ETT) technique has attracted the attention of scientists since it can accurately estimate the kinematic states as well as the contour states of irregular-shape targets. Most traditional GP methods consider the ETT problem with a linear measurement model in Cartesian coordinates and ignore the input uncertainty of GP for simplicity. In many applications, however, measurements are generated with high-resolution sensors in polar coordinates. More importantly, the ignorance of input uncertainty in GP will exacerbate the tracking performance in these cases. In order to track an irregular-shape extended target in polar coordinates with input uncertainty, an improved GP-based probabilistic data association (IGP-PDA) algorithm is developed that includes the following enhancements: Firstly, a nonlinear measurement model is used and the unbiased converted measurement technique is invoked for the ETT problem. Secondly, the analytical form of statistical properties of the input uncertainty due to measurements noise and predicted error is derived. Thirdly, an IGP-PDA taking into account measurement origin uncertainty as well as input uncertainty is proposed, and three sub-optimal implementations are provided. Last, the posterior Cramer-Rao lower bound of ETT with input uncertainty is derived. Simulation results verify the effectiveness of the proposed method. Extended target tracking (dpeaa)DE-He213 Gaussian processes (dpeaa)DE-He213 Input uncertainty (dpeaa)DE-He213 Nonlinear measurement (dpeaa)DE-He213 Measurement origin uncertainty (dpeaa)DE-He213 Yang, Dongsheng aut Ren, Lei aut Yan, Lei aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2022(2022), 1 vom: 29. Okt. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2022 year:2022 number:1 day:29 month:10 https://dx.doi.org/10.1186/s13634-022-00940-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022 2022 1 29 10
spelling	10.1186/s13634-022-00940-w doi (DE-627)SPR048496944 (SPR)s13634-022-00940-w-e DE-627 ger DE-627 rakwb eng Guo, Yunfei verfasserin (orcid)0000-0001-7887-4312 aut Gaussian processes based extended target tracking in polar coordinates with input uncertainty 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Gaussian processes (GP) based extended target tracking (ETT) technique has attracted the attention of scientists since it can accurately estimate the kinematic states as well as the contour states of irregular-shape targets. Most traditional GP methods consider the ETT problem with a linear measurement model in Cartesian coordinates and ignore the input uncertainty of GP for simplicity. In many applications, however, measurements are generated with high-resolution sensors in polar coordinates. More importantly, the ignorance of input uncertainty in GP will exacerbate the tracking performance in these cases. In order to track an irregular-shape extended target in polar coordinates with input uncertainty, an improved GP-based probabilistic data association (IGP-PDA) algorithm is developed that includes the following enhancements: Firstly, a nonlinear measurement model is used and the unbiased converted measurement technique is invoked for the ETT problem. Secondly, the analytical form of statistical properties of the input uncertainty due to measurements noise and predicted error is derived. Thirdly, an IGP-PDA taking into account measurement origin uncertainty as well as input uncertainty is proposed, and three sub-optimal implementations are provided. Last, the posterior Cramer-Rao lower bound of ETT with input uncertainty is derived. Simulation results verify the effectiveness of the proposed method. Extended target tracking (dpeaa)DE-He213 Gaussian processes (dpeaa)DE-He213 Input uncertainty (dpeaa)DE-He213 Nonlinear measurement (dpeaa)DE-He213 Measurement origin uncertainty (dpeaa)DE-He213 Yang, Dongsheng aut Ren, Lei aut Yan, Lei aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2022(2022), 1 vom: 29. Okt. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2022 year:2022 number:1 day:29 month:10 https://dx.doi.org/10.1186/s13634-022-00940-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022 2022 1 29 10
allfields_unstemmed	10.1186/s13634-022-00940-w doi (DE-627)SPR048496944 (SPR)s13634-022-00940-w-e DE-627 ger DE-627 rakwb eng Guo, Yunfei verfasserin (orcid)0000-0001-7887-4312 aut Gaussian processes based extended target tracking in polar coordinates with input uncertainty 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Gaussian processes (GP) based extended target tracking (ETT) technique has attracted the attention of scientists since it can accurately estimate the kinematic states as well as the contour states of irregular-shape targets. Most traditional GP methods consider the ETT problem with a linear measurement model in Cartesian coordinates and ignore the input uncertainty of GP for simplicity. In many applications, however, measurements are generated with high-resolution sensors in polar coordinates. More importantly, the ignorance of input uncertainty in GP will exacerbate the tracking performance in these cases. In order to track an irregular-shape extended target in polar coordinates with input uncertainty, an improved GP-based probabilistic data association (IGP-PDA) algorithm is developed that includes the following enhancements: Firstly, a nonlinear measurement model is used and the unbiased converted measurement technique is invoked for the ETT problem. Secondly, the analytical form of statistical properties of the input uncertainty due to measurements noise and predicted error is derived. Thirdly, an IGP-PDA taking into account measurement origin uncertainty as well as input uncertainty is proposed, and three sub-optimal implementations are provided. Last, the posterior Cramer-Rao lower bound of ETT with input uncertainty is derived. Simulation results verify the effectiveness of the proposed method. Extended target tracking (dpeaa)DE-He213 Gaussian processes (dpeaa)DE-He213 Input uncertainty (dpeaa)DE-He213 Nonlinear measurement (dpeaa)DE-He213 Measurement origin uncertainty (dpeaa)DE-He213 Yang, Dongsheng aut Ren, Lei aut Yan, Lei aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2022(2022), 1 vom: 29. Okt. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2022 year:2022 number:1 day:29 month:10 https://dx.doi.org/10.1186/s13634-022-00940-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022 2022 1 29 10
allfieldsGer	10.1186/s13634-022-00940-w doi (DE-627)SPR048496944 (SPR)s13634-022-00940-w-e DE-627 ger DE-627 rakwb eng Guo, Yunfei verfasserin (orcid)0000-0001-7887-4312 aut Gaussian processes based extended target tracking in polar coordinates with input uncertainty 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Gaussian processes (GP) based extended target tracking (ETT) technique has attracted the attention of scientists since it can accurately estimate the kinematic states as well as the contour states of irregular-shape targets. Most traditional GP methods consider the ETT problem with a linear measurement model in Cartesian coordinates and ignore the input uncertainty of GP for simplicity. In many applications, however, measurements are generated with high-resolution sensors in polar coordinates. More importantly, the ignorance of input uncertainty in GP will exacerbate the tracking performance in these cases. In order to track an irregular-shape extended target in polar coordinates with input uncertainty, an improved GP-based probabilistic data association (IGP-PDA) algorithm is developed that includes the following enhancements: Firstly, a nonlinear measurement model is used and the unbiased converted measurement technique is invoked for the ETT problem. Secondly, the analytical form of statistical properties of the input uncertainty due to measurements noise and predicted error is derived. Thirdly, an IGP-PDA taking into account measurement origin uncertainty as well as input uncertainty is proposed, and three sub-optimal implementations are provided. Last, the posterior Cramer-Rao lower bound of ETT with input uncertainty is derived. Simulation results verify the effectiveness of the proposed method. Extended target tracking (dpeaa)DE-He213 Gaussian processes (dpeaa)DE-He213 Input uncertainty (dpeaa)DE-He213 Nonlinear measurement (dpeaa)DE-He213 Measurement origin uncertainty (dpeaa)DE-He213 Yang, Dongsheng aut Ren, Lei aut Yan, Lei aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2022(2022), 1 vom: 29. Okt. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2022 year:2022 number:1 day:29 month:10 https://dx.doi.org/10.1186/s13634-022-00940-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022 2022 1 29 10
allfieldsSound	10.1186/s13634-022-00940-w doi (DE-627)SPR048496944 (SPR)s13634-022-00940-w-e DE-627 ger DE-627 rakwb eng Guo, Yunfei verfasserin (orcid)0000-0001-7887-4312 aut Gaussian processes based extended target tracking in polar coordinates with input uncertainty 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Gaussian processes (GP) based extended target tracking (ETT) technique has attracted the attention of scientists since it can accurately estimate the kinematic states as well as the contour states of irregular-shape targets. Most traditional GP methods consider the ETT problem with a linear measurement model in Cartesian coordinates and ignore the input uncertainty of GP for simplicity. In many applications, however, measurements are generated with high-resolution sensors in polar coordinates. More importantly, the ignorance of input uncertainty in GP will exacerbate the tracking performance in these cases. In order to track an irregular-shape extended target in polar coordinates with input uncertainty, an improved GP-based probabilistic data association (IGP-PDA) algorithm is developed that includes the following enhancements: Firstly, a nonlinear measurement model is used and the unbiased converted measurement technique is invoked for the ETT problem. Secondly, the analytical form of statistical properties of the input uncertainty due to measurements noise and predicted error is derived. Thirdly, an IGP-PDA taking into account measurement origin uncertainty as well as input uncertainty is proposed, and three sub-optimal implementations are provided. Last, the posterior Cramer-Rao lower bound of ETT with input uncertainty is derived. Simulation results verify the effectiveness of the proposed method. Extended target tracking (dpeaa)DE-He213 Gaussian processes (dpeaa)DE-He213 Input uncertainty (dpeaa)DE-He213 Nonlinear measurement (dpeaa)DE-He213 Measurement origin uncertainty (dpeaa)DE-He213 Yang, Dongsheng aut Ren, Lei aut Yan, Lei aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2022(2022), 1 vom: 29. Okt. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2022 year:2022 number:1 day:29 month:10 https://dx.doi.org/10.1186/s13634-022-00940-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022 2022 1 29 10
language	English
source	Enthalten in EURASIP journal on advances in signal processing 2022(2022), 1 vom: 29. Okt. volume:2022 year:2022 number:1 day:29 month:10
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topic_facet	Extended target tracking Gaussian processes Input uncertainty Nonlinear measurement Measurement origin uncertainty
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container_title	EURASIP journal on advances in signal processing
authorswithroles_txt_mv	Guo, Yunfei @@aut@@ Yang, Dongsheng @@aut@@ Ren, Lei @@aut@@ Yan, Lei @@aut@@
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author	Guo, Yunfei
spellingShingle	Guo, Yunfei misc Extended target tracking misc Gaussian processes misc Input uncertainty misc Nonlinear measurement misc Measurement origin uncertainty Gaussian processes based extended target tracking in polar coordinates with input uncertainty
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topic_title	Gaussian processes based extended target tracking in polar coordinates with input uncertainty Extended target tracking (dpeaa)DE-He213 Gaussian processes (dpeaa)DE-He213 Input uncertainty (dpeaa)DE-He213 Nonlinear measurement (dpeaa)DE-He213 Measurement origin uncertainty (dpeaa)DE-He213
topic	misc Extended target tracking misc Gaussian processes misc Input uncertainty misc Nonlinear measurement misc Measurement origin uncertainty
topic_unstemmed	misc Extended target tracking misc Gaussian processes misc Input uncertainty misc Nonlinear measurement misc Measurement origin uncertainty
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title	Gaussian processes based extended target tracking in polar coordinates with input uncertainty
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title_full	Gaussian processes based extended target tracking in polar coordinates with input uncertainty
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title_sort	gaussian processes based extended target tracking in polar coordinates with input uncertainty
title_auth	Gaussian processes based extended target tracking in polar coordinates with input uncertainty
abstract	Abstract Gaussian processes (GP) based extended target tracking (ETT) technique has attracted the attention of scientists since it can accurately estimate the kinematic states as well as the contour states of irregular-shape targets. Most traditional GP methods consider the ETT problem with a linear measurement model in Cartesian coordinates and ignore the input uncertainty of GP for simplicity. In many applications, however, measurements are generated with high-resolution sensors in polar coordinates. More importantly, the ignorance of input uncertainty in GP will exacerbate the tracking performance in these cases. In order to track an irregular-shape extended target in polar coordinates with input uncertainty, an improved GP-based probabilistic data association (IGP-PDA) algorithm is developed that includes the following enhancements: Firstly, a nonlinear measurement model is used and the unbiased converted measurement technique is invoked for the ETT problem. Secondly, the analytical form of statistical properties of the input uncertainty due to measurements noise and predicted error is derived. Thirdly, an IGP-PDA taking into account measurement origin uncertainty as well as input uncertainty is proposed, and three sub-optimal implementations are provided. Last, the posterior Cramer-Rao lower bound of ETT with input uncertainty is derived. Simulation results verify the effectiveness of the proposed method. © The Author(s) 2022
abstractGer	Abstract Gaussian processes (GP) based extended target tracking (ETT) technique has attracted the attention of scientists since it can accurately estimate the kinematic states as well as the contour states of irregular-shape targets. Most traditional GP methods consider the ETT problem with a linear measurement model in Cartesian coordinates and ignore the input uncertainty of GP for simplicity. In many applications, however, measurements are generated with high-resolution sensors in polar coordinates. More importantly, the ignorance of input uncertainty in GP will exacerbate the tracking performance in these cases. In order to track an irregular-shape extended target in polar coordinates with input uncertainty, an improved GP-based probabilistic data association (IGP-PDA) algorithm is developed that includes the following enhancements: Firstly, a nonlinear measurement model is used and the unbiased converted measurement technique is invoked for the ETT problem. Secondly, the analytical form of statistical properties of the input uncertainty due to measurements noise and predicted error is derived. Thirdly, an IGP-PDA taking into account measurement origin uncertainty as well as input uncertainty is proposed, and three sub-optimal implementations are provided. Last, the posterior Cramer-Rao lower bound of ETT with input uncertainty is derived. Simulation results verify the effectiveness of the proposed method. © The Author(s) 2022
abstract_unstemmed	Abstract Gaussian processes (GP) based extended target tracking (ETT) technique has attracted the attention of scientists since it can accurately estimate the kinematic states as well as the contour states of irregular-shape targets. Most traditional GP methods consider the ETT problem with a linear measurement model in Cartesian coordinates and ignore the input uncertainty of GP for simplicity. In many applications, however, measurements are generated with high-resolution sensors in polar coordinates. More importantly, the ignorance of input uncertainty in GP will exacerbate the tracking performance in these cases. In order to track an irregular-shape extended target in polar coordinates with input uncertainty, an improved GP-based probabilistic data association (IGP-PDA) algorithm is developed that includes the following enhancements: Firstly, a nonlinear measurement model is used and the unbiased converted measurement technique is invoked for the ETT problem. Secondly, the analytical form of statistical properties of the input uncertainty due to measurements noise and predicted error is derived. Thirdly, an IGP-PDA taking into account measurement origin uncertainty as well as input uncertainty is proposed, and three sub-optimal implementations are provided. Last, the posterior Cramer-Rao lower bound of ETT with input uncertainty is derived. Simulation results verify the effectiveness of the proposed method. © The Author(s) 2022
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title_short	Gaussian processes based extended target tracking in polar coordinates with input uncertainty
url	https://dx.doi.org/10.1186/s13634-022-00940-w
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author2	Yang, Dongsheng Ren, Lei Yan, Lei
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Gaussian processes based extended target tracking in polar coordinates with input uncertainty

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