Robust AOA-Based Target Localization for Uniformly Distributed Noise via ℓp-ℓ1 Optimization
This paper addresses the problem of robust angle of arrival (AOA) target localization in the presence of uniformly distributed noise which is modeled as the mixture of Laplacian distribution and uniform distribution. Motivated by the distribution of noise, we develop a localization model by using th...
Saved in:
| Published in | Entropy (Basel, Switzerland) Vol. 24; no. 9; p. 1259 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
07.09.2022
MDPI |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1099-4300 1099-4300 |
| DOI | 10.3390/e24091259 |
Cover
| Abstract | This paper addresses the problem of robust angle of arrival (AOA) target localization in the presence of uniformly distributed noise which is modeled as the mixture of Laplacian distribution and uniform distribution. Motivated by the distribution of noise, we develop a localization model by using the ℓp-norm with 0≤p<2 as the measurement error and the ℓ1-norm as the regularization term. Then, an estimator for introducing the proximal operator into the framework of the alternating direction method of multipliers (POADMM) is derived to solve the convex optimization problem when 1≤p<2. However, when 0≤p<1, the corresponding optimization problem is nonconvex and nonsmoothed. To derive a convergent method for this nonconvex and nonsmooth target localization problem, we propose a smoothed POADMM estimator (SPOADMM) by introducing the smoothing strategy into the optimization model. Eventually, the proposed algorithms are compared with some state-of-the-art robust algorithms via numerical simulations, and their effectiveness in uniformly distributed noise is discussed from the perspective of root-mean-squared error (RMSE). The experimental results verify that the proposed method has more robustness against outliers and is less sensitive to the selected parameters, especially the variance of the measurement noise. |
|---|---|
| AbstractList | This paper addresses the problem of robust angle of arrival (AOA) target localization in the presence of uniformly distributed noise which is modeled as the mixture of Laplacian distribution and uniform distribution. Motivated by the distribution of noise, we develop a localization model by using the ℓp-norm with 0≤p<2 as the measurement error and the ℓ1-norm as the regularization term. Then, an estimator for introducing the proximal operator into the framework of the alternating direction method of multipliers (POADMM) is derived to solve the convex optimization problem when 1≤p<2. However, when 0≤p<1, the corresponding optimization problem is nonconvex and nonsmoothed. To derive a convergent method for this nonconvex and nonsmooth target localization problem, we propose a smoothed POADMM estimator (SPOADMM) by introducing the smoothing strategy into the optimization model. Eventually, the proposed algorithms are compared with some state-of-the-art robust algorithms via numerical simulations, and their effectiveness in uniformly distributed noise is discussed from the perspective of root-mean-squared error (RMSE). The experimental results verify that the proposed method has more robustness against outliers and is less sensitive to the selected parameters, especially the variance of the measurement noise.This paper addresses the problem of robust angle of arrival (AOA) target localization in the presence of uniformly distributed noise which is modeled as the mixture of Laplacian distribution and uniform distribution. Motivated by the distribution of noise, we develop a localization model by using the ℓp-norm with 0≤p<2 as the measurement error and the ℓ1-norm as the regularization term. Then, an estimator for introducing the proximal operator into the framework of the alternating direction method of multipliers (POADMM) is derived to solve the convex optimization problem when 1≤p<2. However, when 0≤p<1, the corresponding optimization problem is nonconvex and nonsmoothed. To derive a convergent method for this nonconvex and nonsmooth target localization problem, we propose a smoothed POADMM estimator (SPOADMM) by introducing the smoothing strategy into the optimization model. Eventually, the proposed algorithms are compared with some state-of-the-art robust algorithms via numerical simulations, and their effectiveness in uniformly distributed noise is discussed from the perspective of root-mean-squared error (RMSE). The experimental results verify that the proposed method has more robustness against outliers and is less sensitive to the selected parameters, especially the variance of the measurement noise. This paper addresses the problem of robust angle of arrival (AOA) target localization in the presence of uniformly distributed noise which is modeled as the mixture of Laplacian distribution and uniform distribution. Motivated by the distribution of noise, we develop a localization model by using the ℓp -norm with 0≤p<2 as the measurement error and the ℓ1 -norm as the regularization term. Then, an estimator for introducing the proximal operator into the framework of the alternating direction method of multipliers (POADMM) is derived to solve the convex optimization problem when 1≤p<2 . However, when 0≤p<1 , the corresponding optimization problem is nonconvex and nonsmoothed. To derive a convergent method for this nonconvex and nonsmooth target localization problem, we propose a smoothed POADMM estimator (SPOADMM) by introducing the smoothing strategy into the optimization model. Eventually, the proposed algorithms are compared with some state-of-the-art robust algorithms via numerical simulations, and their effectiveness in uniformly distributed noise is discussed from the perspective of root-mean-squared error (RMSE). The experimental results verify that the proposed method has more robustness against outliers and is less sensitive to the selected parameters, especially the variance of the measurement noise. |
| Author | Yan, Qingli Chen, Yanping Wang, Chunmei |
| AuthorAffiliation | 2 Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts and Telecommunications, Xi’an 710121, China 1 School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China 3 Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an 710121, China |
| AuthorAffiliation_xml | – name: 3 Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an 710121, China – name: 1 School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China – name: 2 Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts and Telecommunications, Xi’an 710121, China |
| Author_xml | – sequence: 1 givenname: Yanping surname: Chen fullname: Chen, Yanping – sequence: 2 givenname: Chunmei orcidid: 0000-0001-9191-5086 surname: Wang fullname: Wang, Chunmei – sequence: 3 givenname: Qingli surname: Yan fullname: Yan, Qingli |
| BookMark | eNp1kctu1DAUhiNUJNrCgjeIxAaQQo8vuXiDNC23SiNGQu3aOnGcwSMnDrZTNKx5A96QJ8HTGVW0go1_y_7O7_Mfn2RHoxt1lj0n8IYxAWeachCEluJRdkxAiIIzgKO_9k-ykxA2AJRRUh1n8otr5xDzxWpRnGPQXX6Ffq1jvnQKrfmB0bgx753Pr0eTZLDb_J0J0Zt2jon-7EzQ-Y3B_PfPX1ORFpKvpmiGQ-nT7HGPNuhnBz3Nrj-8v7r4VCxXHy8vFstCcV7FQhHoW6YETX2qjirVAadUoNJVD9AxaBFKXoFIWlGOJRE9UtoCsL6kTc1Os8u9b-dwIydvBvRb6dDI2wPn1xJ9NMpqSbDqSYmMsRp5w2mLivBGdRopa2iFyev13mseJ9x-R2vvDAnI3Zjl3ZgT_HYPT3M76E7pMXq09zq4fzOar3LtbqTgogHBksHLg4F332YdohxMUNpaHLWbg6Q1qatGiHIX8sUDdONmP6a57qiqpDWveaLO9pTyLgSve6lMvP2M9L6x_8zw6kHF__P-AYHCv8E |
| CitedBy_id | crossref_primary_10_1016_j_sigpro_2024_109685 |
| Cites_doi | 10.1007/978-1-4419-9569-8 10.1109/ICASSP.2015.7178561 10.1109/TAES.2017.2667999 10.1109/TSP.2012.2199314 10.1109/TVT.2018.2880991 10.1109/LWC.2018.2853745 10.1155/2020/2643546 10.1109/TAES.1978.308681 10.1109/TSP.2017.2711501 10.1109/JSEN.2019.2948063 10.1016/j.sigpro.2018.05.010 10.1016/j.sigpro.2021.108248 10.1007/978-1-4419-9467-7 10.1109/JIOT.2019.2920081 10.1109/LCOMM.2021.3051907 10.1109/JSYST.2020.2976827 10.1016/j.dsp.2021.103006 10.1109/ICASSP.2010.5495819 10.1109/TWC.2020.3044217 10.1109/IPIN51156.2021.9662529 10.1109/TII.2020.3048987 10.1109/TSP.2016.2598316 10.1007/s11036-018-1090-3 10.1109/TSP.2019.2931210 10.1109/SAS.2018.8336730 10.1561/2200000016 10.1109/7.256302 10.1109/ROBIO49542.2019.8961815 10.1049/iet-rsn.2017.0117 10.1109/CCDC49329.2020.9164285 10.1109/TSP.2012.2212015 10.1109/TWC.2020.3038565 10.1016/j.sigpro.2005.03.007 10.1109/COMST.2019.2911558 10.1109/TIM.2021.3083558 10.1109/JSAC.2020.3036964 10.1198/jasa.2011.tm10390 10.1109/JSEE.2015.00103 10.1109/ICUFN.2019.8806041 10.1109/TVT.2016.2589923 10.1109/LCOMM.2015.2486769 10.1109/TVT.2021.3064444 10.1109/ACCESS.2017.2677480 10.1109/CVPR.2019.01217 10.1137/080716542 |
| ContentType | Journal Article |
| Copyright | 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022 |
| Copyright_xml | – notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 by the authors. 2022 |
| DBID | AAYXX CITATION 7TB 8FD 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU DWQXO FR3 HCIFZ KR7 L6V M7S PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS 7X8 5PM ADTOC UNPAY DOA |
| DOI | 10.3390/e24091259 |
| DatabaseName | CrossRef Mechanical & Transportation Engineering Abstracts Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central Technology Collection (ProQuest) ProQuest One ProQuest Central Engineering Research Database ProQuest SciTech Premium Collection Civil Engineering Abstracts ProQuest Engineering Collection Engineering Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database Technology Collection Technology Research Database ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest Central Korea ProQuest Central (New) Engineering Collection Civil Engineering Abstracts Engineering Database ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection ProQuest One Academic UKI Edition Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic ProQuest One Academic (New) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 3 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 1099-4300 |
| ExternalDocumentID | oai_doaj_org_article_1a6f15a3337a4842bac148cdea23826a 10.3390/e24091259 PMC9498093 10_3390_e24091259 |
| GrantInformation_xml | – fundername: Science and Technology Project in Shaanxi Province of China grantid: 2021NY-036; 2019ZDLGY07-08 – fundername: Scientific Research Program Funded by Shaanxi Provincial Education Department of China grantid: 21JK0907 – fundername: Youth Innovation Team of Shaanxi Universities “Industrial Big Data Analysis and Intelligent Processing” grantid: 2020-21 – fundername: Xi’an University of Posts and Telecommunications Key Innovation Fund Project of Science and Technology grantid: CXJJZL2021006 – fundername: National Natural Science Foundation of China (NSFC) grantid: 62101443 |
| GroupedDBID | 29G 2WC 5GY 5VS 8FE 8FG AADQD AAFWJ AAYXX ABDBF ABJCF ACIWK ACUHS ADBBV AEGXH AENEX AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS BCNDV BENPR BGLVJ CCPQU CITATION CS3 DU5 E3Z ESX F5P GROUPED_DOAJ GX1 HCIFZ HH5 IAO ITC J9A KQ8 L6V M7S MODMG M~E OK1 OVT PGMZT PHGZM PHGZT PIMPY PQGLB PROAC PTHSS RNS RPM TR2 TUS XSB ~8M 7TB 8FD ABUWG AZQEC DWQXO FR3 KR7 PKEHL PQEST PQQKQ PQUKI PRINS 7X8 PUEGO 5PM ADTOC C1A CH8 IPNFZ RIG UNPAY |
| ID | FETCH-LOGICAL-c446t-c10fb3c92109cd2ccd04229ace6f00d30ba054609ba0624a519fa22b003f52873 |
| IEDL.DBID | UNPAY |
| ISSN | 1099-4300 |
| IngestDate | Fri Oct 03 12:34:30 EDT 2025 Sun Oct 26 04:00:07 EDT 2025 Tue Sep 30 17:19:03 EDT 2025 Fri Sep 05 11:09:10 EDT 2025 Fri Jul 25 12:05:10 EDT 2025 Thu Apr 24 23:11:49 EDT 2025 Thu Oct 16 04:22:31 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 9 |
| Language | English |
| License | Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c446t-c10fb3c92109cd2ccd04229ace6f00d30ba054609ba0624a519fa22b003f52873 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-9191-5086 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://www.mdpi.com/1099-4300/24/9/1259/pdf?version=1663750368 |
| PQID | 2716527474 |
| PQPubID | 2032401 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_1a6f15a3337a4842bac148cdea23826a unpaywall_primary_10_3390_e24091259 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9498093 proquest_miscellaneous_2717689957 proquest_journals_2716527474 crossref_citationtrail_10_3390_e24091259 crossref_primary_10_3390_e24091259 |
| PublicationCentury | 2000 |
| PublicationDate | 20220907 |
| PublicationDateYYYYMMDD | 2022-09-07 |
| PublicationDate_xml | – month: 9 year: 2022 text: 20220907 day: 7 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Entropy (Basel, Switzerland) |
| PublicationYear | 2022 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Naseri (ref_35) 2021; 189 Lingren (ref_18) 1978; AES-14 Zhang (ref_23) 2019; 16 ref_36 She (ref_44) 2011; 106 ref_33 Liu (ref_9) 2015; 19 Noroozi (ref_15) 2018; 12 Yan (ref_27) 2017; 5 Yan (ref_32) 2021; 25 Boyd (ref_40) 2011; Volume 3 Zheng (ref_6) 2021; 20 BniLam (ref_12) 2020; 20 Sun (ref_5) 2021; 70 ref_38 Yan (ref_29) 2008; 152 Wu (ref_30) 2015; 26 Tomic (ref_13) 2017; 66 Wang (ref_4) 2019; 68 Gabbrielli (ref_16) 2021; 70 Chen (ref_7) 2020; 20 Wang (ref_8) 2019; 6 Wang (ref_37) 2021; 39 Yan (ref_31) 2021; 112 Khelifi (ref_2) 2019; 24 Beck (ref_43) 2009; 2 ref_46 Marjanovic (ref_45) 2012; 60 ref_21 Xu (ref_10) 2017; 53 ref_42 Wen (ref_20) 2017; 65 ref_41 ref_1 Gharamaleki (ref_25) 2020; 14 Zheng (ref_11) 2019; 8 Fang (ref_24) 2020; 2020 Zafari (ref_3) 2019; 21 ref_28 Li (ref_14) 2021; 17 ref_26 (ref_19) 2005; 85 Selesnick (ref_39) 2017; 65 Liu (ref_34) 2012; 60 Gavish (ref_17) 1992; 28 Nguyen (ref_22) 2019; 67 |
| References_xml | – ident: ref_42 doi: 10.1007/978-1-4419-9569-8 – ident: ref_33 doi: 10.1109/ICASSP.2015.7178561 – volume: 53 start-page: 1196 year: 2017 ident: ref_10 article-title: Optimal Sensor Placement for 3-D Angle-of-Arrival Target Localization publication-title: IEEE Trans. Aerosp. Electron. Syst. doi: 10.1109/TAES.2017.2667999 – volume: 60 start-page: 4350 year: 2012 ident: ref_34 article-title: Distributed, Robust Acoustic Source Localization in a Wireless Sensor Network publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2012.2199314 – volume: 68 start-page: 483 year: 2019 ident: ref_4 article-title: Cooperative RSS-Based Localization in Wireless Sensor Networks Using Relative Error Estimation and Semidefinite Programming publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2018.2880991 – volume: 8 start-page: 65 year: 2019 ident: ref_11 article-title: Exploiting AoA Estimation Accuracy for Indoor Localization: A Weighted AoA-Based Approach publication-title: IEEE Wirel. Commun. Lett. doi: 10.1109/LWC.2018.2853745 – volume: 2020 start-page: 2643546 year: 2020 ident: ref_24 article-title: Trust-based attack and defense in wireless sensor networks: A survey publication-title: Wirel. Commun. Mob. Comput. doi: 10.1155/2020/2643546 – volume: AES-14 start-page: 564 year: 1978 ident: ref_18 article-title: Position and Velocity Estimation Via Bearing Observations publication-title: IEEE Trans. Aerosp. Electron. Syst. doi: 10.1109/TAES.1978.308681 – volume: 65 start-page: 4481 year: 2017 ident: ref_39 article-title: Sparse Regularization via Convex Analysis publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2017.2711501 – volume: 20 start-page: 1473 year: 2020 ident: ref_7 article-title: Cooperative Multiple Target Nodes Localization Using TOA in Mixed LOS/NLOS Environments publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2019.2948063 – volume: 152 start-page: 13 year: 2008 ident: ref_29 article-title: Robust AOA based acoustic source localization method with unreliable measurements publication-title: Signal Process doi: 10.1016/j.sigpro.2018.05.010 – volume: 189 start-page: 108248 year: 2021 ident: ref_35 article-title: A novel bearing-only localization for generalized Gaussian noise publication-title: Signal Process. doi: 10.1016/j.sigpro.2021.108248 – ident: ref_41 doi: 10.1007/978-1-4419-9467-7 – volume: 6 start-page: 8529 year: 2019 ident: ref_8 article-title: Robust TDOA-Based Localization for IoT via Joint Source Position and NLOS Error Estimation publication-title: IEEE Internet Things doi: 10.1109/JIOT.2019.2920081 – volume: 25 start-page: 1529 year: 2021 ident: ref_32 article-title: Robust AOA-Based Source Localization in Correlated Measurement Noise via Nonconvex Sparse Optimization publication-title: IEEE Commun. Lett. doi: 10.1109/LCOMM.2021.3051907 – volume: 14 start-page: 4883 year: 2020 ident: ref_25 article-title: A New Distributed Fault Detection Method for Wireless Sensor Networks publication-title: IEEE Syst. J. doi: 10.1109/JSYST.2020.2976827 – volume: 112 start-page: 103006 year: 2021 ident: ref_31 article-title: Robust AOA-based source localization using outlier sparsity regularization publication-title: Digit. Signal Process. doi: 10.1016/j.dsp.2021.103006 – ident: ref_36 doi: 10.1109/ICASSP.2010.5495819 – volume: 20 start-page: 2746 year: 2021 ident: ref_6 article-title: Accurate semidefinite relaxation method for elliptic localization with unknown transmitter position publication-title: IEEE Trans. Wirel. Commun. doi: 10.1109/TWC.2020.3044217 – ident: ref_26 doi: 10.1109/IPIN51156.2021.9662529 – volume: 17 start-page: 6303 year: 2021 ident: ref_14 article-title: Simultaneous Localization of Multiple Unknown Emitters Based on UAV Monitoring Big Data publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2020.3048987 – volume: 65 start-page: 105 year: 2017 ident: ref_20 article-title: Robust Sparse Recovery in Impulsive Noise via ℓp-ℓ1 Optimization publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2016.2598316 – volume: 24 start-page: 761 year: 2019 ident: ref_2 article-title: A Survey of Localization Systems in Internet of Things publication-title: Mob. Netw. Appl. doi: 10.1007/s11036-018-1090-3 – volume: 67 start-page: 4795 year: 2019 ident: ref_22 article-title: An iteratively reweighted instrumental-variable estimator for robust 3-D AOA localization in impulsive noise publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2019.2931210 – ident: ref_28 doi: 10.1109/SAS.2018.8336730 – volume: 16 start-page: 146 year: 2019 ident: ref_23 article-title: A single-site positioning method based on TOA and DOA estimation using virtual stations in NLOS environment publication-title: China Commun. – volume: Volume 3 start-page: 1 year: 2011 ident: ref_40 article-title: Distributed optimization and statistical learning via the alternating direction method of multipliers publication-title: Foundations and Trends® in Machine Learning doi: 10.1561/2200000016 – volume: 28 start-page: 817 year: 1992 ident: ref_17 article-title: Performance analysis of bearing-only target location algorithms publication-title: IEEE Trans. Aerosp. Electron. Syst. doi: 10.1109/7.256302 – ident: ref_21 doi: 10.1109/ROBIO49542.2019.8961815 – volume: 12 start-page: 21 year: 2018 ident: ref_15 article-title: lgebraic solution for three-dimensional TDOA/AOA localisation in multiple-input–multiple-output passive radar publication-title: IET Radar Sonar Navig. doi: 10.1049/iet-rsn.2017.0117 – ident: ref_46 doi: 10.1109/CCDC49329.2020.9164285 – volume: 60 start-page: 5714 year: 2012 ident: ref_45 article-title: On ℓq Optimization and Matrix Completion publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2012.2212015 – volume: 20 start-page: 2005 year: 2020 ident: ref_12 article-title: LoRay: AoA Estimation System for Long Range Communication Networks publication-title: IEEE Trans. Wirel. Commun. doi: 10.1109/TWC.2020.3038565 – volume: 85 start-page: 1695 year: 2005 ident: ref_19 article-title: Bearings-only target localization using total least squares publication-title: Signal Process. doi: 10.1016/j.sigpro.2005.03.007 – volume: 21 start-page: 2568 year: 2019 ident: ref_3 article-title: A Survey of Indoor Localization Systems and Technologies publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2019.2911558 – volume: 70 start-page: 6503612 year: 2021 ident: ref_16 article-title: An echo suppression delay estimator for angle of arrival ultrasonic indoor localization publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2021.3083558 – volume: 39 start-page: 31 year: 2021 ident: ref_37 article-title: Model Refinement Learning and an Example on Channel Estimation with Universal Noise Model publication-title: IEEE J. Sel. Areas Commun. doi: 10.1109/JSAC.2020.3036964 – volume: 106 start-page: 626 year: 2011 ident: ref_44 article-title: Outlier detection using nonconvex penalized regression publication-title: J. Am. Stat. Assoc. doi: 10.1198/jasa.2011.tm10390 – volume: 26 start-page: 946 year: 2015 ident: ref_30 article-title: Robust structured total least squares algorithm for passive location publication-title: J. Syst. Eng. Electron. doi: 10.1109/JSEE.2015.00103 – ident: ref_1 doi: 10.1109/ICUFN.2019.8806041 – volume: 66 start-page: 3197 year: 2017 ident: ref_13 article-title: 3-D Target Localization in Wireless Sensor Networks Using RSS and AoA Measurements publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2016.2589923 – volume: 19 start-page: 2218 year: 2015 ident: ref_9 article-title: An Improved Algebraic Solution for TDOA Localization with Sensor Position Errors publication-title: IEEE Commun. Lett. doi: 10.1109/LCOMM.2015.2486769 – volume: 70 start-page: 3926 year: 2021 ident: ref_5 article-title: Robust RSS-Based Source Localization with Unknown Model Parameters in Mixed LOS/NLOS Environments publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2021.3064444 – volume: 5 start-page: 2798 year: 2017 ident: ref_27 article-title: An Improved NLOS Identification and Mitigation Approach for Target Tracking in Wireless Sensor Networks publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2677480 – ident: ref_38 doi: 10.1109/CVPR.2019.01217 – volume: 2 start-page: 183 year: 2009 ident: ref_43 article-title: A fast iterative shrinkage-thresholding algorithm for linear inverse problems publication-title: SIAM J. Imaging Sci. doi: 10.1137/080716542 |
| SSID | ssj0023216 |
| Score | 2.2944887 |
| Snippet | This paper addresses the problem of robust angle of arrival (AOA) target localization in the presence of uniformly distributed noise which is modeled as the... |
| SourceID | doaj unpaywall pubmedcentral proquest crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Enrichment Source Index Database |
| StartPage | 1259 |
| SubjectTerms | Accuracy Algorithms alternating direction method of multipliers Angle of arrival Computational geometry Convexity Error analysis Localization Methods Noise measurement Optimization algorithms Optimization models outliers Outliers (statistics) Parameter sensitivity Propagation Regularization Robustness (mathematics) Root-mean-square errors Sensors sparse regularization ℓp-norm |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3LSsQwFA3iRjeiqFgdJT4WborNo2mzHF-I-ABRcFfSNMWBsTM4M4p7_8A_9Eu8t-0UK4obNy00t48kN73ntMm5hOyxMI0jHYc-s5nzpcgFfmgKfKkwr7m0kpfrKy6v1NmdPL8P77-k-sI5YZU8cNVwB8yonIVGCBEZGUueGgsIHi5sINhwVUKjINZTMlVTLcGZqnSEBJD6Awd31BDKdSv6lCL9LWT5fV7k3KQYmtcX0-9_CTqni2ShRou0Wz3lEplxxTJJbgbpZDSm3euufwhBKKO35XRueoFxqV5XSQGMUgCUiEn7r_QY9XExtRVYXw16I0efe4Z-vL0Pfdgweg1vjsf61BVyd3pye3Tm13kSfAtkbuxbFuSpsBrYm7YZtzZDYS9trFN5EGQiSA0AMxVo2CsuDYC23HCOAzoPgTGJVTJbDAq3RqhTmUyVtCpmVjKrEP5BGAVUByU2jDyyP22_xNYi4pjLop8AmcCmTpqm9shOYzqslDN-MjrETmgMUOy6PAAukNQukPzlAh7pTLswqUfgKOFABEOk3NIj200xjB38IWIKN5iUNsC2tMZ6Ra2ubz1Qu6ToPZQq3FrqONDCI7uNk_xez_X_qOcGmee4-AJ_Z0UdMjt-mrhNgETjdKv0_k85UQrN priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELbK9gAXVASIQEHmceBiNX7EiQ-o2oVWFYItqlqpt8hxnLLSkizdXVDv_Qf9h_wSZrJOIAi4JFI8kfyY8XzjxzeEvOJJkaUmSxh3pWdKVhIXmmKmNOY1V06J9n7Fx6k-OlPvz5PzLTLt7sLgscpuTmwn6rJxuEa-JwDYJxhCqf3FV4ZZo3B3tUuhYUNqhfJNSzF2i2wLZMYake3JwfTTSR-CScH1hl9IQrC_56EmBly8GXillrx_gDj_PC95e10v7NV3O5__5owOd8jdgCLpeDPs98iWr--T_KQp1ssVHR-P2QScU0lP22Pe9AP6q3DfkgJIpQA0EavOr-g75M3FlFcgPW1mS0-_zSz9cX2zYPDg9BhmlC_h1wfk7PDg9O0RC_kTmIMgb8Ucj6tCOgMdYVwpnCuR8MtY53UVx6WMCwuATccG3looC2CuskKgoVcJRFLyIRnVTe0fEep1qQqtnM64U9xphIXgXgHtQYlL0oi87vovd4FcHHNczHMIMrCr876rI_KiF11sGDX-JjTBQegFkAS7_dBcXuTBpnJudcUTK6VMrcqUKKyD4A50zgIOEdpGZLcbwjxY5jL_pUcRed4Xg03hRomtfbNuZSAKMwbblQ6GflChYUk9-9yycxtlstjIiLzsleTf7Xz8_yo-IXcEXrfADax0l4xWl2v_FEDQqngWNPsndHkI5A priority: 102 providerName: ProQuest |
| Title | Robust AOA-Based Target Localization for Uniformly Distributed Noise via ℓp-ℓ1 Optimization |
| URI | https://www.proquest.com/docview/2716527474 https://www.proquest.com/docview/2717689957 https://pubmed.ncbi.nlm.nih.gov/PMC9498093 https://www.mdpi.com/1099-4300/24/9/1259/pdf?version=1663750368 https://doaj.org/article/1a6f15a3337a4842bac148cdea23826a |
| UnpaywallVersion | publishedVersion |
| Volume | 24 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVFSB databaseName: Free Full-Text Journals in Chemistry customDbUrl: eissn: 1099-4300 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023216 issn: 1099-4300 databaseCode: HH5 dateStart: 19990101 isFulltext: true titleUrlDefault: http://abc-chemistry.org/ providerName: ABC ChemistRy – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1099-4300 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023216 issn: 1099-4300 databaseCode: KQ8 dateStart: 19990101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1099-4300 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023216 issn: 1099-4300 databaseCode: DOA dateStart: 20160101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1099-4300 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023216 issn: 1099-4300 databaseCode: ABDBF dateStart: 20081201 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1099-4300 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023216 issn: 1099-4300 databaseCode: GX1 dateStart: 19990101 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1099-4300 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023216 issn: 1099-4300 databaseCode: M~E dateStart: 19990101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 1099-4300 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023216 issn: 1099-4300 databaseCode: RPM dateStart: 20180101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1099-4300 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023216 issn: 1099-4300 databaseCode: BENPR dateStart: 19990301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 1099-4300 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023216 issn: 1099-4300 databaseCode: 8FG dateStart: 19990301 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Pb9MwFLdYe4ALAwEibFTmz4FL1thxnPiEWlg3IeimaZXKKThOAhUlqdZkaJz5BnxDPgnvpW5EJpCQuCRS_JzE8rPf7-dnv0fIcxYkUaiiwGUmzVzh5z4uNHmukJjXXBjBm_MV76byeCbezIO5zXO6ttsqgYovmkkavTZQ3_OGXAzVEGyxGq7S_OWlXUpiYC7RDyejHdKXAYDxHunPpqej942P01bexBPygdwPM_iywtd0rFATrL-DMK_vj7xZFyt99VUvl78Zn8ku-bD97c2ek88HdZUcmG_XIjr-R7vukNsWmNLRRpPukhtZcY_EZ2VSrys6Ohm5Y7B3KT1vdo7Tt2gC7RFOCriXAnZF-Lu8oq8xFC9m0QLpablYZ_RyoenP7z9WLlwYPYFJ6outep_MJofnr45dm5LBNcAbK9cwL098o4AoKpNyY1KMIaa0yWTueanvJRowoPQU3CUXGvBhrjnHuSMPgJz5D0ivKIvsIaGZTEUihZERM4IZiUgTLDYASCgxQeiQF9suio2NV45pM5Yx8BbszbjtTYc8bUVXmyAdfxIaYz-3AhhXu3lQXnyM7TCNmZY5C7Tv-6EWkeCJNsAXQY01QBsutUP2t1oS28G-jjlwzgDZvXDIk7YYhin6XnSRlXUjA8ROKWxX2NGuzg91S4rFpybgtxIq8pTvkGetHv69nY_-SWqP3OJ4kANdY-E-6VUXdfYY4FWVDMhONDkakP74cHp6NmgWKeB6NGcDO7h-AXJxJIg |
| linkProvider | Unpaywall |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3NbtQwELZKeygXVASILS2YP4lL1MR2nPhQoV3aaku3W1Rtpd6C4ziw0pIs3V2qvfMGvA8Pw5Mwk3UCQcCtl0SKJ5F_xp7viz0zhLwIwjSOVBx6gcmsJ3jO8UeT7wmJec2FEazyrzgdyv6FeHsZXq6R77UvDB6rrNfEaqHOSoP_yPcYAPsQKZR4Pf3sYdYo3F2tU2hol1oh269CjDnHjhO7vAYKN9s_PoDxfsnY0eHoTd9zWQY8A1Ro7pnAz1NuFHAfZTJmTIZhsZQ2Vua-n3E_1QBrpK_gLpnQAHlyzRhOhzwEvsHhu7fIhuBCAfnb6B0O3503lI-zQK7iGXGu_D0LLVcAKVTLClbJAloI98_zmZuLYqqX13oy-c34HW2ROw610u5Kze6SNVvcI8l5mS5mc9o963o9MIYZHVXHyukA7aPz76QAiikAW8TGkyU9wDi9mGILpIfleGbpl7GmP75-m3pwCegZrGCf3Kv3ycWN9OQDsl6UhX1IqJWZSKUwMg6MCIxEGArmHNAllJgw6pBXdf8lxgUzx5wakwRIDXZ10nR1hzxrRKerCB5_E-rhIDQCGHS7elBefUjcHE4CLfMg1JzzSItYsFQbIJOg4xpwD5O6Q3bqIUzcSjBLfulthzxtimEO48aMLmy5qGSA9SmF7YpaQ9-qULukGH-sooEroWJf8Q553ijJv9u5_f8qPiGb_dHpIBkcD08ekdsMXT1w8yzaIevzq4XdBQA2Tx87Lafk_U1PrJ8VN0TW |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1fb9MwED-NIQEvCASIwADzT-IlamI7Tv2AUEcpGxsdQpu0t8xxHKhUkrK2TH3nG_Bt-Dh8Eu7SJBAEvO2lkepLFNt3vt8v9t0BPAmjtB_rfuSHNnO-FLmgD02BLxXVNZdW8iq-4u1Y7RzJN8fR8QZ8b2Jh6FhlsyZWC3VWWvpG3uMI7COiULKX18ci3g1HL2affaogRTutTTmNtYrsudUZ0rf5890hzvVTzkevDl_u-HWFAd8iDVr4NgzyVFiNvEfbjFubUUosbaxTeRBkIkgNQhoVaLwqLg3CndxwTqaQR8g1BD73AlyMKYs7RamPXrdkT_BQrTMZCaGDnsM-awQTuuP_qjIBHWz758nMy8tiZlZnZjr9ze2NrsHVGq-ywVrBrsOGK25A8r5Ml_MFGxwM_G10gxk7rA6Us33yjHVkJ0M4zBDSEiqertiQMvRScS2UHpeTuWNfJob9-Ppt5uNPyA5w7fpU33oTjs5lHG_BZlEW7jYwpzKZKmlVP7QytIoAKDpyxJXYYqPYg2fN-CW2TmNO1TSmCdIZGuqkHWoPHrWis3Xujr8JbdMktAKUbrv6ozz9kNTWm4RG5WFkhBCxkX3JU2ORRqJ2G0Q8XBkPtpopTOo1YJ780lgPHrbNaL20JWMKVy4rGeR7WlO_4s7Ud16o21JMPlZ5wLXU_UALDx63SvLvft75_ys-gEtoTsn-7njvLlzhFONBu2bxFmwuTpfuHiKvRXq_UnEGJ-dtUz8BQVZCcA |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NbtQwELZge4ALFAEitFTm58AljWM7TnxC20JVIdgi1JXKKThOUlZsk1U3KSpn3oA35Ek6k_VGpAIJiUsixeMklsee7_PYM4S8CKMsiXUS-aHNC1-KUuBCE_Olwrzm0krena94P1GHU_n2JDpxeU6XblslUPFZN0mj1wbqMxZwGegAbLEOFnn56sItJYVgLtEPp5KbZENFAMZHZGM6-TD-1Pk4XeVVPCEB5D4o4MsaXzOwQl2w_gHCvL4_8lZbLczlNzOf_2Z8Du6Sz-vfXu05-brbNtmu_X4touN_tGuT3HHAlI5XmnSP3Ciq-yT9WGftsqHjo7G_B_Yup8fdznH6Dk2gO8JJAfdSwK4If-eX9DWG4sUsWiA9qWfLgl7MDP314-fCh0tIj2CSOnNVH5DpwZvj_UPfpWTwLfDGxrchKzNhNRBFbXNubY4xxLSxhSoZywXLDGBAxTTcFZcG8GFpOMe5o4yAnImHZFTVVfGI0ELlMlPSqiS0MrQKkSZYbACQUGKj2CMv112UWhevHNNmzFPgLdibad-bHnnWiy5WQTr-JLSH_dwLYFzt7kF9fpq6YZqGRpVhZIQQsZGJ5JmxwBdBjQ1AG66MR7bXWpK6wb5MOXDOCNm99MjTvhiGKfpeTFXUbScDxE5rbFc80K7BDw1LqtmXLuC3ljphWnjkea-Hf2_n43-S2iK3OR7kQNdYvE1GzXlbPAF41WQ7bghdAQAuICI |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Robust+AOA-Based+Target+Localization+for+Uniformly+Distributed+Noise+via+%E2%84%93p-%E2%84%931+Optimization&rft.jtitle=Entropy+%28Basel%2C+Switzerland%29&rft.au=Chen%2C+Yanping&rft.au=Wang%2C+Chunmei&rft.au=Yan%2C+Qingli&rft.date=2022-09-07&rft.issn=1099-4300&rft.eissn=1099-4300&rft.volume=24&rft.issue=9&rft_id=info:doi/10.3390%2Fe24091259&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1099-4300&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1099-4300&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1099-4300&client=summon |