Practical classification of different moving targets using automotive radar and deep neural networks
In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro-Doppler extraction is propos...
Uloženo v:
| Vydáno v: | IET radar, sonar & navigation Ročník 12; číslo 10; s. 1082 - 1089 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
The Institution of Engineering and Technology
01.10.2018
|
| Témata: | |
| ISSN: | 1751-8784, 1751-8792 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro-Doppler extraction is proposed in conjunction with the automotive radar transceiver TEF810X and microcontroller unit SR32R274 manufactured by NXP Semiconductors. Three different types of neural networks are considered, namely a classic convolutional network, a residual network, and a combination of convolutional and recurrent network, for different classification problems across the four classes of targets recorded. Considerable accuracy (close to 100% in some cases) and low latency of the radar pre-processing prior to classification (∼0.55 s to produce a 0.5 s long spectrogram) are demonstrated in this study, and possible shortcomings and outstanding issues are discussed. |
|---|---|
| AbstractList | In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro-Doppler extraction is proposed in conjunction with the automotive radar transceiver TEF810X and microcontroller unit SR32R274 manufactured by NXP Semiconductors. Three different types of neural networks are considered, namely a classic convolutional network, a residual network, and a combination of convolutional and recurrent network, for different classification problems across the four classes of targets recorded. Considerable accuracy (close to 100% in some cases) and low latency of the radar pre-processing prior to classification (∼0.55 s to produce a 0.5 s long spectrogram) are demonstrated in this study, and possible shortcomings and outstanding issues are discussed. In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro‐Doppler extraction is proposed in conjunction with the automotive radar transceiver TEF810X and microcontroller unit SR32R274 manufactured by NXP Semiconductors. Three different types of neural networks are considered, namely a classic convolutional network, a residual network, and a combination of convolutional and recurrent network, for different classification problems across the four classes of targets recorded. Considerable accuracy (close to 100% in some cases) and low latency of the radar pre‐processing prior to classification (∼0.55 s to produce a 0.5 s long spectrogram) are demonstrated in this study, and possible shortcomings and outstanding issues are discussed. |
| Author | Angelov, Aleksandar Fioranelli, Francesco Murray-Smith, Roderick Robertson, Andrew |
| Author_xml | – sequence: 1 givenname: Aleksandar surname: Angelov fullname: Angelov, Aleksandar organization: 1School of Engineering, University of Glasgow, Glasgow, UK – sequence: 2 givenname: Andrew surname: Robertson fullname: Robertson, Andrew organization: 2NXP Semiconductors, Glasgow, UK – sequence: 3 givenname: Roderick surname: Murray-Smith fullname: Murray-Smith, Roderick organization: 3School of Computing Science, University of Glasgow, Glasgow, UK – sequence: 4 givenname: Francesco surname: Fioranelli fullname: Fioranelli, Francesco email: francesco.fioranelli@glasgow.ac.uk organization: 1School of Engineering, University of Glasgow, Glasgow, UK |
| BookMark | eNqFkMlKBDEQhoMouD6At1w99Jj0Mul4U3GDQUXnHmqSikR7kiHJKL693Y6IiMspFfi_Wr5tsu6DR0L2ORtxVstDh7mIyY9KxtsR46xaI1tcNLxohSzXP-u23iTbKT0y1jTjWm4RcxtBZ6eho7qDlJzt6-yCp8FS46zFiD7TeXh2_oFmiA-YE12m4QfLHOYhu2ekEQxECt5Qg7igHpex7-gxv4T4lHbJhoUu4d7Hu0Om52fT08ticnNxdXo8KXTNuSgQ25lsSzbmFmthJMoGAMqqrXCsW2ks4yCtZtogE1yCHmOFZQXCCNPObLVD-KqtjiGliFYtoptDfFWcqcGS6i2p3pIaLKnBUs-Ib4x2-V1AjuC6P8mjFfniOnz9f5S6u78uT84Zk43o4YMVPMQewzL63ou6OpsOqS_MwgxnFT9kf1_sDSznoyM |
| CitedBy_id | crossref_primary_10_3389_fnins_2022_1075538 crossref_primary_10_1515_freq_2021_0062 crossref_primary_10_1109_TIM_2022_3229703 crossref_primary_10_3390_rs15153742 crossref_primary_10_1109_TMTT_2022_3218304 crossref_primary_10_1109_JSEN_2020_3036047 crossref_primary_10_1109_JSEN_2021_3095674 crossref_primary_10_1016_j_measurement_2022_110743 crossref_primary_10_3390_s25113337 crossref_primary_10_3390_s24092813 crossref_primary_10_1109_JSTSP_2021_3058895 crossref_primary_10_3390_s21061951 crossref_primary_10_1049_iet_rsn_2019_0190 crossref_primary_10_1109_TAP_2022_3175716 crossref_primary_10_1109_LRA_2022_3147324 crossref_primary_10_1080_09205071_2020_1713226 crossref_primary_10_1109_TIV_2022_3220435 crossref_primary_10_1016_j_micpro_2020_103182 crossref_primary_10_1049_iet_rsn_2019_0471 crossref_primary_10_1016_j_desal_2023_116676 crossref_primary_10_1109_ACCESS_2022_3141543 crossref_primary_10_1109_JIOT_2023_3305513 crossref_primary_10_1109_JSEN_2021_3057450 crossref_primary_10_3390_e21100963 crossref_primary_10_1109_JSEN_2022_3141213 crossref_primary_10_3390_electronics11091383 crossref_primary_10_3390_rs16224256 crossref_primary_10_1109_ACCESS_2020_2966222 crossref_primary_10_1049_rsn2_12181 crossref_primary_10_1049_rsn2_12182 crossref_primary_10_1109_TCE_2023_3343460 crossref_primary_10_1109_LRA_2020_2967272 crossref_primary_10_1109_TAES_2023_3291335 crossref_primary_10_1049_iet_rsn_2019_0044 crossref_primary_10_1631_FITEE_1900523 crossref_primary_10_3390_s23146472 crossref_primary_10_1109_JSEN_2023_3276798 crossref_primary_10_1109_LGRS_2021_3102039 crossref_primary_10_1016_j_ins_2021_03_024 crossref_primary_10_1049_iet_rsn_2019_0601 crossref_primary_10_3390_s21072317 crossref_primary_10_1109_JSEN_2020_3041615 crossref_primary_10_3390_s23218901 crossref_primary_10_1049_iet_rsn_2018_5331 crossref_primary_10_1109_TITS_2025_3554781 crossref_primary_10_1109_ACCESS_2020_2977922 crossref_primary_10_3390_app10113861 crossref_primary_10_3390_electronics13204070 crossref_primary_10_3390_s22114208 crossref_primary_10_1145_3628453 crossref_primary_10_1049_iet_rsn_2019_0493 crossref_primary_10_1109_JSEN_2025_3564189 crossref_primary_10_1109_TIV_2022_3167733 crossref_primary_10_3390_electronics10101144 crossref_primary_10_3389_fnins_2022_851774 crossref_primary_10_1080_01431161_2024_2398823 crossref_primary_10_1109_ACCESS_2020_2977938 crossref_primary_10_1109_TGRS_2021_3138687 crossref_primary_10_3390_math11020361 crossref_primary_10_1049_rsn2_12042 crossref_primary_10_1109_ACCESS_2020_2975676 crossref_primary_10_1016_j_eswa_2024_125280 crossref_primary_10_3390_s20123504 crossref_primary_10_3390_s22031048 crossref_primary_10_1109_JSEN_2020_2999548 |
| Cites_doi | 10.1049/el.2016.3543 10.1049/iet-rsn.2015.0118 10.1137/0105003 10.1162/neco.1997.9.8.1735 10.1109/LGRS.2015.2491329 10.1109/CVPR.2016.90 10.1109/LGRS.2017.2771405 10.1109/TAES.2017.2740098 10.1049/iet-rsn.2017.0126 10.1109/TAES.2017.2651678 10.1109/TAES.1983.309350 |
| ContentType | Journal Article |
| Copyright | The Institution of Engineering and Technology 2020 The Institution of Engineering and Technology |
| Copyright_xml | – notice: The Institution of Engineering and Technology – notice: 2020 The Institution of Engineering and Technology |
| DBID | AAYXX CITATION |
| DOI | 10.1049/iet-rsn.2018.0103 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1751-8792 |
| EndPage | 1089 |
| ExternalDocumentID | 10_1049_iet_rsn_2018_0103 RSN2BF00957 |
| Genre | article |
| GrantInformation_xml | – fundername: EPSRC UK Quantum Technology Programme funderid: EP/ M01326X/1 – fundername: EU Horizon 2020 project MoreGrasp funderid: 643955 |
| GroupedDBID | 0R 24P 29I 4.4 4IJ 6IK 8FE 8FG 8VB AAJGR ABJCF ABPTK ACGFS ACIWK AENEX AFKRA ALMA_UNASSIGNED_HOLDINGS ARAPS BENPR BFFAM BGLVJ DU5 EBS EJD ESX GOZPB GRPMH HCIFZ HZ IFIPE IPLJI JAVBF L6V LAI LOTEE LXI M43 M7S MS NADUK NXXTH O9- OCL P62 PTHSS QWB RIE RNS RUI S0W U5U UNMZH UNR ZL0 .DC 0R~ 0ZK 1OC 96U AAHHS AAHJG ABMDY ABQXS ACCFJ ACCMX ACESK ACXQS ADEYR ADZOD AEEZP AEGXH AEQDE AFAZI AIWBW AJBDE ALUQN AVUZU CCPQU F8P GROUPED_DOAJ HZ~ IAO IGS ITC K1G MCNEO MS~ OK1 ROL AAMMB AAYXX AEFGJ AFFHD AGXDD AIDQK AIDYY CITATION IDLOA IMI PHGZM PHGZT PQGLB WIN |
| ID | FETCH-LOGICAL-c4117-ee8b982061fe47d9e95aaa2383e6c89df01a9fc0cde0719ac6e3e23a7d7d8bf3 |
| IEDL.DBID | 24P |
| ISICitedReferencesCount | 94 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000444968600003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1751-8784 |
| IngestDate | Tue Nov 18 21:41:26 EST 2025 Wed Nov 05 20:46:04 EST 2025 Wed Jan 22 16:30:59 EST 2025 Tue Jan 05 21:44:13 EST 2021 Thu May 09 18:03:30 EDT 2019 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 10 |
| Keywords | radar transmitters radar tracking electrical engineering computing radar detection recurrent neural network recurrent neural nets convolutional neural network radar pre-processing object detection microcontroller unit SR32R274 NXP Semiconductors signal classification target classification automotive radar transceiver TEF810X time 0.5 s residual neural network radar receivers object tracking microDoppler extraction Doppler radar road vehicle radar deep neural network microcontrollers |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c4117-ee8b982061fe47d9e95aaa2383e6c89df01a9fc0cde0719ac6e3e23a7d7d8bf3 |
| OpenAccessLink | https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-rsn.2018.0103 |
| PageCount | 8 |
| ParticipantIDs | crossref_primary_10_1049_iet_rsn_2018_0103 iet_journals_10_1049_iet_rsn_2018_0103 wiley_primary_10_1049_iet_rsn_2018_0103_RSN2BF00957 crossref_citationtrail_10_1049_iet_rsn_2018_0103 |
| ProviderPackageCode | RUI |
| PublicationCentury | 2000 |
| PublicationDate | 20181000 October 2018 2018-10-00 |
| PublicationDateYYYYMMDD | 2018-10-01 |
| PublicationDate_xml | – month: 10 year: 2018 text: 20181000 |
| PublicationDecade | 2010 |
| PublicationTitle | IET radar, sonar & navigation |
| PublicationYear | 2018 |
| Publisher | The Institution of Engineering and Technology |
| Publisher_xml | – name: The Institution of Engineering and Technology |
| References | Jokanović, B.; Amin, M. (C22) 2018; 52 Hochreiter, S.; Schmidhuber, J. (C36) 1997; 9 Seyfioğlu, M.S.; Gürbüz, S.Z. (C27) 2017; 14 Lee, J.; Kim, D.; Jeong, S. (C11) 2016; 52 Kim, Y.; Moon, T. (C21) 2016; 13 Rohling, H. (C33) 1983; AES-19 Srivastava, N.; Hinton, G.; Krizhevsky, A. (C35) 2014; 15 Fioranelli, F.; Ritchie, M.; Gürbüz, S. (C20) 2017; 53 Tahmoush, D. (C19) 2015; 9 Patel, J.S.; Fioranelli, F.; Ritchie, M. (C30) 2017 Lee, S.; Yoon, Y.J.; Lee, J.E. (C12) 2017; 11 Munkres, J. (C34) 1957; 5 2017; 53 2012 2011 2017; 14 2017; 11 2009 1983; AES‐19 2014; 15 2016; 52 2017 2016 2018; 52 2015 2015; 9 1957; 5 1997; 9 2016; 13 e_1_2_7_5_1 (e_1_2_7_4_1) 2017 e_1_2_7_3_1 Sorowka P. (e_1_2_7_10_1) 2015 Schubert E. (e_1_2_7_11_1) 2015 e_1_2_7_2_1 Jokanovic B. (e_1_2_7_25_1) 2017 e_1_2_7_13_1 Patel J.S. (e_1_2_7_31_1) 2017 e_1_2_7_12_1 e_1_2_7_28_1 Trommel R.P. (e_1_2_7_29_1) 2016 Heuel S. (e_1_2_7_8_1) 2012 Hasch J. (e_1_2_7_6_1) 2015 Belgiovane D. (e_1_2_7_18_1) 2016 Heuel S. (e_1_2_7_7_1) 2011 Belgiovane D. (e_1_2_7_17_1) 2017 Srivastava N. (e_1_2_7_36_1) 2014; 15 Khashbat J. (e_1_2_7_33_1) 2012 Seyfioğlu M.S. (e_1_2_7_26_1) 2017 Marchetti E. (e_1_2_7_15_1) 2017 Saebboe J. (e_1_2_7_9_1) 2009 Parashar K.N. (e_1_2_7_27_1) 2017 e_1_2_7_32_1 Gashinova M. (e_1_2_7_16_1) 2016 e_1_2_7_23_1 e_1_2_7_22_1 e_1_2_7_34_1 e_1_2_7_21_1 e_1_2_7_35_1 e_1_2_7_20_1 e_1_2_7_37_1 e_1_2_7_38_1 Jokanovic B. (e_1_2_7_24_1) 2016 Klarenbeek G. (e_1_2_7_30_1) 2017 Marchetti E. (e_1_2_7_14_1) 2017 Stolz M. (e_1_2_7_19_1) 2017 |
| References_xml | – start-page: 1 year: 2017 end-page: 10 ident: C30 article-title: Multistatic radar classification of armed vs unarmed personnel using neural networks publication-title: Evol. Syst. – volume: 52 start-page: 180 issue: 1 year: 2018 end-page: 189 ident: C22 article-title: Fall detection using deep learning in range-Doppler radars publication-title: IEEE Trans. Aerosp. Electron. Syst., PP, (99) – volume: 9 start-page: 1140 issue: 9 year: 2015 end-page: 1146 ident: C19 article-title: Review of micro-Doppler signatures publication-title: IET Radar Sonar Navig. – volume: 13 start-page: 8 issue: 1 year: 2016 end-page: 12 ident: C21 article-title: Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 14 start-page: 2462 issue: 12 year: 2017 end-page: 2466 ident: C27 article-title: Deep neural network initialization methods for micro-Doppler classification with low training sample support publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 5 start-page: 32 issue: 1 year: 1957 end-page: 38 ident: C34 article-title: Algorithms for the assignment and transportation problems publication-title: J. Soc. Ind. Appl. Math. – volume: 53 start-page: 640 issue: 2 year: 2017 end-page: 654 ident: C20 article-title: Feature diversity for optimized human micro-Doppler classification using multistatic radar publication-title: IEEE Trans. Aerosp. Electron. Syst. – volume: 15 start-page: 1929 year: 2014 end-page: 1958 ident: C35 article-title: Dropout: a simple way to prevent neural networks from overfitting publication-title: J. Mach. Learn. Res. – volume: 9 start-page: 1735 year: 1997 end-page: 1780 ident: C36 article-title: Long short-term memory publication-title: Neural Comput. – volume: 52 start-page: 2061 issue: 25 year: 2016 end-page: 2063 ident: C11 article-title: Target classification scheme using phase characteristics for automotive FMCW radar publication-title: IET Electron. Lett. – volume: AES-19 start-page: 608 issue: 4 year: 1983 end-page: 621 ident: C33 article-title: Radar CFAR thresholding in clutter and multiple target situations publication-title: IEEE Trans. Aerosp. Electron. Syst. – volume: 11 start-page: 1589 issue: 10 year: 2017 end-page: 1596 ident: C12 article-title: Human–vehicle classification using feature-based SVM in 77-GHz automotive FMCW radar publication-title: IET Radar Sonar Navig. – start-page: 167 year: 2015 end-page: 173 – volume: 53 start-page: 640 issue: 2 year: 2017 end-page: 654 article-title: Feature diversity for optimized human micro‐Doppler classification using multistatic radar publication-title: IEEE Trans. Aerosp. Electron. Syst. – start-page: 94 year: 2017 end-page: 97 – start-page: 350 year: 2016 end-page: 353 – start-page: 1 year: 2009 end-page: 5 – volume: 14 start-page: 2462 issue: 12 year: 2017 end-page: 2466 article-title: Deep neural network initialization methods for micro‐Doppler classification with low training sample support publication-title: IEEE Geosci. Remote Sens. Lett. – start-page: 1 year: 2017 end-page: 7 – start-page: 477 year: 2011 end-page: 484 – volume: 5 start-page: 32 issue: 1 year: 1957 end-page: 38 article-title: Algorithms for the assignment and transportation problems publication-title: J. Soc. Ind. Appl. Math. – volume: 52 start-page: 2061 issue: 25 year: 2016 end-page: 2063 article-title: Target classification scheme using phase characteristics for automotive FMCW radar publication-title: IET Electron. Lett. – start-page: 81 year: 2016 end-page: 84 – volume: 15 start-page: 1929 year: 2014 end-page: 1958 article-title: Dropout: a simple way to prevent neural networks from overfitting publication-title: J. Mach. Learn. Res. – volume: 52 start-page: 180 issue: 1 year: 2018 end-page: 189 article-title: Fall detection using deep learning in range‐Doppler radars publication-title: IEEE Trans. Aerosp. Electron. Syst., PP, (99) – year: 2016 – volume: 13 start-page: 8 issue: 1 year: 2016 end-page: 12 article-title: Human detection and activity classification based on micro‐Doppler signatures using deep convolutional neural networks publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 9 start-page: 1735 year: 1997 end-page: 1780 article-title: Long short‐term memory publication-title: Neural Comput. – start-page: 1 year: 2016 end-page: 5 – start-page: 57 year: 2017 end-page: 60 – year: 2012 – start-page: 2912 year: 2017 end-page: 2916 – start-page: 167 year: 2017 end-page: 170 – start-page: 39 year: 2012 end-page: 44 – start-page: 174 year: 2015 end-page: 179 – volume: AES‐19 start-page: 608 issue: 4 year: 1983 end-page: 621 article-title: Radar CFAR thresholding in clutter and multiple target situations publication-title: IEEE Trans. Aerosp. Electron. Syst. – volume: 11 start-page: 1589 issue: 10 year: 2017 end-page: 1596 article-title: Human–vehicle classification using feature‐based SVM in 77‐GHz automotive FMCW radar publication-title: IET Radar Sonar Navig. – start-page: 1125 year: 2017 end-page: 1130 – start-page: 1 year: 2015 end-page: 4 – start-page: 1 year: 2017 end-page: 10 article-title: Multistatic radar classification of armed vs unarmed personnel using neural networks publication-title: Evol. Syst. – year: 2017 – start-page: 0948 year: 2017 end-page: 0952 – start-page: 1739 year: 2017 end-page: 1744 – volume: 9 start-page: 1140 issue: 9 year: 2015 end-page: 1146 article-title: Review of micro‐Doppler signatures publication-title: IET Radar Sonar Navig. – year: 2015 – start-page: 167 volume-title: Pedestrian classification with 24 GHz chirp sequence radar year: 2015 ident: e_1_2_7_10_1 – start-page: 1 volume-title: Effect of data representations on deep learning in fall detection year: 2016 ident: e_1_2_7_24_1 – ident: e_1_2_7_12_1 doi: 10.1049/el.2016.3543 – start-page: 1739 volume-title: Micro‐Doppler feature extraction using convolutional auto‐encoders for low latency target classification year: 2017 ident: e_1_2_7_27_1 – start-page: 1 volume-title: Harmonic automotive radar for VRU classification year: 2009 ident: e_1_2_7_9_1 – volume: 15 start-page: 1929 year: 2014 ident: e_1_2_7_36_1 article-title: Dropout: a simple way to prevent neural networks from overfitting publication-title: J. Mach. Learn. Res. – ident: e_1_2_7_20_1 doi: 10.1049/iet-rsn.2015.0118 – start-page: 2912 volume-title: Micro‐Doppler characteristics of pedestrians and bicycles for automotive radar sensors at 77 GHz year: 2017 ident: e_1_2_7_17_1 – ident: e_1_2_7_35_1 doi: 10.1137/0105003 – ident: e_1_2_7_3_1 – volume-title: Determining the driver's reaction time in the stationary and real‐life environments (comparative study) year: 2012 ident: e_1_2_7_33_1 – ident: e_1_2_7_37_1 doi: 10.1162/neco.1997.9.8.1735 – start-page: 174 volume-title: Clustering of high‐resolution automotive radar detections and subsequent feature extraction for classification of road users year: 2015 ident: e_1_2_7_11_1 – start-page: 1 volume-title: Bicycles and human riders backscattering at 77 GHz for automotive radar year: 2016 ident: e_1_2_7_18_1 – start-page: 1 volume-title: Comparison of pedestrian reflectivities at 24 and 300 GHz year: 2017 ident: e_1_2_7_15_1 – ident: e_1_2_7_22_1 doi: 10.1109/LGRS.2015.2491329 – start-page: 1 volume-title: Driving towards 2020: automotive radar technology trends year: 2015 ident: e_1_2_7_6_1 – ident: e_1_2_7_5_1 – start-page: 477 volume-title: Two‐stage pedestrian classification in automotive radar systems year: 2011 ident: e_1_2_7_7_1 – start-page: 1125 volume-title: Deep learning of micro‐Doppler features for aided and unaided gait recognition year: 2017 ident: e_1_2_7_26_1 – start-page: 167 volume-title: Multi‐target human gait classification using LSTM recurrent neural networks applied to micro‐Doppler year: 2017 ident: e_1_2_7_30_1 – ident: e_1_2_7_2_1 – start-page: 81 volume-title: Multi‐target human gait classification using deep convolutional neural networks on micro‐Doppler spectrograms year: 2016 ident: e_1_2_7_29_1 – ident: e_1_2_7_32_1 doi: 10.1109/CVPR.2016.90 – ident: e_1_2_7_38_1 – start-page: 39 volume-title: Two‐stage pedestrian classification in automotive radar systems year: 2012 ident: e_1_2_7_8_1 – start-page: 57 volume-title: Radar reflectivity and motion characteristics of pedestrians at 300 GHz year: 2017 ident: e_1_2_7_14_1 – ident: e_1_2_7_28_1 doi: 10.1109/LGRS.2017.2771405 – start-page: 1 year: 2017 ident: e_1_2_7_31_1 article-title: Multistatic radar classification of armed vs unarmed personnel using neural networks publication-title: Evol. Syst. – ident: e_1_2_7_23_1 doi: 10.1109/TAES.2017.2740098 – ident: e_1_2_7_13_1 doi: 10.1049/iet-rsn.2017.0126 – ident: e_1_2_7_21_1 doi: 10.1109/TAES.2017.2651678 – start-page: 94 volume-title: Multi‐target reflection point model of cyclists for automotive radar year: 2017 ident: e_1_2_7_19_1 – start-page: 0948 volume-title: Multiple joint‐variable domains recognition of human motion year: 2017 ident: e_1_2_7_25_1 – start-page: 350 volume-title: Predicted sensitivity of a 300 GHz FMCW radar to pedestrians year: 2016 ident: e_1_2_7_16_1 – ident: e_1_2_7_34_1 doi: 10.1109/TAES.1983.309350 – volume-title: Expect the unexpected – an IET transport sector report on the unintended consequences of connected and autonomous vehicles year: 2017 ident: e_1_2_7_4_1 |
| SSID | ssj0055649 |
| Score | 2.5424938 |
| Snippet | In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar... |
| SourceID | crossref wiley iet |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 1082 |
| SubjectTerms | automotive radar transceiver TEF810X convolutional neural network deep neural network Doppler radar electrical engineering computing microcontroller unit SR32R274 microcontrollers microDoppler extraction NXP Semiconductors object detection object tracking radar detection radar pre‐processing radar receivers radar tracking radar transmitters recurrent neural nets recurrent neural network residual neural network road vehicle radar signal classification Special Issue: Advanced Automotive Sensing – Towards Car Autonomy target classification time 0.5 s |
| Title | Practical classification of different moving targets using automotive radar and deep neural networks |
| URI | http://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2018.0103 https://onlinelibrary.wiley.com/doi/abs/10.1049%2Fiet-rsn.2018.0103 |
| Volume | 12 |
| WOSCitedRecordID | wos000444968600003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library Free Content customDbUrl: eissn: 1751-8792 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0055649 issn: 1751-8784 databaseCode: WIN dateStart: 20130101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Open Access customDbUrl: eissn: 1751-8792 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0055649 issn: 1751-8784 databaseCode: 24P dateStart: 20130101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Na9swFH-kWQ_roVvbjaX7QIzSQ8FdZDmWddxKwwIjhC7Q3IysjxLYnGC7_fv7npxkDYUMxi4-yE-2ed-Wnn4P4EworiyXaSS0SaLE6TjSacyj1GcyLnghhA8HhX_I8TibzdSkA1frszAtPsRmwY0sI_hrMnBdtF1IMKlFIc5dE1U1QZjy7JK6FezBC86FJNWOk8naHQ8GaciBMUxyNP0s2Wxtqi_PHrEVnPbw9nbKGmLO8NV_-drXcLhKOdnXVkeOoOPKYzh4AkR4AraFLUJ5MUPpNNUPBZGxhWfrHioN-x2WH1hbPV4zqpm_Y_q-CRV9D45V2uqK6dIy69ySEVYmPrFsK83rNzAdXk-vvker_guRSTgGL-eyQhG-O_cukVY5NdBaY4wXLjWZsr7PtfKmb6zDREVpkzrhYqGllTYrvHgL3XJRunfAMon-vRDKaNpVRPUYxIUx6FAM_lApX_Sgv-Z7blbY5NQi41ce9sgTlSP_cuRfTvzLiX89uNhMWbbAHLuIz2lsZZ71LsLPW4Sj62l-83P8hyBfWt8DEUT99_fS3PjbkPJYefpPs97DSxpvywc_QLep7t1H2DcPzbyuPgVNx-vtaPwIBAcDvQ |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3dT9swED_xMQl4gAFDdAxmoYkHpEAdu0n8uI1WRZQKjT7wZjn-mJAgVEng75_PaQvVJJAQr8mdE935PmyffwfwgwkqDE2TiCnNI25VHKkkplHisjTOac6YCxeFB-lwmN3ciKsFOJvehWnwIWYbbmgZwV-jgeOGdLPg5AiSeWvrqKwQw5RmJ9iuYBGWuY822Mcg5ldTf9zpJCEJ9nGSetvP-OxsU5z-N8RcdFr0r-dz1hB0ehsf87ufYX2SdJKfzSzZhAVbbMHaCyjCbTANcJHXGNGYUGMFUVAaeXBk2kWlJvdhA4I09eMVwar5v0Q91qGm78mSUhlVElUYYqwdE0TL9CMWTa159QVGve7odz-adGCINKc-fFmb5QIR3qmzPDXCio5Sykd5ZhOdCePaVAmn29pYn6oIpRPLbMxUalKT5Y7twFLxUNhdIFnqPXzOhFZ4rugnSCfOtfYuRfsllXB5C9pTwUs9QSfHJhl3MpyScyG9_KSXn0T5SZRfC45nLOMGmuM14iN8NjHQ6jXCwznC8-5I_rkePhPIsXEtYEHXb38XeeNfPcxk06_v4voOK_3R5UAOzocXe7CKNE0x4TdYqstHuw-f9FN9W5UHYdr_A0LyBpA |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1La9wwEB6StJTkkD5DtklTUUoPBacrS37omNeS0LAs7R5yE7I0KoHGWWwnvz8aeXfbpZBC6dUeyWZG85Bm9A3AR6G4crzIE2GsTCSaNDF5ypPcl0Va8UoIHy8KXxbjcXl1pSZrcLq4C9PjQywP3Egzor0mBceZ8_2GUxJI5jV2SdMShikvD6ldwTo8kVmwtYTvLCcLe5xleQyCg5_kQfdLucxtqi9_TLHindbD69WYNTqd0fP_87svYHsedLKjfpW8hDWsX8HWb1CEr8H1wEVBYsxSQE0VRFFo7NazRReVjt3EAwjW14-3jKrmfzBz18WavntkjXGmYaZ2zCHOGKFlhhnrvta8fQPT0dn05DyZd2BIrOTBfSGWlSKEd-5RFk6hyowxwcsLzG2pnB9yo7wdWochVFHG5igwFaZwhSsrL3Zgo76tcRdYWQQLXwllDeUVwwLJ0sraYFJs2FIpXw1guGC8tnN0cmqS8VPHLLlUOvBPB_5p4p8m_g3g83LIrIfmeIz4Ez2bK2j7GOGHFcKLs6n-9n38i0AHsQ5ARFn__bs0Nj0eUSRbvP2nUe_h2eR0pC8vxl_3YJNI-lrCfdjomjt8B0_tfXfdNgdx1T8A3zYGFA |
| 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=Practical+classification+of+different+moving+targets+using+automotive+radar+and+deep+neural+networks&rft.jtitle=IET+radar%2C+sonar+%26+navigation&rft.au=Angelov%2C+Aleksandar&rft.au=Robertson%2C+Andrew&rft.au=Murray-Smith%2C+Roderick&rft.au=Fioranelli%2C+Francesco&rft.date=2018-10-01&rft.pub=The+Institution+of+Engineering+and+Technology&rft.issn=1751-8784&rft.eissn=1751-8792&rft.volume=12&rft.issue=10&rft.spage=1082&rft.epage=1089&rft_id=info:doi/10.1049%2Fiet-rsn.2018.0103&rft.externalDocID=10_1049_iet_rsn_2018_0103 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1751-8784&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1751-8784&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1751-8784&client=summon |