Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking
Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection thresho...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Haowei [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Modelling SARS-CoV-2 transmission in a UK university setting - Hill, Edward M. ELSEVIER, 2021, a review journal, Orlando, Fla |
|---|---|
| Übergeordnetes Werk: |
volume:126 ; year:2022 ; day:30 ; month:06 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.dsp.2021.103379 |
|---|
| Katalog-ID: |
ELV057712212 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV057712212 | ||
| 003 | DE-627 | ||
| 005 | 20230626045615.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220808s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.dsp.2021.103379 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001773.pica |
| 035 | |a (DE-627)ELV057712212 | ||
| 035 | |a (ELSEVIER)S1051-2004(21)00418-8 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 610 |q VZ |
| 084 | |a 44.75 |2 bkl | ||
| 100 | 1 | |a Zhang, Haowei |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking |
| 264 | 1 | |c 2022transfer abstract | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results. | ||
| 520 | |a Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results. | ||
| 650 | 7 | |a Nonconvex optimization |2 Elsevier | |
| 650 | 7 | |a Predicted conditional-Cramér-Rao lower bound (PC-CRLB) |2 Elsevier | |
| 650 | 7 | |a Bayesian detection |2 Elsevier | |
| 650 | 7 | |a Cognitive radar |2 Elsevier | |
| 650 | 7 | |a Multiple-input multiple-output (MIMO) radar |2 Elsevier | |
| 700 | 1 | |a Liu, Weijian |4 oth | |
| 700 | 1 | |a Fei, Taiyong |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Academic Press |a Hill, Edward M. ELSEVIER |t Modelling SARS-CoV-2 transmission in a UK university setting |d 2021 |d a review journal |g Orlando, Fla |w (DE-627)ELV006540295 |
| 773 | 1 | 8 | |g volume:126 |g year:2022 |g day:30 |g month:06 |g pages:0 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.dsp.2021.103379 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 44.75 |j Infektionskrankheiten |j parasitäre Krankheiten |x Medizin |q VZ |
| 951 | |a AR | ||
| 952 | |d 126 |j 2022 |b 30 |c 0630 |h 0 | ||
| author_variant |
h z hz |
|---|---|
| matchkey_str |
zhanghaoweiliuweijianfeitaiyong:2022----:ondtcinhehlajsmnadoealctosrtgfront |
| hierarchy_sort_str |
2022transfer abstract |
| bklnumber |
44.75 |
| publishDate |
2022 |
| allfields |
10.1016/j.dsp.2021.103379 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001773.pica (DE-627)ELV057712212 (ELSEVIER)S1051-2004(21)00418-8 DE-627 ger DE-627 rakwb eng 610 VZ 44.75 bkl Zhang, Haowei verfasserin aut Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results. Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results. Nonconvex optimization Elsevier Predicted conditional-Cramér-Rao lower bound (PC-CRLB) Elsevier Bayesian detection Elsevier Cognitive radar Elsevier Multiple-input multiple-output (MIMO) radar Elsevier Liu, Weijian oth Fei, Taiyong oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:126 year:2022 day:30 month:06 pages:0 https://doi.org/10.1016/j.dsp.2021.103379 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 126 2022 30 0630 0 |
| spelling |
10.1016/j.dsp.2021.103379 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001773.pica (DE-627)ELV057712212 (ELSEVIER)S1051-2004(21)00418-8 DE-627 ger DE-627 rakwb eng 610 VZ 44.75 bkl Zhang, Haowei verfasserin aut Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results. Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results. Nonconvex optimization Elsevier Predicted conditional-Cramér-Rao lower bound (PC-CRLB) Elsevier Bayesian detection Elsevier Cognitive radar Elsevier Multiple-input multiple-output (MIMO) radar Elsevier Liu, Weijian oth Fei, Taiyong oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:126 year:2022 day:30 month:06 pages:0 https://doi.org/10.1016/j.dsp.2021.103379 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 126 2022 30 0630 0 |
| allfields_unstemmed |
10.1016/j.dsp.2021.103379 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001773.pica (DE-627)ELV057712212 (ELSEVIER)S1051-2004(21)00418-8 DE-627 ger DE-627 rakwb eng 610 VZ 44.75 bkl Zhang, Haowei verfasserin aut Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results. Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results. Nonconvex optimization Elsevier Predicted conditional-Cramér-Rao lower bound (PC-CRLB) Elsevier Bayesian detection Elsevier Cognitive radar Elsevier Multiple-input multiple-output (MIMO) radar Elsevier Liu, Weijian oth Fei, Taiyong oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:126 year:2022 day:30 month:06 pages:0 https://doi.org/10.1016/j.dsp.2021.103379 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 126 2022 30 0630 0 |
| allfieldsGer |
10.1016/j.dsp.2021.103379 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001773.pica (DE-627)ELV057712212 (ELSEVIER)S1051-2004(21)00418-8 DE-627 ger DE-627 rakwb eng 610 VZ 44.75 bkl Zhang, Haowei verfasserin aut Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results. Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results. Nonconvex optimization Elsevier Predicted conditional-Cramér-Rao lower bound (PC-CRLB) Elsevier Bayesian detection Elsevier Cognitive radar Elsevier Multiple-input multiple-output (MIMO) radar Elsevier Liu, Weijian oth Fei, Taiyong oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:126 year:2022 day:30 month:06 pages:0 https://doi.org/10.1016/j.dsp.2021.103379 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 126 2022 30 0630 0 |
| allfieldsSound |
10.1016/j.dsp.2021.103379 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001773.pica (DE-627)ELV057712212 (ELSEVIER)S1051-2004(21)00418-8 DE-627 ger DE-627 rakwb eng 610 VZ 44.75 bkl Zhang, Haowei verfasserin aut Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results. Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results. Nonconvex optimization Elsevier Predicted conditional-Cramér-Rao lower bound (PC-CRLB) Elsevier Bayesian detection Elsevier Cognitive radar Elsevier Multiple-input multiple-output (MIMO) radar Elsevier Liu, Weijian oth Fei, Taiyong oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:126 year:2022 day:30 month:06 pages:0 https://doi.org/10.1016/j.dsp.2021.103379 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 126 2022 30 0630 0 |
| language |
English |
| source |
Enthalten in Modelling SARS-CoV-2 transmission in a UK university setting Orlando, Fla volume:126 year:2022 day:30 month:06 pages:0 |
| sourceStr |
Enthalten in Modelling SARS-CoV-2 transmission in a UK university setting Orlando, Fla volume:126 year:2022 day:30 month:06 pages:0 |
| format_phy_str_mv |
Article |
| bklname |
Infektionskrankheiten parasitäre Krankheiten |
| institution |
findex.gbv.de |
| topic_facet |
Nonconvex optimization Predicted conditional-Cramér-Rao lower bound (PC-CRLB) Bayesian detection Cognitive radar Multiple-input multiple-output (MIMO) radar |
| dewey-raw |
610 |
| isfreeaccess_bool |
false |
| container_title |
Modelling SARS-CoV-2 transmission in a UK university setting |
| authorswithroles_txt_mv |
Zhang, Haowei @@aut@@ Liu, Weijian @@oth@@ Fei, Taiyong @@oth@@ |
| publishDateDaySort_date |
2022-01-30T00:00:00Z |
| hierarchy_top_id |
ELV006540295 |
| dewey-sort |
3610 |
| id |
ELV057712212 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV057712212</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626045615.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.dsp.2021.103379</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001773.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV057712212</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1051-2004(21)00418-8</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.75</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhang, Haowei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Nonconvex optimization</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Predicted conditional-Cramér-Rao lower bound (PC-CRLB)</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Bayesian detection</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Cognitive radar</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Multiple-input multiple-output (MIMO) radar</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Weijian</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fei, Taiyong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Academic Press</subfield><subfield code="a">Hill, Edward M. ELSEVIER</subfield><subfield code="t">Modelling SARS-CoV-2 transmission in a UK university setting</subfield><subfield code="d">2021</subfield><subfield code="d">a review journal</subfield><subfield code="g">Orlando, Fla</subfield><subfield code="w">(DE-627)ELV006540295</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:126</subfield><subfield code="g">year:2022</subfield><subfield code="g">day:30</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.dsp.2021.103379</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.75</subfield><subfield code="j">Infektionskrankheiten</subfield><subfield code="j">parasitäre Krankheiten</subfield><subfield code="x">Medizin</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">126</subfield><subfield code="j">2022</subfield><subfield code="b">30</subfield><subfield code="c">0630</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| author |
Zhang, Haowei |
| spellingShingle |
Zhang, Haowei ddc 610 bkl 44.75 Elsevier Nonconvex optimization Elsevier Predicted conditional-Cramér-Rao lower bound (PC-CRLB) Elsevier Bayesian detection Elsevier Cognitive radar Elsevier Multiple-input multiple-output (MIMO) radar Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking |
| authorStr |
Zhang, Haowei |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV006540295 |
| format |
electronic Article |
| dewey-ones |
610 - Medicine & health |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
610 VZ 44.75 bkl Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking Nonconvex optimization Elsevier Predicted conditional-Cramér-Rao lower bound (PC-CRLB) Elsevier Bayesian detection Elsevier Cognitive radar Elsevier Multiple-input multiple-output (MIMO) radar Elsevier |
| topic |
ddc 610 bkl 44.75 Elsevier Nonconvex optimization Elsevier Predicted conditional-Cramér-Rao lower bound (PC-CRLB) Elsevier Bayesian detection Elsevier Cognitive radar Elsevier Multiple-input multiple-output (MIMO) radar |
| topic_unstemmed |
ddc 610 bkl 44.75 Elsevier Nonconvex optimization Elsevier Predicted conditional-Cramér-Rao lower bound (PC-CRLB) Elsevier Bayesian detection Elsevier Cognitive radar Elsevier Multiple-input multiple-output (MIMO) radar |
| topic_browse |
ddc 610 bkl 44.75 Elsevier Nonconvex optimization Elsevier Predicted conditional-Cramér-Rao lower bound (PC-CRLB) Elsevier Bayesian detection Elsevier Cognitive radar Elsevier Multiple-input multiple-output (MIMO) radar |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
w l wl t f tf |
| hierarchy_parent_title |
Modelling SARS-CoV-2 transmission in a UK university setting |
| hierarchy_parent_id |
ELV006540295 |
| dewey-tens |
610 - Medicine & health |
| hierarchy_top_title |
Modelling SARS-CoV-2 transmission in a UK university setting |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV006540295 |
| title |
Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking |
| ctrlnum |
(DE-627)ELV057712212 (ELSEVIER)S1051-2004(21)00418-8 |
| title_full |
Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking |
| author_sort |
Zhang, Haowei |
| journal |
Modelling SARS-CoV-2 transmission in a UK university setting |
| journalStr |
Modelling SARS-CoV-2 transmission in a UK university setting |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
zzz |
| container_start_page |
0 |
| author_browse |
Zhang, Haowei |
| container_volume |
126 |
| class |
610 VZ 44.75 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Zhang, Haowei |
| doi_str_mv |
10.1016/j.dsp.2021.103379 |
| dewey-full |
610 |
| title_sort |
joint detection threshold adjustment and power allocation strategy for cognitive mimo radar target tracking |
| title_auth |
Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking |
| abstract |
Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results. |
| abstractGer |
Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results. |
| abstract_unstemmed |
Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA |
| title_short |
Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking |
| url |
https://doi.org/10.1016/j.dsp.2021.103379 |
| remote_bool |
true |
| author2 |
Liu, Weijian Fei, Taiyong |
| author2Str |
Liu, Weijian Fei, Taiyong |
| ppnlink |
ELV006540295 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth |
| doi_str |
10.1016/j.dsp.2021.103379 |
| up_date |
2024-07-06T16:56:28.001Z |
| _version_ |
1803849551579185152 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV057712212</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626045615.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.dsp.2021.103379</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001773.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV057712212</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1051-2004(21)00418-8</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.75</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhang, Haowei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Nonconvex optimization</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Predicted conditional-Cramér-Rao lower bound (PC-CRLB)</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Bayesian detection</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Cognitive radar</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Multiple-input multiple-output (MIMO) radar</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Weijian</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fei, Taiyong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Academic Press</subfield><subfield code="a">Hill, Edward M. ELSEVIER</subfield><subfield code="t">Modelling SARS-CoV-2 transmission in a UK university setting</subfield><subfield code="d">2021</subfield><subfield code="d">a review journal</subfield><subfield code="g">Orlando, Fla</subfield><subfield code="w">(DE-627)ELV006540295</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:126</subfield><subfield code="g">year:2022</subfield><subfield code="g">day:30</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.dsp.2021.103379</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.75</subfield><subfield code="j">Infektionskrankheiten</subfield><subfield code="j">parasitäre Krankheiten</subfield><subfield code="x">Medizin</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">126</subfield><subfield code="j">2022</subfield><subfield code="b">30</subfield><subfield code="c">0630</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| score |
7.402501 |