Cognitive Waveform Optimization for Phase-Modulation-Based Joint Radar-Communications System
A dual-function radar communication (DFRC) system enables the implementation of a primary radar operation and a secondary communication function concurrently. A bank of transmit beamforming weight vectors are guaranteed to have the same transmitted radiation pattern to satisfy in the target detectio...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9001099/ |
| _version_ | 1818444390542082048 |
|---|---|
| author | Yu Yao Pu Miao Zhimin Chen |
| author_facet | Yu Yao Pu Miao Zhimin Chen |
| author_sort | Yu Yao |
| collection | DOAJ |
| description | A dual-function radar communication (DFRC) system enables the implementation of a primary radar operation and a secondary communication function concurrently. A bank of transmit beamforming weight vectors are guaranteed to have the same transmitted radiation pattern to satisfy in the target detection requirements, while the phase symbol is selected from a preset dictionary so that communication information can be embedded. However, as the radar channel is time-variant due to the fluctuation in the radar cross-section (RCS) of the target and the Doppler shift that results from the relative motion of the target, it is necessary for a successive waveform design and selection scheme to continually obtain target information. Our work aims at enhancing the target detection performance by maximizing the relative entropy (RE) between two hypotheses (in the first hypothesis we assume the target is not present in the echoes while in the second hypothesis we assume the target exists in the echoes) and by minimizing the mutual information (MI) between successive target echoes. The proposed scheme overcomes the coexisting communication and radar detection problems in intelligent transportation systems (ITSs), where it is necessary to extract the features of target information that is obtained from a vehicle-mounted sensor. Our simulation results demonstrate an improvement in the target detection performance by the proposed two-stage approach. In addition, the system can transmit data of several Mbps with low symbol error rates. |
| first_indexed | 2024-12-14T19:15:11Z |
| format | Article |
| id | doaj.art-c3e0efdb2fe34399b4f170c7dcd6252f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T19:15:11Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c3e0efdb2fe34399b4f170c7dcd6252f2022-12-21T22:50:38ZengIEEEIEEE Access2169-35362020-01-018332763328810.1109/ACCESS.2020.29747879001099Cognitive Waveform Optimization for Phase-Modulation-Based Joint Radar-Communications SystemYu Yao0https://orcid.org/0000-0002-7282-491XPu Miao1https://orcid.org/0000-0003-4096-4501Zhimin Chen2https://orcid.org/0000-0001-8477-3163School of Information Engineering, East China Jiaotong University, Nanchang, ChinaSchool of Electronic and Information Engineering, Qingdao University, Qingdao, ChinaSchool of Information Science and Engineering, Shanghai Dianji University, Shanghai, ChinaA dual-function radar communication (DFRC) system enables the implementation of a primary radar operation and a secondary communication function concurrently. A bank of transmit beamforming weight vectors are guaranteed to have the same transmitted radiation pattern to satisfy in the target detection requirements, while the phase symbol is selected from a preset dictionary so that communication information can be embedded. However, as the radar channel is time-variant due to the fluctuation in the radar cross-section (RCS) of the target and the Doppler shift that results from the relative motion of the target, it is necessary for a successive waveform design and selection scheme to continually obtain target information. Our work aims at enhancing the target detection performance by maximizing the relative entropy (RE) between two hypotheses (in the first hypothesis we assume the target is not present in the echoes while in the second hypothesis we assume the target exists in the echoes) and by minimizing the mutual information (MI) between successive target echoes. The proposed scheme overcomes the coexisting communication and radar detection problems in intelligent transportation systems (ITSs), where it is necessary to extract the features of target information that is obtained from a vehicle-mounted sensor. Our simulation results demonstrate an improvement in the target detection performance by the proposed two-stage approach. In addition, the system can transmit data of several Mbps with low symbol error rates.https://ieeexplore.ieee.org/document/9001099/Dual-function radar communication (DFRC)waveform optimizationmutual information (MI)relative entropy (RE)target detectioncognitive learning |
| spellingShingle | Yu Yao Pu Miao Zhimin Chen Cognitive Waveform Optimization for Phase-Modulation-Based Joint Radar-Communications System IEEE Access Dual-function radar communication (DFRC) waveform optimization mutual information (MI) relative entropy (RE) target detection cognitive learning |
| title | Cognitive Waveform Optimization for Phase-Modulation-Based Joint Radar-Communications System |
| title_full | Cognitive Waveform Optimization for Phase-Modulation-Based Joint Radar-Communications System |
| title_fullStr | Cognitive Waveform Optimization for Phase-Modulation-Based Joint Radar-Communications System |
| title_full_unstemmed | Cognitive Waveform Optimization for Phase-Modulation-Based Joint Radar-Communications System |
| title_short | Cognitive Waveform Optimization for Phase-Modulation-Based Joint Radar-Communications System |
| title_sort | cognitive waveform optimization for phase modulation based joint radar communications system |
| topic | Dual-function radar communication (DFRC) waveform optimization mutual information (MI) relative entropy (RE) target detection cognitive learning |
| url | https://ieeexplore.ieee.org/document/9001099/ |
| work_keys_str_mv | AT yuyao cognitivewaveformoptimizationforphasemodulationbasedjointradarcommunicationssystem AT pumiao cognitivewaveformoptimizationforphasemodulationbasedjointradarcommunicationssystem AT zhiminchen cognitivewaveformoptimizationforphasemodulationbasedjointradarcommunicationssystem |