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Cognitive Waveform Optimization for Phase-Modulation-Based Joint Radar-Communications System
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DOI：10.1109/ACCESS.2020.2974787.png)
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Abstract
En 中文
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.
Keywords:
Radar cross-sections
Object detection
Receivers
Radar antennas
Radar detection
Wireless communication
Dual-function radar communication (DFRC)
waveform optimization
mutual information (MI)
relative entropy (RE)
target detection
cognitive learning
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