Secure Radar-Communication Systems With Malicious Targets: Integrating Radar, Communications and Jamming Functionalities

This article studies the physical layer security in a multiple-input-multiple-output (MIMO) dual-functional radar-communication (DFRC) system, which communicates with downlink cellular users and tracks radar targets simultaneously. Here, the radar targets are considered as potential eavesdroppers which might eavesdrop the information from the communication transmitter to legitimate users. To ensure the transmission secrecy, we employ artificial noise (AN) at the transmitter and formulate optimization problems by minimizing the signal-to-noise ratio (SNR) received at radar targets, while guaranteeing the signal-to-interference-plus-noise ratio (SINR) requirement at legitimate users. We first consider the ideal case where both the target angle and the channel state information (CSI) are precisely known. The scenario is further extended to more general cases with target location uncertainty and CSI errors, where we propose robust optimization approaches to guarantee the worst-case performance. Accordingly, the computational complexity is analyzed for each proposed method. Our numerical results show the feasibility of the algorithms with the existence of instantaneous and statistical CSI error. In addition, the secrecy rate of secure DFRC system grows with the increasing angular interval of location uncertainty.

Automated summary

This article investigates the physical layer security in a MIMO dual-functional radar-communication system, utilizing artificial noise for transmission secrecy and formulating optimization problems to guarantee worst-case performance. Computational complexity is analyzed for different scenarios and robust optimization approaches are proposed to handle target location uncertainty and CSI errors. Numerical results demonstrate the feasibility of the algorithms in the presence of CSI errors, with the secrecy rate increasing as the angular interval of location uncertainty grows.