Performance Optimization in IRS-Aided Maritime Joint Sensing and Communication Systems

Intelligent reflecting surface (IRS) aided maritime joint sensing and communication (JSAC) can effectively avoid signal attenuation challenges caused by sea surface fluctuations. To improve the maritime JSAC performance further, a sensing signal-to-noise ratio maximization problem is proposed by optimizing active and passive beamforming while maintaining regular maritime communication. Due to the coupled variables of the non-convexity problem and the high complexity and overhead of the channel estimation and beam scanning, a two-phase performance optimization scheme with two sub-problems, the angle estimation sub-problem, and the joint active and passive beamforming sub-problem is developed. Accordingly, the minimum variance distortionless response-based angle estimation algorithm and the alternating optimization beamforming algorithm are proposed. Numerical results show the convergence and effectiveness of the proposed algorithms compared to other benchmark algorithms. In addition, the effect of IRS key parameters on the algorithms’ performance is investigated.