Reliable Clutter Suppression for Slow-Moving Weak Target Radar Detection

Reliable slow-moving weak target detection in complicated environments is challenging due to the masking effects from the surrounding strong reflectors. The traditional Moving Target Indication (MTI) may suppress the echoes from not only the static interference objects (IOs), but also the desired slow-moving weak target. According to the low-rank and sparse properties of the range-velocity maps across different radar scans, a novel clutter suppression scheme based on the Go decomposition (Godec) framework is proposed in this paper. The simulation results show that with the existence of masking effects, the target detection scheme based on Godec clutter suppression can reliably detect the slow-moving weak target, compared to the traditional MTI-based scheme. Besides, the time consumption comparison is conducted, demonstrating that the proposed solution is one that sacrifices time complexity in exchange for enhanced reliability. Additionally, the tradeoffs among the number of false alarm cells, the detection probability and the iteration times for convergence have been revealed, guiding parameter settings of the proposed solution in practical applications. Experiment validation is also conducted to verify the proposed solution, providing further insight into the scenarios where the solution is most applicable.

Multi-Objective Resource Allocation for IRS-Aided SWIPT

In this letter, we study the resource allocation for a multiuser intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) system. Specifically, a multi-antenna base station (BS) transmits energy and information signals simultaneously to multiple energy harvesting receivers (EHRs) and information decoding receivers (IDRs) assisted by an IRS. Under this setup, we introduce a multi-objective optimization (MOOP) framework to investigate the fundamental trade-off between the data sum-rate maximization and the total harvested energy maximization, by jointly optimizing the energy/information beamforming vectors at the BS and the phase shifts at the IRS. This MOOP problem is first converted to a single-objective optimization problem (SOOP) via the $\epsilon$-constraint method and then solved by majorization minimization (MM) and inner approximation (IA) techniques. Simulation results unveil a non-trivial trade-off between the considered competing objectives, as well as the superior performance of the proposed scheme as compared to various baseline schemes.