Metaheuristic-Driven Intelligent Generation of Multidimensional False Targets Using Time-Modulated Metasurfaces
Haoran Han,
Jiwei Zhao,
Wei Deng,
Weilu Lin,
Peixuan Zhu,
Huan Lu,
Fanyi Tang,
Kai Wang,
Rongrong Zhu,
Bin Zheng and
Hongsheng Chen
Time-modulated metasurfaces (TMMs) enable low-observable and reconfigurable radar deception, but existing approaches remain limited in synthesizing complex range-velocity (R-V) false-target clusters because of forward-designed modulation schemes and single-platform operation. This paper presents a task-oriented framework for multidimensional false-target synthesis against frequency-modulated continuous-wave (FMCW) radars. An analytical model explicitly connects TMM temporal modulation with the FMCW R-V estimation chain. Intra-frame modulation reshapes the echo spectrum to generate programmable range offsets, while inter-frame control modifies the slow-time response to generate programmable velocity offsets. The modulation-sequence design is then formulated as a combinatorial inverse-design problem and solved using Physics-Informed Initialization-Assisted Particle Swarm Optimization (PIIA-PSO), which exploits analytical R-V relations to construct a physically informative initial population, thereby reducing ineffective early-stage exploration and improving convergence efficiency toward high-quality realizable solutions. The framework is further extended to distributed multi-platform cooperation to increase spatial degrees of freedom and enable coherent energy aggregation. Results show range and velocity errors below 10% for single-platform synthesis and improve the overall SSIM by 7-10 percentage points under distributed configurations, demonstrating enhanced synthesis fidelity, flexibility, and scalability.
