@inproceedings{zhengnitinolhollowbowtieharmonictransponder2026,
  title = {{5.8/11.6 GHz Nitinol Wire-Based Hollow Bowtie Harmonic Transponder with Parasitic Resonance for Unique Tag Identification via High-Order Signature}},
  author = {Zheng, Yao and Cai, Haofan and Yang, Thomas and Siderhurst, Matthew S. and Jenkins, Daniel M.},
  booktitle = {{2026 IEEE Radar Conference (RadarConf)}},
  year = {2026},
  month = may,
  pages = {1--5},
  publisher = {IEEE},
  doi = {10.1109/RadarConf2663773.2026.11675473},
  url = {https://doi.org/10.1109/RadarConf2663773.2026.11675473},
  abstract = {Conventional harmonic radar tags for insect tracking operate at low microwave frequencies, resulting in bulky geometries, short detection ranges, and limited ability to distinguish individual targets. This paper introduces an AI-assisted design framework for a miniaturized Nitinol wire-based hollow bowtie harmonic transponder operating at 5.8/11.6 GHz, enabling compact size, enhanced range, and unique tag identification through high-order harmonic signatures. The system leverages generative electromagnetic optimization and AI-driven parameter search within ANSYS to evolve antenna geometries that maximize nonlinear conversion efficiency while maintaining mechanical resilience. A Schottky diode integrated at the bowtie feed provides broadband impedance matching, while parasitic resonant loops embedded in the hollow arms induce controlled field asymmetries that reinforce higher-order mixing products. The resulting amplitude–phase distributions across harmonic bands form a physically unclonable spectral signature that remains invariant under attenuation and multipath. This AI-guided, passive physical-layer fingerprinting approach eliminates digital encoding or power sources, providing a scalable path toward lightweight, long-range, and uniquely identifiable insect-borne transponders for ecological monitoring and invasive species management.}
}