An Adaptively Sampled HFM Pulsed Radar for Energy-Efficient Multi-Target Sensing
IEEE Transactions on Green Communications and Networking, cilt.10, ss.2365-2378, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 10
- Basım Tarihi: 2026
- Doi Numarası: 10.1109/tgcn.2026.3667454
- Dergi Adı: IEEE Transactions on Green Communications and Networking
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.2365-2378
- Anahtar Kelimeler: Adaptive sampling, aliasing resistance, energy-efficient sensing, hyperbolic frequency modulated radar, multi-target sensing, pulsed radar, sub-Nyquist sampling
- İstanbul Medipol Üniversitesi Adresli: Evet
Özet
The next generation of wireless networks necessitates high-resolution and accurate sensing across diverse operational conditions. Pulse compression radars assist in achieving this goal at the cost of expensive high-rate analog-to-digital converters (ADCs). Applying a uniform Nyquist sampling rate for all scenarios leads to unnecessary energy consumption, particularly when targets are well-separated and do not require fine-grained resolution. To address this challenge, in this paper, a novel energy-efficient sensing framework for multi-target scenarios is introduced, employing adaptively sampled hyperbolic frequency modulated (HFM) pulsed radar. The system intelligently adjusts its fast-time sampling rate in accordance with a motion-separation metric that quantifies range and velocity differences between targets. Depending on the scene-complexity, it selects an appropriate sampling factor, with updates guided by a confidence metric and periodic resets to the Nyquist rate to ensure consistent long-term performance. The HFM pulses exhibiting non-linear time-frequency signature are exploited for inherent resilience to aliasing, enabling reliable range and velocity estimation under sub-Nyquist sampling. To evaluate the performance, closed-form Cramér-Rao lower bounds for range and velocity estimation are derived, that incorporates the impact of noise folding on the effective signal-to-noise ratio. Simulation results reveal that reduced sampling rates lead to lower energy consumption while preserving estimation accuracy and maintaining reliable detection performance for well-separated targets.