TY - JOUR
T1 - Truncated Nuclear Norm Matrix Completion for Random Stepped-Frequency Waveforms in Multi-Radar Cooperative Sensing Systems
AU - Hu, Xueyao
AU - Wang, Zaiyang
AU - Cui, Zihang
AU - Liang, Can
AU - Li, Yang
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2025
Y1 - 2025
N2 - Random Stepped Frequency (RSF) waveform, known for its superior anti-interference capability, effectively mitigates mutual interference issues commonly encountered in multi-radar cooperative sensing scenarios. However, its sparse sampling pattern in the time-frequency domain results in incomplete echo signal, degrading the range-Doppler spectrum estimation quality. This paper proposes a coherent processing approach for RSF waveform echoes based on matrix completion (MC), significantly enhancing RSF waveform practical value in multi-radar sensing. The proposed method first incorporates a Hankel matrix structure to strengthen the low-rank characteristics of time-frequency data matrix. Subsequently, we utilize the MC approach that combines truncated nuclear norm regularization with an accelerated proximal gradient linear algorithm to effectively address noisy data and improve the completion performance. The efficacy of the proposed method is verified via numerical simulations and further validated with real-collected sensing data.
AB - Random Stepped Frequency (RSF) waveform, known for its superior anti-interference capability, effectively mitigates mutual interference issues commonly encountered in multi-radar cooperative sensing scenarios. However, its sparse sampling pattern in the time-frequency domain results in incomplete echo signal, degrading the range-Doppler spectrum estimation quality. This paper proposes a coherent processing approach for RSF waveform echoes based on matrix completion (MC), significantly enhancing RSF waveform practical value in multi-radar sensing. The proposed method first incorporates a Hankel matrix structure to strengthen the low-rank characteristics of time-frequency data matrix. Subsequently, we utilize the MC approach that combines truncated nuclear norm regularization with an accelerated proximal gradient linear algorithm to effectively address noisy data and improve the completion performance. The efficacy of the proposed method is verified via numerical simulations and further validated with real-collected sensing data.
KW - Integrated sensing and communication
KW - matrix completion
KW - multi-radar cooperative sensing
KW - random stepped-frequency waveform
KW - range-Doppler estimation
KW - time-frequency under-sampled data recovery
UR - https://www.scopus.com/pages/publications/105017662257
U2 - 10.1109/JSEN.2025.3612297
DO - 10.1109/JSEN.2025.3612297
M3 - Article
AN - SCOPUS:105017662257
SN - 1530-437X
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
ER -