TY - JOUR
T1 - Doppler Sidelobe Suppression via Quasi-Neural Network for ST-CDMA MIMO Radar
AU - Liang, Can
AU - Hu, Xueyao
AU - Zhu, Rui
AU - Zhang, Liang
AU - Wang, Yanhua
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2024/4/15
Y1 - 2024/4/15
N2 - In co-located multiple-input multiple-output (MIMO) radar, slow-time code division multiple access (ST-CDMA) is an essential option of orthogonal waveforms, because the transmit power and bandwidth are well exploited with a relatively low level of hardware complexity. However, the code produces high sidelobes in the Doppler spectrum, which poses a negative impact on weak target detection. To address this issue, we propose a novel sidelobe suppression method that leverages the CLEAN framework in conjunction with a quasi-neural network (Quasi-NN). The novelty lies in the application of Quasi-NN for signal modeling in the target parameter estimation step of CLEAN. Specifically, Quasi-NN is employed to represent the signal after range compression. Its inputs, outputs, and internal weights are determined by the characteristics of antennas, waveforms, and targets. In this way, the estimation of target parameters is transformed into the optimization of weights, which thereby can be solved using a back-propagation (BP) algorithm. Simulation results demonstrate the superior performance of the proposed method under various scenarios. Real-data results using a 77-GHz radar also show that the proposed method achieves lower sidelobes and thus improves the detection of weak targets.
AB - In co-located multiple-input multiple-output (MIMO) radar, slow-time code division multiple access (ST-CDMA) is an essential option of orthogonal waveforms, because the transmit power and bandwidth are well exploited with a relatively low level of hardware complexity. However, the code produces high sidelobes in the Doppler spectrum, which poses a negative impact on weak target detection. To address this issue, we propose a novel sidelobe suppression method that leverages the CLEAN framework in conjunction with a quasi-neural network (Quasi-NN). The novelty lies in the application of Quasi-NN for signal modeling in the target parameter estimation step of CLEAN. Specifically, Quasi-NN is employed to represent the signal after range compression. Its inputs, outputs, and internal weights are determined by the characteristics of antennas, waveforms, and targets. In this way, the estimation of target parameters is transformed into the optimization of weights, which thereby can be solved using a back-propagation (BP) algorithm. Simulation results demonstrate the superior performance of the proposed method under various scenarios. Real-data results using a 77-GHz radar also show that the proposed method achieves lower sidelobes and thus improves the detection of weak targets.
KW - Multiple-input multiple-output (MIMO) radar
KW - quasi-neural network (Quasi-NN)
KW - sidelobe suppression
KW - slow-time code division multiple access (ST-CDMA)
UR - http://www.scopus.com/inward/record.url?scp=85187339158&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2024.3368482
DO - 10.1109/JSEN.2024.3368482
M3 - Article
AN - SCOPUS:85187339158
SN - 1530-437X
VL - 24
SP - 13545
EP - 13559
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 8
ER -