A waveform optimization method for sparse frequency agile radar

TY – GEN

T1 – A waveform optimization method for sparse frequency agile radar

AU – Chen, Shaohua

AU – Wang, Xiangyu

AU – Lu, Shanshan

AU – Liu, Nan

AU – Liang, Can

AU – Zhao, Jiawen

N1 – Publisher Copyright:
© 2024 IEEE.

PY – 2024

Y1 – 2024

N2 – The sparse frequency agile (SFA) radar is able to achieve the same range resolution as the frequency agile (FA) radar with fewer frequencies. Sparse reconstruction methods are used to estimate target parameters for SFA radars to avoid high sidelobe problem caused by traditional matched filtering methods. The outcome of sparse reconstruction is determined by the sensing dictionary matrix, which is related to the selection of waveform frequency sequence. Therefore, this paper proposes a waveform optimization design method for SFA radars. Firstly, we determine the sensing dictionary matrix’s maximum correlation coefficient (MCC) as our optimization objective. Then, an improved particle swarm optimization algorithm is employed to solve the above problem. It contains mutation steps and simulated annealing operations which can help particles escape from local optimal solutions. Finally, the simulation results prove that the proposed method can obtain the optimal SFA waveform with lower MCC, thus improving the accuracy of sparse reconstruction.

AB – The sparse frequency agile (SFA) radar is able to achieve the same range resolution as the frequency agile (FA) radar with fewer frequencies. Sparse reconstruction methods are used to estimate target parameters for SFA radars to avoid high sidelobe problem caused by traditional matched filtering methods. The outcome of sparse reconstruction is determined by the sensing dictionary matrix, which is related to the selection of waveform frequency sequence. Therefore, this paper proposes a waveform optimization design method for SFA radars. Firstly, we determine the sensing dictionary matrix’s maximum correlation coefficient (MCC) as our optimization objective. Then, an improved particle swarm optimization algorithm is employed to solve the above problem. It contains mutation steps and simulated annealing operations which can help particles escape from local optimal solutions. Finally, the simulation results prove that the proposed method can obtain the optimal SFA waveform with lower MCC, thus improving the accuracy of sparse reconstruction.

KW – particle swarm optimization (PSO) algorithm

KW – sparse frequency agile (SFA) radar

KW – sparse reconstruction

KW – waveform optimization

UR – www.scopus.com/pages/publications/86000028671

U2 – 10.1109/ICSIDP62679.2024.10868098

DO – 10.1109/ICSIDP62679.2024.10868098

M3 – Conference contribution

AN – SCOPUS:86000028671

T3 – IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

BT – IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

PB – Institute of Electrical and Electronics Engineers Inc.

T2 – 2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

Y2 – 22 November 2024 through 24 November 2024

ER –