This paper considers multi-frequency passive radar and develops a multi-frequency joint direction of arrival(DOA)estimation algorithm to improve estimation accuracy and resolu-tion.The developed algorithm exploits the sparsity of targets in the spatial domain.Specifically,we first extract the required fre-quency channel data and acquire the snapshot data through a series of preprocessing such as clutter suppression,coherent integration,beamforming,and constant false alarm rate(CFAR)detection.Then,based on the framework of sparse Bayesian learning,the target's DOA is estimated by jointly extracting the multi-frequency data via evidence maximization.Simulation results show that the developed algorithm has better estimation accuracy and resolution than other existing multi-frequency DOA estimation algorithms,especially under the scenarios of low sig-nal-to-noise ratio(SNR)and small snapshots.Furthermore,the effectiveness is verified by the field experimental data of a multi-frequency FM-based passive radar.

WEN Jinfang;YI Jianxin;WAN Xianrong;GONG Ziping;SHEN Ji

School of Electronic Information,Wuhan University,Wuhan 430072,China

系统工程与电子技术（英文版）

[1]WEN Jinfang;YI Jianxin;WAN Xianrong;GONG Ziping;SHEN Ji-.DOA estimation based on multi-frequency joint sparse Bayesian learning for passive radar)[J].系统工程与电子技术（英文版）,2022(05):1052-1063

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