DOA estimation based on multi-frequency joint sparse Bayesian learning for passive radar

This paper considers multi-frequency passive radar and develops a multi-frequency joint direction of arrival (DOA) estimation algorithm to improve estimation accuracy and resolution. The developed algorithm exploits the sparsity of targets in the spatial domain. Specifically, we first extract the required frequency 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 signal-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.