IEEE Access (Jan 2021)

Robust Wideband DOA Estimation Based on Element-Space Data Reconstruction in a Multi-Source Environment

  • Chuanqi Zhu,
  • Shiliang Fang,
  • Qisong Wu,
  • Liang An,
  • Xinwei Luo

DOI
https://doi.org/10.1109/ACCESS.2021.3063410
Journal volume & issue
Vol. 9
pp. 43522 – 43539

Abstract

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In the presence of multiple sources, the performance of direction-of-arrival (DOA) estimation based on beam power maximization is susceptible to the energy leaking from the interference beams to the target beam, especially in the case that the signal of interest (SOI) is quite weak. To address this issue, a robust wideband DOA estimation method is proposed in this paper. Unlike those conventional high-resolution methods which mitigate the influence of energy leakage by reducing beamwidths and sidelobe levels, the proposed method achieves this by directly reconstructing the element-space data to approach the received hydrophone data of the single-source scenario containing only the SOI. Element-space data reconstruction (ESDR) for the SOI is achieved by removing the element-space waveforms of all the interference signals from the received hydrophone data. Moreover, an iterative algorithm is developed to adaptively extract the element-space waveform of each interference signal, without requiring the prior information of the array amplitude response coefficients and hydrophone coordinates of a distorted towed array. Simulation results show that the proposed method outperforms its counterparts in terms of estimation accuracy for a multi-source scenario. Meanwhile, the DOA estimation performance of the proposed method in a multi-source environment is close to that obtained by the beam power maximization method in the single-source scenario, even if the signal-to-interference ratio (SIR) is as low as −25 dB. At-sea experimental results prove that, even though the number of signal sources is as many as seven and the SOI is contaminated by real ocean ambient noise, the proposed method still achieves a better DOA estimation performance compared to existing state-of-the-art methods.

Keywords