Abstract
Near-field interference suppression for a towed linear array (TLA) is investigated in this paper. The existing eigencomponent association (ECA) scheme and multiple signal classification interference suppression (MUSIC-IS) scheme require the prior information of a target bearing in order to achieve satisfactory performance. To improve this, we propose the use of an enhanced ECA (EECA) scheme that achieves interference suppression in a non-cooperative scenario. It identifies non-target eigenvectors by scanning the tail direction zone of the TLA. With the non-target-only eigenvectors subtracted, the beam power spectrum of the EECA manifests null troughs at the target bearings. Numerical simulations show the superiority of the EECA method. This method can effectively suppress strong interference without prior information, capture a target even at a low signal-to-interference (SIR) level of −25 dB, and obtain dozens of dB processing gains compared to the ECA and MUSIC-IS.
Highlights
In non-cooperative underwater acoustic pulse detection systems, using the towed linear array (TLA) to intercept weak targets in the far-field is one of the key steps of pulse signal detection
As a classical subspace-based method, multiple signal classification (MUSIC) is widely used for interference suppression, but it is limited by the problem of accurate subspace division [8]
Post-processing beamforming algorithms depend on the cross-spectral density matrix (CSDM), where CSDM is similar to the time-domain cross-correlation, which is used to estimate the correlation between two signals
Summary
In non-cooperative underwater acoustic pulse detection systems, using the towed linear array (TLA) to intercept weak targets in the far-field is one of the key steps of pulse signal detection. The first category eliminates interference components based on the eigendecomposition of the covariance matrix in the element-space domain [1,2] The success of such algorithms relies on large amount of snapshots, which may not be available in real environments. A spatial filtering matrix is applied to the measured data, suppressing the out-of-sector interference while keeping the sector-of-interest signal [10,11] It works well for far-field interferences while suffering performance degradation in the case of near-field interference [12,13]. To address this issue, we propose a new scheme that relies on CSDM eigendecomposition as the eigencomponent association (ECA) method [3,4]. The high performance of the proposed scheme was verified by numerical simulations
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