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Passive detection of target-of-interest (TOI) within strong interferences poses a challenge. This paper introduces an adaptive interference suppression based on an invariant subspace of matrix matching. Assume that the TOI-bearing intervals are known. We define a correlation ratio for each eigenvector to obtain the highest one. Then, we use invariant subspace of matrix matching to measure the distance between two invariant projection matrices of this eigenvector. This identifies and removes the eigenvectors associated with TOI. Finally, the remaining eigenvectors are subtracted from the sample covariance matrix to suppress interference and noise. The viability of the proposed method is demonstrated experimentally. [ABSTRACT FROM AUTHOR]

For bistatic detection, the desired target is always smeared by the direct blast, limited by the performance of direction-of-arrival (DOA) estimation algorithms. Thus, a deconvolved Multiple-Signal-Classification (D-MUSIC) DOA estimation algorithm is proposed. D-MUSIC beam power is obtained by deconvolving MUSIC beam power using the point-scattering-function (PSF) and Richardson-Lucy (R-L) iteration algorithm. Under low signal-to-noise ratios (SNRs), the background level of D-MUSIC beam power is much lower than that of MUSIC beam power. Besides, D-MUSIC can realize the weak target detection with an adjacent strong interferer in the mainlobe. Finally, via processing the actual data collected during the Sanya sea trial, we analyze the bearing time record (BTR) of MUSIC and D-MUSIC, verifying the superiority of D-MUSIC algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we considered the real-time modeling of an underwater channel impulse response (CIR), exploiting the inherent structure and sparsity of such channels. Building on the recent development in the modeling of acoustic channels using a Kronecker structure, we approximated the CIR using a structured and sparse model, allowing for a computationally efficient sparse block-updating algorithm, which can track the time-varying CIR even in low signal-to-noise ratio (SNR) scenarios. The algorithm employs a conjugate gradient formulation, which enables a gradual refinement if the SNR is sufficiently high to allow for this. This was performed by gradually relaxing the assumed Kronecker structure, as well as the sparsity assumptions, if possible. The estimated CIR was further used to form a residual signal containing (primarily) information of the time-varying signal responses, thereby allowing for the detection of weak target signals. The proposed method was evaluated using both simulated and measured underwater signals, clearly illustrating the better performance of the proposed method. [ABSTRACT FROM AUTHOR]