The movement of sonar platform and the time-varying underwater acoustic environment introduce additional unstable reverberation component, resulting in a degradation of the performance of traditional methods that exploit the low-rank characteristics of multi-frame reverberation backgrounds for reverberation suppression. In order to solve this problem, a reverberation suppression method for moving sonar platform based on tensor decomposition is proposed. In this method, the multi-frame data received on a moving sonar platform containing both target echoes and background reverberation is modeled as the superposition of spatiotemporal continuous reverberation, random reverberation and sparse target in the form of a tensor. A cost function is formed in which anisotropic three-dimensional (range, bearing, time) total variation and non-convex rank approximation constrain continuous reverberation, the Frobenius norm constrains random reverberation, and a tensor-weighted Lp norm constrains sparse target. The constrained optimization problem is solved using the alternating direction multiplier method (ADMM), enhanceing computational efficiency. The performance of this method is evaluated on a dataset containing a moving target and reverberation that changes rapidly in time due to the motion of the sonar platform. Experimental results show that compared with several traditional methods, the proposed method is more robust to rapidly evolving reverberation caused by the motion of the sonar platform and can retain the target energy to a greater extent.

In this letter, inspired by the maximum inter-element spacing (IES) constraint (MISC) criterion, an enhanced MISC-based (EMISC) sparse array (SA) with high uniform degrees-of-freedom (uDOFs) and low mutual-coupling (MC) is proposed, analyzed and discussed in detail. For the EMISC SA, an IES set is first determined by the maximum IES and number of elements. Then, the EMISC SA is composed of seven uniform linear sub-arrays (ULSAs) derived from an IES set. An analysis of the uDOFs and weight function shows that, the proposed EMISC SA outperforms the IMISC SA in terms of uDOF and MC. Simulation results show a significant advantage of the EMISC SA over other existing SAs.

Two estimate methods of the Doppler effects in mobile underwater acoustic communication have been proposed. For the first method the Doppler coefficients are obtained by estimating frequency change of CW impulse signal with notch filter. For the other method the Doppler coefficients are obtained by estimating chirp rate change of LFM signal with fractional Fourier transform (FRFT). And the performance of the Doppler effects estimation method based on notch filter or the FRFT are compared by the computer simulation. The advantages, shortcoming and the application occasion of the two methods are elaborated. The effectiveness and robustness of the two methods have been proved.

Multitarget tracking algorithms based on sonar usually run into detection uncertainty, complex channel and more clutters, which cause lower detection probability, single sonar sensors failing to measure when the target is in an acoustic shadow zone, and computational bottlenecks. This paper proposes a novel tracking algorithm based on multisensor data fusion to solve the above problems. Firstly, under more clutters and lower detection probability condition, a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter with computational advantages was used to get local estimations. Secondly, this paper provided a maximum-detection capability multitarget track fusion algorithm to deal with the problems caused by low detection probability and the target being in acoustic shadow zones. Lastly, a novel feedback algorithm was proposed to improve the GMPHD filter tracking performance, which fed the global estimations as a random finite set (RFS). In the end, the statistical characteristics of OSPA were used as evaluation criteria in Monte Carlo simulations, which showed this algorithm’s performance against those sonar tracking problems. When the detection probability is 0.7, compared with the GMPHD filter, the OSPA mean of two sensor and three sensor fusion was decrease almost by 40% and 55%, respectively. Moreover, this algorithm successfully tracks targets in acoustic shadow zones.