Adaptive line enhancers (ALEs) have been widely used in passive sonars for enhancing narrowband discrete components (known simply as lines or tonals) radiated by surface or underwater targets. Unfortunately, the performance of the conventional ALE based on the least‐mean‐square (LMS) adaptive algorithm is limited by the high steady‐state misadjustment, which limits the output signal‐to‐noise ratio (SNR) of the conventional ALE. To break through the limit of the conventional ALE, this study proposes a sparsity‐based ALE (SALE). The SALE takes into account the frequency‐domain sparsity of narrowband discrete components and realises the adaption in the frequency domain. A ‐norm sparse penalty is incorporated into authors’ frequency‐domain adaption to reduce the misadjustment dramatically. The simulations show that the SNR gain of the SALE is 6.3 dB higher than that of the conventional ALE. The results of processing the real data collected in the sea trial also demonstrate that authors’ proposed SALE outperforms the conventional ALE. To reduce the computational complexity of the SALE, a fast SALE (FSALE) is also proposed. In authors’ setup, the computational complexity of the FSALE is one order of magnitude lower than that of the SALE, with comparable SNR performance.

Broadband acoustic Doppler current profilers (BBADCPs) are instruments that are widely used for measuring ocean currents. The ambiguity velocity of the conventional method used in BBADCPs must accommodate all possible measurement velocities. Unfortunately, allowing a high-ambiguity velocity results in a high measurement deviation in conventional BBADCPs. We propose a method to break through the limitation imposed by the ambiguity velocity to improve BBADCPs. Our proposed method involves designing an orthogonal coprime signal to replace the conventional transmit signal in BBADCPs. The proposed orthogonal coprime signal consists of two orthogonal subsignals, whose ambiguity velocities are designed to be low and coprime. Utilizing the coprime property, we then employ the robust Chinese remainder theorem to resolve the velocity ambiguity from the two independent measurements made via the two orthogonal subsignals. Simulations show that our proposed method decreases the standard deviation of measurement velocity by nearly three times, compared to the conventional method used in BBADCPs. Our simulations also show that the proposed method can yield a 12-dB improvement of signal-to-noise ratio over the conventional method. This can help increase the profiling range significantly.

Detection of acoustic tonals from surfaces and underwater vehicles is important for passive sonar systems. Enhancements of the tonals are usually necessary in passive sonar prior to detections. Conventionally, passive sonars employ adaptive line enhancers (ALE) in order to realise enhancements of the tonals. However, ALEs have requirements on their input signal‐to‐noise ratios (SNR). When the SNR inputs are too low, the ALEs cannot perform well. Therefore, for the purpose of overcoming the limitations of the SNR inputs to ALEs, this study proposes to enhance tonals using unsupervised deep‐learning techniques. The proposed deep‐learning‐based line enhancer (DLE) is based on an autoencoder neural network. The simulation results show that when the input SNR is −28 dB, the proposed DLE still achieves an SNR gain of 15 dB. However, the reference ALE fails. The experimental results also demonstrate the superiority of the proposed DLE.

A planar array of sensors and beamforming algorithms are required for underwater 3-D acoustic imaging system. However, due to limited aperture and sensor numbers, conventional beamforming usually results in wide mainlobes and high sidelobes, which severely degrade the image quality. To date, using unconventional beamforming algorithms to improve the imaging quality of underwater 3-D acoustic systems have been rarely investigated. This article proposes a feature-enhanced beamforming (FEBF) algorithm, with the goal of improving the imaging quality by utilizing more appropriate prior knowledge that objects in the angular domain in underwater 3-D imaging scenes are generally sparse but locally dense. The proposed FEBF employs the $l_{1}$ -total variation (L1-TV) mixed norm regularization. The penalized beamforming optimization is reformed into convex programming with linear constraints and solved by alternating direction method of multipliers. The results of simulation and experiment demonstrate that the proposed FEBF has the ability to effectively improve the image quality when compared with the other existing beamforming methods. This study also proposes a 2-D fast Fourier transform based acceleration strategy, which effectively reduces the computational time of the FEBF algorithm by about 4.3 × 10 ³ times for forming 200 × 200 beams. The accelerated version of the proposed FEBF method is considered to have potential for real-time imaging systems.

Pulse-to-pulse coherent Doppler sonars are widely used to explore boundary layer characteristics of oceans. Multifrequency pulse-to-pulse coherent Doppler sonars have been proposed in the literature to tackle the velocity ambiguity problem faced by single-frequency systems. A robust algorithm for resolving the velocity ambiguity is crucial in such systems. This paper proposes a method based on the robust Chinese remainder theorem to resolve the velocity ambiguity for multifrequency pulse-to-pulse coherent Doppler sonars. We evaluate the proposed method for resolving the velocity ambiguity in terms of the trial fail rate. The simulations show that the proposed method achieves 1/8 trial failure rate as the reference method. A theoretical criterion is also derived to support the observation that in most cases our proposed method is more robust to estimation errors than the reference method.

In order to detect weak underwater tonals, adaptive line enhancers (ALEs) have been widely applied in passive sonars. Unfortunately, conventional ALEs cannot perform well amid impulse noise generated by ice cracking, snapping shrimp or other factors. This kind of noise has a different noise model compared to Gaussian noise and leads to noise model mismatch problems in conventional ALEs. To mitigate the performance degradation of conventional ALEs in under-ice impulse noise, in this study, a modified ALE is proposed for passive sonars. The proposed ALE is based on the least mean p-power (LMP) error criterion and the prior information of the frequency domain sparsity to improve the enhancement performance under impulse noise. The signal-to-noise ratio (SNR) gain is chosen as the metric for evaluating the proposed ALE. The simulation results show that the output SNR gain of the proposed ALE was, respectively, 9.3 and 2.6 dB higher than that of the sparsity-based ALE (SALE) and the least mean p-power ALE (PALE) when the input GSNR was −12 dB. The results of processing the under-ice noise data also demonstrate that the proposed ALE is distinguished among the four ALEs.