The Conventional Beamforming (CBF) Method has been widely used for underwater target detection. However, the performance and resolution of the CBF are insufficient in low signal-to-noise ratio (SNR) cases. To circumvent such a situation, this article proposes a new short angle weight (SAW) algorithm to improve the performance and resolution for target detection. Compared with the CBF, the proposed approach only uses the main lobe of the azimuth spectrum, while cutting off the side lobes due to redundancy. For obtaining the spatial power distribution and estimating the target azimuth, a weighted dictionary matrix is constructed to deconvolute the azimuth spectrum. Since the resolution of the azimuth spectrum is determined by the rate at which it changes, by properly selecting the size of the support set of its Fourier-transformed function, the resolution of the azimuth spectrum can apparently be enhanced. Moreover, the performance of the proposed SAW method is validated by pool trials and lake trials. The corresponding results show that the proposed algorithm provides much better resolution than other representative methods, at least for all the tested instances.

This article proposes a new active sonar detector based on a beamformed deep neural network (BDNN) in three steps. The process involves a preprocessing step, a deep neural network (DNN) application step, and a subsequent postprocessing step. In the preprocessing step, partial spectra are extracted from multiple directions through frequency-domain beamforming. These partial spectra from different directions serve as DNN input, yielding estimated target probabilities as output in the DNN application step. In the postprocessing step, a multiframe probability multiplication technique is proposed, and the number of frames is determined adaptively. The proposed BDNN generates a gridded azimuth-distance graph, where each grid cell represents the probability of a target's presence at a specific azimuth and distance. To guarantee real-time application, we also propose a graphics processing unit based parallel acceleration method, which increases the computation speed of the beamforming process by nearly two orders of magnitude compared to the CPU. The proposed BDNN is verified through sea and lake trials. The results demonstrate that the proposed BDNN achieves better detection performance compared to the conventional matched filter method and exhibits remarkable generalization capabilities.

A wideband Doppler Effect is a significant challenge for underwater acoustic communications (UAC). This paper proposes a new two-stage structure of direct adaptive multi-resampling turbo equalizer (DAM-TEQ) for solving the problem of large timescale errors in time-varying channels, which uses an innovative adaptive time-domain resampling method for Doppler estimation and compensation. In this equalizer, the received signal is first fed into the first-stage structure, in which an adaptive resampling is performed using equalization coefficient detection to achieve a Doppler rough estimation. After the processing is completed, it is fed into the second-stage structure for joint equalization and decoding, effectively reducing the error of information transmission. Compared with the conventional turbo equalizer (TEQ) based on timescale estimation, the proposed equalizer can avoid the problem of the Doppler Effect not being accurately estimated in time-varying channels, with only a slight increase in complexity. Simulations and lake trails show that the equalizer can effectively perform a Doppler estimation and compensation in time-varying channels, and has a better bit error rate (BER) performance than the traditional timescale-based TEQ.

The background of the active sonar deviates far from the Rayleigh model when encountering reverberation or clutter, which causes unexpected false alarm rates in conventional detectors. Especially for the low-frequency active sonar, due to its relatively low measurement resolution, there are relatively few features that can be utilized for target recognition. In this article, a Joint-Distribution (Joint-D) based fitting method is proposed for non-Rayleigh sonar data, to enhance the performance of conventional constant false alarm rate (CFAR) detection. Subsequently, other than single energy criterion, multi feature fusion based on Joint-D is implemented for both the local and spatial subregions of the detection results. Finally, target recognition is performed using a classifier trained by dual-stage support vector machine (SVM), where the first stage utilizes local features and the second stage utilizes spatial features. Receiver operating characteristic (ROC) curves show that the proposed method outperforms conventional counterparts across SRR from 0-25 dB. When achieving the same detection rate, particularly the 90% threshold, it requires an average of 5 dB less SRR than conventional counterparts. In the case study using sea trial data, the detection rate reaches 100%, and the false alarm rate is consistent with the theoretical value.

Deconvolution algorithms often rely on conventional beamforming methods to obtain beamforming vectors, which limit their resolution. To enhance parameter estimation resolution, this article introduces the Parameter Estimation by Alternating Reconstruction and Sensation (PEARS) algorithm. In the proposed algorithm, direction estimation leverages a linearly constrained quadratic programming method and weighted L1-norm to solve the objective function, achieving higher resolution in the direction spectrum under fixed weighted vector conditions. The algorithm utilizes the gradient descent method to update the weighted vector, and the relationship among the dictionary matrix, direction spectrum, and weighted vector is computed using the chain rule. This process improves direction estimation results, particularly in scenarios with low signal-to-noise ratios. By alternating between target parameter estimation and weight vector calculation, the PEARS algorithm achieves highly accurate target azimuth estimation. Simulation results validate the algorithm's ability to accurately estimate target azimuth angles. In addition, lake and sea experimental results demonstrate the algorithm's effectiveness in correctly estimating direction in complex environments.

By transferring external information between the equalizer and the decoder iteratively, the performance of turbo equalization is close to the channel capacity. Conventional turbo equalization (CTE) relies on channel coding, while the transmission of external information is a problem in an uncoded system, and turbo equalization without channel coding (TECC) remains unexplored. Therefore, this paper introduces a TECC framework with a virtual decoder constructed by bidirectional processing. The main innovation is that the existence of the virtual decoder enables the transmission of external information. Under this new framework, we implement it with a minimum mean square error decision feedback equalizer (MMSE-DFE) and evaluate its performance across stationary channels and multipath fading channels. Simulation results demonstrate significant communication performance enhancement after three to four iterations, surpassing both conventional bidirectional and unidirectional equalization. In addition, the proposed TECC is verified through underwater acoustic communication in a sea trial. The results also demonstrate that the TECC achieves better bit error performance.