Reduced-complexity simultaneous beamforming and equalization for underwater acoustic communications

This is a collection of articles written by members of the Underwater Acoustic Signal Processing (UASP) Technical Committee. The first article, by D. W. Tufts, deals with the history of UASP prior to 1980. In this period, initial mathematical models were developed and the first experimental investigations of underwater acoustic propagation were performed. It was also recognized during this time that there are many similarities between radar and sonar signal processing. The article by J.P. Ianniello deals with research in passive and active sonar from 1980 to the present. Work in this period included experimental verification of algorithms that had been developed in the 1960s and 1970s (e.g. for adaptive beamforming), as well as the development of new approaches, which include acoustic propagation modeling in the design of signal processing algorithms. Such processing is referred to as matched field processing. A common task in passive sonar systems is to estimate the difference in times at which different sensors receive the same signal. Time-delay estimation is a first stage that feeds into subsequent processing blocks. I. Lourtie provides a concise review of work in this field. The article by J.C. Preisig deals with underwater acoustic communications. The underwater channel has several features that make reliable communication a challenging problem. Nevertheless, progress is being made by combining results from ocean acoustic modeling, communication theory, and signal processing. The final article, by J.M.F. Moura, deals with the future of signal processing in the ocean. In addition to considering advances in detection and localization, he deals with new applications such as acoustic tomography, physical oceanography, and synthetic aperture sonar.

Session MP1a: Underwater acoustic communications and signal processing

Underwater acoustic signal denoising model based on secondary variational mode decomposition

International audience

With the advancement of optical communication technologies, such as fiber-optic hydrophones and photonic crystal fibers, the high bandwidth and low noise characteristics of these systems provide new avenues for preprocessing underwater acoustic signals. These technologies enhance the quality of signal acquisition and offer potential for improving underwater acoustic communication systems. As the demand for exploration of marine resources continues to rise, related technologies in the field of underwater acoustic communication are increasingly gaining the attention of researchers from various sectors. Among them, technology for the classification and recognition of underwater acoustic multibeam signals based on deep learning combines the hot topic of artificial intelligence with traditional signal processing techniques and represents an important branch of research in the field of underwater acoustic communication. In traditional signal processing, time-frequency analysis is one of the effective methods for analyzing time-varying signals, which can accurately and efficiently estimate the instantaneous frequency of the signal at various moments. However, according to the uncertainty principle, time-frequency analysis based on traditional methods has an upper limit on time-frequency resolution, which often cannot meet the requirements of precision in fields such as military and exploration that demand high accuracy. This paper proposes a lightweight deep neural network for the classification of underwater acoustic multibeam signals. Drawing inspiration from the basic ideas of AlexNet and GoogLeNet, this deep learning model is implemented in a modular fashion. The network mainly consists of an improved multiscale feature extraction module innovatively assembled through the concept of contextual encoding. Compared to traditional deep neural network structures, the proposed model has better classification performance and fewer network parameters, thus reducing the consumption of computational resources in terms of both space and time.

The papers in this special issue focus on the rapidly evolving techniques on shallow water acoustic communications, and how underwater acoustic (UWA) propagation models based on shallow water physics may be harnessed with signal processing techniques for high-resolution channel estimation, equalization, and resolving cross-layer challenges to achieve high-data-rate communications in shallow water. The definition of shallow water is relatively broad in scope, and not limited to any particular frequency range. Any UWA channel that exhibits significant multipath and rapid fluctuations in delay spread can be included in the paradigm of interest.

Aiming at the problem of passive detection of broadband sources in underwater acoustic vector signal processing, a novel detection algorithm based on beam-space transformation is proposed. The principle of spatial spectrum detection with human eyes is employed for reflerence, and the generalized likelihood ratio test (GLRT) is applied to the beam-space. First, the design criterion of beam-space transformation matrix is studied for the compreflensive consideration of the environment of multiple targets and the characteristic of vector ambient noise field, so that the analytical solution is obtained. Second, assuming that the number of beams not containing the target signal is given, the probability density function (PDF) model of beam-space data is constructed, and the new GLR test is made by calculating the maximum likelihood estimate of the unknown variables in PDF. Finally, the information of theoretical criterion is adopted in order to estimate the number of beams not containing target signals. The processing gain and the threshold value of this test statistics are also discussed, and the specific implement is explained in detail. Theoretical analysis and simulation results show that under the complex conditions of strong target interference and ambient noise with undulated and time-variant power spectrum, the proposed algorithm can give the processing result with higher gain and detection threshold at constant false alarm rate (CFAR); the results of lake experiment further prove the favorable and robust detection performance.

Beamforming technology is the core part of the underwater target location research. It is very important to study adaptive beamforming algorithm. Adaptive beamforming technology can not only effectively extract the desired target signal, but also form a null in the interference direction to suppress interference and noise, thereby reducing the output power of interference and noise, maximizing the signal to interference noise ratio of array output, and then improving the detection performance of sonar. However, in the actual beamforming environment, the performance of adaptive beamforming algorithm will be affected by steering vector error, sampling covariance matrix error and array response error. This paper studies the traditional methods of beamforming, introduces the methods to improve the robustness of adaptive beamforming algorithm, and briefly describes the application of adaptive beamforming algorithm in underwater acoustic array signal processing.

Modulation recognition of non-cooperation underwater acoustic communication signals using principal component analysis

Because of the difficulties in accurately characterizing the statistical behavior of underwater acoustic channels, tolerance to uncertain statistical modeling is an important property for underwater acoustic signal processing procedures to possess. This paper provides an overview of a number of techniques developed in recent years that can be applied to provide uncertainty tolerance in underwater signal processing applications. In addition to a discussion of general concepts of uncertainty tolerance, specific methods for attaining tolerance to uncertainty in temporal/spatial statistics for procedures such as Wiener and matched filtering, time-delay estimation, sonar system design, and signal prediction are reviewed. Tolerance to uncertainty in amplitude statistics is also a key issue in underwater channels, and techniques for achieving this goal are discussed in the contexts of signal estimation, identification, and detection procedures.

With the rapid development and progress of information technology and the wide usage of audio signal processing in underwater acoustic signal processing, acoustic audio signal acquisition, conversion, and transmission technology always play an important role. To enhance the ability of signal acquisition and conversion with high reliability, this paper designs an embedded multi-channel audio conversion module. The module achieves multi-channel, multi-sample rates with synchronous analog-to-digital/digital-to-analog conversion (ADC/DAC) function, and additionally equips with dual network redundancy. The module uses a chip integrated with ARM and programmable logic FPGA as the main control chip. The writing of the underlying driver and application code ensures the reliable operation of the DAC chip and ADC chip and the steady of the network transmission. Through experimental verification, the audio conversion module performs well in multi-channel and multi-sample rates with synchronous ADC/DAC, and the crosstalk between channels is less than -50 dB. The dual network redundancy design and the application of the RS232 serial port ensure the high fidelity, reliability, and reprogramming of the audio conversion function, which shows the design can be used in a wide range of scenarios.

Although the ocean environment in shallow water is very complex, there still exists stable interference pattern for broadband low frequency sound propagation. The waveguide invariant concept is introduced to describe the broadband interference structure of the acoustic pressure field in a waveguide and now it is widely used in underwater acoustic signal processing. Acoustic vector sensor can measure the particle velocity in the ocean and provides more information for the underwater sound field. In this paper, the interference phenomena of broadband vector acoustic signals in shallow water are investigated by numerical simulation. Energy spatial‐frequency distributions are shown for energy flux density vector obtained by combination of pressure and particle velocity signals and they are analyzed according to normal mode theory. Comparisons of the interference structure between the scale acoustic field and vector acoustic field also have been made. The waveguide invariant concept is extended to describe the interference structure of vector acoustic field in shallow water. A method for extraction of the waveguide invariant from interference patterns in vector acoustic field spectrograms is presented, which can be used in matched‐field processing and geoacoustic inversion. It is shown that this method may have more advantages than the traditional methods which calculate the waveguide invariant using measured sound pressure in the ocean.

In underwater acoustic signal processing, direction of arrival (DOA) estimation can provide important information for target tracking and localization. To address underdetermined wideband signal processing in underwater passive detection system, this paper proposes a novel underdetermined wideband DOA estimation method equipped with the nested array (NA) using focused atomic norm minimization (ANM), where the signal source number detection is accomplished by information theory criteria. In the proposed DOA estimation method, especially, after vectoring the covariance matrix of each frequency bin, each corresponding obtained vector is focused into the predefined frequency bin by focused matrix. Then, the collected averaged vector is considered as virtual array model, whose steering vector exhibits the Vandermonde structure in terms of the obtained virtual array geometries. Further, the new covariance matrix is recovered based on ANM by semi-definite programming (SDP), which utilizes the information of the Toeplitz structure. Finally, the Root-MUSIC algorithm is applied to estimate the DOAs. Simulation results show that the proposed method outperforms other underdetermined DOA estimation methods based on information theory in term of higher estimation accuracy.

: The reports cover progress to develop signal processing structure that exploits available knowledge of the environment and of signal and noise variability induced by the environment. The research is directed toward passive sonar detection and classification, continuous wave (CW) and broadband signals, shallow water operation, both platform-mounted and distributed systems, and frequencies below 1 kHz. The results of this research are expected to lead to new passive sonar detectors and classifiers that take advantage of knowledge of medium variability and uncertainty. The results are mainly applicable to passive processing. However, the active processor can be considered a detector matched to the estimated ocean. These results could have significant impact on Navy sonar system applications.

It is well known that underwater acoustic channels are particularly complicated and difficult for typical signal processing procedures due to the time-varying, homogeneous volume, rough boundaries, and the abundance of interference noise sources in these channels. Most traditional methods of statistical signal processing employ simplified assumptions (linear, stationary, Gaussian) for the sake of mathematical tractability that inevitably lead to inadequate performance.