Sonar Image Target Detection for Underwater Communication System Based on Deep Neural Network

Target signal acquisition and detection based on sonar images is a challenging task due to the complex underwater environment.In order to solve the problem that some semantic information in sonar images is lost and model detection performance is degraded due to the complex imaging environment,we proposed a more effective and robust target detection framework based on deep learning,which can make full use of the acoustic shadow information in the forward-looking sonar images to assist underwater target detection.Firstly,the weighted box fusion method is adopted to generate a fusion box by weighted fusion of prediction boxes with high confidence,so as to obtain accurate acoustic shadow boxes.Further,the acoustic shadow box is cut down to get the feature map containing the acoustic shadow information,and then the acoustic shadow feature map and the target information feature map are adaptively fused to make full use of the acoustic shadow feature information.In addition,we introduce a threshold processing module to improve the attention of the model to important feature information.Through the underwater sonar dataset provided by Pengcheng Laboratory,the proposed method improved the average accuracy by 3.14%at the IoU threshold of 0.7,which is better than the current traditional target detection model.

New Wideband Beam-forming Method Used in Underwater Communication System

A novel wideband beam-forming structure with constant beam width based on complex coefficients (FIR) digital filters used in underwater acoustic communication is proposed. First,the received signals are compensated with integer sampling period by using delay line. Then their complex envelopes are calculated by using frequency shift method. Finally,the envelopes are weighted by using complex coefficients FIR digital filters whose coefficients are optimized. Simulation results show that,in the communication band,the maximum difference between the designed beam and desired beam is less than 0.3 dB when the ratio of communication band to carrier frequency is 0.85.

Single Photon Detection Technology in Underwater Wireless Optical Communication:Modulation Modes and Error Correction Coding Analysis

This study explores the application of single photon detection(SPD)technology in underwater wireless optical communication(UWOC)and analyzes the influence of different modulation modes and error correction coding types on communication performance.The study investigates the impact of on-off keying(OOK)and 2-pulse-position modulation(2-PPM)on the bit error rate(BER)in single-channel intensity and polarization multiplexing.Furthermore,it compares the error correction performance of low-density parity check(LDPC)and Reed-Solomon(RS)codes across different error correction coding types.The effects of unscattered photon ratio and depolarization ratio on BER are also verified.Finally,a UWOC system based on SPD is constructed,achieving 14.58 Mbps with polarization OOK multiplexing modulation and 4.37 Mbps with polarization 2-PPM multiplexing modulation using LDPC code error correction.

Underwater Optical Wireless Communications （UOWC） Based on Visible Light

In this paper, we investigate the effect of water attenuation on an underwater optical wireless communication based on LOS model. We take into account parameters including the chlorophyll concentration and also discuss the choice of suitable wavelength for underwater optical wireless communication. Using analytical expressions and calculating the Jerlov water type attenuation, the received signal power is studied. The characteristics of bit error rate for four kinds of optical modulation techniques （OOK, 2FSK, 2DPSK, and L-PPM） are analyzed. The results show that the performance of OOK and 2DPSK are more suitable for underwater optical wireless communication. On the other hand, the wavelength 450 nm is better compared with the wavelength 600 nm.

Fractional Fourier domain hopped communication method based on chirp modulation for underwater acoustic channels

To improve the data rate of underwater acoustic frequency-hopped communications, frequency hopping is applied to different orders of fractional Fourier domain (FrFD), to enable non-intrusive, bandwidth-limited acoustic communications. An FrFD frequency-hopped communication method based on chirp modulation, namely multiple chirp shift keying-FrFD hopping (MCSK-FrFDH), is proposed for underwater acoustic channels. Validated by both simulations and experimental results, this method can reach a bandwidth efficiency twice more than conventional frequency-hopped methods with the same data rate and anti-multipath capability, suggesting that the proposed method achieves a better performance than the traditional frequency hopped communication in underwater acoustic communication channels. Results also show that in practical scenarios, the MCSK-FrFDH system with longer symbol length performs better at the low signal-to-noise ratio (SNR), while the system with larger frequency sweeping range performs better at a high SNR.

Shipborne Underwater Acoustic Communication System and Sea Trials with Submersible Shenhai Yongshi

The Shipborne acoustic communication system of the submersible Shenhai Yongshi works in vertical, horizontal and slant channels according to the relative positions. For ease of use, an array combined by a vertical-cone directional transducer and a horizontal-toroid one is installed on the mothership. Improved techniques are proposed to combat adverse channel conditions, such as frequency selectivity, non-stationary ship noise, and Doppler effects of the platform’s nonlinear movement. For coherent modulation, a turbo-coded single-carrier scheme is used. In the receiver, the sparse decision-directed Normalized Least-Mean-Square soft equalizer automatically adjusts the tap pattern and weights according to the multipath structure, the two receivers’ asymmetry, the signal’s frequency selectivity and the noise’s spectrum fluctuation. The use of turbo code in turbo equalization significantly suppresses the error floor and decreases the equalizer’s iteration times, which is verified by both the extrinsic information transfer charts and bit-error-rate performance. For noncoherent modulation, a concatenated error correction scheme of nonbinary convolutional code and Hadamard code is adopted to utilize full frequency diversity. Robust and lowcomplexity synchronization techniques in the time and Doppler domains are proposed. Sea trials with the submersible to a maximum depth of over 4500 m show that the shipborne communication system performs robustly during the adverse conditions. From the ten-thousand communication records in the 28 dives in 2017, the failure rate of the coherent frames and that of the noncoherent packets are both below 10%, where both synchronization errors and decoding errors are taken into account.

Soft Direct-Adaptation Based Bidirectional Turbo Equalization for MIMO Underwater Acoustic Communications

This paper proposes a soft direct-adaptation based bidirectional turbo equalizer for multiple-input multiple-output underwater acoustic communication systems. Soft, rather than hard, direct-adaptation based equalizer combined with the fast self-optimized least mean square algorithm is employed to achieve a faster convergence rate, and the second-order phase-locked loop is embedded into the equalizer to track the time-varying channel. Meanwhile, by utilizing a weighted linear combining scheme, the conventional soft direct-adaptation based equalizer is combined with the time-reversed soft direct-adaptation based equalizer to exploit bidirectional diversity and mitigate error propagation. Both the simulation and experimental results demonstrate that the soft direct-adaptation based bidirectional turbo equalizer outperforms the single-direction soft direct-adaptation based turbo equalizer, and achieves a faster convergence rate than the hard direct-adaptation based bidirectional turbo equalizer.

Null subcarriers based Doppler scale estimation with polynomial interpolation for multicarrier communication over ultrawideband underwater acoustic channels

This paper addresses the extremal problem of the null subcarriers based Doppler scale estimation in underwater acoustic （UWA） orthogonal frequency division multiplexing （OFDM） communication. The cost function constructed of the total energy of null subcarriers through discrete Fourier transform （DFT） is proposed. The frequencies of null subcarriers are identified from non-uniform Doppler shift at each tentative scaling factor. Then it is proved that the cost function can be fitted as a quadratic polynomial near the global minimum. An accurate Doppler scale estimation is achieved by the location of the global scarifying precision and increasing the computation minimum through polynomial interpolation, without complexity. A shallow water experiment is conducted to demonstrate the performance of the proposed method. Excellent performance results are obtained in ultrawideband UWA channels with a relative bandwidth of 67%, when the transmitter and the receiver are moving at a relative speed of 5 kn, which validates the proposed method.

Novel joint encoding/decoding algorithms of fountain codes for underwater acoustic communication

Fountain codes are considered to be a promising coding technique in underwater acoustic communication（UAC） which is challenged with the unique propagation features of the underwater acoustic channel and the harsh marine environment. And Luby transform（LT） codes are the first codes fully realizing the digital fountain concept. However, in conventional LT encoding/decoding algorithms, due to the imperfect coverage（IC） of input symbols and short cycles in the generator matrix, stopping sets would occur and terminate the decoding. Thus, the recovery probability is reduced,high coding overhead is required and decoding delay is increased.These issues would be disadvantages while applying LT codes in underwater acoustic communication. Aimed at solving those issues, novel encoding/decoding algorithms are proposed. First,a doping and non-uniform selecting（DNS） encoding algorithm is proposed to solve the IC and the generation of short cycles problems. And this can reduce the probability of stopping sets occur during decoding. Second, a hybrid on the fly Gaussian elimination and belief propagation（OFG-BP） decoding algorithm is designed to reduce the decoding delay and efficiently utilize the information of stopping sets. Comparisons via Monte Carlo simulation confirm that the proposed schemes could achieve better overall decoding performances in comparison with conventional schemes.

Modeling and Channel Estimation for Piezo-Acoustic Backscatter Assisted Underwater Acoustic Communications

Relying on direct and converse piezoelectric effects,piezo-acoustic backscatter(PAB)technology reflects ambient acoustic signals to enable underwater backscatter communications at near-zero power,which was first realized through a prototype.In this paper,we propose a mathematical model of the PAB assisted underwater acoustic(UWA)communication,and address the sparse channel estimation problem.First,we present a five-stage backscatter process to derive the backscatter coefficient,and propose the channel model for the shallow-water communications.Then,we formulate the shallow-water acoustic channel estimation problem as a sparse vector recovery one according to the compressed sensing theory,and leverage the orthogonal matching pursuit(OMP)algorithm to obtain the channel estimator.Finally,simulation results are provided to corroborate our proposed studies.

Collision Classification MAC Protocol for Underwater Acoustic Communication Networks Using Directional Antennas

Traditional underwater acoustic communication networks(UACNs)generally use omnidirectional transmission technology that causes a large number of data-packet collisions,thus resulting in low network throughput and high end-to-end delays.Compared with omnidirectional transmission technology,directional technology only sends and receives data packets in a specified direction.This can significantly reduce the probability of collisions and improve network performance.However,it also causes a deafness problem,which occurs when the sending node sends a data packet to the receiving node but the receiving node is unable to reply to the sender,because its antenna beam is closed.To resolve this issue,this study proposes a collision classification media access control(CC-MAC)protocol for UACNs.With this protocol,the underwater acoustic channel is divided into two subchannels,and the nodes transmit corresponding data types on them.The sending node can estimate the current status of the receiving node(i.e.,no collision,normal collision,deafness)according to the type of the data packet received and the sub-channel it arrived on,and it can choose correct options to improve network efficiency.Finally,we verify the performance of CC-MAC via simulations,showing that the protocol achieved higher network throughput and lower end-toend delays.

Low Complexity Adaptive Equalizers for Underwater Acoustic Communications

Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm （APA）, selective regressor APA（SR-APA）, family of selective partial update （SPU） algorithms, family of set-membership （SM） algorithms and selective partial update selective regressor APA （SPU-SR-APA） are compared with conventional algorithms such as the least mean square （LMS） in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error （MSE） of SR-APA, SPU-APA0 SPU-normalized least mean square （SPU-NLMS）, SPU-SR-APA0 SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.

Space-division multiple access for CDMA multiuser underwater acoustic communications

Time reversal mirror （TRM） can use the physical characteristics of the underwater acoustic （UWA） channel to focus on the desired user in multi-user UWA communication. The active average sound intensity （AASI） detector can estimate all azimuths of users with the same frequency band at the same time in order to achieve directional communication by vector combination. Space-division multiple access （SDMA） based on TRM combined with the AASI detector is proposed in this paper, which can make the capacity of the code division multiple access （CDMA） UWA system significantly increase. The simulation and lake test results show that the 7-user UWA multi-user system can achieve low bit error communication.

OTFS on Overspread Doubly Selective Channel

The orthogonal time frequency space(OTFS)modulation proposed in recent years is considered to have superior performance than orthogonal frequency division multiplexing(OFDM)for the doubly selective(DS)channels.The works in the existing literature on OTFS mainly focus on the cases where the channels are underspread(i.e.,the product of the delay spread and the Doppler spread is less than 1).In the scenario of overspread DS channel,which has large delay spread and severe Doppler spread,such as underwater acoustic(UWA)channel,the channel model in delay-Doppler(DD)Domain derived by existing work is no longer applicable.In this paper,we derive a more generalized expression of the channel model in delay-Doppler domain,which allows the product of the delay spread and Doppler spread to be larger than1.The result shows that the existing channel model is just a special case of the one we proposed.Using the proposed channel matrix in DD domain,we build the OTFS detectors with the minimum mean square error(MMSE)and message passing(MP)algorithms on overspread doubly selective channel.Finally,simulation results are presented to verify the theoretical derivation and the effectiveness of the detectors.

Channel estimation-based time-frequency neural network for post-equalization in underwater visible light communication

This Letter proposes a post-equalizer for underwater visible light communication(UVLC) systems that combines channel estimation and joint time-frequency analysis, named channel-estimation-based bandpass variable-order time-frequency network(CBV-TFNet). By utilizing a bandpass variable-order loss function with communication prior knowledge, CBVTFNet enhances communication performance and training stability. It enables lightweight implementation and faster convergence through a channel estimation-based mask. The superior performance of the proposed equalization method over Volterra and deep neural network(DNN)-based methods has been studied experimentally. Using bit-power loading discrete multitone (DMT) modulation, the proposed method achieves a transmission bitrate of 4.956 Gbps through a 1.2 m underwater channel utilizing only 38.15% of real multiplication calculations compared to the DNN equalizer and achieving a bitrate gain of440 Mbps and a significantly larger dynamic range over the LMS-Volterra equalizer. Results highlight CBV-TFNet's potential for future post-equalization in UVLC systems.

基于信道短化和圆球译码的水声通信迭代序列检测

The demand for high-data-rate underwater acoustic communications(UACs)in marine development is increasing;however,severe multipaths make demodulation a challenge.The decision feedback equalizer(DFE)is one of the most popular equalizers in UAC;however,it is not the optimal algorithm.Although maximum likelihood sequence estimation(MLSE)is the optimal algorithm,its complexity increases exponentially with the number of channel taps,making it challenging to apply to UAC.Therefore,this paper proposes a complexity-reduced MLSE to improve the bit error rate(BER)performance in multipath channels.In the proposed algorithm,the original channel is first shortened using a channel-shortening method,and several dominant channel taps are selected for MLSE.Subsequently,sphere decoding(SD)is performed in the following MLSE.Iterations are applied to eliminate inter-symbol interference caused by weak channel taps.The simulation and sea experiment demonstrate the superiority of the proposed algorithm.The simulation results show that channel shortening combined with SD can drastically reduce computational complexity,and iterative SD performs better than DFE based on recursive least squares(RLS-DFE),DFE based on improved proportionate normalized least mean squares(IPNLMS-DFE),and channel estimation-based DFE(CE-DFE).Moreover,the sea experimental results at Zhairuoshan Island in Zhoushan show that the proposed receiver scheme has improved BER performance over RLSDFE,IPNLMS-DFE,and CE-DFE.Compared with the RLS-DFE,the BER,after five iterations,is reduced from 0.0076 to 0.0037 in the 8–12 k Hz band and from 0.1516 to 0.1145 in the 13–17 k Hz band at a distance of 2000 m.Thus,the proposed algorithm makes it possible to apply MLSE in UAC in practical scenarios.

Pilot-Free End-to-End Underwater Acoustic Communication System Based on Autoencoder

The long delay spreads and significant Doppler effects of underwater acoustic(UWA)channels make the design of the UWA communication system more challenging.In this paper,we propose a learning-based end-to-end framework for UWA communications,leveraging a double feature extraction network(DFEN)for data preprocessing.The DFEN consists of an attentionbased module and a mixer-based module for channel feature extraction and data feature extraction,respectively.Considering the diverse nature of UWA channels,we propose a stack-network with a two-step training strategy to enhance generalization.By avoiding the use of pilot information,the proposed network can learn data mapping that is robust to UWA channels.Evaluation results show that our proposed algorithm outperforms the baselines by at least 2 dB under bit error rate(BER)10^(−2)on the simulation channel,and surpasses the compared neural network by at least 5 dB under BER 5×10^(−2)on the experiment channels.

Immune particle swarm optimization of linear frequency modulation in acoustic communication

With the exploration of the ocean, underwater acoustic communication has attracted more and more attention in recent years. The underwater acoustic channel is considered to be one of the most complicated channels because it suffers from more serious multipath effect, fewer available bandwidths and quite complex noise. Since the signals experience a serious distortion after being transmitted through the underwater acoustic channel, the underwater acoustic communication experiences a high bit error rate （BER）. To solve this problem, carrier waveform inter- displacement （CWlD） modulation is proposed. It has been proved that CWlD modulation is an effective method to decrease BER. The linear frequency modulation （LFM） carrier-waves are used in CWlD modulation. The performance of the communication using CWID modulation is sensitive to the change of the frequency band of LFM carrier-waves. The immune particle swarm optimization （IPSO） is introduced to search for the optimal frequency band of the LFM carrier-waves, due to its excellent performance in solving complicated optimization problems. The multi-objective and multi- peak optimization nature of the IPSO gives a suitable description of the relationship between the upper band and the lower band of the LFM carrier-waves. Simulations verify the improved perfor- mance and effectiveness of the optimization method.

Self-adjusting decision feedback equalizer for variational underwater acoustic channel environments

Aimed at the abominable influences to blind equaliza-tion algorithms caused by complex time-space variability existing in underwater acoustic channels, a new self-adjusting decision feedback equalization （DFE） algorithm adapting to different under-water acoustic channel environments is proposed by changing its central tap position. Besides, this new algorithm behaves faster convergence speed based on the analysis of equalizers’ working rules, which is more suitable to implement communications in dif-ferent unknown channels. Corresponding results and conclusions are validated by simulations and spot experiments.

QMCR:A Q-Learning-Based Multi-Hop Cooperative Routing Protocol for Underwater Acoustic Sensor Networks

Routing plays a critical role in data transmission for underwater acoustic sensor networks(UWSNs)in the internet of underwater things(IoUT).Traditional routing methods suffer from high end-toend delay,limited bandwidth,and high energy consumption.With the development of artificial intelligence and machine learning algorithms,many researchers apply these new methods to improve the quality of routing.In this paper,we propose a Qlearning-based multi-hop cooperative routing protocol(QMCR)for UWSNs.Our protocol can automatically choose nodes with the maximum Q-value as forwarders based on distance information.Moreover,we combine cooperative communications with Q-learning algorithm to reduce network energy consumption and improve communication efficiency.Experimental results show that the running time of the QMCR is less than one-tenth of that of the artificial fish-swarm algorithm(AFSA),while the routing energy consumption is kept at the same level.Due to the extremely fast speed of the algorithm,the QMCR is a promising method of routing design for UWSNs,especially for the case that it suffers from the extreme dynamic underwater acoustic channels in the real ocean environment.