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Time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) is presented to enhance the spectral and energy efficiency over the conventional cyclic prefix OFDM and zero padding OFDM, but the interbloc interference (IBI) between the data block and adjacent pilot symbols deteriorates its performance. The IBI is considered as the main TDS-OFDM drawback in the harsh long-delay underwater acoustic (UWA) multipath channel. This article proposes a new scheme called X-transform time-domain synchronous index modulation (IM) OFDM to eliminate the TDS-OFDM IBI, explore the multipath diversity, and reduce the peak-to-average power ratio (PAPR). Particularly, X-transform, which merges the discrete Hartley transform and discrete Fourier transform in a very low-complexity unitary matrix, and IM techniques have been employed to remove the effects of IBI existing in underwater acoustic (UWA) TDS-OFDM. Moreover, compressive sensing has been adopted to guarantee a reliable UWA channel estimation based on a guard interval IBI-free region. The proposed scheme is evaluated over simulated and experimental UWA channels. The computer simulation and the real experiment show the outperformance of the proposed scheme in terms of bit error rate, computational complexity, PAPR, energy efficiency, and spectral efficiency over its conventional benchmarks.

As an enhanced system for marine monitoring and autonomous navigation, the space-based automatic identification system (AIS) has attracted extensive attention and become a hot topic for research. However, it encounters the problem of Doppler shift stemmed from the relative motion between satellites and ships, which leads to performance degradation. To circumvent this issue, in this correspondence, we propose an improved Doppler shift estimation algorithm for down-link signals of space-based AIS, utilizing the calculation of the autocorrelation and ratio on the Rice factor. Specifically, the addressed method is robust to the non-Gaussian noise. Further, the suggested approach has low complexity compared with the existing algorithm. Finally, numerical simulations, such as the standard deviation of the estimated Doppler shift versus signal-to-noise ratio, Rice factor and non-Gaussian noise, and the complexity comparisons, are carried out to validate the theoretical analysis, and demonstrate the superior performance of the proposed estimation approach.

Noise fitting plays a key role in underwater acoustic communications. Traditional approximate models can fit global heavy-tail distribution of the impulsive noise with fixed parameters. These models are unable to cover local distributions with arbitrary lengths. In this paper, we propose a generative-adversarial-network-based underwater noise simulator (GUNS), which constructs a deep-neural-network-based generator and a convolutional-neural-network-based discriminator are constructed to learn the heavy-tail distribution of the impulsive noise. Based on the noise collected in the Wuyuanwan Bay, Xiamen, probability distribution function of the underwater acoustic noise simulated by the proposed GUNS performs lower Kullback-Leibler divergence, Jensen-Shannon divergence and mean square error than that employed traditional approximate models.

This letter presents a new scheme to solve the issue of high redundant energy in Unique Word Orthogonal Frequency Division Multiplexing (UW-OFDM). The proposed scheme, called Low-Redundant Energy UW-OFDM (LRE-UW-OFDM), is suitable for time-varied harsh channels such as the Underwater Acoustic (UWA) channel were inserting the UW data in the time domain leads to huge redundant energy and a dramatic deterioration in Bit Error Rate (BER) performance. The insertion of UW in the proposed LRE-UW-OFDM is done in frequency domain to be any desired sequences in time domain with constant redundant subcarriers energy regardless of the size and the location of data subcarriers. The proposed scheme is evaluated over a long-delay UWA channel demonstrating its outperformance against conventional schemes in terms of BER and mean symbol energy.

In this letter, a new method on the ${K}$ -factor estimation is proposed for wireless communications over Rician frequency-flat fading channels. Firstly, the mean and variance of the squared envelope of the received signal are respectively derived. Then, we employ them to further derive the closed-form expression for the ${K}$ -factor and finally present its estimation formula. Performance analyses, supported by Monte Carlo simulation results for a range of channel conditions, reveal that our proposed estimator outperforms prior techniques, and illustrate the approximate robustness of our suggested work to the signal-to-noise ratio, maximum Doppler shift, and angle of arrival without considering a priori knowledge.

The ubiquitous commercial Wi-Fi has brought unlimited possibilities to the smart city and the Internet of Things. Wi-Fi device-free sensing technology has received more and more attention in recent years. Counting the people in queuing is an uneasy task due to labile information. Most current counting schemes have existed in an ideal environment with idealistic people's behavior. In this article, we propose a more realistic counting scheme called Que-Fi, a queue number identification system based on Wi-Fi channel state information and a deep learning network. In the proposed Que-Fi scheme, the nonnegligible interference of human motion and the surrounding environment is first analyzed based on the Fresnel zone. Then, we proposed a static model based on the convolutional long short-term memory fully connected deep neural network in order to overcome the interference. A dynamic Que-Fi model is proposed to identify the entering/leaving people's behavior and update the counting number. In this article, different preprocessing methods are analyzed and compared to test and evaluate the proposed Que-Fi. Experiments have shown that the proposed Que-Fi outperforms the traditional support vector machine and provide accuracy up to 95% and 96.67% for static and dynamic models, respectively.

Multiple features can be extracted from time-frequency representation (TFR) of signals for the purpose of acoustic event detection. However, many underwater acoustic signals are formed by multiple events (impulsive and tonal), which generates difficulty on the high-resolution TFR for each component. For the characterization of such different events, we propose an anisotropic chirplet transform to achieve the TFR with high energy concentration. Such transform applies a time-frequency varying Gaussian window to compensate the energy of each component while suppressing unwanted noise. Using a set of directional chirplet ridges from the obtained TFR, a structure-split-merge algorithm is designed to reconstruct a multimodal sparse representation, which provides instantaneous frequency and time features. Specifically, a pulsed-to-tonal ratio, based on these features, is computed to distinguish two acoustic signals. The presented method is validated using shallow water experimental underwater acoustic communication signals, and large sequences of harmonics and pulsed bursts from common whales.

Illegally forged signals in automatic identification system (AIS) pose a threat to maritime traffic safety management. In this paper, a multi-level sparse representation based identification (MSRI) algorithm is proposed for specific emitter identification (SEI) in the AIS. The MSRI innovatively combines neural networks with sparse representation based classification (SRC). Channel attention mechanism is introduced to a multi-scale convolutional neural network (CNN) for extracting hidden features in the signal. These extracted features are divided into shallow and deep features according to the depth of the network layer they are extracted from. The original AIS signals and the two-level features are spliced together to form a multi-level dictionary. Subsequently, a sparse representation based identification is performed on the decorrelated multi-level dictionary using the principal components analysis (PCA) method. The proposed MSRI is evaluated on a dataset composed of real-world AIS signals, and compared with the state-of-the-art identification algorithms. The evaluation is based on several factors including computational complexity, number of training samples, and number of emitters. Numerical results indicate that the proposed algorithm can identify emitters with higher accuracy and requires lower training time compared to other methods. Given more than 15 training samples at each emitter, the MSRI can identify nine emitters with an accuracy higher than 90%.

In this article, a wideband Direction-of-Arrival (DOA) estimation method for underdetermined scenarios is proposed, which effectively solves the basis mismatch problem. Based on the fractional Fourier transform (FRFT), the wideband received signal model with a coprime array is first derived by exploiting the aggregation characteristic of wideband linear frequency modulated (LFM) signals in the fractional Fourier (FRF) domain. Then, in order to increase the degree of freedom, an extended uniform linear array is built, and the covariance matrix of the signal is reconstructed by employing the penalized atomic norm minimization with the consecutive spatial dictionary. Meanwhile, without the knowledge of the noise level, the noise variance is estimated from the noisy incomplete data, which is utilized to improve the covariance matrix reconstruction performance. Additionally, for the unconditional model, the Cramer–Rao bound for the wideband DOA estimation based on a coprime array is derived. Different from the existing methods, the proposed method not only can estimate more DOAs of wideband signals than the number of physical sensors in the presence of unknown amplitude distortion but also can obtain more accurate DOA estimation performance without basis mismatch. The effectiveness of the proposed method is verified by our numerical results.

In this letter, we propose a new orthogonal coding-based modulation (OCBM) scheme, in which all available transmitting subcarriers and antennas are activated at a time instant to transmit the data constellation symbols, and the indices of the activated subcarriers and antennas are also harnessed to carry information bits. Our OCBM especially addresses a novel scheme in spatial modulation (SM) domain via employing virtual subarray, and improves the spectral efficiency compared with the existing orthogonal frequency division multiplexing (OFDM)-spread spectrum (SS), index modulation (IM)-OFDM-SS and IM-OFDM based SM (the number of transmit antennas is more than 2). In addition, our investigated detection scheme of the OCBM reduces the complexity of the receiver design by using the unique behavior of the orthogonal code, especially for the detection of the transmitted antenna index bits. Our simulated experiments have been carried out to test the performance of the proposed scheme, and demonstrate the benefits of our suggested approach.

An audio signal detection system based on laser speckle and multi-channel data fusion is presented. A linear CMOS array is used as the detector, which owns a fast line rate and suitable sensing size. The signals from the pixels are selected and fused to enhance the reconstructed signal. The reconstructed audio signals are evaluated with a segmental SNR (SegSNR) algorithm. The experimental results of three categories of audio sources (single voice audio, conversation and music) show that data fusion can improve the SegSNR scores. Especially, direct phase-error based filtering (pbf) fusion gives a nearly 3.0 dB increase and obtains another 1.0 dB increase with the combination of single channel process. The experimental results show that the fusion algorithms are not sensitive to audio types and the performance of multi-channel data fusion is not weakened with the increase of measuring distance. This feature has potential applications in remote sensing. The intelligibility of the fused audio signals is evaluated with normalized subband envelope correlation (NSEC) algorithm and the evaluation results shows that fusion can also enhance the intelligibility of the recovered signal.

This book details research on underwater acoustic communications that mainly originated from the research work performed within Underwater Sensor Network University Of Connecticut. The test is primarily focused on the study of OFDM for UWA communication. The content is solely dedicated to the study of OFDM and issues that are encountered during the early exploration of UWA communication. Authors propose many tools and techniques, which provide solutions to the different problems in OFDM transmission. While reviewing the whole book, it came to our notice that both theoretical description of OFDM and its experimental demonstration in UWA environment are precisely well defined in the text. Suitability and clarity of different sections are impressive. The results also provided with enough descriptions, which are essential for the research community in clearing their concepts. Many published research articles are well cited and presented concisely by authors. Moreover, for a graduate student, this book provides substantial information for the many areas of research, which are important measures in today’s scientific world. Talking about the scope of this book, it demands some advance level of expertise from the researchers. Therefore, readers are strongly recommended to have some prior knowledge of different techniques that can be used in the transmission of OFDM such as modulation techniques, channel estimation, and equalization techniques.

The recent growth in the maritime industry stemming to increase in global commerce as well as searching for hydrocarbons at offshore locations, the high data rate and cost affordable maritime communication is acquired. A mesh network is envisaged for maritime communications because of its expanded coverage, self-healing, and high-capacity. With the development of multi-radio technology, the frequency interference can be decreased sharply with proper channel assignment. The static channel assignment optimization is often applied to decrease the wireless frequency interference which is known as an NP-hard problem. In this paper, we focus on the static channel assignment issue and propose a heuristic algorithm to solve the optimization problem. The problem is addressed by assigning channels to communication links to minimize the interference from overall network. A modified particle swarm optimization (PSO) algorithm is proposed to optimize the problem, and a new merging solution is adopted to reassign channels for nodes, which violate the radio constraints. Multi-radio simulation is performed in NS-3 to validate the effectiveness of the proposed channel assignment algorithm. The results show that the algorithm is able to find an optimized assignment with fewer iterations than the previous work and improve network performance.

Whales communicate using whistle vocalizations that are essentially underwater acoustic frequency-modulated tones. Inevitable environmental noise decreases recognition accuracy of these sounds during wide range detection. In this paper, we propose a robust time - frequency analysis method that combines resonance sparse signal decomposition (RSSD) and spectrogram ridge extraction. We apply RSSD to extract whistle components from the raw signal, and then we segment the ridge regions of the whistle spectrograms. By applying a partial derivative method, we extract the whistle spectrogram ridge representing an accurate trace of the whistle vocalization. From these results, we extract ridge features and use an SVM or a random forest to identify the whale species. We evaluated our method using experiments with samples for four whale species. Compared with direct ridge extraction directly without RSSD, our proposed method achieved better extraction of frequency characteristics of the vocalizations. Our proposed method achieved an accuracy rate of over 98% for sounds from four species when using five training samples.

The novel time-frequency transform, called synchro-compensating chirplet transform, is proposed to represent optimally the overlapping signals in a time-frequency domain. By the control of the self-tuning demodulated operator, an instantaneous rotating operator is introduced in the proposed algorithm to blur the noise and compensate the energy of each component simultaneously. The effectiveness of the method for micro-Doppler signals with a high noise is validated through examples.

Time division synchronization orthogonal frequency division multiplexing (TDS-OFDM) has been attractive due to its fast synchronization and efficient spectral efficiency over conventional cyclic prefix orthogonal frequency division multiplexing (OFDM) and zero padding OFDM. However, inter-block interference (IBI) affects its performance because of delay over multipath channels. To evade IBI, dual pseudo-random noise (DPN) sequences have been introduced that causes to reduce spectral and energy efficiency. But, DPN is unprepared for underwater acoustic (UWA) communication because of battery-based nature and limited bandwidth. To overcome these issues, this paper exploits compressive sensing theory algorithm for obtaining the time-varying channel state information by utilizing sparse property of UWA channels. In this paper, the IBI free region is utilized to estimate accurate UWA channel impulse response and mitigate its interference. Look-ahead backtracking orthogonal matching pursuit-based sparse channel estimation technique is proposed for underwater TDS-OFDM in a real sparse time-varying multipath channel (channel taps are randomly distributed). Furthermore, Doppler-shift of UWA channel is estimated and compensated by PN sequence in time domain. The performance of the proposed technique is evaluated and demonstrated through numerical computation of bit error rate (BER) and mean square error (MSE) using Monte Carlo iterations. Simulation analysis confirms the superiority of the proposed scheme not only in terms of BER and MSE over the conventional ones but also achieve high energy and spectral efficiency.

Mitigation of impulsive noise has been extensively studied in wireline, wireless radio, and powerline communication systems. However, its study in underwater acoustic (UWA) systems is quite limited. This paper considers impulsive noise mitigation for underwater orthogonal frequency-division multiplexing (OFDM) systems, where the system performance is severely impacted by the channel Doppler effect. We propose a practical approach based on a least squares formulation: First, the positions of impulsive noise are determined in the time domain based on the signal amplitude, and second, impulsive noise samples are jointly estimated with the Doppler shift based on the measurements of the OFDM null subcarriers. Based on the available channel estimate and tentative data symbol decisions, an iterative receiver is further developed. Data sets have been acquired in a recent sea experiment near Kaohsiung city, Taiwan, in May 2013. Performance results based on extensive simulations and collected data sets demonstrate that the proposed receivers effectively mitigate impulsive noise for UWA OFDM systems.