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Due to the combined effects of different acoustic propagation, reflection patterns in different time-scale, time-varying underwater acoustic (UWA) channel generally exhibits hybrid sparsity, i.e., contains not only rapidly time-varying elements, but also stationary or slowly time-varying ones. While the classic sparse reconstruction algorithms such as orthogonal matching pursuit (OMP) generally do not take it into account, the existence of static multipath will unavoidably hinder the tracking of rapid time variations. In this paper, a sequential adaptive observation length orthogonal matching pursuit (SAOLOMP) approach is proposed to sequentially explore rapidly time-varying sparsity in a two-step sequential manner. Specifically, the static components are firstly separated via simultaneous orthogonal matching pursuit (SOMP) among continuous measurements. Upon the residual error of the SOMP, the measurement-wise OMP is applied for estimating the remained dynamic components. As the variation of the SOMP residual error indicates how rapidly the remained dynamic multipath components vary, the observation length of the OMP is adjusted according to the gradient of residual error to adapt to rapidly time-varying components. Finally, both types of sparse components are summed up to obtain the whole channel response. Numerical simulations as well as experimental results from field UWA communication data show that the proposed algorithm exhibits better performance in exploiting rapidly time-varying sparsity than the state-of-the-art compressive methods do under UWA channel. [ABSTRACT FROM AUTHOR]

Data collection in underwater acoustic sensor networks (UASNs) and the exposure of node location information pose a threat to the security of the entire network. Therefore, the main challenge for underwater acoustic sensor network security is to protect the security and privacy of the node locations. Compared to active attacks, the characteristics of passive attacks are indistinguishable. Therefore, this research focuses on passive attacks in underwater acoustic sensor networks, and an autonomous underwater vehicle (AUV)-aided data-importance-based scheme for protecting location privacy (DIS-PLP) is proposed. The DIS-PLP comprises three main parts. First, the anchor node calculates the importance of the current data based on the historical dataset and decides whether to send data packets to the base station according to the importance result. Subsequently, an AUV privacy violation is defined, and an AUV path planning method is proposed to prevent attackers from tracking the AUV to obtain the location of the source node. Finally, to prevent nodes from waiting too long for AUVs, a secure multi-hop transmission method is proposed that uses fake source nodes to send fake source data packets to hide the real traffic. The simulation results show that the DIS-PLP offers well performance in terms of safety time and delay, which has strong practical significance for Smart Ocean to improve the level of network security. [ABSTRACT FROM AUTHOR]

We investigate the peak age of information (PAoI) in underwater wireless sensor networks (UWSNs), where Internet of underwater things (IoUT) nodes transmit the latest packets to the sink node, which is in charge of adjusting the sleep-scheduling to match network demands. In order to reduce PAoI, we propose active queue management (AQM) policy of the IoUT node, beneficially compresses the packets having large waiting time. Moreover, we deduce the closed-form expressions of the average PAoI as well as the energy cost relying on probability generating function and matrix-geometric solutions. Numerical results verify that the IoUT node relying on the AQM policy has a lower PAoI and energy cost in comparison to those using non-AQM policy. [ABSTRACT FROM AUTHOR]

The impulsive noise and the time-varying channel are two major detrimental factors which greatly constrain the performance of underwater acoustic communications (UACs). Utilizing the joint sparsity of the impulsive noise and the channel impulse response, the paper proposes the generalized approximate message passing (GAMP)-based sparse Bayesian learning (SBL) frameworks for joint impulsive noise mitigation and channel estimation and tracking for orthogonal frequency division multiplexing (OFDM) UACs. Firstly, the SBL framework for the joint estimation is employed. To reduce the computational complexity, the GAMP is introduced into the expectation-maximization (EM) algorithm, and a low-complexity GAMP based SBL framework is formulated without performance degradation. To further estimate and track the impulsive noise and channel state information in the slow time-varying scenarios, we propose a novel GAMP-based temporal SBL framework. The factor graph and GAMP are used to achieve the approximated estimation for the posterior statistics of both the channel state information and the impulsive noise. The algorithm formulates the message passing scheduling for the EM algorithm to solve the joint multiple sparse signal recovery problem. Simulations and sea-trial results demonstrate that the proposed algorithms significantly improve the performance in terms of the mean square error of channel estimation, impulsive noise estimation, bit error rate and computational complexity compared with their corresponding SBL-based counterparts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Due to the problems of high bit error rate and delay, low bandwidth and limited energy of sensor nodes in underwater acoustic sensor network (UASN), it is particularly important to design a routing protocol with high reliability, strong robustness, low end-to-end delay and high energy efficiency which can flexibly be employed in dynamic network environment. Therefore, a reinforcement learning-based opportunistic routing protocol (RLOR) is proposed in this paper by combining the advantages of opportunistic routing and reinforcement learning algorithm. The RLOR is a kind of distributed routing approach, which comprehensively considers nodes’ peripheral status to select the appropriate relay nodes. Additionally, a recovery mechanism is employed in RLOR to enable the packets to bypass the void area efficiently and continue to forward, which improves the delivery rate of data in some sparse networks. The simulation results show that, compared with other representative underwater routing protocols, the proposed RLOR performs well in end-to-end delay, reliability, energy efficiency and other aspects in underwater dynamic network environments. [ABSTRACT FROM AUTHOR]

The localization and tracking of underwater objects have many applications. In a proactive underwater sensor array, some nodes will periodically broadcast linear frequency modulated (LFM) signals, which will hit the targets, get reflected and received by the other nodes. Depending on the target's position and velocity, the received signals will also be LFM signals of different frequencies and frequency rates. We can use the Fractional Fourier Transform (FrFT) to analyze the received signal's spectrum and find the peak. Based on the location of the peak, the target's distance and radial velocity can be estimated. However, the accuracy is highly dependent on the sampling interval of the spectrum. Smaller sampling interval leads to higher accuracy but also induces considerable complexity. To overcome this issue, we propose a machine learning-based approach to automatically detect the existence of the target, and roughly estimate the peak's location if targets exist. Then over-sampling can be conducted for a small area around the peak, leading to improved accuracy and reduced complexity. The idea is based on the following observation: if a target exists, we will be able to observe an “X” pattern on the spectrum. Extensive simulations are conducted to verify the effectiveness of the proposed architecture. [ABSTRACT FROM AUTHOR]

The unique characteristic of bandwidth-distance relationship in underwater acoustic (UWA) channels makes standard uplink non-orthogonal multiple access (NOMA) scheme not be applicable in general underwater acoustic networks (UWANs). In this article, we propose an uplink partial overlapping non-orthogonal multiple access (PONOMA) scheme for UWANs, and investigate capacity region of uplink PONOMA in UWANs. By formulating the problem of the optimal energy distribution for uplink PONOMA based on UWA channel modeling, the closed-form solutions for optimal power spectral density (p.s.d.) and capacity region are derived. Furthermore, we modify the channel modeling by building in parameter uncertainty induced by the empirical nature of UWA channel modeling using box sets, and derived robust capacity region under parameter uncertainty. Numerical results are provided to demonstrate the performance of our proposed scheme. [ABSTRACT FROM AUTHOR]

Accurate sensor localization is a crucial requirement for the deployment of underwater acoustic sensor networks (UASNs) in a large variety of applications. However, the asynchronous clock, stratification effect and mobility characteristics of underwater environment make it challenging to realize accurate node localization for UASNs. This paper develops an autonomous underwater vehicle (AUV) aided localization solution for UASNs, subjected to asynchronous clock, stratification effect and mobility constraints in cyber channels. A hybrid architecture including surface buoys, AUVs, active and passive sensor nodes, is first presented to construct a cooperative location-aware network. Then, an iterative least squares estimator is developed for AUVs to capture the unknown water current parameters, through which the relationship between propagation delay and location estimation can be established. With the assistance of AUVs, two asynchronous localization algorithms are designed to estimate the locations of active and passive sensor nodes. Particularly, motion and ray compensation strategies are jointly employed to improve the localization accuracy. It is worth noticing that, the proposed localization algorithms incorporate the current field estimation into the localization process of UASNs, and more importantly, they can eliminate the influences of asynchronous clock, stratification effect and node mobility together. Moreover, performance analyses for the proposed localization solution are also presented. Finally, simulation and experimental results reveal that the node localization accuracy in this paper can be significantly improved as compared with the other works. [ABSTRACT FROM AUTHOR]

Traditional beamforming (BF) methods can be used to suppress the Gaussian interferers in underwater acoustic communication systems. However, multiple unsteady sub-Gaussian interferers may exist that degrade the performance of BF. In this paper, a new beamformer is proposed for the suppression of multiple unsteady sub-Gaussian interferers (SMUSGI). The proposed BF combines the multiplier and the quasi-Newton methods to obtain the optimal weight vector. Simulation experiments are conducted to validate the performance of the proposed beamformer. In addition, the proposed beamformer is compared with the traditional methods including the MVDR, Subspace, Bartlett, MDDR, LCMD and LCMV beamformers. The obtained results demonstrate that the proposed beamformer can effectively suppress multiple unsteady sub-Gaussian interferers with a higher output signal-to-interference-plus-noise ratio (SINR), and faster convergence compared with the traditional methods. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UASNs) are widely used in a variety of ocean applications, such as exploring ocean resources or monitoring abnormal ocean environments. However, data collection schemes in UASNs are significantly different from those in wireless sensor networks due to high power consumption, severe propagation delay, and so on. Furthermore, previous research has overlooked practical conditions, such as characteristics of water delamination and energy constraint on autonomous underwater vehicles (AUVs). In this paper, a stratification-based data collection scheme for three-dimensional UASNs is proposed to solve these problems. In this scheme, the network is divided into two layers on the basis of the Ekman drift current model. The upper layer, called the Ekman layer, suffers large water velocity. Thus, nodes in the upper layer will follow the water flow. In this case, we employ a forward set-based multihop forwarding algorithm for data collection. The lower layer suffers small water velocity so that nodes in this layer are considered as relatively static. A neighbor density clustering-based AUV data gathering algorithm is applied in this layer for data collection. By employing different data collection algorithms in different layers, we can integrate the advantages of a multihop transmission scheme and AUV-aided data collection scheme to reduce network consumption and improve network lifetime. The simulation results also confirm the proposed method has good performance. [ABSTRACT FROM AUTHOR]

In this article, we theoretically investigate the dynamic aspects of the coverage degree and target detection in the underwater environment resulting from the given moving scenarios of the autonomous underwater vehicles (AUVs). With the help of the continuous moving AUVs, the underwater targets, that cannot be detected by the stationary underwater acoustic sensor network, can be detected with an expected probability, which is determined on the basis of the selected moving scenario of the AUVs. We prove that, for an AUV with randomly selected initial starting point and initial direction, the straight trajectory is the optimal route to achieve the maximum coverage and target detection probability. Then, we present a mathematical model to quantitatively analyze the coverage degree in the underwater environment by using AUVs, as well as formulating the target detection probability of both static target detection and mobile target detection cases. Furthermore, by taking the exposure time of the target into account, we mathematically formulate and analyze the impact of the features of an AUV (i.e., sensing range and velocity) and the moving speed of the mobile target to the mobile target detection probability. We carry out intensive simulation experiments to evaluate the proposed mathematical model, and the experimental results further verify the correctness of our theoretical results. [ABSTRACT FROM AUTHOR]

The underwater wireless sensor networks enable the telemonitoring and communications technologies of underwater information play important roles in the gathering process of scientific data in collaborative monitoring missions. However, to design such a system, several aspects must be considered to make fewer resources required, such as energy efficiency, miniaturization, the required functionality, etc. Conventional methods of data compression and reconstruction fail in energy efficiency. Different from the conventional compression methods, compressive sampling (CS) provides a new perspective to compress huge data with low energy consumption. Unfortunately, the recordings of underwater acoustic signal (UAS) are nonsparse in time domain. Hence, the current CS methods cannot be used directly for compression and reconstruction of UAS. This study adopts the wavelet-transform-based dictionary matrix to build a framework for sparse representation; then, introduces an approach based on structured approximation $l_0$ (SAL0) norm, which is designed by exploring and exploiting the correlation structure of UAS. The proposed method searches the optimal sparse solution via steepest descent method and then projects the solution to its feasible set. Combing with the compression matrix and dictionary matrix, the estimation of SAL0 method is used for reconstructing nonsparse UAS. Experimental results confirm its recovery quality for practical use, and a clear improvement than the traditional methods including $l_1$ (L1) and orthogonal matching pursuit methods. [ABSTRACT FROM AUTHOR]

Differential orthogonal frequency division multiplexing (OFDM) is practically attractive for underwater acoustic communications since it has the potential to obviate channel estimation. However, similar to coherent OFDM, it may suffer from severe inter-carrier interference over time-varying channels. To alleviate the induced performance degradation, we adopt the newly emerging partial fast Fourier transform (FFT) demodulation technique in this paper and propose an eigendecomposition-based algorithm to compute the combining weights. Compared to existing adaptive methods, the new algorithm can avoid error propagation and eliminate the need for parameter tuning. Moreover, it guarantees global optimality under the narrowband Doppler assumption, with the optimal weight vector of partial FFT demodulation achieved by the eigenvector associated with the smallest eigenvalue of the pilot detection error matrix. Finally, the algorithm can also be extended straightforwardly to perform subband-wise computation to counteract wideband Doppler effects. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UW-ASNs) have been attracting more and more research interests recently due to their various promising applications. In this paper, we focus on the time division multiple access (TDMA) based media access control (MAC) protocol design for UW-ASNs in order to improve the network performance. Different from existing work, we consider a more general underwater communication scenario, where the communication network topology is modeled as a three-dimensional (3-D) scenario and the mobility of sensor nodes is also taken into consideration. We introduce a dynamic and flexible spatial reuse strategy for the TDMA protocol design, and formulate the interference scenario as a dynamic interference-free graph according to the nodes’ current position distribution and a preset interference-free threshold. To achieve high spatial reuse efficiency, we propose two interference-free graph (IG) clustering algorithms, which lead to the optimal IG-TDMA protocol and the heuristic IG-TDMA protocol, respectively. The former can achieve the optimal network throughput but may not be feasible for high traffic networks due to its high computational complexity. The latter can achieve near-optimal network performance with much lower computational complexity and thus is more practical. Simulations verify the benefits of our proposed protocols in comparison with the CSMA/CA protocol and some existing TDMA protocols with and without spatial reuse. [ABSTRACT FROM PUBLISHER]

Underwater acoustic (UWA) signal is wideband in nature and suffers doubly selective fading in time and frequency. In the presence of relative motion, a typical UWA channel can be described by a multiscale multilag (MSML) model. To compensate the scaling effect of UWA channels, many UWA communication systems employ a signal-resampling operation by a single scale at the front end of the receiver. For orthogonal frequency division multiplexing systems, since the path scales are different from each other, the resampling can alleviate but not clean out the intercarrier interference (ICI) resulting from the MSML channel. In this paper, we first describe the resampled signal by an equivalent MSML model with residual time scales, which are much smaller than that of the original MSML channel. With the given statistical parameters of the equivalent MSML channel, the closed-form expressions of the average subchannel gains and ICI coefficients are derived. Based on the ICI coefficients, we derive the analytical expression of a lower bound on the average achievable rate of the system as a function of the power allocation. We then investigate the power allocation to improve the lower bound on the achievable rate. Our key idea is that, on subcarriers whose ICIs dominate the additive noise, nothing is transmitted to avoid low energy efficiency. After proving that the ICI coefficient increases monotonically as the subcarrier frequency rises, we propose an ON–OFF uniform power allocation method, in which subcarriers with odd indices keep inactive starting from a certain index to avoid ICI. The performance of the proposed power allocation is evaluated via numerical simulations, whose results show that the proposed method outperforms the existing methods when the residual time scales cannot be neglected. [ABSTRACT FROM AUTHOR]

Most applications of underwater acoustic sensor networks (UASNs) rely upon reliable location information of sensor nodes. However, the harsh underwater environment makes localization more challenging as compared to terrestrial sensor networks. This paper is concerned with a localization issue for UASNs, subject to clock asynchronization and node mobility. A hybrid architecture including autonomous underwater vehicles (AUVs), active and passive sensor nodes is designed, where AUVs act as anchor nodes to provide localization information for sensor nodes. In order to eliminate the effect of asynchronous clocks and compensate the mobility of sensor nodes, an asynchronous localization algorithm with mobility prediction is provided for active and passive sensor nodes. Then, two localization optimization problems are formulated as minimizing the sum of all measurement errors. To solve the localization optimization problems, iterative least squares estimators are designed. In addition, the convergence analysis and Cramé r–Rao lower bound for the localization errors are also provided. Finally, simulation results show that the proposed localization approach can reduce the localization time by comparing with the exhaustive search-based localization method. Meanwhile, the asynchronous algorithm in this paper can effectively eliminate the impact of the clock asynchronization and node mobility. [ABSTRACT FROM AUTHOR]

Impulsive noise is one key factor that limits the performance of underwater acoustic (UA) communications. In this paper, two pilot-subcarrier based algorithms are proposed to improve the performance of channel estimation and impulsive noise mitigation for UA orthogonal frequency-division multiplexing (OFDM) systems. The first algorithm jointly estimates the channel and the impulsive noise based on the least-squares principle. The second algorithm is developed with the aim to reduce the computational complexity, where the expectation-maximization principle is applied to estimate the channel and the impulsive noise iteratively. We compare the proposed algorithms by simulations and apply them to process the data collected during an experiment conducted in December 2015 in the estuary of the Swan River, Western Australia. The results show that both proposed algorithms have better performance than existing methods in mitigating impulsive noise in UA OFDM systems. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

In this paper, we study the performance of single carrier (SC) modulation with frequency-domain equalization (FDE) over underwater acoustic (UWA) channels. The underlying channels are time-varying intersymbol interference (ISI) channels, where time variation arises from the relative motion between the transceivers, which induces one or more Doppler scaling factors. We study two scenarios: a point-to-point (P2P) system, where each cluster of paths has its own distinct Doppler scaling factor, and a multiple access (MAC) system, where $M$ users communicate with a multiple-antenna common receiver in which each user has its own Doppler scaling factor. First, the maximum likelihood (ML) receiver is derived for both scenarios, and it is shown that a preprocessing stage is necessary to reduce the time variation; this is referred to as multiple resampling (MR). The proposed receiver consists of multiple branches where each branch corresponds to a cluster/user and performs frequency shifting and resampling followed by an integrator. Since the output of the MR stage is contaminated by ISI for P2P systems and ISI and interuser interference for MAC systems, an additional equalization stage is necessary, which is ideally the ML sequence detector (MLSD). Since the complexity of MLSD exponentially grows with the number of symbols per block, the alphabet size, and the number of users, a linear minimum-mean-square-error FDE equalizer is used instead. To further reduce the complexity, instead of resampling the received signal multiple times by different scaling factors, it is resampled only by one scaling factor, which is a function of all Doppler scaling factors. The resulting suboptimal preprocessing scheme is called single resampling (SR). Simulation results for uncoded systems show that MR outperforms its SR counterpart at the expense of some additional hardware complexity. Moreover, it is shown that SC-FDE is more resilient than orthogonal frequency-division multiplexing (OFDM) to the Doppler scaling effect in UWA channels at lower overall complexity for the MR case, whereas both have the same overall complexity for the SR case. [ABSTRACT FROM AUTHOR]

Data forwarding techniques in opportunistic underwater sensor networks (OUSNs) are significantly different from those of wireless sensor networks or delay-tolerant networks, due to the irregular mobility of nodes. To this end, an Opportunistic Forwarding Algorithm based on Irregular Mobility (OFAIM) is proposed hereby, which intermittently computes and chooses the appropriate paths of an end-to-end data session. In OFAIM, the contacting probabilities among nodes are estimated according to current nodes statuses. OFAIM is particularly suitable for heterogeneous networks composed of nodes with different communication and movement ranges. The performance of OFAIM is also analyzed through simulations, which indicate that OFAIM achieves a satisfactory delivery ratio (larger than 67% at the worst case) within a limited duration, while the message cost is largely reduced (the number of forwarding copies at every slot is restricted to two or three) compared with epidemic forwarding, motion vector forwarding, and predict and spread forwarding. [ABSTRACT FROM PUBLISHER]

The unique propagation features of underwater acoustic channels (UACs) and the harsh marine environment conditions lead to frequent packet losses in data transmissions. The lossy UAC needs a strong forward error correction (FEC) scheme to improve transmission reliability. To this end, we propose a stochastically optimized fountain-based transmission scheme to improve the transmission goodput over UACs. The fountain-based transmission is formulated as a stochastic optimization problem and solved using discrete stochastic approximation (DSA) approach, whose iterations are in accordance with the channel state estimation. We evaluate the performance of our proposal using the recently published UAC model. The simulation results show the iterative optimality in terms of goodput and statistic convergence of our proposal. The simulation result with node mobility also shows that our proposal can capture the dynamic changes of UACs. [ABSTRACT FROM PUBLISHER]

We propose a new method of modeling the signal transmission in underwater acoustic communications when the transmitter and receiver are moving. The motion-induced channel time variations can be modeled by sampling the transmitter/receiver trajectory at the signal sampling rate and calculating, for each position, the channel impulse response from the acoustic-field computation. This approach, however, would result in high complexity. To reduce the complexity, the channel impulse response is calculated for fewer (waymark ) positions and then interpolated by local splines to recover it at the signal sampling rate. To allow higher distances between waymarks and, thus, further reduction in the complexity, the multipath delays are appropriately adjusted before the interpolation. Because, for every time instant, this method only requires local information from the trajectory, the impulse response can recursively be computed, and therefore, the signal transmission can be modeled for arbitrarily long trajectories. An approach for setting the waymark sampling interval is suggested and investigated. The proposed method is verified by comparing the simulated data with data from real ocean experiments. For a low-frequency shallow-water experiment with a moving source that transmits a tone set, we show that the Doppler spectrum of the received tones is similar in the simulation and experiment. For a higher frequency deep-water experiment with a fast-moving source that transmits orthogonal frequency-division multiplexing (OFDM) communication signals, we investigate the detection performance of a receiver and show that it is similar in the simulation and experiment. [ABSTRACT FROM PUBLISHER]

Using traditional sonar arrays may be difficult and impractical in some mission-critical scenarios, because they should be mounted on or towed by a ship or a submersible. Alternatively, underwater acoustic sensor networks (UW-ASNs) offer a promising solution approach. In this paper, a target-tracking algorithm for UW-ASNs, i.e., 3-D underwater target tracking (3DUT), is presented. The objective of 3DUT is to collaboratively accomplish accurate tracking of underwater targets with minimum energy expenditure. Based on the time of arrival of the echoes from the target after transmitting acoustic pulses from the sensors, the ranges of the nodes to the target are determined, and trilateration is used to obtain the location of the target. The location and the calculated velocity of the target are then exploited to achieve tracking. To realize energy-effective target tracking, 3DUT incorporates a new target-movement-based duty-cycle mechanism. To avoid rapid depletion of the energy resources of boundary nodes due to continuous surveillance, 3DUT employs an adaptive procedure to find, designate, and activate new boundary nodes. Performance evaluation shows that 3DUT is a promising alternative to the traditional sonar-based target-tracking approaches, particularly for on-demand surveillance applications. [ABSTRACT FROM PUBLISHER]

This paper proposes a geometry-based statistical model for wideband multiple-input–multiple-output (MIMO) mobile-to-mobile (M-to-M) shallow-water acoustic (SWA) multipath fading channels. Based on the reference model, the corresponding MIMO time–frequency correlation function, Doppler power spectral density, and delay cross-power spectral density are derived. These statistics are important tools for the design and performance analysis of MIMO M-to-M SWA communication and sonar systems. Finally, the derived statistics are compared with the experimentally obtained channel statistics, and close agreement is observed. [ABSTRACT FROM PUBLISHER]