An ambiguity-free direction-of-arrival (DOA) estimation scheme is proposed for sparse uniform linear arrays under low signal-to-noise ratios (SNRs) and non-stationary broadband signals. First, for achieving better DOA estimation performance at low SNRs while using non-stationary signals compared to the conventional frequency-difference (FD) paradigms, we propose parameterized time-frequency transform-based FD processing. Then, the unambiguous compressive FD beamforming is conceived to compensate the resolution loss induced by difference operation. Finally, we further derive a coarse-to-fine histogram statistics scheme to alleviate the perturbation in compressive FD beamforming with good DOA estimation accuracy. Simulation results demonstrate the superior performance of our proposed algorithm regarding robustness, resolution, and DOA estimation accuracy.

The thriving field of multi-agent reinforcement learning (MARL) studies how a group of interacting agents make decisions autonomously in a shared dynamic environment. Existing theoretical studies in this area suffer from at least two of the following obstacles: memory inefficiency, the heavy dependence of sample complexity on the long horizon and the large state space, the high computational complexity, non-Markov policy, non-Nash policy, and high burn-in cost. In this work, we take a step towards settling this problem by designing a model-free self-play algorithm \emph{Memory-Efficient Nash Q-Learning (ME-Nash-QL)} for two-player zero-sum Markov games, which is a specific setting of MARL. We prove that ME-Nash-QL can output an $\varepsilon$-approximate Nash policy with remarkable space complexity $O(SABH)$, sample complexity $\widetilde{O}(H^4SAB/\varepsilon^2)$, and computational complexity $O(T\mathrm{poly}(AB))$, where $S$ is the number of states, $\{A, B\}$ is the number of actions for the two players, $H$ is the horizon length, and $T$ is the number of samples. Notably, our approach outperforms in terms of space complexity compared to existing algorithms for tabular cases. It achieves the lowest computational complexity while preserving Markov policies, setting a new standard. Furthermore, our algorithm outputs a Nash policy and achieves the best sample complexity compared with the existing guarantee for long horizons, i.e. when $\min \\{ A, B \\} \ll H^2$. Our algorithm also achieves the best burn-in cost $O(SAB\,\mathrm{poly}(H))$, whereas previous algorithms need at least $O(S^3 AB\,\mathrm{poly}(H))$ to attain the same level of sample complexity with ours.

As a result of the development of the Internet of Underwater Things (IoUT), underwater connected devices generate a large volume of data with varying values and time sensitivity. Previous data collection strategies cannot accommodate the varying time requirements of various data types. To address the aforementioned issues, this article proposes a cooperative data collection method (MADC-DV) for multiple autonomous underwater vehicles (AUVs) based on local global deep$Q$learning (LG-DQN) and data value, which divides data into emergency and nonemergency and achieves hybrid data collection. First, the MAC protocol for communication between AUVs and clusters is designed to divide nonemergency data into high-value data and low-value data, with low-value data not needing to reply to ACK acknowledgment packets, thereby reducing the nonemergency data collection delay. Second, nonemergency data are collected cooperatively using multiple AUVs, and the LG-DQN approach is used to plan the paths for multiple AUV data collection in order to reduce the overall energy consumption of underwater wireless sensor networks (UWSNs). Finally, emergency data are collected using a multihop routing approach to assist in the collection. A routing method is proposed to compensate for the inability of AUVs to be applied to emergency data collection. The experimental results indicate that the method can improve the network life cycle by 18.7%, reduce the delay in the collection of nonemergency data by 40%, and reduce the delay in the collection of emergency data by 26.3%, thereby meeting the varying time requirements for different types of data.

With little or no human intervention, almost every object in Internet of Things (IoT) has ability to communicate, sense, and process information to make everything connected. Noise in complex environment has a great impact on machine-to-machine (M2M) interaction in IoT. Adaptive wavelet threshold function (AWTF)-based M2M Gaussian Noise Removal Method is proposed in this article. First, a bilateral enhanced wavelet threshold function is derived based on the adjustable zeroing window. Further, threshold is used to construct a bilateral enhanced wavelet threshold function, which can eliminate the oscillations in the existing wavelet threshold function. This ensures that there is no break in wavelet coefficients during the reconstruction process and that stable wavelet decomposition and reconstruction can be achieved. The signal-to-noise ratio (SNR) of a signal is estimated based on the variance of the noise-containing signal, and the zeroing window parameters are adjusted adaptively according to the SNR value to eliminate the noisy wavelet coefficients and improve the denoising performance. In addition, when the wavelet coefficients are reconstructed, the proposed algorithm can select a suitable threshold function according to a particular threshold value, which improves the robustness of the algorithm. “Doppler” and “Bumps” standard test signals are used as interactive signals to simulate the proposed algorithms. For “Doppler” signals, the SNR, root mean square error (RMSE), and noise suppression ratio (NSR) of AWTF increased by 8.48%, 42.59%, and 1.69%, respectively, compared with the GDES+ABC algorithm. For “Bumps” signal, the SNR, RMSE, and NSR of AWTF are improved by 11.67%, 24.46%, and 2.99%, respectively, compared with GDES+ABC algorithm. In addition, we also use different modulated signals to carry out real field experiments in Qingdao cruise ship home port, which proves the effectiveness of the proposed algorithm.

Although deep learning based beamformers have achieved promising performance using small microphone arrays, they suffer from performance degradation in very challenging environments, such as extremely low Signal-to-Noise Ratio (SNR) environments, e.g., SNR$\le$−10 dB. A large-scale microphone array with dozens or hundreds of microphones can improve the performance of beamformers in these challenging scenarios because of its high spatial resolution. While a dramatic increase in the number of microphones leads to feature redundancy, causing difficulties in feature extraction and network training. As an attempt to improve the performance of deep beamformers for speech extraction in very challenging scenarios, this paper proposes a novel all neural Kronecker product beamforming denoted by ANKP-BF for large-scale microphone arrays by taking the following two aspects into account. Firstly, a larger microphone array can provide higher performance of spatial filtering when compared with a small microphone array, and deep neural networks are introduced for their powerful non-linear modeling capability in the speech extraction task. Secondly, the feature redundancy problem is solved by introducing the Kronecker product rule to decompose the original one high-dimension weight vector into the Kronecker product of two much lower-dimensional weight vectors. The proposed ANKP-BF is designed to operate in an end-to-end manner. Extensive experiments are conducted on simulated large-scale microphone-array signals using the DNS-Challenge corpus and WSJ0-SI84 corpus, and the real recordings in a semi-anechoic room and outdoor scenes are also used to evaluate and compare the performance of different methods. Quantitative results demonstrate that the proposed method outperforms existing advanced baselines in terms of multiple objective metrics, especially in very low SNR environments.

This paper proposes a joint multi-task Bayesian channel estimation and data detection algorithm for Turbo equalization (TEQ) in underwater acoustic (UWA) communication. The joint channel estimation and data detection (JCED) problem is formulated as a multi-task sparse Bayesian learning framework in single carrier (SC) communications. The framework treats the equalized symbols as unknown variables for improving the performance of iterative equalization and leverages temporal correlation in UWA channels by partitioning received symbols into subblocks. Furthermore, a JCED algorithm is derived with variational Bayesian inference. The proposed algorithm was evaluated based on the underwater field data collected during a lake experiment conducted in Qiandao Lake, Zhejiang province, China, in May 2016. The performance of the proposed algorithm has been validated with simulation and experiment results.

Underwater acoustic positioning using ultrashort baseline (USBL) technology is essential for underwater navigation and ocean surveillance. Many research institutions and commercial organizations have conducted extensive studies on USBL, with wideband processing widely applied to enhance the capabilities of related methods. However, the properties of the wideband correlation spectrum have not been thoroughly explored. This study introduces a linear fitting technique, leveraging the frequency-phase relationship within the spectrum to enhance the USBL performance. By combining iterative reweighted least squares and a random sampling and consensus (RANSAC)-type approach, the method reduces the impact of outliers, particularly in multiple-path and low signal-to-noise ratio (SNR) scenarios. Furthermore, we operate a matched filter before the linear fitting process and suggest rescreening based on the consistency of all array elements in the frequency domain to eliminate more outliers. The proposed method significantly reduces positioning errors in both numerical simulations and sea trial data processing.

In orthogonal time sequency multiplexing (OTSM) modulation, the information symbols are conveyed in the delay-sequency domain upon exploiting the inverse Walsh Hadamard transform (IWHT). It has been shown that OTSM is capable of attaining a bit error ratio (BER) similar to that of orthogonal time-frequency space (OTFS) modulation at a lower complexity, since the saving of multiplication operations in the IWHT. Hence we provide its BER performance analysis and characterize its detection complexity. We commence by deriving its generalized input-output relationship and its unconditional pairwise error probability (UPEP). Then, its BER upper bound is derived in closed form under both ideal and imperfect channel estimation conditions, which is shown to be tight at moderate to high signal-to-noise ratios (SNRs). Moreover, a novel approximate message passing (AMP) aided OTSM detection framework is proposed. Specifically, to circumvent the high residual BER of the conventional AMP detector, we proposed a vector AMP-based expectation-maximization (VAMP-EM) detector for performing joint data detection and noise variance estimation. The variance auto-tuning algorithm based on the EM algorithm is designed for the VAMP-EM detector to further improve the convergence performance. The simulation results illustrate that the VAMP-EM detector is capable of striking an attractive BER vs. complexity trade-off than the state-of-the-art schemes as well as providing a better convergence. Finally, we propose AMP and VAMP-EM turbo receivers for low-density parity-check (LDPC)-coded OTSM systems. It is demonstrated that our proposed VAMP-EM turbo receiver is capable of providing both BER and convergence performance improvements over the conventional AMP solution.

In this article, we consider the receiver-only-based time synchronization in underwater wireless sensor networks with exponential delays emerging during the message exchanges. This article is motivated by the fact that 25% of timing messages are discarded in the existing receiver-only-synchronization (ROS)-based schemes to cope with the interaction between the discontinuity of the likelihood function and the unknown clock skews, which would result in a massive waste of energy. To account for this issue, a general ROS model under the exponential delay assumption, with clock skews taken into consideration and all timing messages utilized, is introduced. Following the proposed ROS model, we develop a three-step method to jointly estimate clock offsets and skews of both active and silent nodes, as well as other unknown parameters. Specifically, an estimation on clock offset and skew for the active node using support vector machine is followed by a joint maximum-likelihood estimation of the clock skew and clock offset for the silent node as well as clock offset for the active node, and a minimum variance unbiased estimation of the offsets for both active and silent nodes. Such method further improves the estimation accuracy of the proposed ROS model. The effectiveness and robustness of the proposed method is verified by simulations results.

High-accuracy real-time Doppler estimation is one of the key problems in underwater acoustic communication systems. Using the dual-hyperbolic-frequency-modulation (dual-HFM) synchronous signal is an effective solution; however, the Doppler estimation accuracy is limited by the sampling frequency in the traditional methods. In this letter, we propose a high-accuracy speed spectrum scanning method based on the Doppler invariant and the frequency spectrum properties of the HFM signal. This signal processing system uses dual-HFM waveform as a synchronous signal, constructs the speed spectrum function at the receiver, and then obtains the Doppler estimate through one-dimensional scanning. This method breaks through the speed resolution limit induced by the sampling frequency in the traditional method and obtains a high-accuracy estimate. The influences of signal transmission and processing parameters on the Doppler estimation is analyzed, and the parameter selection criteria is presented to improve the reliability of the estimation. The results of both numerical simulation and underwater communication experiments prove the effectiveness of the proposed method.

This letter addresses the problem of distributed target detection in structured interference and non-homogeneous disturbance. The target signal and interference locate in two linearly independent subspaces with unknown coordinates, while the disturbance is partially homogeneous with an unknown covariance matrix. By incorporating the persymmetric structure of received data, we propose a persymmetric subspace detector for a distributed target, which includes the detector for a point-like target as a special case. Remarkably, the proposed detector is shown to ensure a constant false alarm rate property with respect to both the covariance matrix structure and the power scaling factor. In addition, analytical expressions for the probabilities of false alarm and detection of the special case of the proposed detector for point-like targets are derived, which are verified by Monte Carlo trials. Numerical examples illustrate that the proposed detector can achieve better detection performance, as well as anti-interference ability in training-limited situations.

In this letter, we address adaptive radar detection of point-like targets in Gaussian clutter with an unknown covariance matrix. To this end, we first exploit the symmetrically structured power spectral density of the clutter to transfer data from the complex to the real domain. Then, the spillover of target energy is incorporated into the design criteria to come up with two architectures capable of guaranteeing improved detection performances and range estimation. The performance assessments, conducted on both simulated data and real recorded datasets, demonstrate the effectiveness of the newly proposed detectors compared with the state-of-the-art counterparts, which ignore either the clutter spectral symmetry or the energy spillover.

In this paper, we propose a design method for 3-D higher order ambisonics (3-D HOA) encoding matrices which offers the possibility to impose spatial stop-bands in the directivity patterns of all the spherical-harmonic audio channels while keeping the transformed audio channels still compatible with the 3-D HOA reproduction sound format. This might be useful as an encoding technique which suppresses interfering signals from specific directions in a 3-D HOA recording, or in other situations where certain spatial areas should be suppressed. The design method is adapted from recent work on the optimization of spherical microphone array beamforming. Using the proposed optimization method and the spherical harmonics mathematics framework, the relationship between several design factors, e.g., distortions in the desired response, the dynamic range of matrix coefficients, can be analyzed and illustrated as function of frequency. Based on the proposed optimization formulation, additional constraints can also be easily included and solved. In some of the formulations, the processing can be applied as a matrix multiplication to recorded spherical harmonics coefficients, that is, already encoded 3-D HOA format signals. The modified signals can be of the same or a lower spherical harmonics order. For a full optimization that gives a globally optimal solution, on the other hand, the processing must be applied to the microphone signals themselves. Numerical and experimental results validate the proposed method.

A robust minimum sidelobe beamforming approach based on the spherical harmonics framework for spherical microphone arrays is proposed. It minimizes the peaks of sidelobes while keeping the distortionless response in the look direction and maintaining the mainlobe width. A white noise gain constraint is also derived and employed to improve the robustness against array errors. The resulting beamformer can provide optimal tradeoff between the sidelobe level, the beamwidth and robustness, so it could be more practical than the existing spherical array Dolph-Chebyshev modal beamformer in the presence of array errors. The optimal modal beamforming problem is formulated as a tractable convex second-order cone programming program, which is more efficient than conventional element-space based approaches, since the dimension of array weight vectors can be significantly decreased by using the properties of spherical harmonics and Legendre polynomials. For the purpose of performance comparison, we also formulate current robust modal beamformers as equivalent optimization problems based on the proposed array model. Numerical results show the high flexibility and efficiency of the proposed beamforming approach.

Most of the existing spherical array modal beamformers are implemented in the frequency domain, where a block of snapshots is required to perform the discrete Fourier transform. In this paper, an approach to real-valued time-domain implementation of the modal beamformer for broadband spherical microphone arrays is presented. The microphone array data are converted to the spherical harmonics domain by using the discrete spherical Fourier transform, and then steered to the look direction followed by the pattern generation unit implemented using the filter-and-sum structure. We derive the expression for the array response, the beamformer output power against both isotropic noise and spatially white noise, and the mainlobe spatial response variation in terms of the finite impulse response (FIR) filters' tap weights. A multiple-constraint problem is then formulated to find the filters' tap weights with the aim of providing a suitable tradeoff among multiple conflicting performance measures such as directivity index, robustness, sidelobe level, and mainlobe response variation. Simulation and experimental results show good performance of the proposed time-domain broadband modal beamforming approach.

An approach to optimal array pattern synthesis based on spherical harmonics is presented. The array processing problem in the spherical harmonics domain is expressed with a matrix formulation. The beamformer weight vector design problem is written as a multiply constrained problem, so that the resulting beamformer can provide a suitable trade-off among multiple conflicting performance measures such as directivity index, robustness, array gain, sidelobe level, mainlobe width, and so on. The multiply constrained problem is formulated as a convex form of second-order cone programming which is computationally tractable. We show that the pure phase-mode spherical microphone array can be viewed as a minimum variance distortionless response (MVDR) beamformer in the spherical harmonics domain for the case of spherically isotropic noise. It is shown that our approach includes the delay-and-sum beamformer and a pure phase-mode beamformer as special cases, which leads to very flexible designs. Results of simulations and experimental data processing show good performance of the proposed array pattern synthesis approach. To simplify the analysis, the assumption of equidistant spatial sampling of the wavefield by microphones on a spherical surface is used and the aliasing effects due to noncontinuous spatial sampling are neglected.

A space domain optimal beamforming approach for spherical microphone arrays is presented, which is based on original microphone signals rather than spherical harmonics. A simple correlated multipath signal model is employed. The beamforming optimization criteria, including adaptive beamforming, multi-beam steering, sidelobe control, correlated interference cancellation and robustness control, are proposed and reformulated in a convex form of second order cone programming, which is computationally tractable. Using the spherical harmonics framework, we also prove that, given a perfect spatial sampling scheme, the conventional plan-wave decomposition (PWD) beamformer can be interpreted as a space domain minimum variance distortionless response (MVDR) beamformer under the condition of a spherically isotropic sound field.

H.264 adopts variable block size motion estimation and rate-distortion-optimization based mode decision to improve video quality and compression ratio. These techniques have made H.264 better than other existing video coding standards. However, they are computationally intensive and time-consuming. In this paper, a fast mode selection scheme is proposed for H.264 inter prediction. Firstly, the first few frames are encoded and thresholds are acquired through a statistical learning process. Then, for the rest of frames, motion estimation and mode decision are only performed for the candidate modes which are selected with the proposed fast mode selection scheme. The proposed approach is applicable to all existing motion search algorithms. Besides, thresholds are on-line computed separately for each sequence. Results show that the total encoding time is saved by 57.2% on average with negligible video quality degradation.

An approach to the optimal design of FIR broadband beamformer with frequency invariant patterns via joint optimizing the spatial and frequency responses is proposed. The beam responses are jointly optimized to satisfy some spatial and frequency domain specifications by designing a bank of FIR filters corresponding to the input channels. It minimizes the maximum error between the designed beam patterns and the desired ones over the working frequencies within the mainlobe area while guaranteeing the sidelobes and the array patterns over the stopband to be below some given threshold values. White noise gain constraint is applied to improve the robustness of the beamformer against random errors. The beam response is expressed as a linear function of the FIR filters tap weights, and the design problem is formulated as the second-order cone programming, which can be solved efficiently via the well-established interior point methods. Results of computer simulation for a twelve-element semicircular array confirm satisfactory performance of the approach proposed in this paper.