Acoustic vector sensor (AVS) has been recently researched and developed for acoustic wave capturing and signal processing. Conventional array generally employs spatially displayed sensors for signal enhancement, source localisation, target tracking, etc. However, the large size usually limits its implementations on some portable devices. AVS which generally includes one omni-directional sensor and three orthogonally co-located directional sensors has been recently introduced. An AVS is able to provide the four-dimensional information of sound field in space: the acoustic pressure and its three-dimensional particle velocities. A compact assembled AVS could be as small as a match head and the weight can be <50 g. Benefits from these properties, AVS tends to be more attractive for exploitation and commercialisation than conventional sensor array. To have a well understanding of the research progress on AVS, an overview on its recent developments is first given in this study. Then, discussions of challenges on AVS and extensions on its possible future prospects are presented.

In multi-platform surveillance system, a prerequisite for successful fusion is the transformation of data from different platforms to a common coordinate system. However, some stochastic system biases arise during this transformation, and they seriously downgrade the global surveillance performance. Considering that the target state and the system biases are coupled and interactive, the authors present a new recursive joint estimation (RJE) algorithm for registering stochastic system biases and estimating target state. First, the relationship between system biases estimation and target state estimation is derived. Second, the RJE framework is introduced on the basis of the proposed relationship. Representing the different behavioural aspects of the motion of a maneuvering target is difficult to achieve with a single model in a multi-platform target tracking system. By accounting for the non-linear and/or non-Gaussian property of the dynamic system, they modify the interacting multiple model–particle filter framework to estimate parameters. This approach considers not only the influence of the system biases, but also the covariance of state on the basis of multiple-particle statistics. Simulation results reveal the superior performance of the proposed approach with respect to the traditional algorithm under the same conditions.

One way to improve automatic speech recognition (ASR) performance on the latest mobile devices, which can be employed on a variety of noisy environments, consists of taking advantage of the small microphone arrays embedded in them. Since the performance of the classic beamforming techniques with small microphone arrays is rather limited, specific techniques are being developed to efficiently exploit this novel feature for noise-robust ASR purposes. In this study, a novel dual-channel minimum mean square error-based feature compensation method relying on a vector Taylor series (VTS) expansion of a dual-channel speech distortion model is proposed. In contrast to the single-channel VTS approach (which can be considered as the state-of-the-art for feature compensation), the authors’ technique particularly benefits from the spatial properties of speech and noise. Their proposal is assessed on a dual-microphone smartphone (a particular case of interest) by means of the AURORA2-2C synthetic corpus. Word recognition results, also validated with real noisy speech data, demonstrate the higher accuracy of their method by clearly outperforming minimum variance distortionless response beamforming and a single-channel VTS feature compensation approach, especially at low signal-to-noise ratios.

In this study, new infinite impulse response (IIR) digital differentiators of second, third and fourth orders based on optimising the L ₁-error fitness function using the bat algorithm (BA) are proposed. The coefficients of numerator and denominator of the differentiators are computed by minimising the L ₁-norm of the error fitness function along with imposing the constraint for the location of poles and zeros within the unit circle to ensure minimum phase. The transfer function of the differentiators are inverted and transformed into the digital integrators of the same orders. The results obtained for the solutions by the proposed L ₁-based BA (L ₁-BA) are superior to the designs using other techniques such as particle swarm optimisation and real-coded genetic algorithm. The designed optimal differentiator and integrator are compared with the existing models and are found to be of high accuracy and flatness in a wide frequency range along with minimum absolute magnitude error. The mean relative error (dB) is obtained as low as −67 dB and −73 dB for the proposed differentiators and integrators, respectively.

Conventional methods for block-based compressive sensing consider an equal number of samples for all blocks. However, the sparsity order of blocks in natural images could be different and, therefore, a various number of samples could be required for their reconstruction. In this study, the authors propose an adaptive block-based compressive sensing scheme, which collects a different number of samples from each block. The authors show that by adapting the sampling rate, in addition to reducing the whole required number of measurements, the reconstruction performance would be improved, simultaneously. Simulation results verify the effectiveness of the proposed scheme, especially for multi-level pixel value images like Mondrian test image.

This paper proposed a novel weighted multidimensional scaling (MDS) estimator for estimating the position of a stationary emitter with sensor position uncertainties using time-difference-of-arrival measurements. The solution is closed form and unbiased. It is shown analytically to achieve the Cramer–Rao lower bound performance in small noise region. Simulation results show that the proposed estimator offers smaller bias and mean square error than the two-step weighted least square approach and traditional MDS estimator ignoring sensor position uncertainties at moderate noise level. Additionally, the computation complexities of them are comparable.

In this study, the authors investigate the resource allocation (RA) problem for the downlink orthogonal frequency division multiplexing based non-orthogonal multiple access (OFDM-NOMA) system. The RA problem is decomposed into two subproblems of subcarrier allocation (SA) and power allocation (PA). For the SA, a user grouping based greedy algorithm is proposed under the assumption that power is uniformly distributed among all the selected users. For the PA, the authors propose the iterative water-filling and specific user rate maximising criterion with minimum rate constraints (iterative WF + SURMC-MRC) scheme to jointly consider the PA problem among the selected users on one subcarrier and the PA problem among subcarriers. The simulation results show that the spectral efficiency performance of the proposed iterative WF + SURMC-MRC scheme outperforms those of the (non-iterative) WF + SURMC-MRC scheme and the uniform distribution (UD) + SURMC-MRC scheme. Moreover, the iterative WF + SURMC-MRC scheme has advantages in resisting against the user overloading compared with the (non-iterative) WF + SURMC-MRC scheme.

In this study, the authors propose a novel method, namely Toeplitz-based singular value decomposition (or TL-SVD for short) for the reconstruction and basis function construction of electromagnetic interference (EMI) source signals. Given a specific EMI source signal, they first construct a Toeplitz type data matrix. By applying singular value decomposition (SVD) to the constructed matrix, they obtain a set of singular values, which are further divided into two parts, corresponding to the clear and noisy components of input signals, respectively. The de-noised signal can then be reconstructed by reserving relatively larger singular values and abandoning smaller ones. Finally, by utilising the compositions of certain vectors resulting from the previous SVD step, the basis function can be constructed. To evaluate the performance of the proposed method, they conduct extensive experiments on both the synthetic data and real EMI signals, by comparing with several state-of-the-art signal reconstruction methods, such as discrete wavelet transform, EEMD, sparse representation based on K-SVD and OMP. Experimental results demonstrate that the proposed method can outperform comparison approaches.

The filter bank multicarrier (FBMC) with offset quadrature amplitude modulation (OQAM) is one of the alternative modulation schemes to orthogonal frequency division multiplexing for next generation broadband wireless access systems. The non-linearity of high-power amplifiers (HPA) has a crucial effect on the performance of FBMC systems. In this study, the impacts of non-linear distortion effects are considered in a FBMC-OQAM system when the signal is passed through a HPA for medium- and high-power signals which are modelled as amplitude and phase distortions. Specifically, memory-less HPA non-linear distortion models such as solid-state power amplifier (SSPA), soft-envelope limiter (SEL), travelling wave tube amplifier are proposed for FBMC systems. A closed-form expression for bit error rate expression is derived and analysed for SSPA and SEL models for FBMC/OQAM system with non-linear HPA in frequency-selective Rayleigh channel by varying constellation size in OQAM modulation and input back-off. The performance is compared for the models with 64 sub-channels and input back-off for 6 and 8 dB. In lieu of validating the obtained simulation results, theoretical results are being compared.

The authors propose a diagnostic technique for the state-space model fitting of time series by deleting some observations and measuring the change in the parameter estimates. They consider this approach in order to distinguish an observational outlier from an innovational one. Thus, they present a robust subspace identification algorithm that is less sensitive to outliers. A Monte Carlo simulation for a vibrating structure model demonstrates the effectiveness of the proposed algorithm and its ability to detect outliers in the measurements as well as the dynamical state.

The classic two-step approach for time difference of arrival (TDOA) geolocation is suboptimal since the TDOA measurements have not followed the constraint that all measurements should be consistent for a geolocation of a single emitter. In this study, the direct TDOA geolocation approach is proposed for frequency-hopping (FH) emitters. It makes use of the sparsity of the FH signals in frequency domain, and constructs a cross correlation function (CCF) matrix in frequency domain, then the location estimate is obtained by searching the maximum eigenvalue of the CCF matrix in a two dimensional grid. The Cramer–Rao lower bound has been derived. The resolution for a single FH signal geolocation is also analysed. Further, an extension of the new method for multiple FH emitters direct TDOA geolocation has been presented. The performance comparison between the direct approach and the conventional two-step method has been made by simulations. The results demonstrated that the proposed method outperforms the conventional two-step method. The simulations also demonstrated the effectiveness of the new method in locating multiple FH emitters.

In this study, the authors propose an l ₀-norm penalised shrinkage linear least mean squares (l ₀-SH-LMS) algorithm and an l ₀-norm penalised shrinkage widely linear least mean squares (l ₀-SH-WL-LMS) algorithm for sparse system identification. The proposed algorithms exploit the priori and the posteriori errors to calculate the varying step-size, thus they can adapt to the time-varying channel. Meanwhile, in the cost function they introduce a penalty term that favours sparsity to enable the applicability for sparse condition. Moreover, the l ₀-SH-WL-LMS algorithm also makes full use of the non-circular properties of the signals of interest to improve the tracking capability and estimation performance. Quantitative analysis of the convergence behaviour for the l ₀-SH-WL-LMS algorithm verifies the capabilities of the proposed algorithms. Simulation results show that compared with the existing least mean squares-type algorithms, the proposed algorithms perform better in the sparse channels with a faster convergence rate and a lower steady-state error. When channel changes suddenly, a filter with the proposed algorithms can adapt to the variation of the channel quickly.

Remote sensing images are widely used for different areas from mineral exploration to agricultural applications and poor quality of hyperspectral (HS) images will directly have adverse effect on these applications. In this study, a method is proposed to restore degraded HS images. To achieve this aim, another multispectral (MS) observation of the same scene is supposed to be available and restoration is fulfilled by fusion of HS images and MS images. The proposed method gains maximum a posteriori estimation and is based on expectation maximisation algorithm. Deblurring and denoising are performed separately. Deblurring is done in spatial domain via non-overlapping blocks, whereas denoising is implemented in wavelet domain. To represent the coefficients in wavelet domain, instead of multinormal model, Gaussian scale mixture is exploited. The proposed method is validated on airborne visible/infrared imaging spectrometer (AVIRIS) and HS digital imagery collection experiment (HYDICE) databases and experimental results signify that the proposed method outperforms state-of-the-art techniques cited in the literature and signal-to-noise ratio is improved as much as 15.71 dB for Moffett database and 16.26 dB for HYDICE database.

A novel scheme is proposed to locate the dispersive region, whose location is essential for parallel echo cancellation. In the scheme, a first filter adapts to a subsampled version of the input signal to roughly identify the impulse response. After each adaptation, a squaring function and a moving window integration procedure are performed on the first filter, and the region with the maximum integration value is considered to be the dispersive region. Finally, a second short filter is used to precisely identify the active coefficients belonging to the located dispersive region to implement the actual echo cancellation. Simulation results suggest that the parallel structure improves its convergence speed 3 times and its computation can be reduced by 3/8 compared with the traditional NLMS algorithm by decreasing the filter length. Due to the more accurate estimate of the location, the misalignment noise of the proposed algorithm is at least 10 dB lower than that of the conventional dispersive region locating algorithm. Moreover, the proposed algorithm based on the parallel structure outperforms other sparse adaptive algorithms in all aspects.

Exact compressed sensing (CS) recovery theoretically depends on a large number of random measurements. In this study, the authors present a novel CS measurement technique based on the cellular automata chaos (CAC) model. The proposed method selects original signal thresholding (OST) as its initial seed to realise CS signal coding. The benefits of CS coding with CAC-OST are that: (i) the signal compression ratio of this coding method can be far below the signal sparsity level and (ii) the signal can be recovered perfectly, even with slow CS measurements. This study reports some experiments that demonstrate the excellent performance of CAC-OST in CS coding.