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Direction-of-arrival (DOA) estimation for wideband sources has attracted a growing interest in the recent decade because wideband sources are incorporated in many real-world applications such as communication systems, radar, sonar and acoustics. One way to estimate the DOAs of wideband signals is to decompose it into narrowband signals using discrete Fourier transform (DFT) and then apply well-established narrowband algorithms to each signal. Afterwards, results are averaged to yield the final DOAs. These techniques require scanning the full band of wideband sources, ultimately degrading the resolution and increasing complexity. This paper aims to propose a new DOA estimation methodology to solve these problems.

The new DOA estimation methodology is based on incoherent signal subspace method (ISSM). The proposed approach presents a criterion to select a single sub-band of the selected narrowband signals instead of scanning the whole signal spectrum. Then, the DOAs of wideband signals are estimated using the selected sub-band. Therefore, it is named as single sub-band (SSB)-ISSM.

The computational complexity of the proposed method is much lower than that of traditional DFT-based methods. The effectiveness and advantages of the proposed methodology are theoretically investigated, and computational complexity is also addressed.

To verify the theoretical analysis, computer simulations are implemented, and comparisons with other algorithms are made. The simulation results show that the proposed method achieves better performance and accurately estimates the DOAs of wideband sources.

The existing dimensionality reduction algorithms suffer serious performance degradation under low signal-to-noise ratio (SNR) owing to the presence of noise. To address these problems, an enhanced spatial smoothing scheme is proposed that exploits the subarray time-space correlation matrices to reconstruct the data matrix to overcome this weakness. This method uses the strong correlation of signal and the weak correlation of noise in time and space domains, which improves the noise suppression ability.

In this paper, an enhanced spatial smoothing method is proposed. By using the strong correlation of signal and the weak correlation of noise, the time-space smoothed array covariance matrix based on the subarray time-space correlation matrices is constructed to improve the noise suppression ability. Compared with the existing Toeplitz matrix reconstruction and spatial smoothing methods, the proposed method improves the DOA estimation performance at low SNR.

Theoretical analysis and simulation results show that compared with the existing dimensionality reduction processing algorithms, the proposed method improves the DOA estimation performance in cases with a low SNR. Furthermore, in cases where the DOAs between the coherent sources are closely spaced and the snapshot number is low, our proposed method significantly improves the performance of the DOA estimation performance.

The proposed method improves the DOA estimation performance at low SNR. In particular, for the cases with a low SNR, the proposed method provides a better RMSE. The convergence of the proposed method is also faster than other methods for the low number of snapshots. Our analysis also confirms that in cases where the DOAs between the coherent sources are closely spaced, the proposed method achieves a much higher angular resolution than that of the other methods.

The purpose of this paper is to design and analyze the performance of live model of Bessel beamformer for thorough comprehension of beamforming in adaptive environment and compared with live model of least mean square (LMS) in terms of gain and mean square error (MSE). It presents the principal elements of communication system. The performance of designed live model is tested for its efficiency in terms of signal recovery, directive gain by minimizing MSE using the “wavrecord” function to bring live audio data in WAV format into the MATLAB workspace. These adaptive techniques are illustrated by appropriate examples.

The proposed algorithm framework relies on MATLAB software with the goal to obtain high efficiency in terms of signal recovery, directive gain by minimizing MSE using the “wavrecord” function to bring live audio data in WAV format. It is assumed that this audio signal is only the message or the baseband signal received by the computer. Here the authors consider computer (laptop) as a base station containing adaptive signal processing algorithm and source (mobile phone) as a desired user, so the experiment setup is designed for uplink application (user to base station) to differentiate between desired signal, multipath and interfering signals as well as to calculate their directions of arrival.

The presented adaptive live model is reliable, robust and lead to a substantial reduction of MSE, signal recovery in comparison with the LMS technique. The paper contains experimental data. Obtained results are presented clearly and the conclusion comes directly from the presented experimental data. The paper shows that the presented method leads to superior results in comparison with the popular LMS method and can be used as a better alternative in many practical applications.

The adaptive processes described in the paper are still limited to simulation. It is because of the non-availability of real system for testing, therefore chosen research approach that is platform of MATLAB is opted for simulation. Therefore, researchers are encouraged to test the proposed algorithms on real system if possible.

The paper contains experimental data. The paper's impact on the society is acceptable. These implications are consistent with the findings and the conclusions of the paper. However, there is a need to extend this paper to a next level by implementing the proposed algorithms in the real time environment using FPGA technology.

This research will improve the signal quality of wireless cellular system by increasing capacity and will reduce the total cost of the system so that cost toward subscribers be decreased.

The live model presented in this paper is shown to provide better results. It is the original work and can provide scientific contribution to signal processing community.

This paper reports some investigative results obtained through the application of differential geometry to the array manifold of a direction finding (DF) sensor array. It emphasises the crucial but so far disregarded role of the array manifold in the performance of subspace‐based direction finding (DF) algorithms and then proceeds to a compact mathematical analysis of the array manifold using the tools of differential geometry. The results thus obtained are used to quantify the effects of the array manifold properties on the performance of a DF system and to design superresolution sensor arrays.

The purpose of this paper is to exploit the multiple-Toeplitz matrices reconstruction method combined with quadratic spatial smoothing processing to improve the direction-of-arrival (DOA) estimation performance of coherent signals at low signal-to-noise ratio (SNRs).

An improved multiple-Toeplitz matrices reconstruction method is proposed via quadratic spatial smoothing processing. Our proposed method takes advantage of the available information contained in the auto-covariance matrices of individual Toeplitz matrices and the cross-covariance matrices of different Toeplitz matrices, which results in a higher noise suppression ability.

Theoretical analysis and simulation results show that, compared with the existing Toeplitz matrix processing methods, the proposed method improves the DOA estimation performance in cases with a low SNR. Especially for the cases with a low SNR and small snapshot number as well as with closely spaced sources, the proposed method can achieve much better performance on estimation accuracy and resolution probability.

The study investigates the possibility of reusing pre-existing designs for the DOA estimation of the coherent signals. The proposed technique enables achieve good estimation performance at low SNRs.

The paper includes implications for the DOA problem at low SNRs in communication systems.

The proposed method proved to be useful for the DOA estimation at low SNR.

Design, fabricate and evaluate all-solid-state wearable sensor systems that can monitor ion concentrations in human sweat to provide real time health analysis and disease diagnosis capabilities.

A human health monitoring system includes disposable customized flexible electrode array and a compact signal transmission-processing electronic unit.

Patterned rGO (reduced-graphene oxide) layers can replace traditional metal electrodes for the fabrication of free-standing all solid film sensors to provide improved flexibility, sensitivity, selectivity, and stability in ion concentration monitoring. Electrochemical measurements show the open circuit potential of current selective electrodes exhibit near Nernst responses versus Na+ and K+ ion concentration in sweat. These signals show great stability during a typical measurement period of 3 weeks. Sensor performances evaluated through real time measurements on human subjects show strong correlations between subject activity and sweating levels, confirming high degree of robustness, sensitivity, reliability and practicality of current sensor systems.

In improving flexibility, stability and interfacial coherency of chemical sensor arrays, rGO films have been the developed as a high-performance alternative to conventional electrode with significant cost and processing complexity reduction. rGO supported solid state electrode arrays have been found to have linear potential response versus ion concentration, suitable for electrochemical sensing applications. Current sweat sensor system has a high degree of integration, including electrode arrays, signal processing circuits, and data visualization interfaces.

The purpose of this paper is to present and solve the problem of adaptive beamforming (ABF) for a uniform linear array (ULA) as an optimization problem. ABF mainly concerns with estimation of weights of antenna array so as to direct the major lobe in the direction of desired user and nulls in the direction of interfering signals with reduced side lobe level (SLL).

The potential of gravitational search algorithm is explored to optimize multi-objective fitness function for ABF using MATLAB software.

The performance of the algorithm has been compared by considering different number of interference signals at different power levels. The proposed algorithm presents good convergence rate and accurate steering of main lobe and nulls with reduced SLL compared to the well-known ABF technique, namely, minimum variance distortionless response (MVDR) and previously reported results. The simulation results are presented in tabular form.

The present work is limited to simulation. The researchers are encouraged to solve the problem of ABF using the proposed approach in hardware.

The application of proposed algorithm is to optimize multi-objective function for ABF with reduced SLL in linear antenna arrays.

The aero-engine array electrostatic monitoring technology (AEMT) can provide more and more accurate information about the direct product of the fault, and it is a novel condition monitoring technology that is expected to solve the problem of high false alarm rate of traditional electrostatic monitoring technology. However, aliasing of the array electrostatic signals often occurs, which will greatly affect the accuracy of the information identified by using the electrostatic sensor array. The purpose of this paper is to propose special solutions to the above problems.

In this paper, a method for de-aliasing of array electrostatic signals based on compressive sensing principle is proposed by taking advantage of the sparsity of the distribution of multiple pulse signals that originally constitute aliased signals in the time domain.

The proposed method is verified by finite element simulation experiments. The simulation experiments show that the proposed method can recover the original pulse signal with an accuracy of 96.0%; when the number of pulse signals does not exceed 5, the proposed method can recover the pulse peak with an average absolute error of less than 5.5%; and the recovered aliased signal time-domain waveform is very similar to the original aliased signal time-domain waveform, indicating that the proposed method is accurate.

The proposed method is one of the key technologies of AEMT.

The purpose of this paper is to extract the angle information of direct P wave within multidimensional vibration signals obtained through the sensor array, and to realize high precision shallow burst point localization based on direct of angle (DOA).

This paper presents a method which combines adaptive covariance matrix (ACM) algorithm with geometric constraint conditions for extracting the angle information of direct P wave by using its polarization characteristics. First, modify the obtained three-dimensional vibration data by using attitude rotation matrix and unify the coordinate system of vibration field. Next, construct the beam model of direct P wave by making use of ACM algorithm and extract its angle information. Finally, modify P wave beam model by taking advantage of the space geometric constraint relations between nodes.

The results of numerical simulation show that this method not only can extract the angle information of direct P wave arriving at each node effectively, but also can evaluate the quality of extracted angle information of direct P wave. Meanwhile, the results of underground shallow explosion experiment show that this method can extract the angle information of direct P wave of each node significantly and can realize underground shallow explosion source localization based on DOA by using this information, the location error can be limited less than 50 cm and satisfies the location requirements of shallow burst point.

This paper provides a method for various problems of underground localization based on the sensor array, such as directional demolition blasting, underground damage assessment, earth-penetrating projectile burst point positioning in weaponry industry testing plant, etc., and has definite value to engineering application in underground space positioning field.

The purpose of this paper is to provide a thin tactile force sensor array based on conductive rubber and to offer descriptions of the sensor design, fabrication and test.

The sensor array consists of a sandwich structure. Sensing elements are distributed discretely in the sensor. Each sensing element has two electrodes and a piece of conductive rubber with piezoresistive property. The electrodes, as well as the conductive trace for signal transmission, are printed on the substrate layer by the screen printing technique. A scanning circuit based on zero potential method and an experimental set‐up based on balance to characterize the sensor array are designed and implemented in the test of the sensor array.

Experimental results verify the validity of the sensor array in measuring the vertical tactile force between the sensing elements and the object.

In this paper, all the sensors are tested without calibration procedures and the procedure of the dynamic test is implemented by manual operation.

The sensor array could be applied to measure the plantar force for gait detection in clinical applications.

The paper presents a tactile force sensor array with discrete sensing elements to essentially restrict the cross‐talk among sensing elements. This paper will provide many practical details that can help others in the field.