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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.

This paper aims to accurately estimate harmonics/interharmonics in modern power system. There are several high spectral resolution techniques that have been in use for several years like Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), Prony methods, etc. but these techniques require prior knowledge of number of modes present in the signal. Model Order (MO) estimation techniques have to make a trade-off between accuracy and their speed i.e., computational burden. Therefore, there is always a requirement of a technique that is fast as well as accurate.

The proposed standard deviation (SD) method eliminates the requirement of energy validation test and analyses the distribution pattern, i.e. standard deviation of eigenvalues to identify the number of modes present in the signal. Signal is reconstructed using estimated modes and reconstruction error is obtained to show accuracy of the proposed estimation.

Six test synthetic signals as well as one practical signal have been taken for validating the proposed method. The paper shows that proposed methodology has a better accuracy compared to modified exact model order (MEMO) method in high noise environment and takes very less computation time compared to the exact model order (EMO) method.

The proposed method has been practically implemented for harmonic/interharmonic analysis at a sewage treatment plant at GIFT City, Gujarat, India. Apart from this the proposed method is modeled in python-based tool and is run into low-cost Raspberry Pi like hardware to create an onsite as well as remote monitoring device.

SD-based approach for model order estimation is novel to this area. Further, the proposed method is compared with EMO and MEMO under varying noise conditions to check for accuracy and estimation time.

We present an approach for blind separation of acoustic sources produced from multiple speakers mixed in realistic room environments. We first transform recorded signals into the time‐frequency domain to make mixing become instantaneous. We then separate the sources in each frequency bin based on an independent component analysis (ICA) algorithm. For the present paper, we choose the complex version of fixedpoint iteration (CFPI), i.e. the complex version of FastICA, as the algorithm. From the separated signals in the time‐frequency domain, we reconstruct output‐separated signals in the time domain. To solve the so‐called permutation problem due to the indeterminacy of permutation in the standard ICA, we propose a method that applies a special property of the CFPI cost function. Generally, the cost function has several optimal points that correspond to the different permutations of the outputs. These optimal points are isolated by some non‐optimal regions of the cost function. In different but neighboring bins, optimal points with the same permutation are at almost the same position in the space of separation parameters. Based on this property, if an initial separation matrix for a learning process in a frequency bin is chosen equal to the final separation matrix of the learning process in the neighboring frequency bin, the learning process automatically leads us to separated signals with the same permutation as that of the neighbor frequency bin. In each bin, but except the starting one, by chosen the initial separation matrix in such a way, the permutation problem in the time domain reconstruction can be avoided. We present the results of some simulations and experiments on both artificially synthesized speech data and real‐world speech data, which show the effectiveness of our approach.

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.

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.

Classical techniques to estimate the spectrum of the multi‐component signal are based on Fourier‐based transformations. The frequency estimates obtained from their spectral peaks are affected by the window length and phase of signal component, thus presenting a large variance even in the absence of noise. The spectrum of the signals is estimated with the help of the Wigner‐Ville distribution and its time‐frequency representation is obtained. For the same purpose, the min‐norm method (subspace method) is used. The accuracy of the tested methods was investigated and compared with the parameters of the frequency estimation via FFT. The proposed methods were also tested with non‐stationary multiple‐component signals occurring during the fault operation of inverter‐fed drives and transmission lines.

The purpose of this paper is to show a comparative study of different direction-of-arrival (DOA) estimation techniques, namely, multiple signal classification (MUSIC) algorithm, delay-and-sum (DAS) beamforming, support vector regression (SVR), multivariate linear regression (MLR) and multivariate curvilinear regression (MCR).

The relative delay between the microphone signals is the key attribute for the implementation of any of these techniques. The machine-learning models SVR, MLR and MCR have been trained using correlation coefficient as the feature set. However, MUSIC uses noise subspace of the covariance-matrix of the signals recorded with the microphone, whereas DAS uses the constructive and destructive interference of the microphone signals.

Variations in root mean square angular error (RMSAE) values are plotted using different DOA estimation techniques at different signal-to-noise-ratio (SNR) values as 10, 14, 18, 22 and 26dB. The RMSAE curve for DAS seems to be smooth as compared to PR1, PR2 and RR but it shows a relatively higher RMSAE at higher SNR. As compared to (DAS, PR1, PR2 and RR), SVR has the lowest RMSAE such that the graph is more suppressed towards the bottom.

DAS has a smooth curve but has higher RMSAE at higher SNR values. All the techniques show a higher RMSAE at the end-fire, i.e. angles near 90°, but comparatively, MUSIC has the lowest RMSAE near the end-fire, supporting the claim that MUSIC outperforms all other algorithms considered.

This paper aims to present an context evaluation and frequency measurement method for surface acoustic wave (SAW) resonant sensor.

This method is based on a signal subspace construction, which, along with assembling optional value set, provides the results.

The method can assess the application context and improve the resolution and accuracy of the passive wireless SAW resonator sensor system.

Passive wireless SAW resonators have been used as sensor elements for different physical parameters such as temperature, pressure and force in a number of industrial and medical applications. Various wireless channel environments introduce different application contexts.

This paper aims to present a method for the efficient reduction of networks modelling parasitic couplings in very‐large‐scale integration (VLSI) circuits.

The parasitic effects are modelled by large RLC networks and current sources for the digital switching currents. Based on the determined behaviour of the digital modules, an efficient description of these networks is proposed, which allows for a more efficient model reduction than standard methods.

The proposed method enables a fast and efficient simulation of the parasitic effects. Additionally, an extension of the reduction method to elements, which incorporate some supply voltage dependence to model the internal currents more precisely than independent current sources is presented.

The presented method can be applied to large electrical networks, used in the modelling of parasitic effects, for reducing their size. A reduced model is created which can be used in investigations with circuit simulators requiring a lowered computational effort.

Contrary to existing methods, the presented method includes the knowledge of the behaviour of the sources in the model to enhance the model reduction process.

During recent years, higher order statistics (HOS) have found a wide applicability in many diverse fields, e.g. biomedicine, harmonic retrieval and adaptive filtering. In power spectrum estimation, the signal under consideration is processed in such a way that the distribution of power among its frequency is estimated and phase relations between the frequency components are suppressed. Higher order statistics and their associated Fourier transforms reveal not only amplitude information about a signal, but also phase information. If a non‐Gaussian signal is received along with additive Gaussian noise, a transformation to higher order cumulant domain eliminates the noise. These are some methods for estimation of signal components, based on HOS. In the paper we apply the MUSIC method both for the correlation and the fourth order cumulant, to investigate the state of asynchronous running of synchronous machines and the fault operation of inverter‐fed induction motors. When the investigated signal is distorted by a coloured noise, more exact results can be achieved by applying cumulants.