Conventional Multiple Signal Classification Algorithm Research Articles

Frequency estimation of multiple complex sinusoids using noise suppressing predictive FIR filter

The paper presents a predictive filter based frequency estimation algorithm (PF-FE) for multiple complex sinusoids corrupted by additive white Gaussian noise. The proposed frequency estimator combines the noise suppression capability of an FIR filter with a least squares approach minimizing the sum of the squared errors of a prediction filter. The FIR filter coefficients are chosen such that the sinusoidal components are predicted with minimum distortion. Computer simulation results are given to contrast the performance of the proposed PF-FE method with six other state-of-the-art frequency-estimators and the Cramer-Rao lower bound. Furthermore, the paper provides a complexity analysis of the PF-FE method and compares it with those of the conventional MUltiple SIgnal Classification algorithm and some selected state-of-the-art methods from the literature. Performance of the proposed PF-FE method is validated with effective mean square error (EMSE) plots for model orders of three and five and under different observation lengths and signal-to-noise ratio values. Finally, the paper demonstrates how EMSE varies for the proposed method when the prediction filter length is increased from 20 to 75. After extensive simulations, it is observed that when filter length is larger than half the observation length the EMSE will start to degrade.

DOA Estimation of Indoor Sound Sources Based on Spherical Harmonic Domain Beam-Space MUSIC

The Multiple Signal Classification (MUSIC) algorithm has become one of the most popular algorithms for estimating the direction-of-arrival (DOA) of multiple sources due to its simplicity and ease of implementation. Spherical microphone arrays can capture more sound field information than planar arrays. The collected multichannel speech signals can be transformed from the space domain to the spherical harmonic domain (SHD) for processing through spherical modal decomposition. The spherical harmonic domain MUSIC (SHD-MUSIC) algorithm reduces the dimensionality of the covariance matrix and achieves better DOA estimation performance than the conventional MUSIC algorithm. However, the SHD-MUSIC algorithm is prone to failure in low signal-to-noise ratio (SNR), high reverberation time (RT), and other multi-source environments. To address these challenges, we propose a novel joint spherical harmonic domain beam-space MUSIC (SHD-BMUSIC) algorithm in this paper. The advantage of decoupling the signal frequency and angle information in the SHD is exploited to improve the anti-reverberation property of the DOA estimation. In the SHD, the broadband beamforming matrix converts the SHD sound pressure to the beam domain output. Beamforming enhances the incoming signal in the desired direction and reduces the SNR threshold as well as the dimension of the signal covariance matrix. In addition, the 3D beam of the spherical array has rotational symmetry and its beam steering is decoupled from the beam shape. Therefore, the broadband beamforming constructed in this paper allows for the arbitrary adjustment of beam steering without the need to redesign the beam shape. Both simulation experiments and practical tests are conducted to verify that the proposed SHD-BMUSIC algorithm has a more robust adjacent source discrimination capability than the SHD-MUSIC algorithm.

Multi-Source DoA Estimation of EM Waves Impinging Spherical Antenna Array With Unknown Mutual Coupling Using Relative Signal Pressure Based Multiple Signal Classification Approach

Spherical antenna array (SAA) is a configuration that scans almost all the radiation sphere with constant directivity. It finds applications in spacecraft and satellite communication. Multiple signal classification (MUSIC) is a widely used multiple source direction-of-arrival (DoA) estimation method because of its low complexity implementation in practical applications. Conversely, it is susceptible to noise, which consequently affects its accuracy of localization. In this paper, MUSIC-based methods that operate at low signal-to-noise ratio (SNR) are developed via relative electromagnetic (EM) wave pressure measurements of a SAA. The proposed methods are the relative pressure MUSIC (RP-MUSIC), and in spherical domain (SH-RP-MUSIC). The developed SH-RP-MUSIC algorithm is in spherical domain thereby allows frequency-smoothing approach for the de-correlation of the coherent source signals towards an enhanced accuracy of localization. Both RP-MUSIC and SH-RP-MUSIC algorithms developed have the ability to estimate the number of active sources that is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge of the conventional MUSIC algorithm. Numerical experiments were used to demonstrate the adequacy of the developed algorithms. In addition, measured data from experiment, which is the practically acceptable way to examine any procedure is employed to demonstrate the merits of the developed algorithms against the conventional MUSIC algorithm and other recent multiple source localization method in literature. Finally, in order to achieve DoA estimations with adequate localization accuracy at low SNR using SAA, SH-RP-MUSIC algorithm is a better choice.

Ameliorated-multiple signal classification (Am-MUSIC) for damage imaging using a sparse sensor network

Multiple Signal Classification (MUSIC) – a directional scanning and searching algorithm, has gained its prominence in phased array-facilitated nondestructive evaluation. Nevertheless, prevailing MUSIC algorithms are largely bound up with the use of a dense linear array, which fail to access the full planar area of an inspected sample, leaving blind zones to which an array fails to scan, along with the incapability of differentiating multiple damage sites that are close one from another. To break above limitations, conventional MUSIC algorithm is ameliorated in this study, by manipulating the signal representation matrix at each pixel using the excitation signal series, instead of the scattered signal series, which enables the use of a sparse sensor network with arbitrarily positioned transducers. In the ameliorated MUSIC (Am-MUSIC), the orthogonal attributes between the signal subspace and noise subspace inherent in the signal representation matrix is quantified, in terms of which the Am-MUSIC yields a full spatial spectrum of the inspected sample, and damage, if any, can be visualized in the spectrum. Am-MUSIC is validated, in both simulation and experiment, by evaluating single and multiple sites of damage in plate-like waveguides with a sparse sensor network. Results verify that i) detectability of Am-MUSIC-driven damage imaging is not limited by damage quantity; ii) Am-MUSIC has full access to a sample, eliminating blind zones; and iii) the amelioration expands conventional MUSIC from phased array-facilitated nondestructive evaluation to health monitoring using built-in sparse sensor networks.

High-Efficiency Super-Resolution FMCW Radar Algorithm Based on FFT Estimation.

This paper proposes a high-efficiency super-resolution frequency-modulated continuous-wave (FMCW) radar algorithm based on estimation by fast Fourier transform (FFT). In FMCW radar systems, the maximum number of samples is generally determined by the maximum detectable distance. However, targets are often closer than the maximum detectable distance. In this case, even if the number of samples is reduced, the ranges of targets can be estimated without degrading the performance. Based on this property, the proposed algorithm adaptively selects the number of samples used as input to the super-resolution algorithm depends on the coarsely estimated ranges of targets using the FFT. The proposed algorithm employs the reduced samples by the estimated distance by FFT as input to the super resolution algorithm instead of the maximum number of samples set by the maximum detectable distance. By doing so, the proposed algorithm achieves the similar performance of the conventional multiple signal classification algorithm (MUSIC), which is a representative of the super resolution algorithms while the performance does not degrade. Simulation results demonstrate the feasibility and performance improvement provided by the proposed algorithm; that is, the proposed algorithm achieves average complexity reduction of compared to the conventional MUSIC algorithm while achieving its similar performance. Moreover, the improvement provided by the proposed algorithm was verified in practical conditions, as evidenced by our experimental results.

Damage identification for plate-like structures using ultrasonic guided wave based on improved MUSIC method

The ultrasonic guided wave has emerged as one of the most prominent and promising tools for metal and composite structures in the fields of structural health monitoring (SHM) and nondestructive testing (NDT). This paper presents a novel model-based 2D multiple signal classification (MUSIC) damage identification algorithm for plate-like structures. Unlike the conventional MUSIC algorithm, the proposed model-based 2D MUSIC damage identification algorithm is deduced based on the assumption of near-field according to the propagation model of guided waves. Since scattered signals contain the location information of damage, the cross-correlation function of residual signals received by experiment and scattered signals received by damage scattering model are developed for spatial spectrum estimation MUSIC algorithm. Due to the uncorrelation of signal and noise, the damage can be successfully identified by searching the peak point of spatial spectrum in the monitored area employing the orthogonality of signal subspace and noise subspace. The accuracy and effectiveness of the proposed method are firstly validated by numerical simulation on aluminum plate, and the general applicability is further verified by experiments for the damage identification of laminated composite plate. The numerical and experimental results demonstrate the proposed damage identification algorithm is appropriate for damage identification of plate-like structures with high estimation accuracy and resolution.

Two‐dimensional direction of arrival estimation for coprime planar arrays via a computationally efficient one‐dimensional partial spectral search approach

The authors investigate the problem of two-dimensional (2D) direction of arrival (DOA) estimation of multiple signals for coprime planar arrays (CPAs) in this study and they propose a computationally efficient 1D partial spectral search approach based on multiple signal classification (MUSIC) algorithm. The conventional 2D MUSIC algorithm for CPAs has a great DOA estimation performance, but suffers from a tremendously expensive computational complexity due to the 2D spectral search. To this end, the proposed approach first decreases the dimension of the spectrum function to one dimension and then utilises the linear relationship between the true and ambiguous DOA estimates to form a 1D partial spectral search over a small sector. Finally, the true DOA estimates can be achieved based on the coprime property. The proposed approach can have an impressively good DOA estimation performance, but with a low computational cost. Simulation results validate the effectiveness and superiority of the proposed approach.

Harmonic Frequency Estimation Algorithm Based on MSWF in Power System

This paper pointed out the disadvantage of harmonic frequency estimation algorithm in current power system, presented the MUSIC (multiple signal classification)-based harmonic frequency estimation algorithm in power system, and analyzed the computational complexity of the MUSIC algorithm. In order to reduce the computational complexity of conventional MUSIC algorithm and to increase the real-time characteristic of harmonic frequency estimation algorithm, we combined the multi-stage wiener filter (MSWF) recursive algorithm and MUSIC algorithm so as to avoid the subspace decomposition process of the conventional MUSIC algorithm, thus realizing the purpose of significantly reducing the computational complexity of the MUSIC algorithm. Through theoretical analysis and simulation experiments, we find that the algorithm proposed in this paper is of excellent resolution characteristic, and less dependent on data volume.

A Root-MUSIC Algorithm for Angle Estimation in Bistatic MIMO Radar

This paper discusses the problem of direction of departure (DOD) and direction of arrival (DOA) estimation using the root multiple signal classification (MUSIC) algorithm in a bistatic multiple input and multiple output (MIMO) radar. The proposed algorithm gets the estimation of DOA and DOD via computing the roots of polynomials and it avoids the spectral peak searching in the conventional MUSIC algorithm. Thus the Root-MUSIC algorithm has much lower computational load. Simulation results illustrate our proposed algorithm has better angle estimation performance than the conventional algorithms.

Noise source localization by applying multiple signal classification with wavelet transformation

In an inverse acoustic problem with nearfield sources, it is important to separate multiple acoustic sources and to measure the position of each target. This paper proposes a new algorithm by applying multiple signal classification (MUSIC) to the outputs of discrete wavelet transformation with subband selection based on the entropy threshold. Some numerical experiments show that the proposed method can estimate the more precise positions than a conventional MUSIC algorithm under moderately correlated signal and relatively low signal-to-noise ratio case.

Improved MUSIC Algorithm for the Code-Timing Estimation of DS-CDMA Multipath-Fading Channels in Multiantenna Systems

A multiple-signal-classification (MUSIC) approach of estimating the code timings of a desired user is considered for direct-sequence code-division multiple-access (DS-CDMA) multipath-fading channels when exploiting multiple receive antennas with either spatially uncorrelated or fully correlated fading. The acquisition performance of the conventional MUSIC timing estimator employing a single antenna is not good for the small size of observation windows and low signal-to-noise ratios (SNRs). Multiple antennas allow for rapid acquisition and lowers the range of detectable SNR. An efficient and improved MUSIC algorithm of estimating the multipath timings of a desired user for DS-CDMA systems is presented. In multipath-fading channels, the solution of the proposed algorithm is based on successively optimizing the cost function for increasing numbers of multipath delays, which does not require a multidimensional search for multidelay paths. Furthermore, the estimate of code timing at each path is obtained by finding the zeros of second-order polynomials, which is computationally efficient. The proposed MUSIC algorithm significantly improves the acquisition performance of conventional MUSIC algorithm in the presence of multipath time-varying Rayleigh-fading channels with arbitrary time delays. The acquisition performance of multiple antennas-based MUSIC timing estimators is much better than that of a single-antenna-based timing estimator. The Crame/spl acute/r-Rao bound for the code-timing estimator based on multiple antennas is presented.

Direction of Arrival Estimation with a Novel Single-Port Smart Antenna

A novel direction of arrival (DOA) estimation technique that uses the conventional multiple-signal classification (MUSIC) algorithm with periodic signals is applied to a single-port smart antenna. Results show that the proposed method gives a high-resolution (1 degree) DOA estimation in an uncorrelated signal environment. The novelty lies in that the MUSIC algorithm is applied to a simplified antenna configuration. Only 1 analogue-to-digital converter (ADC) is used in this antenna, which features low power consumption, low cost, and ease of fabrication. Modifications to the conventional MUSIC algorithm do not bring much additional complexity. The proposed technique is also free from the negative influence by the mutual coupling among antenna elements. Therefore, it offers an economical way to extensively implement smart antennas into the existing wireless mobile communications systems, especially at the power consumption limited mobile terminals such as laptops in wireless networks.