Classical Multiple Signal Classification Research Articles

Simultaneous Correlative Interferometer Technique for Direction Finding of Signal Sources.

In this paper, we propose a novel simultaneous Correlative Interferometer (CI) technique that elaborately estimates the Direction of Arrival (DOA) of multiple source signals incident on an antenna array. The basic idea of the proposed technique is that the antenna-array-based receiver compares the phase of the received signal with one of the candidates at each time sample and jointly exploits these multiple time samples to estimate the DOAs of multiple signal sources. The proposed simultaneous CI-based DOA estimation technique collectively utilizes multiple time-domain samples and can be regarded as a generalized version of the conventional CI algorithm for the case of multiple time-domain samples. We first thoroughly review the conventional CI algorithm to comprehensively explain the procedure of the direction-finding algorithm that adopts the phase information of received signals. We also discuss several technical issues of conventional CI-based DOA estimation techniques that are originally proposed for the case of a single time-domain sample. Then, we propose a simultaneous CI-based DOA estimation technique with multi-sample diversity as a novel solution for the case of multiple time-domain samples. We clearly compare the proposed simultaneous CI technique with the conventional CI technique and we compare the existing Multiple Signal Classification (MUSIC)-based DOA estimation technique with the conventional CI-based technique by using the DOA spectrum as well. To the best of our knowledge, the simultaneous CI-based DOA estimation technique that effectively utilizes the characteristics of multiple signal sources over multiple time-domain samples has not been reported in the literature. Through extensive computer simulations, we show that the proposed simultaneous CI technique significantly outperforms both the conventional CI technique in terms of DOA estimation even in harsh environments and with various antenna array structures. It is worth noting that the proposed simultaneous CI technique results in much better performance than the classical MUSIC algorithm, which is one of the most representative subspace-based DOA estimation techniques.

Enhanced DOA Estimation With Co-Prime Array in the Scenario of Impulsive Noise: A Pseudo Snapshot Augmentation Perspective

Co-prime array configuration is popular in the recent development of array signal processing by exploiting the sparse arrangement of arrays and the co-prime feature of the number of subarray elements. However, the assumption of Gaussian noise in most co-prime array processing research might lead to model mismatch in practical scenarios of impulsive noise, and therefore have an adverse impact on the estimation of direction of arrival (DOA). Moreover, co-prime array builds an enlarged virtual array by vectorizing the covariance matrix of the received signals, where the equivalent received signals of the virtual array have only a single snapshot. In this paper, we propose an enhanced fractional low-order method (EFLOM) for co-prime array configuration in scenarios of impulsive noise, from the perspective of pseudo snapshot increment. Since impulsive noise does not have finite second-order statistics or high-order cumulant, we construct a series of equivalent covariance matrices by using phased fractional low-order moments with different orders of the received signals. Then, the vectorization of multiple equivalent covariance matrices can be formed as the equivalent received signals of the virtual array with multiple snapshots. Since the reformed observations of the equivalent received signals are from the same sources, additional spatial smoothing preprocessing operations are still needed for decorrelation. While in this paper, we propose an improved spatial smoothing (ISS) technique by applying the information of both autocorrelation and cross subarray correlation of the covariance matrix. Afterwards, the classical multiple signal classification (MUSIC) is applied for the estimation of DOAs. The performance of the proposed method is theoretically verified and simulations are also provided to show its effectiveness in terms of the generalized signal to noise ratio (GSNR), parameter of impulsive noise, and angle separation.

Eliminating Space Scanning: Fast mmWave Beam Alignment with UWB Radios

Due to their large bandwidth and impressive data speed, millimeter-wave (mmWave) radios are expected to play a key role in the 5G and beyond (e.g., 6G) communication networks. Yet, to release mmWave’s true power, the highly directional mmWave beams need to be aligned perfectly. Most existing beam alignment methods adopt an exhaustive or semi-exhaustive space scanning, which introduces up to seconds of delays. To eliminate the need for complex space scanning, this article presents an Ultra-wideband (UWB)-assisted mmWave communication framework, which leverages the co-located UWB antennas to estimate the best angles for mmWave beam alignment. One major challenge of applying this idea in the real world is the barrier of limited antenna numbers. Commercial-Off-The-Shelf (COTS) devices are usually equipped with only a small number of UWB antennas, which are not enough for the existing algorithms to provide an accurate angle estimation. To solve this challenge, we design a novel Multi-Frequency MUltiple SIgnal Classification (MF-MUSIC) algorithm, which extends the classic MUltiple SIgnal Classification (MUSIC) algorithm to the frequency domain and overcomes the antenna limitation barrier in the spatial domain. Extensive real-world experiments and numerical simulations illustrate the advantage of the proposed MF-MUSIC algorithm. MF-MUSIC uses only three antennas to achieve an accurate angle estimation, which is a mere 0.15° (or a relative difference of 3.6%) different from the state-of-the-art 16-antenna-based angle estimation method.

Analysis and Design of a MuSiC-Based Angle of Arrival Positioning System

In this research article, a concept for a secondary RAdio Direction And Ranging (RADAR) angle of arrival based system with cooperative targets transmitting at 2.4 GHz and using Multiple Signal Classification (MuSiC) to determine the angles of incidence is investigated. In addition to introducing common algorithms and presenting thorough derivations, the system is first examined through simulations. To prove the concept, hardware, firmware, and software are developed. For MuSiC, we propose three novel methods to obtain the correct incident angle from the spectrum, especially in strong multipath environments. These methods work either for a single spectrum or for a combination recorded at multiple times. Together with the estimated angles of incidence, our methods determine measures on the respective likelihoods. Based on this, we additionally propose two algorithms for computing the final position. Our system is characterized in both a simple 20 m × 15 m outdoor and a 17 m × 13 m multipath indoor environment, where we achieve a mean angular error of 3∘ and a mean positioning error of 0.67 m for the former using only four base stations with four antennas each. Our novel approach shows position accuracy improvements of 15% outdoors and 25% indoors compared to classical MuSiC estimation.

A novel displaced coprime parallel array for two-dimensional direction of arrival estimation

ABSTRACT The traditional coprime parallel array (TrCPA) is prone to suffer from the problem of angle ambiguity, due to its larger inter-element spacing and limited array aperture, for two-dimensional direction of arrival estimation. And the classic multiple signal classification (MUSIC) algorithm has heavy complexity and has poor estimation ability in some unsatisfactory situations. Both restrict the improvement of angle estimation performance. In this paper, we design a novel displaced coprime parallel array termed NDCPA, by rearranging and misplacing all subarrays of TrCPA along the coordinate axis, which brings about larger array aperture making contributions to superior angle estimation accuracy. Besides, we propose an improved two-step reduced-dimensional MUSIC algorithm to further enhance angle estimation accuracy. The proposed method reconstructs the covariance matrix, which can fully exploit the information contained in uniform linear subarrays. Meanwhile, the transformation of angle domain divides the original two-dimensional search into two one-dimensional searches, reducing complexity to a certain extent. Finally, simulation results verify the effectiveness and applicability of the proposed configuration and method compared with the other structures and methods.

An Efficient MUSIC Algorithm Enhanced by Iteratively Estimating Signal Subspace and Its Applications in Spatial Colored Noise

The classical multiple signal classification (MUSIC) algorithms mainly have two limitations. One is an insufficient number of snapshots, which usually causes an ill-posed sample covariance matrix in many real applications. The other limitation is the intense space-colored and time-white noise, which also breaks the separability between signal and noise subspaces. In the case of the insufficient sample, there are few signal components in the non-zero delay sample covariance matrix (SCM), where the space-colored and time-white noise components are suppressed by the temporal method. A set of non-zero delay sample covariance matrices are constructed, and a nonlinear object function is formulated. Hence, the sufficient non-zero delay SCMs ensure that enough signal components are used for signal subspace estimation. Then, the constrained optimization problem is converted into an unconstrained one by exploiting the Lagrange multiplier method. The nonlinear equation is solved by Newton’s method iteratively. Moreover, a proper initial value of the new algorithm is given, which can improve the convergence of the iterative algorithm. In this paper, the noise subspace is removed by the pre-projection technique in every iteration step. Then, an improved signal subspace is obtained, and a more efficient MUSIC algorithm is proposed. Experimental results show that the proposed algorithm achieves significantly better performance than the existing methods.

A sequential MUSIC algorithm for scatterers detection in SAR tomography enhanced by a robust covariance estimator

Synthetic aperture radar (SAR) tomography (TomoSAR) is an appealing tool for the extraction of height information of urban infrastructures. Due to the widespread applications of the multiple signal classification (MUSIC) algorithm in source localization, it is a suitable solution in TomoSAR when multiple snapshots (looks) are available. While the classical MUSIC algorithm aims to estimate the whole reflectivity profile of scatterers, sequential MUSIC algorithms are suited for the detection of sparse point-like scatterers. In this class of methods, successive cancellation is performed through orthogonal complement projections on the MUSIC power spectrum. In this work, a new sequential MUSIC algorithm named recursive covariance cancelled MUSIC (RCC-MUSIC), is proposed. This method brings higher accuracy in comparison with the previous sequential methods at the cost of a negligible increase in computational cost. Furthermore, to improve the performance of RCC-MUSIC, it is combined with the recent method of covariance matrix estimation called correlation subspace. Utilizing the correlation subspace method results in a denoised covariance matrix which in turn, increases the accuracy of subspace-based methods. Several numerical examples are presented to compare the performance of the proposed method with the relevant state-of-the-art methods. As a subspace method, simulation results demonstrate the efficiency of the proposed method in terms of estimation accuracy and computational load.

An adaptive segmentation and template matching based on SSA for interference suppression in millimeter wave radar

The mutual interference between multiple-input multiple-output (MIMO) millimeter wave (mmWave) radars reduces the signal-to-noise ratio (SNR) of the target, which in turn affects the angle estimation of the target. The singular spectrum analysis (SSA) can be used to suppress the interference by signal reconstruction. However, when there is interference in the intermediate frequency (IF) signal, it is hard to select the principal components (PCs) to reconstruct the IF signal. Therefore, an adaptive segmentation and template matching based on SSA (ASTM-SSA) is proposed. In the adaptive segmentation, the IF signal is divided into interference and interference-free segments based on the location of the interference. In the template matching, the desired PCs of the interference-free segment are considered as the templates to match with PCs of the interference segment, and the matched PCs are used to reconstruct the IF signal. After all the interference are suppressed, the classical multiple signal classification (MUSIC) is performed to estimate the angle of the target. The simulation and field experiments are conducted to verify the effectiveness of the ASTM-SSA, and the results indicate that the ASTM-SSA can effectively suppress interference and accurately estimate the angle of the target.

An Improved co-Prime Parallel Array With Conjugate Augmentation for 2-D DOA Estimation

This paper proposes an improved co-prime parallel array (CPPA) configuration for two-dimensional direction of arrival (2-D DOA) estimation. Specifically, the proposed array consists of a CPPA and two extra sensors along the vertical axis. The array aperture is extended through two steps. Firstly, a virtual parallel symmetric co-prime array (ViP-SCPA), consisting of two symmetric co-prime arrays(SCPAs), is constructed by using the conjugate augmented processing(CAP). Secondly, a one-dimensional (1-D) virtual co-array of ViP-SCPA is derived by exploiting the difference and sum co-array of the CPPA. Based on the obtained 1-D virtual co-array of ViP-SCPA, the 2-D DOA estimation is cast as a 1-D DOA estimation problem, for which both subspace-based methods and sparse representation algorithms can be adopted. To evaluate the array apertures of the proposed array, analytical expressions are derived to quantify the achievable number of unique lags, and the maximum number of consecutive lags of the resultant 1-D virtual co-array of ViP-SCPA. Simulation results verify the effectiveness of the proposed configuration by using the classical multiple signal classification (MUSIC) and sparse representation(SR) algorithm respectively.

A focusing MUSIC algorithm for baseline-free Lamb wave damage localization

The dispersion effect of Lamb waves can cause different degree of phase distortion of array signals, which will make the classical multiple signal classification (MUSIC) algorithm no longer valid in Lamb wave based damage localization. At the same time, baseline subtraction and estimation of the number of scattering sources are required in the classical MUSIC algorithm, which limits its application. A focusing MUSIC algorithm is proposed for baseline-free Lamb wave damage localization on isotropic materials. Based on the virtual time reversal technique, the dispersive wave packets adaptively back propagate according to their respective propagation distances so that the dispersion can be completely compensated and focusing signals are obtained. Wave packet truncation is implemented on the focusing signals and then let the truncated signals forward propagate with the group velocity at center frequency. The two operations, back propagation and forward propagation, can compensate the dispersion effect and avoid estimating of the number of scattering sources as well as baseline subtraction. Experiments are conducted on an aluminum plate to investigate the performance of the proposed algorithm. A uniform linear PZT array is glued on the aluminum plate to transmit and receive Lamb waves. Experimental results of the proposed algorithm as well as the classical MUSIC algorithm are given and compared. The comparison verifies the effectiveness of the proposed focusing MUSIC algorithm.

Ground target finding mechanism for unmanned aerial vehicles to secure crop field data

AbstractUnmanned aerial vehicles (UAVs) are being used in many military and civil applications while their use along wireless sensors is emerging abruptly as it upgrades the network in terms of reliability, lifetime, and connectivity. The system performance and throughput can be further enhanced if the UAVs in use are capable to locate ground sensor nodes as a means to address them smartly. In typically localization, the direction of arrival of incoming signals from a target node is measured by using a uniformed linear array (ULA) of antennas having signal processing capabilities. However, adding numerous antennas (transmitter receivers set) on a UAV is an extra burden which ultimately effects its speed, flight time, agility, and payload capacity. Considering the issue, a typical ULA antenna system is modeled by using virtual phased array (VPA) antenna. In the proposed VPA, a single antenna installed on a moving UAV will achieve the desire results. The well‐known multiple signal classification algorithm is modified to adapt this virtual antenna system. Four extra modules including calibrator, virtual antenna, rectifier, and adjustment are added in classical multiple signal classification algorithm to make it enable to work with single antenna. The proposed system mitigates the existing challenges of physical ULA and improves system throughput. Furthermore, due to its virtualness, VPA can adjust the number of antenna elements and interelement spacing, which adds multifrequency support and adaptive precision mechanism. A simulation model is developed in MATLAB to verify the performance of the VPA where the proposed system is evaluated against different scenarios with a varying number of parameters. It is found that VPA gives the same resolution and precision as a physical ULA antenna but it is more flexible, user‐friendly, cost‐effective, reliable, and most importantly, does not affect the UAV characteristics.

Recovery analysis of damped spectrally sparse signals and its relation to MUSIC

Abstract One of the classical approaches for estimating the frequencies and damping factors in a spectrally sparse signal is the MUltiple SIgnal Classification (MUSIC) algorithm, which exploits the low-rank structure of an autocorrelation matrix. Low-rank matrices have also received considerable attention recently in the context of optimization algorithms with partial observations, and nuclear norm minimization (NNM) has been widely used as a popular heuristic of rank minimization for low-rank matrix recovery problems. On the other hand, it has been shown that NNM can be viewed as a special case of atomic norm minimization (ANM), which has achieved great success in solving line spectrum estimation problems. However, as far as we know, the general ANM (not NNM) considered in many existing works can only handle frequency estimation in undamped sinusoids. In this work, we aim to fill this gap and deal with damped spectrally sparse signal recovery problems. In particular, inspired by the dual analysis used in ANM, we offer a novel optimization-based perspective on the classical MUSIC algorithm and propose an algorithm for spectral estimation that involves searching for the peaks of the dual polynomial corresponding to a certain NNM problem, and we show that this algorithm is in fact equivalent to MUSIC itself. Building on this connection, we also extend the classical MUSIC algorithm to the missing data case. We provide exact recovery guarantees for our proposed algorithms and quantify how the sample complexity depends on the true spectral parameters. In particular, we provide a parameter-specific recovery bound for low-rank matrix recovery of jointly sparse signals rather than use certain incoherence properties as in existing literature. Simulation results also indicate that the proposed algorithms significantly outperform some relevant existing methods (e.g., ANM) in frequency estimation of damped exponentials.

Robust Modified Multiple Signal Classification Algorithm for Direction of Arrival Estimation

A modified robust multiple signal classification (MUSIC) algorithm for direction-of-arrival (DOA) estimation of coherent source signals is devised in this work. Classical and subspace-based methods require large antenna elements and huge computations for the estimation of intended signals. It also deviates from its performance in low signal-to-noise ratio (SNR) conditions. Hence efficient and robust DOA estimators are required to tackle the real-time problems in wireless communication applications. To overcome these problems, the classical MUSIC algorithm is modified by assimilating Jordon canonical matrix in the covariance matrix for the reconstruction of data. With this modification, it is possible to make an accurate estimation of coherent sources even under an extremely low SNR environment. Furthermore, the proposed method requires fewer antenna elements, fewer snapshots, and less computation as compared to the classical MUSIC algorithm. The experimental results signify the effectiveness of the new method.

Accurate DOA Estimation With Adjacent Angle Power Difference for Indoor Localization

Indoor localization with high accuracy plays a key role in the field of Internet of Things in 5th-Generation (5G) era. With the introduction of Multiple Input Multiple Output (MIMO) technology in 5G, the direction-of-arrival (DOA) method is highly feasible in indoor localization. However, the direction of arrival is susceptible to complex indoor environment. To improve the accuracy and stability of DOA estimation, an adjacent angle power difference (AAPD) method is proposed based on Orthogonal Matching Pursuit (OMP). This method uses OMP to obtain an initial estimate of the direction, and then, adjusts the estimation by calculating the difference power of adjacent points at initial value point to get the fractional DOA. In the scenario of continuous movement, beamforming is further applied, which reduces the amount of calculation. Both simulation and experimental results show that the proposed method can achieve high accuracy and eliminate the error jitter. Compared with the classical Multiple Signal Classification (MUSIC) method for DOA estimation, the proposed method can increase accuracy by 46% under the condition of low SNR (Signal-to-Noise Ratio). The probability that the measurement error does not exceed 5° in the actual movement tests is 97.5%.

Adaptive Channel Estimation and Tracking for URA-Based Massive MIMO Systems

In this paper, an adaptive channel estimation and tracking method is proposed for the uniform rectangular array (URA) based massive multiple-input and multiple-output (M-MIMO) systems. To decrease the computational complexity while tracking the change on the channel state information (CSI) in time, a principal subspace analysis (PSA) based scheme is proposed to estimate and track the signal subspace based on the received signal covariance matrix. Based on the estimated signal subspace, the channel parameters including the direction of arrival (DoA) angle and the channel gain corresponding to each resolvable path of the channel, can be recovered via the classic multiple signal classification (MUSIC) algorithm. Then, the Cramer-Rao lower bound (CRLB) is derived to evaluate the performance of the proposed scheme. Simulation results are provided to verify the accuracy of the proposed scheme on the channel estimation.

One-Bit MUSIC

In this letter, we consider the problem of direction-of-arrival (DOA) estimation with one-bit quantized array measurements. With analysis, it is shown that, under mild conditions the one-bit covariance matrix can be approximated by the sum of a scaled unquantized covariance matrix and a scaled identity matrix. Although the scaling parameters unknown because of the extreme quantization, they do not affect the subspace-based DOA estimators. Specifically, the signal and noise subspaces can be straightforwardly determined through the eigendecomposition of the one-bit covariance matrix, without pre-processing such as unquantized covariance matrix reconstruction. With so-obtained subspaces, the most classical multiple signal classification (MUSIC) technique can be applied to determine the signal DOAs. The resulting method is thus termed as one-bit MUSIC. Thanks to the simplicity of this method, it can be very easily implemented in practical applications, whereas the DOA estimation performance is comparable to the case with unquantized covariance matrix reconstruction, as demonstrated by various simulations.

Reduced Dimension Based Two-Dimensional DOA Estimation with Full DOFs for Generalized Co-Prime Planar Arrays.

In this paper, we investigate the problem of two-dimensional (2D) direction-of-arrival (DOA) estimation for generalized co-prime planar arrays. The classic multiple signal classification (MUSIC)-based methods can provide a superior estimation performance, but suffer from a tremendous computational burden caused by the 2D spectral search. To this end, we reduce the 2D problem into a one-dimensional (1D) one and propose a reduced dimension partial spectral search estimation method, which can compress the search region into a small 1D sector. Moreover, the proposed method can utilize the full information of the entire array without degrees-of-freedom loss. Furthermore, an iterative approach is also proposed to reduce complexity and improve performance. Simulation results show that the proposed methods can provide improved performance with substantially reduced complexity, as compared to other state-of-the-art methods.

A Linear Synthetic Focusing Method for Microwave Imaging of 2-D Objects

This paper presents a linear synthetic focusing method (SFM) in order to mitigate the drawbacks of the classical multiple signal classification (MUSIC) algorithm for microwave imaging (MI). The determination of noise subspace dimension for MUSIC algorithm is a challenge when imaging an extended target. That is because an extended target comprises many point scatterers and a practical MI system provides an incomplete multistatic response (IMSR) matrix. The SFM obtains a focused multistatic response matrix for each location in the imaging region, which contains, mostly, the scattered field of a point scatterer at that location. In this way, the signal subspace is reduced to the one for a point scatterer, whose singular values are well-separated from those of the noise subspace. The proposed SFM is applied to the breast cancer detection using synthetic data. It is also applied to the experimental data given by Institute Fresnel to test the SFM for scenarios other than biomedical uses. It is shown that the SFM provides superior results over the classical MUSIC algorithm, especially for extended targets and IMSR matrices.

A Novel Algorithm to Estimate Closely Spaced Source DOA

<p>In order to improve resolution and direction of arrival (DOA) estimation of two closely spaced sources, in context of array processing, a new algorithm is presented. However, the proposed algorithm combines both spatial sampling technic to widen the resolution and a high resolution method which is the Multiple Signal Classification (MUSIC) to estimate the DOA of two closely spaced sources impinging on the far-field of Uniform Linear Array (ULA). Simulations examples are discussed to demonstrate the performance and the effectiveness of the proposed approach (referred as Spatial sampling MUSIC SS-MUSIC) compared to the classical MUSIC method when it’s used alone in this context.</p>

Wind speed and direction measurement based on arc ultrasonic sensor array signal processing algorithm

This article investigates a kind of method to measure the wind speed and the wind direction, which is based on arc ultrasonic sensor array and combined with array signal processing algorithm. In the proposed method, a new arc ultrasonic array structure is introduced and the array manifold is derived firstly. On this basis, the measurement of the wind speed and the wind direction is analyzed and discussed by means of the basic idea of the classic MUSIC (Multiple Signal Classification) algorithm, which achieves the measurements of the 360° wind direction with resolution of 1° and 0–60m/s wind speed with resolution of 0.1m/s. The implementation of the proposed method is elaborated through the theoretical derivation and corresponding discussion. Besides, the simulation experiments are presented to show the feasibility of the proposed method. The theoretical analysis and simulation results indicate that the proposed method has superiority on anti-noise performance and improves the wind measurement accuracy.