2D Multiple Signal Classification Research Articles

Cognitive dual coprime frequency diverse array MIMO radar network for target discrimination and main‐lobe interference mitigation

AbstractThe authors propose a novel dual coprime frequency diverse array (FDA) multiple input multiple output (DCFDA‐MIMO) radar network design, empowered by cognitive capabilities, aimed at target discrimination and mitigation of interference present in the standalone radar systems. That is, the proposed DCFDA‐MIMO design capitalises on the complementary advantages of FDAs for target discrimination and coprime arrays for enhanced resolution, resulting in superior performance. Additionally, the proposed DCFDA‐MIMO network employs a 2D multiple signal classification algorithm to achieve high‐resolution target localisation. By incorporating cognitive techniques based on the action‐perception cycle, the proposed approach demonstrates notable improvements in multiple target detection and tracking accuracy with fewer number of antenna elements as compared to existing techniques. Furthermore, it enhances individual radar beamforming performance for interference suppression and true target detection without prior information.

Localization of Dielectric Anomalies with Multi-Monostatic S11 Using 2D MUSIC Algorithm with Spatial Smoothing.

This article demonstrates that the complex value of S11 of an antenna, acquired in a multi-monostatic configuration, can be used for localization of a dielectric anomaly hidden inside a dielectric background medium when the antenna is placed close (~5 mm) to the geometry. It uses an Inverse Synthetic Aperture Radar (ISAR) imaging framework where data is acquired at multiple frequencies and look-angles. Initially, near-field scattering data are used for simulation to validate this methodology since the basic derivation of the Multiple Signal Classification (MUSIC) algorithm is based on the plain wave assumption. Later on, from an applications perspective, data acquisition is performed using an antipodal Vivaldi antenna that has eight constant-width slots on each arm. This antenna operates in a frequency range of 1 to 8.5 GHz and its S11 is fed to the 2D MUSIC algorithm with spatial smoothing whereas the antenna artifact and background effect are removed by subtracting the average S11 at each antenna location. Measurements reveal that this methodology gives accurate results with both homogeneous and inhomogeneous backgrounds because the size of data sub-arrays trades between the image noise and resolution, hence reducing the effect of inhomogeneity in the background. In addition to near-field ISAR imaging, this study can be used in the ongoing research on breast tumors and brain stroke detection, among others.

Joint Doppler and DOA estimation using (Ultra-)Wideband FMCW signals

The joint Doppler and Direction-of-Arrival (DOA) estimation of moving targets using an (Ultra-)Wideband (UWB) frequency modulated continuous-wave (FMCW) antenna array radar is investigated. Besides the well-known range migration problem, another concern for wideband signals is the DOA estimation problem. For the first time, both problems are considered in this paper simultaneously, where the wideband DOA is transformed into a second-order coupling system similar to the range migration problem by using the property of the FMCW signal. A novel embedded compensation approach to eliminate the coupling terms caused by range migration and wideband DOA is proposed and 2D multiple signal classification (2D MUSIC) algorithm is subsequently applied with dynamic noise subspace to joint estimation of Doppler and DOA. Further, to reduce the computational load caused by multiple eigendecompositions of large matrices, efficient implementation methods are proposed and their performance in speed, accuracy and robustness is compared. The performance of the proposed methods is validated by the numerical simulations and is compared with Keystone MUSIC. Finally, it is shown that for a small number of targets, the Rayleigh-Ritz is the most efficient approach among them.

Two-Dimensional Direction-of-Arrival Estimation of Noncircular Signals in Coprime Planar Array with High Degree of Freedom

In estimating the two-dimensional (2D) direction-of-arrival (DOA) using a coprime planar array, there are problems of the limited degree of freedom (DOF) and high complexity caused by the spectral peak search. We utilize the time-domain characteristics of signals and present a high DOF algorithm with low complexity based on the noncircular signals. The paper first analyzes the covariance matrix and ellipse covariance matrix of the received signals, vectorizes these matrices, and then constructs the received data of a virtual uniform rectangular array (URA). 2D spatial smoothing processing is applied to calculate the covariance of the virtual URA. Finally, the paper presents an algorithm using 2D multiple signal classification and an improved algorithm using unitary estimating signal parameters via rotational invariance techniques, where the latter solves the closed-form solutions of DOAs replacing the spectral peak search to reduce the complexity. The simulation experiments demonstrate that the proposed algorithms obtain the high DOF and enable to estimate the underdetermined signals. Furthermore, both two proposed algorithms can acquire the high accuracy.

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.

Estimating Angle-of-Arrival and Time-of-Flight for Multipath Components Using WiFi Channel State Information.

Channel state information (CSI) collected during WiFi packet transmissions can be used for localization of commodity WiFi devices in indoor environments with multipath propagation. To this end, the angle of arrival (AoA) and time of flight (ToF) for all dominant multipath components need to be estimated. A two-dimensional (2D) version of the multiple signal classification (MUSIC) algorithm has been shown to solve this problem using 2D grid search, which is computationally expensive and is therefore not suited for real-time localisation. In this paper, we propose using a modified matrix pencil (MMP) algorithm instead. Specifically, we show that the AoA and ToF estimates can be found independently of each other using the one-dimensional (1D) MMP algorithm and the results can be accurately paired to obtain the AoA–ToF pairs for all multipath components. Thus, the 2D estimation problem reduces to running 1D estimation multiple times, substantially reducing the computational complexity. We identify and resolve the problem of degenerate performance when two or more multipath components have the same AoA. In addition, we propose a packet aggregation model that uses the CSI data from multiple packets to improve the performance under noisy conditions. Simulation results show that our algorithm achieves two orders of magnitude reduction in the computational time over the 2D MUSIC algorithm while achieving similar accuracy. High accuracy and low computation complexity of our approach make it suitable for applications that require location estimation to run on resource-constrained embedded devices in real time.

Two-Dimensional Direction-of-Arrival Fast Estimation of Multiple Signals with Matrix Completion Theory in Coprime Planar Array.

In estimating the two-dimensional (2D) direction-of-arrival (DOA) using a coprime planar array, the main issues are the high complexity of spectral peak search and the limited degree of freedom imposed by the number of sensors. In this paper, we present an algorithm based on the matrix completion theory in coprime planar array that reduces the computational complexity and obtains a high degree of freedom. The algorithm first analyzes the covariance matrix of received signals to estimate the covariance matrix of a virtual uniform rectangular array, which has the same aperture as the coprime planar array. Matrix completion theory is then applied to estimate the missing elements of the virtual array covariance matrix. Finally, a closed-form DOA solution is obtained using the unitary estimation signal parameters via rotational invariance techniques (Unitary-ESPRIT). Simulation results show that the proposed algorithm has a high degree of freedom, enabling the estimation of more signal DOAs than the number of sensors. The proposed algorithm has reduced computational complexity because the spectral peak search is replaced by Unitary-ESPRIT, but attains similarly high levels accuracy to those of the 2D multiple signal classification algorithm.

Joint DOA and Doppler frequency estimation of coherent and/or same‐direction signals

SummaryThe smart antenna is one of the most attractive technologies in mobile communications. In this paper, a modified 2D multiple signal classification (MUSIC) algorithm based on eigenspace is presented to jointly estimate the directions of arrival and Doppler frequencies for the coherent and/or same‐direction signals. First, a modified 2D MUSIC (MMUSIC) algorithm is provided using the conjugate data to cope with the estimation for the case of coherent signals and/or same‐direction signals. Then on this basis, we derive an eigenspace‐based MMUSIC (ES‐MMUSIC) algorithm, which uses the information of both signal and noise subspaces. The ES‐MMUSIC algorithm can also estimate signals power. Besides, this method has no influence on directions of arrival and Doppler frequencies estimation of uncorrelated signals; in the meanwhile, the computational complexity will have no obvious increase as well. Finally, the results of several contrastive numerical simulations demonstrate the superiority of the ES‐MMUSIC algorithm compared with the MMUSIC algorithm.

Real-Valued 2D MUSIC Algorithm Based on Modified Forward/Backward Averaging Using an Arbitrary Centrosymmetric Polarization Sensitive Array.

Two-dimensional multiple signal classification (MUSIC) algorithm based on polarization sensitive array (PSA) has excellent performance. However, it suffers a high computational complexity due to a multitude of complex operations. In this paper, we propose a real-valued two-dimensional MUSIC algorithm based on conjugate centrosymmetric signal model, which is applicable to arbitrary centrosymmetric polarization sensitive array. The modified forward/backward averaging, which can be applied to the PSA, is presented. Hence, the eigen-decomposition analysis process and spectrum function computation are converted into real domain, prominently reducing the computational complexity. Then, the direction-of-arrival (DOA) estimation is decoupled from the polarization parameter estimation so that the four-dimensional spectral peak search process is avoided. The theoretical computational complexity is discussed and the Cramer-Rao bound (CRB) of DOA estimation is derived in this paper. The simulation results indicate that the proposed algorithm achieves superior accuracy in DOA estimation and has low computational complexity.

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.

Passive localisation of mixed far‐field and near‐field sources using uniform circular array

Using uniform circular array, a passive localisation algorithm is presented for the scenarios where both far-field and near-field narrow-band signals may exist synchronously. The differencing matrix and the orthogonal projection matrix of the signal subspace are built to classify the mixed signals and to estimate the 2D direction-of-arrivals (DOAs) of the near-field signals (NFSs). Then, the covariance matrix of signals is decomposed to obtain the noise subspace. Meanwhile, 1D multiple signal classification (MUSIC) is used to estimate the ranges of the NFSs and 2D MUSIC is used to estimate the DOAs of far-field signals (FFSs). Compared with two-stage MUSIC (TSMUSIC), the proposed algorithm can provide higher resolution for the DOAs so that the signals can be separated. In addition, compared with TSMUSIC and four-order cumulant MUSIC, the proposed algorithm has higher accuracy for localisation of both FFSs and NFSs. Simulation results are carried out to verify the performance of the proposed algorithm.

Impact energy level assessment of composite structures using MUSIC-ANN approach

Impact damage effects on the residual mechanical properties of the structure can be quite detrimental, and failure patterns depend strongly on the impact energy levels. This paper presents impact energy level assessment method using 2D multiple signal classification (MUSIC) and artificial neural network (ANN) approach for composite structures. Because uniform linear array has the shortcoming of the half-plane mirror effect, 2D MUSIC algorithm using a plum blossom sensor array is firstly applied to locate the position of impact and extract impact energy feature. Secondly, the relation impact energy feature, impact position, and impact energy level at different measurement points are established using ANN approach. Through the trained ANN, the impact energy level can be predicted when the position and impact energy feature results of unknown impact are given as input. Finally, the proposed method is applied to a quasi-isotropic epoxy laminate plate and a large stiffened carbon fiber composite structure showing its successful performance on composite structure. Copyright © 2015 John Wiley & Sons, Ltd.

Joint DFT-ESPRIT Estimation for TOA and DOA in Vehicle FMCW Radars

This letter proposes a joint discrete Fourier transform (DFT)—estimation of signal parameters via rotational invariance techniques (ESPRIT) estimator for time-of-arrival (TOA) and direction-of-arrival (DOA) in vehicle frequency-modulated continuous-wave (FMCW) radars. Since the vehicle FMCW radar should recognize vehicles in the side/rear area when the driver initiates a lane change, the estimation of the joint TOA/DOA between the radar and targets is an important issue for solving complicated location tasks. However, conventional joint estimation methods such as 2D-ESPRIT and 2D-multiple signal classification (MUSIC) cannot be adopted for real-time implementation due to their high computational loads. To satisfy the required accuracy specifications and reduce complexity compared with the conventional estimator, we propose a low-complexity joint TOA and DOA estimator that uses the combined DFT-ESPRIT algorithm for FMCW radars. The performance of the proposed estimation in multitarget environments was derived and compared with the Monte Carlo simulation results. The root-mean-square error (RMSE) of the proposed method was compared with that of 2D-ESPRIT with various parameters. To verify the performance of the proposed combination method, we implemented the FMCW radar and verified its performance in an anechoic chamber environment.

Two-Dimensional Direction-of-Arrivals Estimation Based on One-Dimensional Search Using Rank Deficiency Principle

A novel efficient method for two-dimensional (2D) direction-of-arrivals (DOAs) estimation is proposed to reduce the computational complexity of conventional 2D multiple signal classification (2D-MUSIC) algorithm with uniform rectangular arrays (URAs). By introducing two electrical DOAs, the formula of 2D-MUSIC is transformed into a new one-dimensional (1D) quadratic optimal problem. This 1D quadratic optimal problem is further proved equivalent to finding the conditions of noise subspace rank deficiency (NSRD), which can be solved by an efficient 1D spectral search, leading to a novel NSRD-MUSIC estimator accordingly. Unlike 2D-MUSIC with exhaustive 2D search, the proposed technique requires only an efficient 1D one. Compared with the estimation of signal parameter via rotation invariance techniques (ESPRIT), NSRD-MUSIC has a significantly improved accuracy. Moreover, the new algorithm requires no pair matching. Numerical simulations are conducted to verify the efficiency of the new estimator.

Multi-impact source localisation on aircraft composite structure using uniform linear PZT sensors array

Composite structures are often struck by foreign objects at multiple locations. Multiple dents may be found on the composite structures such that it is hard to determine the number of impact events. This research presents a multiple impacts localisation method based on the near-field 2D multiple signal classification (2D-MUSIC) method for the composite structure. When multiple impacts are loaded on the plate, it is difficult to localise the positions of multiple impacts because of the overlapping of elastic waves produced by multi-impacts and the edge reflection of the plate. The MUSIC approach can provide estimation of the parameters of multiple emitters without any signal separation of mixed multi-impacts. It is well known that the performance of the MUSIC algorithm largely depends on the successful determination of the number of impacts. Gerschgorin disc theorem is applied for estimating the number of impacts. To verify the proposed method, experiments were preformed on the composite structure. The estimation of the number of impacts and localisation results are in good agreement with the actual impact occurrence. Finally, the proposed method was applied to a real aircraft composite oil tank which showed its successful performance on complex composite structures.

Speech recognition in cars by speaker localization using microphone array

AbstractThis paper proposes a speech recognition system for noisy car environments based on speaker localization using a microphone array. In this system, multiple microphones are arranged in two‐dimensional (2D) space to surround the interior of a car, and the speaker location is first estimated by the proposed HE (Harmonic Enhanced) 2D MUSIC (MUltiple SIgnal Classification) technique. The proposed HE 2D MUSIC is a two‐dimensional extension of the DOA (Direction Of Arrival) estimation method called MUSIC [11]. HE 2D MUSIC also uses the harmonic structure of speech signals to separate target speech signals from additive noise. Then, 2D Delay and Sum (DS) is applied to enhance the target speech. Such preprocessing makes robust speech recognition in noisy car environments possible. Moreover, in the proposed system, not only a driver, but also a fellow passenger can control car electronics by their voices regardless of where they are. The results of simulations and experiments in a real car environment are presented to confirm the validity of our proposed system. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 34(8): 1–12, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10415