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

Based on the extended noise subspace, the case of weak signal direction of arrival (DOA) estimation in presence of strong jamming is studied, and a novel approach is given in this paper. Under the condition knowing the number of strong interferences, first, the method forms the extended noise subspace, then, uses the conventional multiple signal classification (MUSIC) algorithm to constrain the strong interferences effectively and estimates weak signals' DOA information accurately based on the extended noise subspace. The method does not require the angle information of strong interferences and it has the same computation burden as conventional MUSIC. Theoretic analysis and computer simulation demonstrate the method is effective and feasible.

ABSTRACTGenerally, a coprime L-shaped array (CLsA) is composed of two uniform L-shaped subarrays with larger spacing among inter-element to accomplish the improved direction of arrival (DOA) estimation performance. In this paper, the two subarrays are unfolded to extend the array aperture and the performance of the unfolded CLsA (UCLsA) for two-dimensional (2D) DOA estimation is investigated. In addition, an all array multiple signals classification (AA-MUSIC) algorithm is proposed for the UCLsA. By stacking the received signals of the two subarrays, the ambiguity problem can be avoided on the basis of the coprime property. Simultaneously, due to the combination of the cross-correlation and auto-correlation, the proposed AA-MUSIC algorithm can achieve the full degrees of freedom (DOFs) and obtain more accurate DOA estimates, nevertheless, the expensive total spectral search is entailed. Consequently, a reduced complexity MUSIC (RC-MUSIC) algorithm is proposed to relieve the computational burden. The Cramer-Rao Bounds (CRBs) are utilised as a theoretical benchmark for the lower bound of unbiased estimate. Furthermore, numerical simulations verify the effectiveness and superiority of the AA-MUSIC algorithm and RC-MUSIC method for the UCLsA.

This paper presents a brief overview of narrowband direction of arrival (DOA) estimation algorithms and techniques. A comprehensive study is carried out in this paper to investigate and evaluate the performance of variety of algorithms for DOA estimation. Two categories of DOA estimation algorithms are considered for discussion which are Classical methods and Subspace based techniques. Classical methods include Sum-and-Delay method and Capon’s Minimum Variance Distortionless Response (MVDR) while Subspace based techniques are multiple signal classification (MUSIC) and The Minimum Norm Technique. Also ESPIRIT technique is evaluated. Inefficiencies are pointed out and solutions are suggested to overcome these shortfalls. Simulation results shows that the MUSIC algorithm is able to better represent the DOAs of signals with more prominent peaks. The Min-Norm algorithm also identifies the DOAs of signals similar to the MUSIC algorithm, but produces spurious peaks at other locations. The MVDR method identifies the DOAs of signals, but the locations are not represented by sharp peaks, due to spectral leakage. The classical beamformer also produces several spurious peaks. MUSIC show higher accuracy and resolution than the other algorithms. It should be noted that MUSIC is more applicable because it can be used for different array geometries.

Multiple-input-multiple-output (MIMO) radar is well-known for providing high-resolution direction-of-arrival (DOA) estimation by forming a large-scaled sum coarray utilizing waveform diversity. However, the sacrifice is that a large number of snapshots are required to estimate the sample covariance matrix. When the number of training snapshots is limited, the performance of subspace-based DOA estimation method, such as multiple signal classification (MUSIC), deteriorates due to the distortion of noise subspace. In order to improve the accuracy of DOA estimation using MIMO radar in the case of few snapshots, we propose a method to refine the covariance matrix iteratively. The sampled covariance matrix is iteratively refined by subtracting cross-correlation terms using generalized inner product based on the previous DOA estimates. Finally, the MUSIC algorithm is implemented based on the refined sample covariance matrix to update the DOA estimates until achieving termination condition. Simulation results demonstrate that the additional covariance matrix refinement step enhances the accuracy of DOA estimation using MIMO radar in the case of limited snapshots significantly.

A single vector hydrophone can complete the estimation of the direction of arrival (DOA) of underwater target. However, the single vector hydrophone can only estimate limited number sources and its performance is degraded when the signal is non-stationary, such as LFM signal. Besides, the DOA estimation performance of wideband LFM can be further improved as the time-frequency characteristics of the wideband LFM signals are not fully utilized in the existing methods. In this paper, a DOA estimation of wideband LFM signal using single vector hydrophone based on LVD-MUSIC method is proposed. Firstly, the Lv Distribution (LVD) algorithm is used to estimate the signal parameters, which has the advantage of high time-frequency concentration and the ability of anti-noise. Then, considering that the single vector hydrophone has the characteristics of the array manifold, the subspace decomposition theory in array signal processing is applied to the array manifold. At the same time, a covariance matrix is constructed based on the LVD, and is introduced into the Multiple Signal Classification (MUSIC) algorithm to estimate DOA. Finally, the simulation results prove the effectiveness of the proposed method, and verify the improvement of the DOA estimation accuracy.

In automotive millimeter-wave (MMW) radar systems, achieving high-precision direction of arrival (DOA) estimation with a limited number of array elements is a crucial research focus. Compressive sensing (CS) techniques have been demonstrated to offer superior performance in DOA estimation compared to spectral estimation methods. However, traditional CS methods suffer from an off-grid effect, which causes their reconstruction results to deviate from the actual positions of the signal sources, thereby reducing the accuracy. Currently, as a gridless method, atomic norm minimization (ANM) has shown effectiveness in DOA estimation for uniform linear arrays (ULAs). However, the performance of ANM is suboptimal in non-uniform linear arrays (NULAs), and their computational efficiency is not satisfactory. In this paper, we propose a novel algorithm for DOA estimation in NULA, drawing inspiration from the alternating descent conditional gradient algorithm framework. First, we construct an atomic set based on the observation scene and select the atoms with the highest correlation to the residuals as potential signal sources for global estimation. Then, we construct a mapping function for the signal sources in the continuous domain and perform conditional gradient descent in the neighborhood of each signal source, addressing the bias introduced by the off-grid effect. We compared the proposed algorithm with ANM, Iterative Shrinkage Thresholding (IST), and Multiple Signal Classification (MUSIC) algorithms. Simulation experiments validate that the proposed algorithm effectively addresses the off-grid effect and is applicable to DOA estimation in coprime and random arrays. Furthermore, real data experiments confirm the effectiveness of the proposed algorithm.

gold-MUSIC based DOA estimation using ULA antenna of DS-CDMA sources with propagation delay diversity

Joint DOA-range-polarization estimation with a novel radar system, i.e., spatially separated polarization sensitive random frequency diverse array based on multiple-input multiple-output (SS-PSRFDA-MIMO) radar, is discussed. The proposed array can obtain not only unambiguous range estimation but also polarization parameter estimation. Firstly, the signal model of SS-PSRFDA-MIMO radar is constructed. Secondly, dimension reduction multiple signal classification (DR-MUSIC) algorithm is extended to parameter estimation with the proposed array. Last, simulations demonstrate the proposed algorithm is effective to estimate parameter, and the performance of proposed array is better than that of polarization sensitive frequency diverse array based on MIMO radar. It is worth mentioning that the Cramér–Rao lower bound (CRLB) of range estimation with the proposed array is much lower than that of PSFDA-MIMO radar.

This paper proposes a low complexity direction of arrival (DOA) estimation method for two-dimensional (2D) massive multiple-input multiple-output (MIMO) systems. The conventional Multiple SIgnal Classification (MUSIC) algorithm of an example of 2D DOA estimation has a great DOA estimation performance, although there is a problem that the calculation amount becomes enormous due to 2D spectral search, especially in massive MIMO systems. By using a part of row or column antenna elements, it is possible to separate search domains azimuth and elevation, respectively. Observation dimension can be degenerated to 1D and subset of row/column-wise antenna elements are sufficient for DOA estimation. The proposed approach can reduce computation complexity while achieving an improved DOA estimation accuracy. Simulation results validate its effectiveness and superiority. The obtained findings will be useful for designing practical massive MIMO systems in future mobile communications.

We address the problem of direction of arrival (DOA) estimation in an array-fed parabolic reflector antenna. We employ the estimated DOA and an adaptive nulling scheme for pattern nulling of a jamming signal. The multiple signal classification (MUSIC) algorithm is used to estimate the DOA. Adaptive nulling is obtained using the linearly constrained minimum variance (LCMV) beam former. The performance of the method is illustrated by specific examples.

In signal processing applications smart antenna arrays are used to locate sources of various signals and for beamforming. Various algorithms have been developed to determine or estimate the Direction Of Arrival (DOA). Direction Of Arrival estimation is utilized in a number of applications, including radar, navigation, communication, etc. Beamforming is a technique used for directive signal transmission and reception. In a smart antenna system antenna arrays adjusts the main lobe towards the desire user and generates null in the direction of interfering users to provide suppression. Objective of this paper is to analyze the performance of Multiple Signal Classification (MUSIC) algorithm a subspace method to estimate DOA and compare with classical Bartlett method of DOA. Beamforming algorithms like LMS and NVSSLMS are studied,analyzed and its performance parameters compared.

Compared with other systems with a single transmit antenna, multiple-input multiple-output (MIMO) radar systems have additional degrees of freedom that can enhance space resolution, improve parameter identifiability and enhance flexibility for transmit beam pattern design. The computational complexity for direction of arrival (DOA) estimation using sensor arrays increases very rapidly with the number of channels. The polynomial-rooting version of multiple signal classification algorithm (root-MUSIC) is computationally more efficient than spectral MUSIC. However, this algorithm can only be applied to uniform linear arrays. The manifold separation technique allows arrays of any geometry to be used with fast DOA estimators designed for linear arrays. In this study, a DOA estimation method that uses manifold separation and polynomial rooting technique is presented for monostatic MIMO radar with arbitrary array configuration. The algorithm offers a low computation complexity and an improved resolution capability for closely spaced sources as compared to conventional spectral MUSIC. Moreover, the wavefield modelling for monostatic MIMO radar and the number of mode selection of the proposed method are also analysed in the study. Finally, the simulation results are presented and the performances of the proposed algorithm are investigated and discussed.

In this paper, we deal with the multiple carrier frequency offsets (CFOs) estimation issues for orthogonal frequency-division multiple access (OFDMA) based uplink system. In such system, the interleaved carrier assignment scheme (CAS) has gained much attention for it brings maximum frequency diversity though it undergoes severe CFO problem than other schemes. The multiple-CFO estimation problem in this scheme uses a similar signal structure as that of Direction of Arrival (DOA) estimation problem in array signal processing, so subspace based multiple signal classification (MUSIC) algorithm can be exploited to estimate multiple CFOs. Compared with the conventional MUSIC algorithm utilizing eigenvalue decomposition of complex-value covariance matrix, the proposed unitary MUSIC and unitary root-MUSIC CFO estimation algorithms have comparable performance but lower computational complexity because of real-value covariance matrix used, which is verified by computer simulation.

Antennas without phase center (AWPC) are applied to resolve the less-sensors-than-sources problem in direction-of-arrival (DOA) estimation using subspace methods, typically, the well-known multiple signal classification (MUSIC) algorithm. This paper focuses on optimization of some design parameters of such antennas, including the rotation angle, the rotation step number and distances of two dipole couples, in order to improve the ambiguity and accuracy of all estimators which use AWPC structure. These optimization problems are formulated and solved by using Cramer-Rao bound (CRB) and ambiguity checking criteria. Specially, the parameters were optimized to avoid issues related to the ambiguity for half space localization problem while minimizing the CRB for improving the DOA estimation accuracy.

Efficient impact monitoring techniques are important to be researched to ensure the safety of in-service composite structures. Multiple signal classification–based method is a new promising method. This method uses small aperture sensor array arrangement instead of sparse sensor arrangement. It has the advantage of easy sensor array arrangement. By processing the whole sensor array outputs using the multiple signal classification algorithm, transfer function and material property of the structure are not needed. Although some researches have shown the potential of this new method, most of the current researches have been performed based on the far-field assumption on aluminum structures. In engineering applications, composite structures are the main structures which need impact monitoring. Deep research needs to be addressed. Aiming at these problems, an improved two-dimensional near-field multiple signal classification processing algorithm is presented for impact localization on composite structure in this article. The proposed method can simultaneously estimate the impact occurring direction and distance. A uniform carbon fiber composite plate and a real aircraft oil tank made of carbon composite structure with varied thickness are adopted to evaluate the presented method. To deal with the anisotropy performance of the complex composite, the two-dimensional multiple signal classification processing algorithm is also improved by taking into account the existing anisotropy performance.