Jie Zhuang

Robust Adaptive Array Beamforming With Subspace Steering Vector Uncertainties

In this letter, the steering vector of the signal-of-interest (SOI) is assumed to belong to a given linear subspace but the associated coordinates are otherwise unknown. In the case where the signal subspace is poorly estimated, a rank-constrained optimization problem is formulated in which the signal subspace is forced to intersect with the known linear subspace (where the SOI steering vector is located). Such problem can be reformulated as two subproblems via the variable alternation method. Also, the analytical solutions of these two subproblems can be found. Numerical results demonstrate that the proposed method can be regarded as an improved subspace-based method, which lowers the requirements in terms of snapshot number or signal-to-noise ratio (SNR) to outperform the diagonal-loading-type methods as compared with the traditional signal-subspace projection method.

An IDFT-based root-MUSIC for arbitrary arrays

Root-MUSIC algorithm, designed for uniform linear arrays, has been extended to arrays of arbitrary geometry by means of manifold separation techniques but at the cost of increased computational complexity. In this paper, an inverse discrete Fourier transform (IDFT)-based method is proposed in which polynomial rooting is avoided. The proposed method asymptotically exhibits the same performance as the extended root-MUSIC, implying that it outperforms the conventional MUSIC in terms of resolution ability. A remarkable property of this algorithm is that it has a computationally efficient implementation because a finite number of IDFT operations can run in parallel.

An Improved Wi-Fi Indoor Positioning Method via Signal Strength Order Invariance

It is well known that Wi-Fi indoor positioning accuracy is vulnerable to environmental fluctuations. In this paper, we propose a novel Wi-Fi indoor positioning method which applies signal strength order invariance (SSOI) to overcome the problem of environment influence and hence improve the positioning accuracy. In the off-line phase we save not only the signal strength of reference points but also the corresponding signal strength order. Then in the online phase, the measured signal strength and the associated order are used jointly to estimate the unknown points coordinate. Simulation and experimental results both demonstrate that our proposed algorithm can achieve better positioning accuracy than the methods using the traditional nearest neighbor (NN) or K-nearest-neighbors (KNN) fingerprinting algorithm only.

Semiblind Channel Estimation for Multiuser MIMO-CDMA Systems with Orthogonal Space-Time Block Codes

In this paper, we concern the channel estimation for a wireless communication system in which the techniques of multiple-input multiple-output, code division multiple access (CDMA) and orthogonal space-time block codes (OSTBCs) are integrated together for the purpose of achieving high data rate. We show that a composite channel information (CCI) vector can be formed, which contains the effects of channel state information, spreading coding and OSTBCs. From the standpoint of the MUltiple SIgnal Classification method, such CCI vector must lie in the signal subspace spanned by the dominant eigenvectors of the received data covariance matrix. Also, this CCI vector is located in another subspace which is associated with the CDMA and OSTBC codes and can be computed off-line. Using the vector space projections method, this CCI vector can be viewed as the intersection of these two subspaces and thus can be computed by alternative projections. In order to reduce the computation complexity, we propose an equivalent but computationally effective single-step solution in which the channel estimation amounts to searching for the principal eigenvector of a certain matrix with moderate size. Additionally, only one training block is required to overcome the problem of sign ambiguity. Numerical results demonstrate that, in addition to improving the bandwidth efficiency, the proposed method offers better performance in terms of channel estimation accuracy and bit-error-rate as compared with the standard nonblind least-squares channel estimation approach.

Matched direction beamforming based on signal subspace

The actual manifold vector (or steering vector) of the signal of interest (SOI) is often imprecise in practical applications. However the true manifold vector can be expressed as a product of a known matrix and an unknown coordinate vector in many cases. This model can accommodate many manifold uncertainties, for instance, the look direction error, local scattering, etc. Matched direction beamformer (MDB) is referred to as the beamformer resolving the signal that is drawn from an unknown direction inside the known subspace. The main contribution of this paper is to propose a new MDB that can estimates the coordinate vector associated with the SOI, without using the knowledge of the interference subspace (IS). Moreover the proposed approach is robust to the dimension overestimation of the signal subspace.

FFT-based adaptive 2-D DOA estimation for arbitrary array structures

Direction-of-arrival (DOA) estimation is a ubiquitous task in array processing. The conventional MUltiple SIgnal Classification (MUSIC) method is search-based and often computationally expensive, particularly in the application of joint azimuth and elevation estimation. In this paper, we propose an adaptive 2-dimensional direction finding framework to track multiple moving targets for arbitrary array structures by using the manifold separation technique (MST). First, we employ the subspace tracking technique to update the eigenbasis recursively on the arrival of a new data snapshot. In addition, by using the shift matrix a fast one-step operation is found to update the coefficient matrix in parallel. Finally, 2-D fast Fourier transform (FFT) algorithm is performed to compute the 2-D spatial spectrum at once. In comparison with the traditional MUSIC or root-MUSIC methods, the proposed method can reduce the computational complexity considerably.

Two-Dimensional DOA Estimation Using Arbitrary Arrays for Massive MIMO Systems

With the quick advancement of wireless communication networks, the need for massive multiple-input-multiple-output (MIMO) to offer adequate network capacity has turned out to be apparent. As a portion of array signal processing, direction-of-arrival (DOA) estimation is of indispensable significance to acquire directional data of sources and to empower the 3D beamforming. In this paper, the performance of DOA estimation for massive MIMO systems is analyzed and compared using a low-complexity algorithm. To be exact, the 2D Fourier domain line search (FDLS) MUSIC algorithm is studied to mutually estimate elevation and azimuth angle, and arbitrary array geometry is utilized to represent massive MIMO systems. To avoid the computational burden in estimating the data covariance matrix and its eigenvalue decomposition (EVD) due to the large-scale sensors involved in massive MIMO systems, the reduced-dimension data matrix is applied on the signals received by the array. The performance is examined and contrasted with the 2D MUSIC algorithm for different types of antenna configuration. Finally, the array resolution is selected to investigate the performance of elevation and azimuth estimation. The effectiveness and advantage of the proposed technique have been proven by detailed simulations for different types of MIMO array configuration.

Low complexity 2-D DOA estimator for arbitrary arrays: A hybrid MUSIC-based method

The traditional search-based MUltiple Signal Classification (MUSIC) method is often computationally expensive, particulary for the application of joint azimuth and elevation estimation. By means of the manifold separation technique (MST), the search-free root-MUSIC method, which is originally designed for the uniform linear array structures, can be extended to arbitrary arrays and reduce the computation burden to some extent. However, a computationally complex polynomial rooting procedure is still required. In this paper, we propose a computation attractive 2-D direction-of-arrival (DOA) estimator which can be viewed as a hybrid MUSIC-based method. First, we use the MST method to convert the 2D-MUSIC cost function into stand 2D-IDFT form. In doing so, we can obtain the 2-D spatial spectrum by using 2D-FFT. Since a relatively small point number for the FFT is chosen, the DOAs are located roughly. Then the MUSIC method with fine angular grid is utilized to search the DOAs finely within a small angular section. The proposed hybrid method not only alleviates the computation burden of root-MUSIC or MUSIC solely used; it also achieves almost the same DOA estimation performance and is easy to implement.

A Variable Diagonal Loading Beamformer with Joint Uncertainties of Steering Vector and Covariance Matrix

In this paper, a novel robust adaptive beamforming is proposed in which both the uncertainties of steering vector and covariance matrix are taken into account. First we develop a min–max optimization problem which aims to find a steering vector with the maximum output power under the worst-case covariance mismatch. Then we relax this min–max optimization problem to a max–min optimization problem which can be solved by using the Karush–Kuhn–Tucker optimality conditions. It is also shown that the proposed technique can be interpreted in terms of variable diagonal loading where the optimal loading factors are related to both the correlations (between the eigenvectors and the signal of interest) and the eigenvalues of the data covariance matrix. The effectiveness of the proposed approach is supported by computer simulation results.

Extension of the signal-subspace projection method to multi-dimension using CCA

The traditional signal-subspace projection (SSP) method combats the problem of array manifold uncertainty to gain the robustness by means of projecting the nominal manifold vector onto the signal subspace so as to eliminate the errors lying in the noise subspace. The main contribution of this paper is to extent the SSP approach from one dimension to multi-dimension. We assume that the actual manifold vector of the desired signal can be expressed as a product of a known matrix and an unknown coordinate vector. Then it is shown that the SSP method can be derived from the perspective of a problem of canonical correlation analysis (CCA) where the dimension of one subspace is one. When the dimension of the subspace (which the actual manifold of the desired signal belongs to) increases to multi-dimension, a novel projection method is developed, which can be viewed as the extension of the SSP method from one dimension to multi-dimension. Numerical results demonstrate the superiority of the proposed beamformer relatively to the conventional SSP method.