Partial Relaxation Approach: An Eigenvalue-Based DOA Estimator Framework

In this paper, the partial relaxation approach is introduced and applied to DOA estimation using spectral search. Unlike existing methods like Capon or MUSIC which can be considered as single source approximations of multi-source estimation criteria, the proposed approach accounts for the existence of multiple sources. At each considered direction, the manifold structure of the remaining interfering signals impinging on the sensor array is relaxed, which results in closed form estimates for the interference parameters. The conventional multidimensional optimization problem reduces, thanks to this relaxation, to a simple spectral search. Following this principle, we propose estimators based on the Deterministic Maximum Likelihood, Weighted Subspace Fitting and covariance fitting methods. To calculate the pseudo-spectra efficiently, an iterative rooting scheme based on the rational function approximation is applied to the partial relaxation methods. Simulation results show that the performance of the proposed estimators is superior to the conventional methods especially in the case of low Signal-to-Noise-Ratio and low number of snapshots, irrespectively of any specific structure of the sensor array while maintaining a comparable computational cost as MUSIC.Comment: This work has been submitted to IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A Compact Formulation for the ℓ2,1\ell_{2,1} Mixed-Norm Minimization Problem

Parameter estimation from multiple measurement vectors (MMVs) is a fundamental problem in many signal processing applications, e.g., spectral analysis and direction-of- arrival estimation. Recently, this problem has been address using prior information in form of a jointly sparse signal structure. A prominent approach for exploiting joint sparsity considers mixed-norm minimization in which, however, the problem size grows with the number of measurements and the desired resolution, respectively. In this work we derive an equivalent, compact reformulation of the $\ell_{2,1}$ mixed-norm minimization problem which provides new insights on the relation between different existing approaches for jointly sparse signal reconstruction. The reformulation builds upon a compact parameterization, which models the row-norms of the sparse signal representation as parameters of interest, resulting in a significant reduction of the MMV problem size. Given the sparse vector of row-norms, the jointly sparse signal can be computed from the MMVs in closed form. For the special case of uniform linear sampling, we present an extension of the compact formulation for gridless parameter estimation by means of semidefinite programming. Furthermore, we derive in this case from our compact problem formulation the exact equivalence between the $\ell_{2,1}$ mixed-norm minimization and the atomic-norm minimization. Additionally, for the case of irregular sampling or a large number of samples, we present a low complexity, grid-based implementation based on the coordinate descent method

Outdoor-to-indoor office MIMO measurements and analysis at 5.2 GHz

The outdoor-to-indoor wireless propagation channel is of interest for cellular and wireless local area network applications. This paper presents the measurement results and analysis based on our multiple-input-multiple-output (MIMO) measurement campaign, which is one of the first to characterize the outdoor-to-indoor channel. The measurements were performed at 5.2 GHz; the receiver was placed indoors at 53 different locations in an office building, and the transmitter was placed at three ”base stations ” positions on a nearby rooftop. We report on the root-mean-square (RMS) angular spread, building penetration, and other statistical parameters that characterize the channel. Our analysis is focused on three MIMO channel assumptions often used in stochastic models. 1) It is commonly assumed that the channel matrix can be represented as a sum of a line-of-sight (LOS) contribution and a zero-mean complex Gaussian distribution. Our investigation shows that this model does not adequately represent our measurement data. 2) It is often assumed that the Rician K-factor is equal to the power ratio of the LOS component and the other multipath components (MPCs). We show that this is not the case, and we highlight the difference between the Rician K-factor often associated with LOS channels and a similar power ratio for th