Tadeu N. Ferreira

Sparsity-Aware Data-Selective Adaptive Filters

We propose two adaptive filtering algorithms that combine sparsity-promoting schemes with data-selection mechanisms. Sparsity is promoted via some well-known nonconvex approximations to the l0 norm in order to increase convergence speed of the algorithms when dealing with sparse/compressible signals. These approximations circumvent some difficulties of working with the l0 norm, thus allowing the development of online data-selective algorithms. Data selection is implemented based on set-membership filtering, which yields robustness against noise and reduced computational burden. The proposed algorithms are analyzed in order to set properly their parameters to guarantee stability. In addition, we characterize their updating processes from a geometrical viewpoint. Simulation results show that the proposed algorithms outperform the state-of-the-art algorithms designed to exploit sparsity.

Covariance-Based Direction-of-Arrival Estimation With Real Structures

Parametric methods for direction-of-arrival (DoA) estimation have become very popular due to their low computational complexity and good accuracy. Among parametric DoA algorithms, ESPRIT is one of the most widely used, since it presents low computational complexity in comparison to other parametric methods. Covariance-based DoA (CB-DoA) estimation algorithm provides an even lower complexity alternative to ESPRIT, while imposing the same constraints on the geometry of the receiving array. This letter presents a new algorithm, based on the CB-DoA approach, comprising only real operations. The constraints on the algorithm are the same imposed to the unitary ESPRIT algorithm, allowing a reduction of about 67% on the required computational effort, for equivalent error measure.

A Volumetric SRP with Refinement Step for Sound Source Localization

This paper proposes an efficient method based on the steered-response power (SRP) technique for sound source localization using microphone arrays: the volumetric SRP (VSRP). As compared to the SRP, by deploying a sparser volumetric grid, the V-SRP achieves a significant reduction of the computational complexity without sacrificing the accuracy of the location estimates. By appending a fine search step to the VSRP, its refined version (RV-SRP) improves on the compromise between complexity and accuracy. Experiments conducted in both simulatedand real-data scenarios demonstrate the benefits of the proposed approaches. Specifically, the RV-SRP is shown to outperform the SRP in accuracy at a computational cost of about ten times lower.This letter proposes an efficient method based on the steered-response power (SRP) technique for sound source localization using microphone arrays: the refined volumetric SRP (RV-SRP). By deploying a sparser volumetric grid, the RV-SRP achieves a significant reduction of the computational complexity without sacrificing the accuracy of location estimates. In addition, a refinement step improves on the compromise between complexity and accuracy. Experiments conducted in both simulated- and real-data scenarios show that the RV-SRP outperforms state-of-the-art methods in accuracy with lower computational cost.

Open-source physical-layer simulator for LTE systems

This article describes a physical-layer simulator for both uplink and downlink connections of LTE systems, whose performances are assessed by simulating standardized environments. The simulator is compliant with Release 9 of LTE standard and it is publicly available for educational purposes, allowing students and researchers to test the performance of Signal Processing and Digital Communications techniques in an easy-to-use MATLAB framework. Users may benefit from implemented features such as channel estimation using different demodulation reference signals, channel coding, equalization, multiple access schemes in which multiple cells are employed, as well as diversity, spatial multiplexing, and beamforming transmissions. As an example, we evaluate the impact on the performance of an uplink connection due to inaccuracy in channel estimation and multi-user interference. In addition, we include the evaluation of using diversity and spatial multiplexing transmissions on downlink connections.

Direction-of-Arrival Estimation using a Low-Complexity Covariance-Based Approach

This article presents a new algorithm for performing direction-of-arrival (DOA) estimation using manipulations on covariance matrices. The proposed algorithm combines a new formulation for data projection on real subspaces, together with beamspace decompositions, reducing the sizes of all data structures and computational complexity of the resulting estimation process. Theoretical analyses as well as computer simulations indicate that the proposed algorithm reduces its ESPRIT equivalent computational complexity by a minimum of 20%, while presenting similar mean-square error (MSE) performance.

Direction-of-Arrival Estimation using a Direct-Data Approach

This paper presents a direct-data (DD) counterpart to the covariance-based (CB) algorithm for direction-of-arrival (DOA) estimation. The proposed DD-DOA scheme provides reduced computational complexity as compared with other ESPRIT variations, filling in a theoretical gap not covered by previously presented schemes. A mean-squared error (MSE) analysis as well as computer simulations are provided allowing performance comparisons between DD-DOA and ESPRIT algorithms. The MSE performance is compared with the Cramer-Rao lower bound (CRLB) for one source. Results indicate that the proposed algorithm represents an efficient trade-off between computational complexity and final MSE as compared with standard ESPRIT schemes.

Beamspace covariance-based DoA estimation

This work presents a new version, with reduced computational complexity, of the covariance-based direction-of-arrival (CB-DoA) algorithm. The new algorithm incorporates the concept of beamspace projection before performing the DoA estimation. Such modification reduces the dimensions of the matrices employed by the elements pace CB-DoA, simplifying the resulting computations while preserving the detectability of the original algorithm. The Beamspace CB-DoA algorithm is compared to the traditional algorithm Beamspace ESPRIT, as well as to element space CB-DoA.

Low-complexity proportionate algorithms with sparsity-promoting penalties

There are two main families of algorithms that tackle the problem of sparse system identification: the proportionate family and the one that employs sparsity-promoting penalty functions. Recently, a new approach was proposed with the l0-IPAPA algorithm, which combines proportionate updates with sparsity-promoting penalties. This paper proposes some modifications to the l0-IPAPA algorithm in order to decrease its computational complexity while preserving its good convergence properties. Among these modifications, the inclusion of a data-selection mechanism provides promising results. Some enlightening simulation results are provided in order to verify and compare the performance of the proposed algorithms.

Low complexity blind estimation of the carrier frequency offset in multicarrier systems

In orthogonal frequency division multiplexing (OFDM), carrier frequency offset (CFO) must be mitigated since it generates interference between received symbols transmitted through different sub-carriers. This paper presents a new algorithm for CFO estimation with reduced computational complexity. The new approach is based on the segmentation of the input-signal autocorrelation matrix into the noise and signal subspaces, the latter being employed to estimate the desired CFO. Simulations validate the effectiveness of the new algorithm in comparison to the traditional parametric estimation technique ESPRIT.

Optimal constraint vectors for set-membership affine projection algorithms☆

Abstract There is a growing interest in adaptive filtering solutions whose learning processes are data selective, bringing about computational reduction and energy savings while improving estimation accuracy. The set-membership affine projection algorithms are a representative family of algorithms including data-selection mechanisms. The update process of these algorithms depends on the choice of a constraint vector (CV) which, up to now, is based on some heuristics. In this paper we propose an optimal CV and discuss some of its inherent properties. The resulting problem falls into a convex optimization framework, allowing some unexpected features to surface; for instance, the widely used simple choice CV is asymptotically optimal for statistically white stationary inputs. Simulations indicate the optimal CV outperforms the simple choice CV regarding update rates and steady-state mean squared errors for statistically colored inputs.