José Picheral

Fast communication: Advantages of nonuniform arrays using root-MUSIC

In this paper, we consider the Direction-Of-Arrival (DOA) estimation problem in the Nonuniform Linear Arrays (NLA) case, particularly the arrays with missing sensors. We show that the root-MUSIC algorithm can be directly applied to this case and that it can fully exploit the advantages of using an NLA instead of a Uniform Linear Array (ULA). Using theoretical analysis and simulations, we demonstrate that employing an NLA with the same number of sensors as the ULA, yields better performance. Moreover, reducing the number of sensors while keeping the same array aperture as the ULA slightly modifies the Mean Square Error (MSE). Therefore, thanks to the NLA, it is possible to maintain a good resolution while decreasing the number of sensors. We also show that root-MUSIC presents good performance and is one of the simplest high resolution methods for this type of arrays. Closed-form expressions of the estimator variance and the Cramer-Rao Bound (CRB) are derived in order to support our simulation results. In addition, the analytical expression of the CRB of the NLA to the CRB of the ULA ratio is calculated in order to show the advantages of the NLA.

A robust super-resolution approach with sparsity constraint for near-field wideband acoustic imaging

Acoustic source imaging has nowadays been widely used in source localization and separation. In this paper, based on the deconvolution methods (DAMAS), we propose a robust super-resolution approach with sparsity constraint (SC-RDAMAS) to estimate both the positions and powers of the sources, as well as the noise variance in low Signal to Noise Ratio (SNR) situation. For effectively applying sparsity constraint, we explore a better initialization of source number to determine the bound of total source powers. By simulations and real data, we show that our SC-RDAMAS can obtain more accurate estimations of source positions and averaging powers, and can be more robust to strong noise interference, by comparison with the state of the art methods: the Beamforming, DAMAS, DAMAS with sparsity constraint (SC-DAMAS) and the Covariance Matrix Fitting (CMF) method. Indeed the computation burden of the proposed method is much lower than the CMF, so that our SC-RDAMAS is more applicable to scan the large region with super resolutions.

Second-order near-field localization with automatic paring operation

Most exiting array signal processing techniques for bearing estimation are strongly relied on the far-field assumption. When the sources are located close to the array, these techniques may no longer perform satisfactorily. In this work, we propose a tensor-based algorithm which is dedicated to the joint estimation of the range and the bearing of multiple narrow-band and near-filed sources in a spatially white Gaussian noise. Automatic paring of the model parameters is achieved for an uniform linear array. By means of numerical simulation, we show that for low signal to noise ratio, the proposed algorithm is more accurate than the higher order statistics (HOS)- based ESPRIT algorithm for small/moderate number of snapshots.

Localization of spatially distributed near-field sources with unknown angular spread shape

In this paper, we propose to localize and characterize coherently distributed (CD) sources in near-field. Indeed, it appears that in some applications, the more the sources are close to the array of sensors, the more they seem to be scattered. It thus appears to be of the biggest importance to take into account the angular distribution of the sources in the joint direction of arrival (DOA) and range estimation methods. The methods of the literature which consider the problem of distributed sources do not handle the case of near field sources and require that the shape of the dispersion is known. The main contribution of the proposed method is to estimate the shape of the angular distribution using an additional shape parameter to address the case of unknown distributions. We propose to jointly estimate the DOA, the range, the spread angle and the shape of the spread distribution. Accurate estimation is then achieved even when the shape of the angular spread distribution is unknown or imperfectly known. Moreover, the proposed estimator improves angular resolution of the sources. HighlightsWe propose an estimator to localize and characterize the shape distribution of CD.This method jointly estimates angle, distance, spread angle and shape.Main idea is to estimate the shape of the distribution with an additional parameter.Accurate estimation is obtained even when the shape distribution is unknown.Good direction of arrival source separation is shown for the proposed estimator.

Direction of Arrival Estimation using EM-ESPRIT with Nonuniform Arrays

This paper deals with the problem of the Direction Of Arrival (DOA) estimation with nonuniform linear arrays. The proposed method is based on the Expectation Maximization method where ESPRIT is used in the maximization step. The key idea is to iteratively interpolate the data to a virtual uniform linear array in order to apply ESPRIT to estimate the DOA. The iterative approach allows one to improve the interpolation using the previously estimated DOA. One of this method’s novelties lies in its capacity of dealing with any nonuniform array geometry. This technique manifests significant performance and computational advantages over previous algorithms such as Spectral MUSIC, EM-IQML and the method based on manifold separation technique. EM-ESPRIT is shown to be more robust to additive noise. Furthermore, EM-ESPRIT fully exploits the advantages of using a nonuniform array over a uniform array: simulations show that for the same aperture and with a smaller number of sensors, the nonuniform array presents almost identical performance as the equivalent uniform array.

Array geometry impact on music in the presence of spatially distributed sources

The MUltiple SIgnal Classification (MUSIC) estimator has been widely studied for a long time for its high resolution capability in the domain of the direction of arrival (DOA) estimation, with the sources assumed to be point. However, when the actual sources are spatially distributed with angular dispersion, the performance of the conventional MUSIC is degraded. In this paper, the impact of the array geometry on the DOA estimation of spatially distributed sources impinging on a sensor array is considered. Taking into account a coherently distributed source model, we establish closed-form expressions of the MUSIC-based DOA estimation error as a function of the positions of the array sensors in the presence of model errors due to the angular dispersion of the signal sources. The impact of the array geometry is studied and particular array designs are proposed to make DOA estimation more robust to source dispersion. The analytical results are validated by numerical simulations.1

Performance analysis of music in the presence of modeling errors due to the spatial distributions of sources

In this paper, the direction of arrival (DOA) localization of spatially distributed sources impinging on a sensor array is considered. The performance of the well known MUSIC estimator is studied in the presence of model errors due to angular dispersion of sources. Taking into account the coherently distributed source model proposed in [1], we establish closed-form expressions of the DOA estimation error and mean square error (MSE) due to both the model errors and the effects of a finite number of snapshots. The analytical results are validated by numerical simulations and allow to analyze the performance of MUSIC for coherently distributed sources.

Vibration signals compression with Time-Frequency Adaptive Quantization

A novel quantization method, well suited to the case of vibration signal compression is introduced in this paper. This method, applied in the domain of the Discrete Cosine Transform, is called Time-Frequency Adaptive Quantization (TFAQ) and it efficiently allocates the coding bits based on the time-frequency properties of the vibration signals. The method is compared to other transform-based compression techniques. Experiments are carried on a rich database of vibration signals, issued by the plane engines, in various stages of the flight. The results prove the superiority of TFAQ versus the other tested methods, for the experimental data set we dispose on.

EM-Esprit Algorithm for Direction Finding with Nonuniform Arrays

This paper deals with the problem of the Direction Of Arrival (DOA) estimation with nonuniform linear arrays. The proposed method is a combination of the Expectation Maximization (EM) and the ESPRIT methods. The EM algorithm interpolates the nonuniform array to an equivalent uniform array, and then, the application of ESPRIT is possible, in order to estimate the DOA. One of this method novelties lies in its capacity of dealing with any nonuniform array geometry. This technique manifests significant performance and computational advantages over previous algorithms such as MUSIC, specially in the preasymptotic domain, and the comparison with the theoretical Cramer-Rao Bounds (CRB) shows its efficiency.

Parametric estimation of space-time channels with spatially correlated noise by JADE-ESPRIT

In mobile communications the antenna array makes it possible to estimate the path delay, angle of arrival (or angle) and amplitude for the multipath propagation channel. This paper considers the problem of parametric channel estimation by the joint angle and delay estimation (JADE) in a time division system. By exploiting the angle/delay invariance of the channel regardless of the fast faded amplitude variation (i.e., angle/delay is assumed quasi-static). The shift invariance method (JADE-ESPRIT) is chosen for its capacity to pair delay and angle for each path. In this paper, it is shown that the consistency of JADE-ESPRIT for a large enough number of slots guarantees the feasibility of the method for spatially correlated noise. Bounds of the channel MSE are derived when JADE is used.