Showing papers by "Mats Viberg published in 2007"

New approaches for channel prediction based on sinusoidal modeling

Abstract: Long-range channel prediction is considered to be one of the most important enabling technologies to future wireless communication systems. The prediction of Rayleigh fading channels is studied in the frame of sinusoidal modeling in this paper. A stochastic sinusoidal model to represent a Rayleigh fading channel is proposed. Three different predictors based on the statistical sinusoidal model are proposed. These methods outperform the standard linear predictor (LP) in Monte Carlo simulations, but underperform with real measurement data, probably due to nonstationary model parameters. To mitigate these modeling errors, a joint moving average and sinusoidal (JMAS) prediction model and the associated joint least-squares (LS) predictor are proposed. It combines the sinusoidal model with an LP to handle unmodeled dynamics in the signal. The joint LS predictor outperforms all the other sinusoidal LMMSE predictors in suburban environments, but still performs slightly worse than the standard LP in urban environments.

Compensation for Nonlinearity in a Hammerstein System Using the Coherence Function With Application to Nonlinear Acoustic Echo Cancellation

Abstract: This correspondence addresses compensation for nonlinearity in Hammerstein nonlinear systems. Specifically, we propose two predistortion methods that are based on coherence function criteria. The proposed methods carry out linearization without knowing the linear block in the Hammerstein system. This is particularly desirable for nonlinear acoustic echo cancellation applications, where dealing with the linear block can be computational cumbersome due to the long room acoustic impulse response.

Optimized Beamforming Calibration in the Presence of Array Imperfections

Abstract: For arrays with position and channel errors the calibration becomes very crucial. In the traditional calibration methods one can choose between an optimal SNR and a beam pattern with low side lobes. In this paper we formulate a beam pattern synthesis method which optimize the trade-off between the two criteria. A classical problem with position errors in an array, is that it is difficult to get low side lobes over the whole side lobe region, since the position errors give rise to direction dependent errors. In this paper this problem is solved by using local (direction dependent) correction matrices in the beam pattern optimization. The new way of using local correction matrices leads to the lowest possible uniform side lobe level, for the chosen SNR, beamwidth and beam pointing direction.

Models and Predictions of Scattered Radio Waves on Rough Surfaces

Abstract: Scattering of radio waves on rough surfaces is investigated using ray tracing techniques, which results in a sinusoidal model with time varying amplitudes. An AR(d) model with nonzero mean is proposed to characterize and predict the time variation of the amplitudes. A covariance sequence based method is proposed to estimate the autoregressive coefficients from the channel observations. An adaptive channel predictor using a Kalman filter is proposed to predict the complex amplitudes of the scattering signal. The proposed method outperforms other sinusoidal modeling based channel predictors and linear predictors with single scattering scenarios in simulations.

Adaptive beamforming using calibration vectors with unknown gain and phase

Abstract: The importance of taking the direction dependent amplitude and phase of the received signals (due to the mutual coupling) into account in the calibration is demonstrated using adaptive beamforming (Capon's method). The method is compared to a direction dependent (local) calibration method, which is shown to be the preferred method if there are position errors in the array. In reality, robust versions of Capon's method is always used. The calibration is considered as a complement to these robust methods, to reduce the array steering vector errors and thereby further improve the performance of the robust Capon methods. The calibration also allows for larger errors in the array, which translates to a potential to decrease the manufacturing cost.

Two-Step Esprit with Compensation for Modelling Errors using a Sparse Calibration Grid

Abstract: In arrays with scan dependent errors, such as large position errors, a dense calibration grid can become necessary. Calibration time is, however, very expensive and keeping the measured calibration grid as sparse as possible is important. In this paper it is shown how interpolation using local models can be used to make the calibration grid more dense without increasing the number of measurements. Furthermore, it is shown how the performance of the DOA estimation with ESPRIT using arrays with large position errors can be improved by a second step including weighted calibration.

Tracking moving objects in video using enhanced mean shift and region-based motion field

Abstract: In this paper, we propose a scheme for moving object tracking from videos by combining mean shift and motion field statistics. For mean shift, we employ an enhanced spatial-range mean shift that enables a reduced number of over-segmentation. For motion statistics, we combine the optical flow and high-order moment to generate motion regions that are associated with moving objects (or object parts). Experiments have been conducted on several indoor and outdoor (color/gray-scale) image sequences ranging from simple to median complexity. To evaluate the performance, three objective criteria are applied in addition to the visual inspection. The results show that the proposed method is promising for moving object tracking in video, with an averaging detection rate of 95%. Further, the proposed scheme is compared with that using the conventional mean shift for the tracking, indicating a significantly reduction in false alarm (≈ 30%).