Showing papers by "Mats Viberg published in 2015"

A numerical implementation of gridless compressed sensing

Abstract: Atomic norm denoising has been recently introduced as a generalization of the Least Absolute Shrinkage and Selection Operator (LASSO) to overcome the problem of off-grid parameters. The method has been found to possess many interesting theoretical properties. However, its implementation has been only discussed in a special case of spectral line estimation by uniform sampling. In this paper, we propose a general numerical method to solve the atomic norm denoising problem. The complexity of the proposed algorithm is proportional to the complexity of a single-parameter search in the parameter space and thus in many interesting cases, including frequency estimation it enjoys fast realization.

Wireless information and power transfer in MIMO channels under Rician fading

Abstract: Simultaneous wireless information and power transfer (SWIPT) offers an attractive alternative to the traditional battery limited or grid dependent communication system design. In this paper, we investigate robust precoder designs for systems with SWIPT capabilities under a stochastic Rician fading framework. Under a multiple-input multiple-output (MIMO) channel model, we formulate the problem of minimizing the average mean-square error at the information receiver (IR) while keeping the average energy harvested at the energy receiver (ER) above given levels. We consider two different strategies that can be adopted by the IR: i) simple estimation filters based solely on the channel mean, ii) robust estimation filters aiming to minimize the average mean-square error. Both of these scenarios lead to non-convex formulations. For the first scenario, we propose a convex relaxation that is tight. For the second scenario, we propose an alternating minimization method that provides precoder designs even in the scenarios where the number of transmit antennas is larger than the length of data vector. Our numerical results show that the proposed designs provide significant performance gains especially when the scattering component of the channel is strong.

Array calibration using array response interpolation and parametric modeling

Abstract: High-performance array applications often require an accurate array response model. A common way to achieve this is by array calibration which involves measuring the response for a finite number of given source directions and employing interpolation. This paper considers the array calibration problem by combing interpolation techniques and parametric modeling. The idea is to model the array response as a product of a mutual coupling matrix, an ideal array response vector (derived from the geometry of antenna array) and an angle-dependent correction vector. Since the major effects are captured by the physical model and the mutual coupling matrix, the correction vector will be a smoother function of angle as compared to direct interpolation of the measured array response. In numerical experiments of a real antenna array, the method is found to improve the performance of the array calibration significantly.

Maximum Aperture Power Transmission in Lossy Homogeneous Matters

Abstract: We apply array signal processing techniques to the transmitting aperture field modes in order to determine the optimal field distribution that maximizes the power transmission through lossy media between a transmitting and an ideally receiving antenna aperture. The optimal aperture distribution is then used as a reference field for developing curves applicable to the design of many near-field systems, such as for the detection of foreign objects in lossy matters (e.g., food contamination detectors), wireless charging of batteries of human body implanted devices, and for in-body communication systems.

Optimal aperture distribution for maximum power transfer in planar lossy multilayered matters

Abstract: We combine array-signal-processing and spectral domain techniques to determine the optimal aperture current distribution that maximizes the power coupling between two antenna apertures which are located on opposing sides of a multilayered lossy structure in the near-field. The resulting optimal distribution can be used as a reference in the design of antennas for near-field applications, such as for the detection of foreign objects in lossy media, wireless charging of implant batteries, or near-field communications with an implant. A MoM approach is formulated to study the performance of the optimal aperture currents in a simulated detection application in the presence of a small PEC patch, and it is shown that the optimal aperture current can improve the system sensitivity and increase the detection probability of the object.

Wideband Waveform and Receiver Filter Bank Design for Clutter Suppression

Abstract: Modern wideband radar systems with long integration time, equipped with arbitrarily waveform generators, raise a demand for advanced signaling transmission schemes. In this paper, we propose two algorithms to select transmit waveforms and receiver filters. The techniques are based on a clutter suppression criterion. For the first algorithm, we employ an optimized filter bank, and for the second algorithm, we use a matched filter bank. Clutter suppression is achieved by minimizing the correlation between receiver filters and interfering clutter echoes. The algorithm, for the optimized filter bank, is extended to adapt the transmission scheme and receiver filters to a time-evolving scenario. Adaptation parameters are based on estimates of a clutter map and detected target characteristics. To estimate the clutter map we propose a Kalman filter, whereas target parameters are calculated using a least-squares fit to data. The efficiency of the algorithms and the adaptation scheme are visualized through a numerical simulation.

Wideband waveform design for robust target detection

Abstract: Future radar systems are expected to use waveforms of a high bandwidth, with an advantage of an improved range resolution Herein, a technique to design robust wideband waveforms is developed The context is detection of a single object with partially unknown parameters The technique achieves an optimal detection speed for a desired resolution, maintaining a high detection performance Many radar systems also require fast adaptation to a variable environment Hence, the technique is devoted to rapidly design waveforms In terms of probabilities of detection and false alarm, numerical evaluation shows the efficiency of the method when compared with a chirp signal and a Gaussian pulse

A Novel Sparsity-Based Approach to Recursive Estimation of Dynamic Parameter Sets

Abstract: We consider the problem of estimating a variable number of parameters with a dynamic nature. A familiar example is finding the position of moving targets using sensor array observations. The problem is challenging in cases where either the observations are not reliable or the parameters evolve rapidly. Inspired by the sparsity based techniques, we introduce a novel Bayesian model for the problems of interest and study its associated recursive Bayesian filter. We propose an algorithm approximating the Bayesian filter, maintaining a reasonable amount of calculations. We compare by numerical evaluation the resulting technique to state-of-the-art algorithms in different scenarios. In a scenario with a low SNR, the proposed method outperforms other complex techniques.

MIMO linear precoder design with non-ideal transmitters

Abstract: We investigate the linear precoder design problem for multiple-input multiple-output (MIMO) channels under non-ideal transmitter hardware. We consider two different non-ideal hardware models: i) an additive noise model in which the level of the noise at an antenna is proportional to the signal power at that antenna, ii) an additive precoder error model. We focus on the problem of minimizing mean-square error at the receiver under transmit power constraints at the transmitter. For the first hardware impairment model, this scenario leads to a non-convex formulation for which we propose a block-coordinate descent technique. The proposed method has a convergence guarantee and provides rank-constrained solutions. For the second model, analytical expressions for the optimum designs are provided. We compare the performance of our hardware impairment aware designs with that of designs developed with ideal hardware assumptions. Our results suggest that significant gains can be obtained by the proposed designs for sufficiently high signal-to-noise ratio values.