Showing papers by "Mats Viberg published in 2014"
TL;DR: This article provides an overview of DOA estimation methods that are relevant in theory and practice and presents estimators based on beamforming, subspace and parametric approaches and compares their performance in terms of estimation accuracy, resolution capability and computational complexity.
Abstract: Estimation of direction of arrival (DOA) from data collected by sensor arrays is of fundamental importance to a variety of applications such as radar, sonar, wireless communications, geophysics and biomedical engineering. Significant progress in the development of algorithms has been made over the last three decades. This article provides an overview of DOA estimation methods that are relevant in theory and practice. We will present estimators based on beamforming, subspace and parametric approaches and compare their performance in terms of estimation accuracy, resolution capability and computational complexity. Methods for processing broadband data and signal detection will be discussed as well. Finally, a brief discussion will be given to application specific algorithms.
42 citations
TL;DR: The ideal data model is introduced and its properties are explored, with a special emphasis on the array response, and the general concepts of beamforming and direction-of-arrival estimation are introduced.
Abstract: The purpose of this chapter is to give some background material on array signal processing, which serves as a more detailed introduction to the remaining chapters. The ideal data model is introduced and its properties are explored, with a special emphasis on the array response. The general concepts of beamforming and direction-of-arrival estimation are introduced, and exemplified by some well-known techniques. Although the focus is on traditional applications involving an array of coherent sensors, we also present some extensions to non-coherent and/or distributed sensors.
18 citations
22 Sep 2014
TL;DR: In this article, the optimal source aperture field distribution was determined to maximize the power transfer through a lossy medium from the transmitter aperture to a receiver aperture located in the near-field region.
Abstract: We determine the optimal source aperture field distribution to maximize the power transfer through a lossy medium from the transmitter aperture to a receiver aperture located in the near-field region. The optimal distribution is determined by applying array signal processing techniques to aperture field modes. By designing the system to be sensitive to the presence of foreign objects in a homogenous matter, the synthesized optimal aperture distribution increases the detection probability of foreign objects in various near-field applications, such as those that can detect potentially hazardous contaminations in food.
11 citations
Book•
01 Jan 2014
TL;DR: This third volume, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in array and statistical signal processing.
Abstract: This third volume, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in array and statistical signal processing. With this reference source you will: * Quickly grasp a new area of research * Understand the underlying principles of a topic and its application* Ascertain how a topic relates to other areas and learn of the research issues yet to be resolved * Quick tutorial reviews of important and emerging topics of research in array and statistical signal processing* Presents core principles and shows their application* Reference content on core principles, technologies, algorithms and applications* Comprehensive references to journal articles and other literature on which to build further, more specific and detailed knowledge* Edited by leading people in the field who, through their reputation, have been able to commission experts to write on a particular topic
11 citations
04 May 2014
TL;DR: A generalizing CS framework is developed which shows that sampling to a finite grid is not necessary toward compressive estimation and an alternative procedure over infinite dictionaries is proposed, which is shown to be theoretically consistent in many cases of interest.
Abstract: The effect of off-grid atoms has become the prominent problem in application of the Compressed Sensing (CS) techniques to the cases where there is an underlying continuous parametrization. In this work, we develop a generalizing CS framework which shows that sampling to a finite grid is not necessary toward compressive estimation. We propose an alternative procedure over infinite dictionaries, which we show to be theoretically consistent in many cases of interest and then propose a robust implementation. We illustrate the general properties of our technique in some difficult practical instances of frequency estimation.
10 citations
TL;DR: In this paper, an electromagnetic measurement system for monitoring the effective permittivity in closed metal vessels, which are commonly used in the process industry, is presented, which exploits the process vessel as a microwave cavity resonator and the relative change in its complex resonance frequencies is related to the complex effective permitivity inside the vessel.
7 citations
TL;DR: This chapter provides techniques that allow the practitioner to acquire the steering vector model of real-world sensor arrays so that various nonidealities are taken into account so that array processing algorithms may avoid performance losses caused by array modeling errors.
Abstract: Real-world sensor arrays are typically composed of elements with individual directional beampatterns and are subject to mutual coupling, cross-polarization effects as well as mounting platform reflections. Errors in the array elements’ positions are also common in sensor arrays built in practice. Such nonidealities need to be taken into account for optimal array signal processing and in finding related performance bounds. Moreover, problems related to beam-steering and cancellation of the signal-of-interest in beamforming applications may be prevented. Otherwise, an array processor may experience a significant performance degradation. In this chapter we provide techniques that allow the practitioner to acquire the steering vector model of real-world sensor arrays so that various nonidealities are taken into account. Consequently, array processing algorithms may avoid performance losses caused by array modeling errors. These techniques include model-based calibration and auto-calibration methods, array interpolation, as well as the wavefield modeling principle or manifold separation technique. Robust methods are also briefly considered since they are useful when the array nonidealities are not described by the employed steering vector model. Extensive array processing examples related to direction-finding and beamforming are included demonstrating that optimal or close-to optimal performance may be achieved despite the array nonidealities.
6 citations
22 Jun 2014
TL;DR: It is demonstrated that the proposed technique can improve estimation performance in difficult cases, as compared to the SAGE technique, and is less computationally complex and its performance is independent of the grid selection.
Abstract: Sparse estimation and compressive sensing techniques have been recently considered for radar estimation problems. It is frequently observed that these methods are robust to model uncertainties and substantially improve performance in scenarios with a low signal-to-noise. However, since current sparsity-based techniques are computationally costly and require a suitable discretization (grid), which strongly restricts resolution, they practically receive less attention. In this work, we present an application of a new sparsity-based technique to the specific problem of range-Doppler estimation. The method, generalizing basis pursuit, is less computationally complex and its performance is independent of the grid selection. We demonstrate that the proposed technique can improve estimation performance in difficult cases, as compared to the SAGE technique.
2 citations
Posted Content•
TL;DR: In this paper, a wideband spectrum sensing method is presented that utilizes a sub-Nyquist sampling scheme to bring substantial savings in terms of the sampling rate, where the correlation matrix of a finite number of noisy samples is computed and used by a nonlinear least square estimator to detect the occupied and vacant channels of the spectrum.
Abstract: For systems and devices, such as cognitive radio and networks, that need to be aware of available frequency bands, spectrum sensing has an important role. A major challenge in this area is the requirement of a high sampling rate in the sensing of a wideband signal. In this paper a wideband spectrum sensing method is presented that utilizes a sub-Nyquist sampling scheme to bring substantial savings in terms of the sampling rate. The correlation matrix of a finite number of noisy samples is computed and used by a non-linear least square (NLLS) estimator to detect the occupied and vacant channels of the spectrum. We provide an expression for the detection threshold as a function of sampling parameters and noise power. Also, a sequential forward selection algorithm is presented to find the occupied channels with low complexity. The method can be applied to both correlated and uncorrelated wideband multichannel signals. A comparison with conventional energy detection using Nyquist-rate sampling shows that the proposed scheme can yield similar performance for SNR above 4 dB with a factor of 3 smaller sampling rate.
2 citations
22 Jun 2014
TL;DR: An optimization in terms of mainlobe and sidelobe properties of the ambiguity function is formulated, then an approximate solution based on convex relaxation is provided, showing the advantage of selecting a smart signal compared to a conventional waveform design.
Abstract: Modern signal generators offer the capability to synthesize arbitrary wideband waveforms for radar and sonar applications. This makes it possible to optimize signals for specific purposes or scenarios. Herein, we discuss how to design a wideband waveform for clutter suppression. We formulate an optimization in terms of mainlobe and sidelobe properties of the ambiguity function, then we provide an approximate solution based on convex relaxation. Numerical evaluation shows the advantage of selecting a smart signal compared to a conventional waveform design. The advantages are shown as a lower probability of false alarm and a higher probability of correct target detection.
2 citations