| 1. |
- Athley, Fredrik, 1968, et al.
(author)
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High-Resolution Space-Time Signal Processing for Radar
- 2003
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In: High-Resolution and Robust Signal Processing, To be published, Dekker, editor Y. Hua and A. Gershman and Q. Cheng. - : CRC Press. - 9781482276404
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Book chapter (other academic/artistic)
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| 2. |
- Chung, Pei-Jung, et al.
(author)
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DOA Estimation Methods and Algorithms
- 2014
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In: Academic Press Library in Signal Processing: Volume 3 Array and Statistical Signal Processing. - 9780124115972 ; , s. 599-650
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Book chapter (other academic/artistic)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.
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| 3. |
- Costa, Mario, et al.
(author)
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Array Processing in the Face of Nonidealities
- 2014
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In: Academic Press Library in Signal Processing: Volume 3 Array and Statistical Signal Processing. - 9780124115972 ; , s. 819-857
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Book chapter (other academic/artistic)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.
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| 4. |
- Jansson, M., et al.
(author)
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Optimal Subspace Techniques for DOA Estimation
- 2005
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In: Space-Time Wireless Systems: From Array Processing to MIMO Communications / edited by Helmut Bölcskei et al..
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Book chapter (other academic/artistic)
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| 5. |
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| 6. |
- Stoica, Petre, et al.
(author)
-
A unified instrumental variable approach to direction finding in colored noise fields
- 2009
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In: The Digital Signal Processing Handbook. - 9781420046045
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Book chapter (other academic/artistic)abstract
- Most parametric methods for direction-of-arrival (DOA) estimation require knowledge of the spatial (sensor-to-sensor) color of the background noise. If this information is unavailable, a serious degradation of the quality of the estimates can result, particularly at low signal-to-noise ratio (SNR) [1-3]. A number of methods have been proposed over the recent years to alleviate the sensitivity to the noise color. If a parametric model of the covariance matrix of the noise is available, the parameters of the noise model can be estimated along with those of the interesting signals [4-7]. Such an approach is expected to performwell in situations where the noise can be accurately modeled with relatively few parameters. An alternative approach, which does not require a precise model of the noise, is based on the principle of instrumental variables (IVs). See Söderström and Stoica [8,9] for thorough treatments of IV methods (IVMs) in the context of identification of linear time-invariant dynamical systems. A brief introduction is given in the appendix. Computationally simple IVMs for array signal processing appeared in [10,11]. These methods perform poorly in difficult scenarios involving closely spaced DOAs and correlated signals.
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| 7. |
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| 8. |
- Viberg, Mats, 1961, et al.
(author)
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Calibration in Array Processing
- 2009
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In: Classical and Modern Direction-of-Arrival Estimation. ; , s. 93-124
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Book chapter (other academic/artistic)abstract
- High-resolution direction-or-arrival estimation has been an active area of research since late 1970's. The methods find a wide range of applications, including passive listening arrays, radar and sonar, and spatial (or space-time) characterization of wireless communication channels. Conventional beamforming-based techniques for direction estimation are limited by the aperture (or physical size) of the array. In contrast, parametric methods promise an unlimited resolution in theory. These methods take advantage of a precise mathematical model of the received array data, for example due to incoming plane waves. In practice, the resolution and estimation accuracy is limited by noise as well as errors in the assumed data model. The focus of this chapter is on modeling errors, and in particular calibration techniques to mitigate such errors. Perhaps the most natural and common approach is to measure the response of the array in an anechoic chamber. These calibration measurements are then used to update the data model, either in the form of explicit unknown parameters or in a non-parametric way. Under favorable conditions it is also possible to estimate the response model together with the unknown directions, so-called auto-calibration. The purpose of this chapter is to give an overview of existing techniques and discuss their respective pros and cons. We will also elaborate on how the methods can be extended to more general situations, for example including frequency and polarization dependence. It should be mentioned that practical calibration also involves hardware adjustments, to compensate for temperature drift etc. The methods considered here can be classified as software calibration, where errors are handled by adjusting the assumed data model rather than correcting for it.
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| 9. |
- Viberg, Mats, 1961
(author)
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Direction of Arrival Estimation
- 2005
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In: Smart Antennas - State-of-the-Art / edited by Thomas Kaiser et al..
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Book chapter (other academic/artistic)
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| 10. |
- Viberg, Mats, 1961
(author)
-
Introduction to Array Processing
- 2014
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In: Academic Press Library in Signal Processing: Volume 3 Array and Statistical Signal Processing. - 9780124115972 ; , s. 463-502
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Book chapter (other academic/artistic)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.
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