| 1. |
- Abrahamsson, Richard, 1976-
(författare)
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Estimation Problems in Array Signal Processing, System Identification, and Radar Imagery
- 2006
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis is concerned with parameter estimation, signal processing, and applications. In the first part, imaging using radar is considered. More specifically, two methods are presented for estimation and removal of ground-surface reflections in ground penetrating radar which otherwise hinder reliable detection of shallowly buried landmines. Further, a study of two autofocus methods for synthetic aperture radar is presented. In particular, we study their behavior in scenarios where the phase errors leading to cross-range defocusing are of a spatially variant kind. In the subsequent part, array signal processing and optimal beamforming is regarded. In particular, the phenomenon of signal cancellation in adaptive beamformers due to array perturbations, signal correlated interferences and limited data for covariance matrix estimation is considered. For the general signal cancellation problem, a class of improved adaptive beamformers is suggested based on ridge-regression. Another set of methods is suggested to mitigate signal cancellation due to correlated signal and interferences based on a novel way of finding a characterization of the interference subspace from observed array data. Further, a new minimum variance beamformer is presented for high resolution non-parametric spatial spectrum estimation in cases where the impinging signals are correlated. Lastly, a multitude of enhanced covariance matrix estimators from the statistical literature are studied as an alternative to other robust adaptive beamforming methods. The methods are also applied to space-time adaptive processing where limited data for covariance matrix estimation is a common problem. In the third and final part the estimation of the parameters of a general bilinear problem is considered. The bilinear model is motivated by the application of identifying submarines from their electromagnetic signature and by the identification of a Hamerstein-Wiener model of a non-linear dynamic system. An efficient approximate maximum-likelihood method with closed form solution is suggested for estimating the bilinear model parameters.
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| 2. |
- Jakobsson, Andreas, et al.
(författare)
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Subspace-based estimation of time delays and Doppler shifts
- 1998
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Ingår i: IEEE TRANSACTIONS ON SIGNAL PROCESSING. - 1053-587X. ; 46:9, s. 2472-2483
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Tidskriftsartikel (refereegranskat)abstract
- This paper considers the problem of estimating the time delays and Doppler shifts of a known waveform received via several distinct paths by an array of antennas. The general maximum likelihood estimator is presented, and is shown to require a Sd-dimensio
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| 3. |
- Mishra, Kumar Vijay, et al.
(författare)
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A Signal Processing Outlook Toward Joint Radar-Communications
- 2024
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Ingår i: Signal Processing for Joint Radar Communications. - : Wiley. ; , s. 3-36
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Synergistic design of communications and radar systems with common spectral and hardware resources is heralding a new era of efficiently utilizing a limited radio-frequency spectrum. Such a joint radar-communications (JRC) model has advantages of low-cost, compact size, less power consumption, spectrum sharing, improved performance, and safety due to enhanced information sharing. Today, millimeter-wave (mm-wave) communications have emerged as the preferred technology for short distance wireless links because they provide transmission bandwidth that is several gigahertz wide. This band is also promising for short-range radar applications, which benefit from the high-range resolution arising from large transmit signal bandwidths. Signal processing techniques are critical in implementation of mmWave JRC systems. Major challenges are joint waveform design and performance criteria that would optimally trade-off between communications and radar functionalities. Novel multiple-input-multiple-output (MIMO) signal processing techniques are required because mmWave JRC systems employ large antenna arrays. There are opportunities to exploit recent advances in cognition, compressed sensing, and machine learning to reduce required resources and dynamically allocate them with low overheads. This article provides a signal processing perspective of mmWave JRC systems with an emphasis on waveform design.
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| 4. |
- Mishra, Kumar Vijay, et al.
(författare)
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Preface
- 2024
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Ingår i: Signal Processing for Joint Radar Communications. - : wiley.
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 5. |
- Mishra, Kumar Vijay, et al.
(författare)
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Signal Processing for Joint Radar Communications
- 2024
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Bok (övrigt vetenskapligt/konstnärligt)abstract
- Signal Processing for Joint Radar Communications A one-stop, comprehensive source for the latest research in joint radar communications In Signal Processing for Joint Radar Communications, four eminent electrical engineers deliver a practical and informative contribution to the diffusion of newly developed joint radar communications (JRC) tools into the sensing and communications communities. This book illustrates recent successes in applying modern signal processing theories to core problems in JRC. The book offers new results on algorithms and applications of JRC from diverse perspectives, including waveform design, physical layer processing, privacy, security, hardware prototyping, resource allocation, and sampling theory. The distinguished editors bring together contributions from more than 40 leading JRC researchers working on remote sensing, electromagnetics, optimization, signal processing, and beyond 5G wireless networks. The included resources provide an in-depth mathematical treatment of relevant signal processing tools and computational methods allowing readers to take full advantage of JRC systems. Readers will also find: Thorough introductions to fundamental limits and background on JRC theory and applications, including dual-function radar communications, cooperative JRC, distributed JRC, and passive JRC Comprehensive explorations of JRC processing via waveform analyses, interference mitigation, and modeling with jamming and clutter Practical discussions of information-theoretic, optimization, and networking aspects of JRC In-depth examinations of JRC applications in cutting-edge scenarios including automotive systems, intelligent reflecting surfaces, and secure parameter estimation Perfect for researchers and professionals in the fields of radar, signal processing, communications, information theory, networking, and electronic warfare, Signal Processing for Joint Radar Communications will also earn a place in the libraries of engineers working in the defense, aerospace, wireless communications, and automotive industries.
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| 6. |
- Mollén, Christopher, 1987-
(författare)
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High-End Performance with Low-End Hardware : Analysis of Massive MIMO Base Station Transceivers
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Massive MIMO (multiple-input–multiple-output) is a multi-antenna technology for cellular wireless communication, where the base station uses a large number of individually controllable antennas to multiplex users spatially. This technology can provide a high spectral efficiency. One of its main challenges is the immense hardware complexity and cost of all the radio chains in the base station. To make massive MIMO commercially viable, inexpensive, low-complexity hardware with low linearity has to be used, which inherently leads to more signal distortion. This thesis investigates how the degenerated linearity of some of the main components—power amplifiers, analog-to-digital converters (ADCs) and low-noise amplifiers—affects the performance of the system, with respect to data rate, power consumption and out-of-band radiation. The main results are: Spatial processing can reduce PAR (peak-to-average ratio) of the transmit signals in the downlink to as low as 0B; this, however, does not necessarily reduce power consumption. In environments with isotropic fading, one-bit ADCs lead to a reduction in effective signal-to-interference-and-noise ratio (SINR) of 4dB in the uplink and four-bit ADCs give a performance close to that of an unquantized system. An analytical expression for the radiation pattern of the distortion from nonlinear power amplifiers is derived. It shows how the distortion is beamformed to some extent, that its gain never is greater than that of the desired signal, and that the gain of the distortion is reduced with a higher number of served users and a higher number of channel taps. Nonlinear low-noise amplifiers give rise to distortion that partly combines coherently and limits the possible SINR. It is concluded that spatial processing with a large number of antennas reduces the impact of hardware distortion in most cases. As long as proper attention is paid to the few sources of coherent distortion, the hardware complexity can be reduced in massive MIMO base stations to overcome the hardware challenge and make massive MIMO commercial reality.
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| 7. |
- Roy, R, et al.
(författare)
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ESPRIT and Uniform Linear Arrays
- 1989
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Ingår i: Proceedings of the 33rd SPIE International Technical Symposium. - : SPIE. ; , s. 370-380
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Konferensbidrag (refereegranskat)abstract
- ESPRIT is a recently developed and patented technique for high-resolution estimation of signal parameters. It exploits an invariance structure designed into the sensor array to achieve a reduction in computational requirements of many orders of magnitude over previous techniques such as MUSIC, Burg's MEM, and Capon's ML, and in addition achieves performance improvement as measured by parameter estimate error variance. It is also manifestly more robust with respect to sensor errors (e.g. gain, phase, and location errors) than other methods as well. Whereas ESPRIT only requires that the sensor array possess a single invariance best visualized by considering two identical but other-wise arbitrary arrays of sensors displaced (but not rotated) with respect to each other, many arrays currently in use in various applications are uniform linear arrays of identical sensor elements. Phased array radars are commonplace in high-resolution direction finding systems, and uniform tapped delay lines (i.e., constant rate A/D converters) are the rule rather than the exception in digital signal processing systems. Such arrays possess many invariances, and are amenable to other types of analysis, which is one of the main reasons such structures are so prevalent. Recent developments in high-resolution algorithms of the signal/noise subspace genre including total least squares (TLS) ESPRIT applied to uniform linear arrays are summarized. ESPRIT is also shown to be a generalization of the root-MUSIC algorithm (applicable only to the case of uniform linear arrays of omni-directional sensors and unimodular cisoids). Comparisons with various estimator bounds, including CramerRao bounds, are presented.
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| 8. |
- Roy, R, et al.
(författare)
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Recent Advances in Multidimensional Sensor Array Signal Processing
- 1989
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Ingår i: Proceedings of the 6th ASSP Multidimensional Signal Processing Workshop.
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Konferensbidrag (refereegranskat)abstract
- Summary form only given, ESPRIT is a patented technique for high-resolution estimation of signal parameters that exploits an invariance structure designed into the sensor array to achieve a reduction in computational requirements of many orders of magnitude over previous techniques such as MUSIC, Burg's MEM, and Capon's ML, with virtually no loss in performance as measured by parameter estimate variance. Whereas ESPRIT only requires that the sensor array possess a single invariance best visualized by considering tow identical but otherwise arbitrary arrays of sensors displaced (but not rotated) with respect to each other, many arrays currently in use in various applications are uniform arrays of identical sensor elements with displacements in more than one dimension. The uniformly sampled phased-array radar is a typical example, and such systems are commonplace in high-resolution direction finding systems. Such arrays possess many invariances in potentially more than one dimension. Recent developments in extending the concepts behind ESPRIT to multiple invariances and multidimensional parameter spaces were examined.
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| 9. |
- Swindlehurst, A. Lee, et al.
(författare)
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Multiple Invariance ESPRIT
- 1992
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 40:4, s. 867-881
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Tidskriftsartikel (refereegranskat)abstract
- A subspace-fitting formulation of the ESPRIT problem is presented that provides a framework for extending the algorithm to exploit arrays with multiple invariances. In particular, a multiple invariance (MI) ESPRIT algorithm is developed and the asymptotic distribution of the estimates is obtained. Simulations are conducted to verify the analysis and to compare the performance of MI ESPRIT with that of several other approaches. The excellent quality of the MI ESPRIT estimates is explained by recent results which state that, under certain conditions, subspace-fitting methods of this type are asymptotically efficient.
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| 10. |
- Swindlehurst, A. Lee, et al.
(författare)
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Subspace Fitting with Diversely Polarized Antenna Arrays
- 1993
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Diversely polarized antenna arrays are widely used in RF applications. The diversity of response provided by diversely polarized antenna arrays can greatly improve direction-finding performance over arrays sensitive to only one polarization component. For d emitters, directly implementing a multidimensional estimation algorithm would require a search for 3d parameters: d directions of arrival (DOAs), and 2d polarization parameters. A more efficient solution is presented based on the noise subspace fitting (NSF) algorithm. It is shown how to decouple the NSF search into a two-step procedure, where the DOAs are estimated separately. The polarization parameters are then obtained by solving a linear system of equations. The advantage of this approach is that the search dimension is reduced by a factor of three, and no initial polarization estimate is required. The algorithm can be shown to yield asymptotically minimum variance estimates: provided no perfectly coherent signals are present. Simulation examples are included.
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