8 research outputs found

    Unit Circle Roots Based Sensor Array Signal Processing

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    As technology continues to rapidly evolve, the presence of sensor arrays and the algorithms processing the data they generate take an ever-increasing role in modern human life. From remote sensing to wireless communications, the importance of sensor signal processing cannot be understated. Capon\u27s pioneering work on minimum variance distortionless response (MVDR) beamforming forms the basis of many modern sensor array signal processing (SASP) algorithms. In 2004, Steinhardt and Guerci proved that the roots of the polynomial corresponding to the optimal MVDR beamformer must lie on the unit circle, but this result was limited to only the MVDR. This dissertation contains a new proof of the unit circle roots property which generalizes to other SASP algorithms. Motivated by this result, a unit circle roots constrained (UCRC) framework for SASP is established and includes MVDR as well as single-input single-output (SISO) and distributed multiple-input multiple-output (MIMO) radar moving target detection. Through extensive simulation examples, it will be shown that the UCRC-based SASP algorithms achieve higher output gains and detection probabilities than their non-UCRC counterparts. Additional robustness to signal contamination and limited secondary data will be shown for the UCRC-based beamforming and target detection applications, respectively

    НСчСтная симмСтрия Π²Π΅ΠΊΡ‚ΠΎΡ€Π° вСсовых коэффициСнтов симмСтричных Π°Π½Ρ‚Π΅Π½Π½Ρ‹Ρ… Ρ€Π΅ΡˆΠ΅Ρ‚ΠΎΠΊ с Π»ΠΈΠ½Π΅ΠΉΠ½Ρ‹ΠΌΠΈ ограничСниями

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    ΠŸΠΎΠ»Π½Ρ‹ΠΉ тСкст доступСн Π½Π° сайтС издания ΠΏΠΎ подпискС: http://radio.kpi.ua/article/view/S0021347018060031Π’ Ρ€Π°Π±ΠΎΡ‚Π΅ ΠΏΡ€ΠΈΠ²Π΅Π΄Π΅Π½ΠΎ Π΄ΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒΡΡ‚Π²ΠΎ Π½Π΅Ρ‡Π΅Ρ‚Π½ΠΎΠΉ симмСтрии Π²Π΅ΠΊΡ‚ΠΎΡ€Π° вСсовых коэффициСнтов, ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Ρ… Π½Π° основС критСрия Π½Π°ΠΈΠΌΠ΅Π½ΡŒΡˆΠΈΡ… ΠΊΠ²Π°Π΄Ρ€Π°Ρ‚ΠΎΠ², Π² симмСтричной Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠΉ Π°Π΄Π°ΠΏΡ‚ΠΈΠ²Π½ΠΎΠΉ Π°Π½Ρ‚Π΅Π½Π½ΠΎΠΉ Ρ€Π΅ΡˆΠ΅Ρ‚ΠΊΠ΅ с Π»ΠΈΠ½Π΅ΠΉΠ½Ρ‹ΠΌΠΈ ограничСниями ΠΈ Ρ‚Ρ€Π΅Π±ΡƒΠ΅ΠΌΡ‹ΠΌ сигналом. ΠŸΠ°Ρ€Ρ‹ симмСтричных элСмСнтов Ρ‚Π°ΠΊΠΎΠ³ΠΎ Π²Π΅ΠΊΡ‚ΠΎΡ€Π° ΡΠ²Π»ΡΡŽΡ‚ΡΡ комплСксно-сопряТСнными ΠΏΠΎ ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΡŽ Π΄Ρ€ΡƒΠ³ ΠΊ Π΄Ρ€ΡƒΠ³Ρƒ. Для обСспСчСния Π΄Π°Π½Π½ΠΎΠ³ΠΎ свойства Π²Π΅ΠΊΡ‚ΠΎΡ€ ΠΎΠ³Ρ€Π°Π½ΠΈΡ‡ΠΈΠ²Π°Π΅ΠΌΡ‹Ρ… ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² (значСния Π΄ΠΈΠ°Π³Ρ€Π°ΠΌΠΌΡ‹ направлСнности Π°Π½Ρ‚Π΅Π½Π½ΠΎΠΉ Ρ€Π΅ΡˆΠ΅Ρ‚ΠΊΠΈ Π² ΠΈΠ½Ρ‚Π΅Ρ€Π΅ΡΡƒΡŽΡ‰ΠΈΡ… направлСниях) Π΄ΠΎΠ»ΠΆΠ΅Π½ Π±Ρ‹Ρ‚ΡŒ Π΄Π΅ΠΉΡΡ‚Π²ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹ΠΌ, Π½ΠΎ Π½Π΅ ΠΎΠ±ΡΠ·Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎ симмСтричным. НСчСтная симмСтрия Π²Π΅ΠΊΡ‚ΠΎΡ€ΠΎΠ² Π²Ρ…ΠΎΠ΄Π½Ρ‹Ρ… сигналов ΠΈ вСсовых коэффициСнтов Π°Π½Ρ‚Π΅Π½Π½ΠΎΠΉ Ρ€Π΅ΡˆΠ΅Ρ‚ΠΊΠΈ позволяСт Ρ€Π°Π·Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°Ρ‚ΡŒ для Ρ‚Π°ΠΊΠΎΠΉ Ρ€Π΅ΡˆΠ΅Ρ‚ΠΊΠΈ Π°Π΄Π°ΠΏΡ‚ΠΈΠ²Π½Ρ‹Π΅ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΡ‹ Π² Π°Ρ€ΠΈΡ„ΠΌΠ΅Ρ‚ΠΈΠΊΠ΅ Π΄Π΅ΠΉΡΡ‚Π²ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… чисСл. Π’ этом случаС число арифмСтичСских ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΉ Ρ‚Π°ΠΊΠΈΡ… Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠΎΠ², приходящихся Π½Π° ΠΎΠ΄Π½Ρƒ ΠΈΡ‚Π΅Ρ€Π°Ρ†ΠΈΡŽ, ΠΏΡ€ΠΈΠΌΠ΅Ρ€Π½ΠΎ Π² Π΄Π²Π° ΠΈΠ»ΠΈ Ρ‡Π΅Ρ‚Ρ‹Ρ€Π΅ Ρ€Π°Π·Π° мСньшС эквивалСнтного числа Π΄Π΅ΠΉΡΡ‚Π²ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… арифмСтичСских ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΉ Π°Π½Π°Π»ΠΎΠ³ΠΈΡ‡Π½Ρ‹Ρ… Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠΎΠ² Π² Π°Ρ€ΠΈΡ„ΠΌΠ΅Ρ‚ΠΈΠΊΠ΅ комплСксных чисСл. Π’ Ρ€Π°Π±ΠΎΡ‚Π΅ прСдставлСны Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ ΡΡ€Π°Π²Π½ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠ³ΠΎ модСлирования Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠΎΠ² Π² Π°Ρ€ΠΈΡ„ΠΌΠ΅Ρ‚ΠΈΠΊΠ΅ комплСксных ΠΈ Π΄Π΅ΠΉΡΡ‚Π²ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… чисСл. Они ΠΏΠΎΠΊΠ°Π·Ρ‹Π²Π°ΡŽΡ‚, Ρ‡Ρ‚ΠΎ Π°Π΄Π°ΠΏΡ‚ΠΈΠ²Π½Ρ‹ΠΉ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌ, ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΡŽΡ‰ΠΈΠΉ Π°Ρ€ΠΈΡ„ΠΌΠ΅Ρ‚ΠΈΠΊΡƒ Π΄Π΅ΠΉΡΡ‚Π²ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… чисСл, обСспСчиваСт Π² 1,5–2 Ρ€Π°Π·Π° Π±ΠΎΠ»Π΅Π΅ ΠΊΠΎΡ€ΠΎΡ‚ΠΊΠΈΠΉ ΠΏΠ΅Ρ€Π΅Ρ…ΠΎΠ΄Π½Ρ‹ΠΉ процСсс ΠΈ Π±ΠΎΠ»Π΅Π΅ Π³Π»ΡƒΠ±ΠΎΠΊΠΈΠ΅ ΠΏΡ€ΠΎΠ²Π°Π»Ρ‹ (2–3 Π΄Π‘) Π² ΡƒΡΡ‚Π°Π½ΠΎΠ²ΠΈΠ²ΡˆΠ΅ΠΌΡΡ Ρ€Π΅ΠΆΠΈΠΌΠ΅ Π² Π΄ΠΈΠ°Π³Ρ€Π°ΠΌΠΌΠ΅ направлСнности Π°Π½Ρ‚Π΅Π½Π½ΠΎΠΉ Ρ€Π΅ΡˆΠ΅Ρ‚ΠΊΠΈ Π² направлСниях Π½Π° источники Π°Π΄Π°ΠΏΡ‚ΠΈΠ²Π½ΠΎ подавляСмых ΠΏΠΎΠΌΠ΅Ρ…, Ρ‡Π΅ΠΌ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌ Π² Π°Ρ€ΠΈΡ„ΠΌΠ΅Ρ‚ΠΈΠΊΠ΅ комплСксных чисСл

    Learning Strategies for Radar Clutter Classification

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    In this paper, we address the problem of classifying clutter returns in order to partition them into statistically homogeneous subsets. The classification procedure relies on a model for the observables including latent variables that is solved by the expectation-maximization algorithm. The derivations are carried out by accounting for three different cases for the structure of the clutter covariance matrix. A preliminary performance analysis highlights that the proposed technique is a viable means to cluster clutter returns over the range.Comment: 12 pages, 13 figure

    A short overview of adaptive multichannel filters SNR loss analysis

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    Many multichannel systems use a linear filter to retrieve a signal of interest corrupted by noise whose statistics are partly unknown. The optimal filter in Gaussian noise requires knowledge of the noise covariance matrix Ξ£\Sigma and in practice the latter is estimated from a set of training samples. An important issue concerns the characterization of the performance of such adaptive filters. This is generally achieved using as figure of merit the ratio of the signal to noise ratio (SNR) at the output of the adaptive filter to the SNR obtained with the clairvoyant -known Ξ£\Sigma- filter. This problem has been studied extensively since the seventies and this document presents a concise overview of results published in the literature. We consider various cases about the training samples covariance matrix and we investigate fully adaptive, partially adaptive and regularized filters

    Abstracts on Radio Direction Finding (1899 - 1995)

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    The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion
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