8 research outputs found
Unit Circle Roots Based Sensor Array Signal Processing
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
ΠΠ΅ΡΠ΅ΡΠ½Π°Ρ ΡΠΈΠΌΠΌΠ΅ΡΡΠΈΡ Π²Π΅ΠΊΡΠΎΡΠ° Π²Π΅ΡΠΎΠ²ΡΡ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠΎΠ² ΡΠΈΠΌΠΌΠ΅ΡΡΠΈΡΠ½ΡΡ Π°Π½ΡΠ΅Π½Π½ΡΡ ΡΠ΅ΡΠ΅ΡΠΎΠΊ Ρ Π»ΠΈΠ½Π΅ΠΉΠ½ΡΠΌΠΈ ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΡΠΌΠΈ
ΠΠΎΠ»Π½ΡΠΉ ΡΠ΅ΠΊΡΡ Π΄ΠΎΡΡΡΠΏΠ΅Π½ Π½Π° ΡΠ°ΠΉΡΠ΅ ΠΈΠ·Π΄Π°Π½ΠΈΡ ΠΏΠΎ ΠΏΠΎΠ΄ΠΏΠΈΡΠΊΠ΅: http://radio.kpi.ua/article/view/S0021347018060031Π ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½ΠΎ Π΄ΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΡΡΠ²ΠΎ Π½Π΅ΡΠ΅ΡΠ½ΠΎΠΉ ΡΠΈΠΌΠΌΠ΅ΡΡΠΈΠΈ Π²Π΅ΠΊΡΠΎΡΠ° Π²Π΅ΡΠΎΠ²ΡΡ
ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠΎΠ², ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΊΡΠΈΡΠ΅ΡΠΈΡ Π½Π°ΠΈΠΌΠ΅Π½ΡΡΠΈΡ
ΠΊΠ²Π°Π΄ΡΠ°ΡΠΎΠ², Π² ΡΠΈΠΌΠΌΠ΅ΡΡΠΈΡΠ½ΠΎΠΉ Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠΉ Π°Π΄Π°ΠΏΡΠΈΠ²Π½ΠΎΠΉ Π°Π½ΡΠ΅Π½Π½ΠΎΠΉ ΡΠ΅ΡΠ΅ΡΠΊΠ΅ Ρ Π»ΠΈΠ½Π΅ΠΉΠ½ΡΠΌΠΈ ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΡΠΌΠΈ ΠΈ ΡΡΠ΅Π±ΡΠ΅ΠΌΡΠΌ ΡΠΈΠ³Π½Π°Π»ΠΎΠΌ. ΠΠ°ΡΡ ΡΠΈΠΌΠΌΠ΅ΡΡΠΈΡΠ½ΡΡ
ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ² ΡΠ°ΠΊΠΎΠ³ΠΎ Π²Π΅ΠΊΡΠΎΡΠ° ΡΠ²Π»ΡΡΡΡΡ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎ-ΡΠΎΠΏΡΡΠΆΠ΅Π½Π½ΡΠΌΠΈ ΠΏΠΎ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ Π΄ΡΡΠ³ ΠΊ Π΄ΡΡΠ³Ρ. ΠΠ»Ρ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ²ΠΎΠΉΡΡΠ²Π° Π²Π΅ΠΊΡΠΎΡ ΠΎΠ³ΡΠ°Π½ΠΈΡΠΈΠ²Π°Π΅ΠΌΡΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² (Π·Π½Π°ΡΠ΅Π½ΠΈΡ Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΠΎΡΡΠΈ Π°Π½ΡΠ΅Π½Π½ΠΎΠΉ ΡΠ΅ΡΠ΅ΡΠΊΠΈ Π² ΠΈΠ½ΡΠ΅ΡΠ΅ΡΡΡΡΠΈΡ
Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡΡ
) Π΄ΠΎΠ»ΠΆΠ΅Π½ Π±ΡΡΡ Π΄Π΅ΠΉΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΡΠΌ, Π½ΠΎ Π½Π΅ ΠΎΠ±ΡΠ·Π°ΡΠ΅Π»ΡΠ½ΠΎ ΡΠΈΠΌΠΌΠ΅ΡΡΠΈΡΠ½ΡΠΌ. ΠΠ΅ΡΠ΅ΡΠ½Π°Ρ ΡΠΈΠΌΠΌΠ΅ΡΡΠΈΡ Π²Π΅ΠΊΡΠΎΡΠΎΠ² Π²Ρ
ΠΎΠ΄Π½ΡΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΈ Π²Π΅ΡΠΎΠ²ΡΡ
ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠΎΠ² Π°Π½ΡΠ΅Π½Π½ΠΎΠΉ ΡΠ΅ΡΠ΅ΡΠΊΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΡΠ°Π·ΡΠ°Π±Π°ΡΡΠ²Π°ΡΡ Π΄Π»Ρ ΡΠ°ΠΊΠΎΠΉ ΡΠ΅ΡΠ΅ΡΠΊΠΈ Π°Π΄Π°ΠΏΡΠΈΠ²Π½ΡΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΡ Π² Π°ΡΠΈΡΠΌΠ΅ΡΠΈΠΊΠ΅ Π΄Π΅ΠΉΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΠΈΡΠ΅Π». Π ΡΡΠΎΠΌ ΡΠ»ΡΡΠ°Π΅ ΡΠΈΡΠ»ΠΎ Π°ΡΠΈΡΠΌΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ ΡΠ°ΠΊΠΈΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ², ΠΏΡΠΈΡ
ΠΎΠ΄ΡΡΠΈΡ
ΡΡ Π½Π° ΠΎΠ΄Π½Ρ ΠΈΡΠ΅ΡΠ°ΡΠΈΡ, ΠΏΡΠΈΠΌΠ΅ΡΠ½ΠΎ Π² Π΄Π²Π° ΠΈΠ»ΠΈ ΡΠ΅ΡΡΡΠ΅ ΡΠ°Π·Π° ΠΌΠ΅Π½ΡΡΠ΅ ΡΠΊΠ²ΠΈΠ²Π°Π»Π΅Π½ΡΠ½ΠΎΠ³ΠΎ ΡΠΈΡΠ»Π° Π΄Π΅ΠΉΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΡΡ
Π°ΡΠΈΡΠΌΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ Π°Π½Π°Π»ΠΎΠ³ΠΈΡΠ½ΡΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² Π² Π°ΡΠΈΡΠΌΠ΅ΡΠΈΠΊΠ΅ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΡΡ
ΡΠΈΡΠ΅Π». Π ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² Π² Π°ΡΠΈΡΠΌΠ΅ΡΠΈΠΊΠ΅ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΡΡ
ΠΈ Π΄Π΅ΠΉΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΠΈΡΠ΅Π». ΠΠ½ΠΈ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡ, ΡΡΠΎ Π°Π΄Π°ΠΏΡΠΈΠ²Π½ΡΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΠΈΠΉ Π°ΡΠΈΡΠΌΠ΅ΡΠΈΠΊΡ Π΄Π΅ΠΉΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΠΈΡΠ΅Π», ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°Π΅Ρ Π² 1,5β2 ΡΠ°Π·Π° Π±ΠΎΠ»Π΅Π΅ ΠΊΠΎΡΠΎΡΠΊΠΈΠΉ ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Π½ΡΠΉ ΠΏΡΠΎΡΠ΅ΡΡ ΠΈ Π±ΠΎΠ»Π΅Π΅ Π³Π»ΡΠ±ΠΎΠΊΠΈΠ΅ ΠΏΡΠΎΠ²Π°Π»Ρ (2β3 Π΄Π) Π² ΡΡΡΠ°Π½ΠΎΠ²ΠΈΠ²ΡΠ΅ΠΌΡΡ ΡΠ΅ΠΆΠΈΠΌΠ΅ Π² Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΠ΅ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΠΎΡΡΠΈ Π°Π½ΡΠ΅Π½Π½ΠΎΠΉ ΡΠ΅ΡΠ΅ΡΠΊΠΈ Π² Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡΡ
Π½Π° ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΈ Π°Π΄Π°ΠΏΡΠΈΠ²Π½ΠΎ ΠΏΠΎΠ΄Π°Π²Π»ΡΠ΅ΠΌΡΡ
ΠΏΠΎΠΌΠ΅Ρ
, ΡΠ΅ΠΌ Π°Π»Π³ΠΎΡΠΈΡΠΌ Π² Π°ΡΠΈΡΠΌΠ΅ΡΠΈΠΊΠ΅ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΡΡ
ΡΠΈΡΠ΅Π»
Learning Strategies for Radar Clutter Classification
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
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 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 - 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
Recommended from our members
Anti-Jam GPS Controlled Reception Pattern Antennas for Man-Portable Applications
Military GPS receivers provide crucial information to soldiers in the field, however, the performance of these devices is degraded by in band RF interference, making GPS susceptible to jamming. Anti-jam techniques for aircraft and vehicular platforms have been developed, but at present there is no system for dismounted soldiers. There is a need for an anti-jam system which meets the demands of a dismounted soldier and conforms to the size, weight, and power requirements of a portable device.
A controlled reception pattern antenna, or CRPA, is a potential solution for jammer mitigation. These devices work by steering reception pattern nulls toward the jammer direction, reducing the jammer power which reaches the GPS receiver. Prior CRPA realizations have been designed for use on vehicular and aircraft applications, however, these platforms do not suffer from the same limitations as a man-portable CRPA. Three considerations which are more pertinent for man-portable designs than prior work are (i) distributed antenna element positions and orientations dynamically change during use changing the reception pattern characteristics, (ii) the user is lower to the ground and moves through the environment meaning that multipath propagation can have a greater effect on CRPA performance, and (iii) the size weight and power constraints for a portable system limit the number of antenna elements reducing the degrees of freedom that can be used for cancellation.
To address these challenges, a framework for man-portable CRPA modeling is presented. This includes development of efficient modeling methods which enable investigations into element perturbations to address the dynamic orientation problem. These and other methods are presented in Chapter 3, along with a discussion of the relative strengths and weaknesses of each. Additionally, a mixed scattering channel model is applied to the CRPA reception patterns, combining diffuse and specular reflection in Chapter 4. Discussion of this model centers around the eigenvalues of the signal covariance matrix and the effect of coherence between multipath components. Following this, Chapter 5 examines the performance of polarimetric CRPAs and space-time adaptive processing for man-portable CRPAs with limited degrees of freedom
Abstracts on Radio Direction Finding (1899 - 1995)
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