Using heterogeneous satellites for passive detection of moving aerial target

Passive detection of a moving aerial target is critical for intelligent surveillance. Its implementation can use signals transmitted from satellites. Nowadays, various types of satellites co-exist which can be used for passive detection. As a result, a satellite signal receiver may receive signals from multiple heterogeneous satellites, causing difficult in echo signal detection. In this paper, a passive moving aerial target detection method leveraging signals from multiple heterogeneous satellites is proposed. In the proposed method, a plurality of direct wave signals is separated in a reference channel first. Then, an adaptive filter with normalized least-mean-square (NLMS) is adopted to suppress direct-path interference (DPI) and multi-path interference (MPI) in a surveillance channel. Next, the maximum values of the cross ambiguity function (CAF) and the fourth order cyclic cumulants cross ambiguity function (FOCCCAF) correspond into each separated direct wave signal and echo signal will be utilized as the detection statistic of each distributed sensor. Finally, final detection probabilities are calculated by decision fusion based on results from distributed sensors. To evaluate the performance of the proposed method, extensive simulation studies are conducted. The corresponding simulation results show that the proposed fusion detection method can significantly improve the reliability of moving aerial target detection using multiple heterogeneous satellites. Moveover, we also show that the proposed detection method is able to significantly improve the detection performance by using multiple collaborative heterogeneous satellites

Passive detection of moving aerial target based on multiple collaborative GPS satellites

Passive localization is an important part of intelligent surveillance in security and emergency applications. Nowadays, Global Navigation Satellite Systems (GNSSs) have been widely deployed. As a result, the satellite signal receiver may receive multiple GPS signals simultaneously, incurring echo signal detection failure. Therefore, in this paper, a passive method leveraging signals from multiple GPS satellites is proposed for moving aerial target detection. In passive detection, the first challenge is the interference caused by multiple GPS signals transmitted upon the same spectrum resources. To address this issue, successive interference cancellation (SIC) is utilized to separate and reconstruct multiple GPS signals on the reference channel. Moreover, on the monitoring channel, direct wave and multi-path interference are eliminated by extensive cancellation algorithm (ECA). After interference from multiple GPS signals is suppressed, the cycle cross ambiguity function (CCAF) of the signal on the monitoring channel is calculated and coordinate transformation method is adopted to map multiple groups of different time delay-Doppler spectrum into the distance−velocity spectrum. The detection statistics are calculated by the superposition of multiple groups of distance-velocity spectrum. Finally, the echo signal is detected based on a properly defined adaptive detection threshold. Simulation results demonstrate the effectiveness of our proposed method. They show that the detection probability of our proposed method can reach 99%, when the echo signal signal-to-noise ratio (SNR) is only −64 dB. Moreover, our proposed method can achieve 5 dB improvement over the detection method using a single GPS satellite

Modified Cramer-Rao bound for M-FSK signal parameter estimation in Cauchy and Gaussian noise

The Cramer-Rao bound (CRB) provides an efficient standard for evaluating the quality of standard parameter estimators. In this paper, a modified Cramer-Rao bounds (MCRB) for modulation parameter estimations of frequency-shift-keying (FSK) signals is proposed under the condition of the Gaussian and non-Gaussian additive interference. We extend the MCRB to the estimation of a vector of non-random parameters in the presence of nuisance parameters. Moreover, the MCRB is applied to the joint estimation of phase offset, frequency offsets, frequency deviation, and symbol period of FSK signal with two important special cases of alpha stable distributions, namely, the Cauchy and the Gaussian. The extensive simulation studies are conducted to contrast the MCRB for the modulation parameter vector in different noise environments

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

Phase estimation in a navigation receiver

Tässä lisensiaatintutkimuksessa esitetään menetelmä näytteistetyn sinimuotoisen signaalin vaiheen estimointiin silloin, kun taajuus on tunnettu. Menetelmän nimi on vaihekorjattu korrelaatio (PCC) ja sillä voi estimoida vaiheen myös niissä tapauksissa, joissa signaalista ei ole kokonaisluvullista määrää jaksoja mittausvälissä. PCC-vaihe-estimaatin suorituskykyä tutkitaan vertaamalla sen neliösummavirhettä (MSE) Cramér-Rao alarajaan (CRLB). Jotta menetelmän analysointi ja vertailu läheisten menetelmien kanssa olisi helpompaa, signaalimallina on yksi sinimuotoinen signaali valkoisessa Gaussisessa kohinassa. Työssä esitetään lisäksi kaksi menetelmää häiriöisen signaalin vaihe-estimaatin neliösummavirheen pienentämiseen. Tyypillisiä häiriölähteitä ovat salamat ja läheisellä taajuudella toimivat lähettimet; menetelmät ovat vastaavasti nimeltään purskehäiriöiden poisto ja virheellisten ositteiden poisto. PCC-taajuusestimaatti saadaan seuraamalla signaalin vaiheen muuttumista peräkkäisissä mittausväleissä ja sen suorituskykyä sekä laskentakuormaa verrataan Interpoloituun DFT:hen (IDFT). Menetelmän sovellusalue on meteorologinen luotausjärjestelmä, joka käyttää VLF-navigointiverkkoja yläilmakehän tuulenmittaukseen. Estimointiongelmana on arvioida Doppler-ilmiön aiheuttama pienenpieni taajuussiirtymä. Venäläisen Alpharadionavigointiverkon lähetystaajuudet ovat erityisen haasteellisia, koska käytetyssä 400 ms:n mittausvälissä ei ole kokonaisluvullista määrää signaalin jaksoja. Useimmat taajuuden- ja vaiheenestimointimenetelmät eivät ole soveliaita tähän estimointiongelmaan. IDFT saattaisi olla käyttökelpoinen ja siksi sitä on käytetty vertailukohtana. Tietokonesimulaatioin osoitetaan, että vaihe-estimaatin MSE on lähellä CRLB:tä. Sama koskee taajuusestimaatteja, jotka on saatu seuraamalla signaalin vaiheen muuttumista peräkkäisissä mittausväleissä. Simulaatiot osoittavat myös, että PCC-taajuusestimaatin MSE on lähempänä CRLB:tä kuin IDFT-taajuusestimaatin MSE. Koska PCC saavuttaa tämän suorituskyvyn pienemmällä laskentakuormalla, se on soveliaampi kyseiseen sovellukseen. Lisäksi osoitetaan, että vaihe-estimaatin MSE pienenee, kun näytteenottotaajuutta tai mittausväliä kasvatetaan, tai kun salamoiden ja läheisellä taajuudella toimivien lähettimien aiheuttamat häiriöt poistetaan purskehäiriöiden poisto ja virheellisten ositteiden poisto -algoritmeilla. Lopuksi esitetään muutamia signaaliprosessoritoteutukseen (DSP) liittyviä yksityiskohtia, joilla voidaan pienentää laskentakuormaa.This thesis proposes a new method for estimating the unknown phase of a sampled sinusoid of known frequency. The method is called phase corrected correlation (PCC) and it is targeted specifically for the case, when there is a non-integer number of cycles in the measurement interval. Performance of the PCC phase estimate is studied by comparing its mean squared error (MSE) with the Cramér-Rao lower bound (CRLB). In order to simplify analysis and comparison with related methods, the selected signal model is a single sinusoid in additive white Gaussian noise. Two additional algorithms, burst noise removal and partition outlier removal, are proposed for decreasing the MSE of phase estimates in the presence of disturbances such as lightnings and interfering transmitters. PCC frequency estimate is obtained by observing signal phase change in consecutive measurement intervals. Frequency estimation performance and computational burden of the PCC is compared with Interpolated DFT (IDFT). The application domain is a meteorological sounding system for upper-air wind finding using Very Low Frequency (VLF) navigation systems. The problem is to estimate a minute frequency offset caused by the Doppler effect. Frequencies transmitted especially by the Russian Alpha radionavigation system are challenging: the estimation algorithm must be able handle a non-integer number of signal cycles in the 400 ms measurement interval. Most of the related frequency and phase estimation methods are not applicable to this estimation problem. Interpolated DFT (IDFT) may be feasible and therefore it is used as a benchmark. It is shown with computer simulations, that MSE of the phase estimate is close to the CRLB. The same applies to frequency estimates obtained by observing signal phase change in consecutive measurement intervals. Comparison with IDFT shows, that MSE of the PCC frequency estimate is closer to the CRLB as MSE of the IDFT frequency estimate. Moreover, PCC achieves this performance with lower computational burden, making it the preferred choice in this application. It is also shown that MSE of the phase estimate decreases as sampling rate or measurement interval is increased, and that MSE of the phase estimate decreases when interference is removed using burst noise removal and partition outlier removal algorithms. Finally, to achieve a computationally efficient digital signal processor (DSP) implementation, a number of implementation issues are covered

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Proceedings of the Fifth International Mobile Satellite Conference 1997

Satellite-based mobile communications systems provide voice and data communications to users over a vast geographic area. The users may communicate via mobile or hand-held terminals, which may also provide access to terrestrial communications services. While previous International Mobile Satellite Conferences have concentrated on technical advances and the increasing worldwide commercial activities, this conference focuses on the next generation of mobile satellite services. The approximately 80 papers included here cover sessions in the following areas: networking and protocols; code division multiple access technologies; demand, economics and technology issues; current and planned systems; propagation; terminal technology; modulation and coding advances; spacecraft technology; advanced systems; and applications and experiments

Aeronautical engineering: A continuing bibliography with indexes (supplement 223)

This bibliography lists 423 reports, articles, and other documents introduced into the NASA scientific and technical information system in January, 1988

Latitude, longitude, and beyond:mining mobile objects' behavior

Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity