Computational Algorithms for Improved Synthetic Aperture Radar Image Focusing

High-resolution radar imaging is an area undergoing rapid technological and scientific development. Synthetic Aperture Radar (SAR) and Inverse Synthetic Aperture Radar (ISAR) are imaging radars with an ever-increasing number of applications for both civilian and military users. The advancements in phased array radar and digital computing technologies move the trend of this technology towards higher spatial resolution and more advanced imaging modalities. Signal processing algorithm development plays a key role in making full use of these technological developments.In SAR and ISAR imaging, the image reconstruction process is based on using the relative motion between the radar and the scene. An important part of the signal processing chain is the estimation and compensation of this relative motion. The increased spatial resolution and number of receive channels cause the approximations used to derive conventional algorithms for image reconstruction and motion compensation to break down. This leads to limited applicability and performance limitations in non-ideal operating conditions.This thesis presents novel research in the areas of data-driven motion compensation and image reconstruction in non-cooperative ISAR and Multichannel Synthetic Aperture Radar (MSAR) imaging. To overcome the limitations of conventional algorithms, this thesis proposes novel algorithms leading to increased estimation performance and image quality. Because a real-time imaging capability is important in many applications, special emphasis is placed on the computational aspects of the algorithms.For non-cooperative ISAR imaging, the thesis proposes improvements to the range alignment, time window selection, autofocus, time-frequency-based image reconstruction and cross-range scaling procedures. These algorithms are combined into a computationally efficient non-cooperative ISAR imaging algorithm based on mathematical optimization. The improvements are experimentally validated to reduce the computational burden and significantly increase the image quality under complex target motion dynamics.Time domain algorithms offer a non-approximated and general way for image reconstruction in both ISAR and MSAR. Previously, their use has been limited by the available computing power. In this thesis, a contrast optimization approach for time domain ISAR imaging is proposed. The algorithm is demonstrated to produce improved imaging performance under the most challenging motion compensation scenarios. The thesis also presents fast time domain algorithms for MSAR. Numerical simulations confirm that the proposed algorithms offer a reasonable compromise between computational speed and image quality metrics

Computational Algorithms for Improved Synthetic Aperture Radar Image Focusing

High-resolution radar imaging is an area undergoing rapid technological and scientific development. Synthetic Aperture Radar (SAR) and Inverse Synthetic Aperture Radar (ISAR) are imaging radars with an ever-increasing number of applications for both civilian and military users. The advancements in phased array radar and digital computing technologies move the trend of this technology towards higher spatial resolution and more advanced imaging modalities. Signal processing algorithm development plays a key role in making full use of these technological developments.In SAR and ISAR imaging, the image reconstruction process is based on using the relative motion between the radar and the scene. An important part of the signal processing chain is the estimation and compensation of this relative motion. The increased spatial resolution and number of receive channels cause the approximations used to derive conventional algorithms for image reconstruction and motion compensation to break down. This leads to limited applicability and performance limitations in non-ideal operating conditions.This thesis presents novel research in the areas of data-driven motion compensation and image reconstruction in non-cooperative ISAR and Multichannel Synthetic Aperture Radar (MSAR) imaging. To overcome the limitations of conventional algorithms, this thesis proposes novel algorithms leading to increased estimation performance and image quality. Because a real-time imaging capability is important in many applications, special emphasis is placed on the computational aspects of the algorithms.For non-cooperative ISAR imaging, the thesis proposes improvements to the range alignment, time window selection, autofocus, time-frequency-based image reconstruction and cross-range scaling procedures. These algorithms are combined into a computationally efficient non-cooperative ISAR imaging algorithm based on mathematical optimization. The improvements are experimentally validated to reduce the computational burden and significantly increase the image quality under complex target motion dynamics.Time domain algorithms offer a non-approximated and general way for image reconstruction in both ISAR and MSAR. Previously, their use has been limited by the available computing power. In this thesis, a contrast optimization approach for time domain ISAR imaging is proposed. The algorithm is demonstrated to produce improved imaging performance under the most challenging motion compensation scenarios. The thesis also presents fast time domain algorithms for MSAR. Numerical simulations confirm that the proposed algorithms offer a reasonable compromise between computational speed and image quality metrics

Käänteisen synteettisen apertuurin tutkan signaalinkäsittely ja liikekompensaatio

Synteettisen apertuurin tutkalla voidaan muodostaa korkearesoluutioisia kaksi- tai kolmiulotteisia tutkakuvia tutkittavista kohteista ja kohdealueista. Korkea resoluutio tutkan ja kohdealueen keskipisteen yhdistävän suoran suuntaisessa etäisyydessä saavutetaan käyttämällä lähetettävälle radioalueen signaalille laajaa taajuuskaistaa. Toinen, etäisyyssuuntaa vastaan kohtisuorassa oleva ulottuvuus tutkakuvaan saadaan kohteen ja tutkan välistä kulma-asentoa muuttavan suhteellisen liikkeen avulla. Tämä kulma-asennon muutos aiheuttaa vastaanotettuun signaaliin Doppler-siirtymiä, joita voidaan hyödyntää sivusuuntaisen erottelukyvyn muodostamiseen. Tämä tutkielma keskittyy yksipaikkaisen käänteisen synteettisen apertuurin tutkan signaalinkäsittelyyn. Tällöin tutkan ja kohteen välinen suhteellinen liike on ideaalisessa tapauksessa kohteen tasaista pyörimisliikettä. Perinteiset etäisyys-Doppler- ja polar-format-prosessointimenetelmät perustuvat Fourier-muunnokseen ja tiettyihin approksimaatioihin. Tutkielmassa esitetään matemaattisesti optimaalinen prosessointimenetelmä, joka ei sisällä approksimaatioita. Tässä käänteiskuvausmenetelmässä tutkakuvan prosessointi suoritetaan erikseen jokaiselle kuvapisteelle korreloimalla kuvapisteestä muodostettua pisteleviämisfunktiota tutkan vastaanottaman signaalin kanssa. Pisteleviämisfunktiolla tarkoitetaan ideaalisen pistemäisen sirottajan vastetta kuvaa muodostavalle systeemille. Kaikissa prosessointimenetelmissä kohteen tuntematon ja epäideaalinen liiketila heikentää tai voi jopa tehdä mahdottomaksi onnistuneen tutkakuvan muodostamisen. Erityisesti käänteiskuvausmenetelmässä on jokaisen kuvapisteen etäisyys tutkasta ajan funktiona tunnettava eksplisiittisesti. Tuntematon liiketila aiheuttaa vastaanotettuun signaaliin vaihemuutoksia, jotka on korjattava ennen kuvan muodostamista liikekompensaation avulla. Tutkielmassa simuloidaan taajuusaskellettua tutkasignaalia, jonka avulla voidaan toteuttaa käänteiskuvausmenetelmä ja tutkia tälle prosessointimenetelmälle sopivia liikekompensaatiomenetelmiä. Rotaatioakselin tarkan sijainnin määrittämiseen käytetään tuloskuvan kontrastin maksimointia. Kontrastiksi määritellään tuloskuvan amplitudien keskihajonnan ja keskiarvon suhde. Käänteiskuvausmenetelmän kanssa yhteensopiviksi liikekompensaatiomenetelmiksi todetaan maksimikorrelaatiomenetelmä ja dominoivia sirottajia hyödyntävät menetelmät. Toteutettujen liikekompensaatiomenetelmien tarkoituksena on muokata vastaanotettu signaali sellaiseen muotoon, että kohteella on ainoastaan tasaista pyörimisliikettä liikekompensaation jälkeen. Jatkotutkimuksen aiheeksi ehdotetaan liikekompensaation yhdistämistä käänteiskuvausmenetelmän prosessointiin

Analysis and comparison of multichannel SAR imaging algorithms

Multichannel synthetic aperture radar (MSAR) systems are essential for applications such as ground moving target indication (GMTI), interferometric SAR (InSAR), and high-resolution wide-swath (HRWS) imaging. In this paper, we analyze and compare MSAR image reconstruction algorithms. Previously, image reconstruction for MSAR has relied heavily on frequency domain matched filtering. Time domain image reconstruction algorithms have several attractive qualities, but their use has been limited due to a high computational burden. In this paper, we utilize digital beamforming and the phase center approximation to develop a fast time domain (fast factorized back-projection, FFBP) algorithm for MSAR. We present two FFBP implementations for MSAR and perform a comparative study between MSAR imaging algorithms. The numerical results confirm the feasibility of the proposed FFBP algorithms for MSAR.acceptedVersionPeer reviewe

Energy and transport in comparison : immaterialisation, dematerialisation and decarbonisation in the EU15 between 1970 and 2000

This article compares the development of transport and energy use with a focus on carbon dioxide (CO2) emissions in the EU15 countries between 1960 and 2000, and separately by each individual EU country between 1970 and 2000. Based on a review on the literature, immaterialisation can be defined as the reduction of energy intensity and transport intensity; dematerialisation can be defined as the reduction in carbon intensity of energy production and the carbon intensity of transport; decarbonisation can be defined as the reduction in (total and transport) carbon intensity of the whole economy. Although there is a clear pattern of reduction in energy intensity of the economy and carbon intensity of energy production, a similar pattern cannot be found in transport. Neither the transport intensity of the economy nor the carbon intensity of transport has been reduced. In particular, freight transport intensity has grown between 1985 and 2000. Data presented by country have shown even more variation. The EU15 countries were aggregated into six groups by cluster analysis to establish the different patterns on each of the three measures. It is concluded that the EU15 countries will have problems in achieving the EU White Paper target of decoupling transport growth from economic growth and the Kyoto target of reducing total CO2 emissions by 8% from the 1990 level between 2008 and 2012. However, there are some weak signals suggesting a more sustainable passenger transport system

Global decarbonisation patterns – total and transport CO2 intensity

Global warming is now considered to be an established fact, and there is little doubt about the contribution of human induced carbon dioxide emissions from fossil fuels to the changing climate. Global CO2 emissions, including international aviation and maritime transport, grew by 50% 1971-1990, and by a further 32% 1990-2006. Carbon intensity of the economy, which can be measured by the carbon dioxide emissions per unit Gross Domestic Product, has reduced over time globally: this is the decarbonisation effect. In this paper, the world’s countries (116 individual countries and five groups of countries) have been grouped through a cluster analysis based on trends in total carbon intensity and transport carbon intensity of the economy (transport CO2 emissions/GDP). Various patterns have been found, some of which are unexpected. Countries with very different GDP per capita levels have similar development patterns in total carbon intensity or transport carbon intensity. It is concluded that economic growth is neither the cause of nor the salvation for CO2 emissions growth, but more subtle measures of emission control are needed for each country

Investigation of environmental effects on coherence loss in SAR interferometry for Snow Water Equivalent retrieval.

Publisher Copyright: AuthorInterferometric Synthetic Aperture Radar (InSAR) is a promising tool for the retrieval of Snow Water Equivalent (SWE) from space. Due to refraction, the interferometric phase changes with snow depth and density, which is exploited by the InSAR method. While the method was first proposed two decades ago, qualitative research using experimental data analyzing factors affecting retrieval performance remains scarce. In this work a tower-based 1-10 GHz, fully polarimetric SAR with InSAR capabilities was used to analyze the effect of meteorological events (air temperature, precipitation intensity, and wind) on the observed temporal decorrelation of interferometric image pairs, at L-, S-, C- and X-bands. These factors were found to be causes of decorrelation in snow, being the temperature the critical variable in the case of snowmelt events. Of the analyzed bands, L-band presented the best coherence conservation properties. Additionally, the phase change between pairs with sufficient coherence was applied to generate estimates of changes in SWE, studying the retrieval errors at different bands and over different temporal baselines. SWE accumulation was calculated from 6 hours up to 12 days temporal baseline over a non-vegetated area. SWE accumulation profiles were successfully reconstructed for short temporal baselines and low frequencies, while an increase in the retrieval error was observed for high frequencies and long temporal baselines, indicating the limitations of higher frequencies for repeat-pass InSAR retrieval. The analysis was also reproduced over a forested area at L-band with similar results as to the non-vegetated area.Peer reviewe