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Record W4254671887 · doi:10.22215/etd/2016-11441

Cramer-Rao Lower Bound Derivation and Performance Analysis for Space-Based SAR SMTI

2016· dissertation· en· W4254671887 on OpenAlexaff
Mamoon Rashid

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsCramér–Rao boundClutterConstellationAlgorithmComputer scienceSatelliteUpper and lower boundsSIGNAL (programming language)Remote sensingSynthetic aperture radarChannel (broadcasting)RadarGeographyMathematicsArtificial intelligenceTelecommunicationsPhysicsEstimation theory

Abstract

fetched live from OpenAlex

This thesis develops the Cramér-Rao Lower Bound (CRLB) for multi-channel spaceborne synthetic aperture radar (SAR) system and provides surface moving target indication (SMTI) performance analysis.CRLB provides a lower bound on the achievable variance of any unbiased estimator.An estimator that achieves this bound is called efficient, however, there is no guarantee that an efficient estimator can be found.Nonetheless, the theoretical variance of the efficient estimator provides a good estimate of the capability of the system and serves as a valuable system performance validation tool.Even if an efficient estimator cannot be found, for radar systems the CRLB provides a necessary, but not sufficient design baseline for measurement parameters such as the number of sub-apertures for transmit and receive, power levels, pulse-repetition frequency (PRF), etc.A multi-channel moving target signal model is derived in satellite earth-fixed earthcentered coordinate system.This model is used in space-time adaptive processing (STAP) approaches for SMTI.A statistical model of the received signal is formed using the derived deterministic target signal, and Gaussian distributions for noise and clutter.CRLB for the statistical model and target parameters is derived by solving the derivatives.The non-trivial derivatives are also verified using a numerical method.CRLB is then used to analyse the SMTI performance of RADARSAT-2, RADARSAT constellation mission (RCM) satellite, and a proposed satellite called "TestSAT", under a variety of switching/toggling modes.The results confirm the SMTI capability of RADARSAT-2 demonstrated previously [1,2], and the optimal switching/toggling mode [3][4][5].The simulations for RCM demonstrate that its SMTI capability will be far inferior to RADARSAT-2.However, by slightly changing the parameters of RCM, as was done for TestSAT, it was shown that an SMTI performance that is comparable to that of RADARSAT-2 can be theoretically achieved with a smaller aperture size and lower transmitted power.The main contributions of this thesis include the derivation of the CRLB for multichannel space-borne SAR, and theoretical SMTI performance analysis using CRLB.The goal of the analysis was two-fold: i) to find the SMTI performance limits of realistic systems over different switching/toggling configurations, and ii) to use CRLB as a benchmark tool to determine if it is possible to have a system that consumes less power than an existing system and provides a comparable or better SMTI performance.The theoretical results demonstrate the usefulness of CRLB as a tool in the theoretical performance evaluation of different systems and switching/toggling schemes for SMTI. Thesis P txTransmit power.V a The satellite velocity in satellite ECEF coordinate system. d(u)The direction-of-arrival (DOA) vector.V eff The effective radar velocity, which is used to model the curved-earth geometry for an accurate representation of the range equation in space-borne applications.The velocity of the radar beam as it moves along the ground. V relThe relative velocity between the moving target and the satellite.The radial velocity component of the moving target.The direction of velocity is along the line-of-sight (los) vector. V xThe along-track velocity component of the moving target, which is in the direction parallel to the satellite velocity vector.V ⊥xThe across-track velocity component of the moving target, which is in the crossrange direction. V yThe component of across-track velocity of the moving target that is in the direction along the positive y-axis in satellite ECEF coordinate system (D). V zThe component of along-track velocity of the moving target that is in the direction along the positive z-axis in satellite ECEF coordinate system (D).A i (u) The look-direction dependent antenna pattern for i t h channel.r s Distance from the center of the earth to the center of the platform.r n Distance from the center of the platform to the n th antenna phase center.t Refers to slow-time in the one-dimensional representation of the signal.y 0 The y-component of the moving target position vector at broadside time in satellite ECEF coordinates (D)*.(check time) z 0 The z-component of the moving target position vector at broadside time in satellite ECEF coordinates (D)*.(check time) Thesis RCS radar cross section RDA range doppler algorithm SAR synthetic aperture radar SBR space-borne radar SMTI surface moving target indication SNR Signal to noise ratio STAP space time adaptive processing

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0030.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.254
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2016
Admission routes1
Has abstractyes

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