arXiv subjects

Khalid El-Darymli

Publications and source records attributed to Khalid El-Darymli.

4 recordsLinked to original sources

arXiv2026

Forward-Scatter Bistatic RCS of a PEC Sphere: A Mie-Theory Analysis

Forward-scatter radar (FSR) represents an extreme bistatic configuration in which the transmitter--target--receiver geometry approaches collinearity, yielding a bistatic angle near $180^\circ$. In this regime, diffraction-dominated scattering can produce bistatic radar cross sections (RCS) that substantially exceed their monostatic counterparts, a property that underpins FS and passive bistatic radar concepts. This paper presents a rigorous electromagnetic study of the bistatic RCS of a perfectly electrically conducting (PEC) sphere using exact Lorenz--Mie theory. The analysis addresses two practical objectives: (i) quantifying the sensitivity of FS enhancement to deviations from the ideal $180^\circ$ bistatic angle as a function of electrical size, and (ii) decomposing the FS response into the canonical Lorenz--Mie polarization channels $S_1$ and $S_2$, thereby establishing a polarization-resolved benchmark. The results, however, are governed by the dimensionless size parameter $ka$ and therefore generalize directly to other frequencies and bistatic configurations. By explicitly relating full-wave simulation results to Babinet-based FS scaling and optical-theorem interpretations in the high-frequency limit, the paper provides a physically transparent benchmark for FS and passive bistatic radar analysis.

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arXiv2026

Frame-Induced Doppler Self-Ambiguity in SiriusXM Satellite Signals for Passive Radar

Satellite Digital Audio Radio Service (SDARS) signals are attractive illuminators of opportunity for passive radar due to their continuous geostationary coverage and relatively high effective radiated power. However, the deterministic time-division multiplexed (TDM) framing employed by these waveforms introduces inherent self-ambiguity that can constrain coherent integration and detection performance. This paper analyzes the self-ambiguity function (SAF) of the XM/SiriusXM (SXM) waveform and explicitly links the observed Doppler periodicity to the repetition of the fast synchronization preamble (FSP) embedded in the TDM master frame. A frame-accurate XM TDM signal simulation is used to establish the expected ambiguity behavior, which is then validated experimentally using real direct-path data collected from the SXM-8 satellite. Results demonstrate pronounced and persistent Doppler replicas at integer multiples of the XM FSP repetition frequency, forming a deterministic Doppler ``comb'' (grating-lobe structure) that spans Doppler extents relevant to GEO-based passive radar operation with multi-second coherent processing intervals.

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arXiv2026

Deep Sigma Point Processes for RCS Modeling in Spaceborne SAR Imagery

Radar cross-section (RCS) modeling is foundational to advancing the utility and sensitivity of spaceborne radar systems. This study introduces a deep sigma-point process (DSPP) model for predicting RCS in synthetic aperture radar (SAR) imagery using a RADARSAT-2 dataset containing 208,191 verified ships. The DSPP model not only strives for predictive accuracy but also characterizes the uncertainty inherent in the intricate relationships among radar signals, ship parameters, and environmental conditions. Unlike traditional approaches that rely on deterministic equations with static parameters, the DSPP uses a hierarchical Gaussian process framework with Bayesian inference to capture variability and uncertainty in RCS predictions. By generating predictive distributions rather than single estimates, the model accounts for the complex dynamics governing radar returns. Using a Matern kernel with automatic relevance determination, the DSPP identifies and ranks critical features across radar, operational, and environmental domains, thereby supporting transparency and interpretability. Performance evaluations demonstrate the model's superiority over linear regression baselines, with a 20.83 percent reduction in root mean squared error, a 25.89 percent increase in R-squared, and a 44.4 percent reduction in both the residual interquartile range and median absolute deviation on the test data. By providing calibrated uncertainty bounds, the DSPP enhances prediction reliability and supports robust decision-making. This work represents a shift toward probabilistic models that incorporate the inherent uncertainty of complex phenomena. By transitioning from fixed equations to distributions over outcomes, the DSPP fosters a deeper understanding of RCS behavior and enables systems to operate effectively in dynamic environments.

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arXiv2018

Target detection in synthetic aperture radar imagery: a state-of-the-art survey

Target detection is the front-end stage in any automatic target recognition system for synthetic aperture radar (SAR) imagery (SAR-ATR). The efficacy of the detector directly impacts the succeeding stages in the SAR-ATR processing chain. There are numerous methods reported in the literature for implementing the detector. We offer an umbrella under which the various research activities in the field are broadly probed and taxonomized. First, a taxonomy for the various detection methods is proposed. Second, the underlying assumptions for different implementation strategies are overviewed. Third, a tabular comparison between careful selections of representative examples is introduced. Finally, a novel discussion is presented, wherein the issues covered include suitability of SAR data models, understanding the multiplicative SAR data models, and two unique perspectives on constant false alarm rate (CFAR) detection: signal processing and pattern recognition. From a signal processing perspective, CFAR is shown to be a finite impulse response band-pass filter. From a statistical pattern recognition perspective, CFAR is shown to be a suboptimal one-class classifier: a Euclidian distance classifier and a quadratic discriminant with a missing term for one-parameter and two-parameter CFAR, respectively. We make a contribution toward enabling an objective design and implementation for target detection in SAR imagery.

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