4,313 research outputs found
Orthogonal Frequency Division Multiplexed Waveform Effects on Passive Bistatic Radar
Communication waveforms act as signals of opportunity for passive radars. However, these signals of opportunity suffer from range-Doppler processing losses due to their high range sidelobes and pulse-diverse waveform aspects. Signals such as the long term evolution (LTE) encode information within the phase and amplitude of the waveform. This research explores aspects of the LTE, such as the encoding scheme and bandwidth modes on passive bistatic Doppler radar. Signal space-time adaptive processing (STAP) performance is evaluated and parameters are compared with the signal to interference-plus-noise ratio (SINR) metric
Modeling Maritime Radar Scattering
The focus of this project was the design and implementation of a maritime radar simulation developed in MATLAB to aid in the understanding of the effects of ocean waves on radar. The purpose of this simulation is to be used as a toolbox for the future development of detection algorithms for small boats on or near the ocean surface. The team delivered three simulations to the MIT Lincoln Laboratory staff. The first simulation focused on the integration of a one-dimensional ocean model and a chirp radar model. The second deliverable extended the first simulation to include a two-dimensional ocean model and a boat wake model. The third simulation introduced a phased array radar model. These simulations were verified against publicly available data and models
Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool
Accelerated by the increasing attention drawn by 5G, 6G, and Internet of
Things applications, communication and sensing technologies have rapidly
evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years.
Enabled by significant advancements in electromagnetic (EM) hardware, mmWave
and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz,
respectively, can be employed for a host of applications. The main feature of
THz systems is high-bandwidth transmission, enabling ultra-high-resolution
imaging and high-throughput communications; however, challenges in both the
hardware and algorithmic arenas remain for the ubiquitous adoption of THz
technology. Spectra comprising mmWave and THz frequencies are well-suited for
synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide
spectrum of tasks like material characterization and nondestructive testing
(NDT). This article provides a tutorial review of systems and algorithms for
THz SAR in the near-field with an emphasis on emerging algorithms that combine
signal processing and machine learning techniques. As part of this study, an
overview of classical and data-driven THz SAR algorithms is provided, focusing
on object detection for security applications and SAR image super-resolution.
We also discuss relevant issues, challenges, and future research directions for
emerging algorithms and THz SAR, including standardization of system and
algorithm benchmarking, adoption of state-of-the-art deep learning techniques,
signal processing-optimized machine learning, and hybrid data-driven signal
processing algorithms...Comment: Submitted to Proceedings of IEE
Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems
Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300
GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including
security sensing, industrial packaging, medical imaging, and non-destructive
testing. Traditional methods for perception and imaging are challenged by novel
data-driven algorithms that offer improved resolution, localization, and
detection rates. Over the past decade, deep learning technology has garnered
substantial popularity, particularly in perception and computer vision
applications. Whereas conventional signal processing techniques are more easily
generalized to various applications, hybrid approaches where signal processing
and learning-based algorithms are interleaved pose a promising compromise
between performance and generalizability. Furthermore, such hybrid algorithms
improve model training by leveraging the known characteristics of radio
frequency (RF) waveforms, thus yielding more efficiently trained deep learning
algorithms and offering higher performance than conventional methods. This
dissertation introduces novel hybrid-learning algorithms for improved mmWave
imaging systems applicable to a host of problems in perception and sensing.
Various problem spaces are explored, including static and dynamic gesture
classification; precise hand localization for human computer interaction;
high-resolution near-field mmWave imaging using forward synthetic aperture
radar (SAR); SAR under irregular scanning geometries; mmWave image
super-resolution using deep neural network (DNN) and Vision Transformer (ViT)
architectures; and data-level multiband radar fusion using a novel
hybrid-learning architecture. Furthermore, we introduce several novel
approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen
Synthetic Aperture Radar Imaging
Simulation programs are used to locate the positions of the input target points and generate a 2D SAR image with the Range Migration Algorithm. Using the same methodology, we can create a scene geometry using the concept of Point cloud and run the simulation program to generate raw SAR data
Parallel MATALAB Techniques
In this chapter, we show why parallel MATLAB is useful, provide a comparison
of the different parallel MATLAB choices, and describe a number of applications
in Signal and Image Processing: Audio Signal Processing, Synthetic Aperture
Radar (SAR) Processing and Superconducting Quantum Interference Filters
(SQIFs). Each of these applications have been parallelized using different
methods (Task parallel and Data parallel techniques). The applications
presented may be considered representative of type of problems faced by signal
and image processing researchers. This chapter will also strive to serve as a
guide to new signal and image processing parallel programmers, by suggesting a
parallelization strategy that can be employed when developing a general
parallel algorithm. The objective of this chapter is to help signal and image
processing algorithm developers understand the advantages of using parallel
MATLAB to tackle larger problems while staying within the powerful environment
of MATLAB
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