2,601 research outputs found
A model-based approach for detection of objects in low resolution passive millimeter wave images
A model-based vision system to assist the pilots in landing maneuvers under restricted visibility conditions is described. The system was designed to analyze image sequences obtained from a Passive Millimeter Wave (PMMW) imaging system mounted on the aircraft to delineate runways/taxiways, buildings, and other objects on or near runways. PMMW sensors have good response in a foggy atmosphere, but their spatial resolution is very low. However, additional data such as airport model and approximate position and orientation of aircraft are available. These data are exploited to guide our model-based system to locate objects in the low resolution image and generate warning signals to alert the pilots. Also analytical expressions were derived from the accuracy of the camera position estimate obtained by detecting the position of known objects in the image
A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications
The commercial availability of low-cost millimeter wave (mmWave)
communication and radar devices is starting to improve the penetration of such
technologies in consumer markets, paving the way for large-scale and dense
deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the
same time, pervasive mmWave access will enable device localization and
device-free sensing with unprecedented accuracy, especially with respect to
sub-6 GHz commercial-grade devices. This paper surveys the state of the art in
device-based localization and device-free sensing using mmWave communication
and radar devices, with a focus on indoor deployments. We first overview key
concepts about mmWave signal propagation and system design. Then, we provide a
detailed account of approaches and algorithms for localization and sensing
enabled by mmWaves. We consider several dimensions in our analysis, including
the main objectives, techniques, and performance of each work, whether each
research reached some degree of implementation, and which hardware platforms
were used for this purpose. We conclude by discussing that better algorithms
for consumer-grade devices, data fusion methods for dense deployments, as well
as an educated application of machine learning methods are promising, relevant
and timely research directions.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys &
Tutorials (IEEE COMST
Concealed Object Detection for Passive Millimeter-Wave Security Imaging Based on Task-Aligned Detection Transformer
Passive millimeter-wave (PMMW) is a significant potential technique for human
security screening. Several popular object detection networks have been used
for PMMW images. However, restricted by the low resolution and high noise of
PMMW images, PMMW hidden object detection based on deep learning usually
suffers from low accuracy and low classification confidence. To tackle the
above problems, this paper proposes a Task-Aligned Detection Transformer
network, named PMMW-DETR. In the first stage, a Denoising Coarse-to-Fine
Transformer (DCFT) backbone is designed to extract long- and short-range
features in the different scales. In the second stage, we propose the Query
Selection module to introduce learned spatial features into the network as
prior knowledge, which enhances the semantic perception capability of the
network. In the third stage, aiming to improve the classification performance,
we perform a Task-Aligned Dual-Head block to decouple the classification and
regression tasks. Based on our self-developed PMMW security screening dataset,
experimental results including comparison with State-Of-The-Art (SOTA) methods
and ablation study demonstrate that the PMMW-DETR obtains higher accuracy and
classification confidence than previous works, and exhibits robustness to the
PMMW images of low quality
Learning to Detect Open Carry and Concealed Object with 77GHz Radar
Detecting harmful carried objects plays a key role in intelligent
surveillance systems and has widespread applications, for example, in airport
security. In this paper, we focus on the relatively unexplored area of using
low-cost 77GHz mmWave radar for the carried objects detection problem. The
proposed system is capable of real-time detecting three classes of objects -
laptop, phone, and knife - under open carry and concealed cases where objects
are hidden with clothes or bags. This capability is achieved by the initial
signal processing for localization and generating range-azimuth-elevation image
cubes, followed by a deep learning-based prediction network and a multi-shot
post-processing module for detecting objects. Extensive experiments for
validating the system performance on detecting open carry and concealed objects
have been presented with a self-built radar-camera testbed and collected
dataset. Additionally, the influence of different input formats, factors, and
parameters on system performance is analyzed, providing an intuitive
understanding of the system. This system would be the very first baseline for
other future works aiming to detect carried objects using 77GHz radar.Comment: 12 page
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
Computational polarimetric microwave imaging
We propose a polarimetric microwave imaging technique that exploits recent
advances in computational imaging. We utilize a frequency-diverse cavity-backed
metasurface, allowing us to demonstrate high-resolution polarimetric imaging
using a single transceiver and frequency sweep over the operational microwave
bandwidth. The frequency-diverse metasurface imager greatly simplifies the
system architecture compared with active arrays and other conventional
microwave imaging approaches. We further develop the theoretical framework for
computational polarimetric imaging and validate the approach experimentally
using a multi-modal leaky cavity. The scalar approximation for the interaction
between the radiated waves and the target---often applied in microwave
computational imaging schemes---is thus extended to retrieve the susceptibility
tensors, and hence providing additional information about the targets.
Computational polarimetry has relevance for existing systems in the field that
extract polarimetric imagery, and particular for ground observation. A growing
number of short-range microwave imaging applications can also notably benefit
from computational polarimetry, particularly for imaging objects that are
difficult to reconstruct when assuming scalar estimations.Comment: 17 pages, 15 figure
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