322 research outputs found
2-D Coherence Factor for Sidelobe and Ghost Suppressions in Radar Imaging
The coherence factor (CF) is defined as the ratio of coherent power to
incoherent power received by the radar aperture. The incoherent power is
computed by the multi-antenna receiver based on only the spatial variable. In
this respect, it is a one-dimensional (1-D) CF, and thereby the image sidelobes
in down-range cannot be effectively suppressed. We propose a two-dimensional
(2-D) CF by supplementing the 1-D CF by an incoherent sum dealing with the
frequency dimension. In essence, we employ both spatial diversity and frequency
diversity which, respectively, enhance imaging quality in cross range and
range. Simulations and experimental results are provided to demonstrate the
performance advantages of the proposed approach.Comment: 7 pages, 21 figure
Multipath Exploitation-Based Indoor Target Localization Model Using Single Marginal Antenna
Recently, indoor target localization became an area of interest due to its diverse applications. In indoor target localization, surrounding environment creates multipath components, which can be exploited to aid in localization process. A number of studies have been proposed to employ multipath exploitation in localizing indoor targets. However, their localization errors can still be improved. This study proposed a new localization model based on multipath exploitation techniques by using triangulation method. Ultra-wide band signals were resolved and associated using marginal antenna-based scheme. The estimate of the target location was then obtained using measured round-trip time delays. The location was determined by applying the simple trigonometry on the triangle in which real radar, virtual radars, and the target location are the vertices of the triangle in question. Simulation results show that the proposed method has improved the localization error over a wide range of timing errors, target locations and room sizes with the overall maximum localization error of 1.4 m equivalent to 22.2% improvement as compared to 1.8 m localization error obtained using the method developed by the Muqaibel et al. (2017)
Through-the-wall radar imaging with compressive sensing; theory, practice and future trends-a review
Through-the-Wall Radar Imaging (TWRI) is anemerging technology which enables us to detect behind the wall targets using electromagnetic signals. TWRI has received considerable attention recently due to its diverse applications. This paper presents fundamentals, mathematical foundations and emerging applications of TWRI with special emphasis on Compressive Sensing (CS) and sparse image reconstruction.Multipath propagation stemming from the surrounding walls and nearby targets are among the impinging challenges.Multipath components produce replicas of the genuine target, ghosts, during image reconstruction which may significantly increase the probability of false alarm. The resulting ghost not only creates confusion with genuine targets but may deteriorate the performance of (CS) algorithms as described in this article. The results from a practical scenario show a promising future of the technology which can be adopted in real-life problems including rescue missions and military purposes.AKey words:Â spect dependence, compressive sensing, multipath ghost, multipath exploitation, through-the-wall-radar imaging
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
Through the Wall Radar Imaging via Kronecker-structured Huber-type RPCA
The detection of multiple targets in an enclosed scene, from its outside, is
a challenging topic of research addressed by Through-the-Wall Radar Imaging
(TWRI). Traditionally, TWRI methods operate in two steps: first the removal of
wall clutter then followed by the recovery of targets positions. Recent
approaches manage in parallel the processing of the wall and targets via low
rank plus sparse matrix decomposition and obtain better performances. In this
paper, we reformulate this precisely via a RPCA-type problem, where the sparse
vector appears in a Kronecker product. We extend this approach by adding a
robust distance with flexible structure to handle heterogeneous noise and
outliers, which may appear in TWRI measurements. The resolution is achieved via
the Alternating Direction Method of Multipliers (ADMM) and variable splitting
to decouple the constraints. The removal of the front wall is achieved via a
closed-form proximal evaluation and the recovery of targets is possible via a
tailored Majorization-Minimization (MM) step. The analysis and validation of
our method is carried out using Finite-Difference Time-Domain (FDTD) simulated
data, which show the advantage of our method in detection performance over
complex scenarios
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