44,008 research outputs found
Super-Resolution Radar
In this paper we study the identification of a time-varying linear system
from its response to a known input signal. More specifically, we consider
systems whose response to the input signal is given by a weighted superposition
of delayed and Doppler shifted versions of the input. This problem arises in a
multitude of applications such as wireless communications and radar imaging.
Due to practical constraints, the input signal has finite bandwidth B, and the
received signal is observed over a finite time interval of length T only. This
gives rise to a delay and Doppler resolution of 1/B and 1/T. We show that this
resolution limit can be overcome, i.e., we can exactly recover the continuous
delay-Doppler pairs and the corresponding attenuation factors, by solving a
convex optimization problem. This result holds provided that the distance
between the delay-Doppler pairs is at least 2.37/B in time or 2.37/T in
frequency. Furthermore, this result allows the total number of delay-Doppler
pairs to be linear up to a log-factor in BT, the dimensionality of the response
of the system, and thereby the limit for identifiability. Stated differently,
we show that we can estimate the time-frequency components of a signal that is
S-sparse in the continuous dictionary of time-frequency shifts of a random
window function, from a number of measurements, that is linear up to a
log-factor in S.Comment: Revised versio
Super-Resolution Time of Arrival Estimation Using Random Resampling in Compressed Sensing
There is a strong demand for super-resolution time of arrival (TOA) estimation techniques for radar applications that can that can exceed the theoretical limits on range resolution set by frequency bandwidth. One of the most promising solutions is the use of compressed sensing (CS) algorithms, which assume only the sparseness of the target distribution but can achieve super-resolution. To preserve the reconstruction accuracy of CS under highly correlated and noisy conditions, we introduce a random resampling approach to process the received signal and thus reduce the coherent index, where the frequency-domain-based CS algorithm is used as noise reduction preprocessing. Numerical simulations demonstrate that our proposed method can achieve super-resolution TOA estimation performance not possible with conventional CS methods
Super-Resolving Quantum Radar: Coherent-State Sources with Homodyne Detection Suffice to Beat the Diffraction Limit
There has been much recent interest in quantum metrology for applications to
sub-Raleigh ranging and remote sensing such as in quantum radar. For quantum
radar, atmospheric absorption and diffraction rapidly degrades any actively
transmitted quantum states of light, such as N00N states, so that for this
high-loss regime the optimal strategy is to transmit coherent states of light,
which suffer no worse loss than the linear Beer's law for classical radar
attenuation, and which provide sensitivity at the shot-noise limit in the
returned power. We show that coherent radar radiation sources, coupled with a
quantum homodyne detection scheme, provide both longitudinal and angular
super-resolution much below the Rayleigh diffraction limit, with sensitivity at
shot-noise in terms of the detected photon power. Our approach provides a
template for the development of a complete super-resolving quantum radar system
with currently available technology.Comment: 23 pages, content is identical to published versio
Bayesian super-resolution with application to radar target recognition
This thesis is concerned with methods to facilitate automatic target recognition using images generated from a group of associated radar systems. Target
recognition algorithms require access to a database of previously recorded or
synthesized radar images for the targets of interest, or a database of features
based on those images. However, the resolution of a new image acquired under
non-ideal conditions may not be as good as that of the images used to generate
the database. Therefore it is proposed to use super-resolution techniques to
match the resolution of new images with the resolution of database images.
A comprehensive review of the literature is given for super-resolution when
used either on its own, or in conjunction with target recognition. A new superresolution algorithm is developed that is based on numerical Markov chain
Monte Carlo Bayesian statistics. This algorithm allows uncertainty in the superresolved image to be taken into account in the target recognition process. It
is shown that the Bayesian approach improves the probability of correct target
classification over standard super-resolution techniques.
The new super-resolution algorithm is demonstrated using a simple synthetically generated data set and is compared to other similar algorithms. A variety
of effects that degrade super-resolution performance, such as defocus, are analyzed and techniques to compensate for these are presented. Performance of the
super-resolution algorithm is then tested as part of a Bayesian target recognition
framework using measured radar data
Scale Dependence Of Radar Rainfall Uncertainty: Initial Evaluation Of NEXRAD\u27s New Super-resolution Data For Hydrologic Applications
This study explores the scale effects of radar rainfall accumulation fields generated using the new super-resolution level II radar reflectivity data acquired by the Next Generation Weather Radar (NEXRAD) network of the Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars. Eleven months (May 2008-August 2009, exclusive of winter months) of high-density rain gauge network data are used to describe the uncertainty structure of radar rainfall and rain gauge representativeness with respect to five spatial scales (0.5, 1, 2, 4, and 8 km). While both uncertainties of gauge representativeness and radar rainfall show simple scaling behavior, the uncertainty of radar rainfall is characterized by an almost 3 times greater standard error at higher temporal and spatial resolutions (15 min and 0.5 km) than at lower resolutions (1 h and 8 km). These results may have implications for error propagation through distributed hydrologic models that require high-resolution rainfall input. Another interesting result of the study is that uncertainty obtained by averaging rainfall products produced from the super-resolution reflectivity data is slightly lower at smaller scales than the uncertainty of the corresponding resolution products produced using averaged (recombined) reflectivity data. © 2010 American Meteorological Society
3D Super-Resolution Imaging Method for Distributed Millimeter-wave Automotive Radar System
Millimeter-wave (mmW) radar is widely applied to advanced autopilot
assistance systems. However, its small antenna aperture causes a low imaging
resolution. In this paper, a new distributed mmW radar system is designed to
solve this problem. It forms a large sparse virtual planar array to enlarge the
aperture, using multiple-input and multiple-output (MIMO) processing. However,
in this system, traditional imaging methods cannot apply to the sparse array.
Therefore, we also propose a 3D super-resolution imaging method specifically
for this system in this paper. The proposed method consists of three steps: (1)
using range FFT to get range imaging, (2) using 2D adaptive diagonal loading
iterative adaptive approach (ADL-IAA) to acquire 2D super-resolution imaging,
which can satisfy this sparsity under single-measurement, (3) using constant
false alarm (CFAR) processing to gain final 3D super-resolution imaging. The
simulation results show the proposed method can significantly improve imaging
resolution under the sparse array and single-measurement
Identification of Parametric Underspread Linear Systems and Super-Resolution Radar
Identification of time-varying linear systems, which introduce both
time-shifts (delays) and frequency-shifts (Doppler-shifts), is a central task
in many engineering applications. This paper studies the problem of
identification of underspread linear systems (ULSs), whose responses lie within
a unit-area region in the delay Doppler space, by probing them with a known
input signal. It is shown that sufficiently-underspread parametric linear
systems, described by a finite set of delays and Doppler-shifts, are
identifiable from a single observation as long as the time bandwidth product of
the input signal is proportional to the square of the total number of delay
Doppler pairs in the system. In addition, an algorithm is developed that
enables identification of parametric ULSs from an input train of pulses in
polynomial time by exploiting recent results on sub-Nyquist sampling for time
delay estimation and classical results on recovery of frequencies from a sum of
complex exponentials. Finally, application of these results to super-resolution
target detection using radar is discussed. Specifically, it is shown that the
proposed procedure allows to distinguish between multiple targets with very
close proximity in the delay Doppler space, resulting in a resolution that
substantially exceeds that of standard matched-filtering based techniques
without introducing leakage effects inherent in recently proposed compressed
sensing-based radar methods.Comment: Revised version of a journal paper submitted to IEEE Trans. Signal
Processing: 30 pages, 17 figure
An Adversarial Super-Resolution Remedy for Radar Design Trade-offs
Radar is of vital importance in many fields, such as autonomous driving,
safety and surveillance applications. However, it suffers from stringent
constraints on its design parametrization leading to multiple trade-offs. For
example, the bandwidth in FMCW radars is inversely proportional with both the
maximum unambiguous range and range resolution. In this work, we introduce a
new method for circumventing radar design trade-offs. We propose the use of
recent advances in computer vision, more specifically generative adversarial
networks (GANs), to enhance low-resolution radar acquisitions into higher
resolution counterparts while maintaining the advantages of the low-resolution
parametrization. The capability of the proposed method was evaluated on the
velocity resolution and range-azimuth trade-offs in micro-Doppler signatures
and FMCW uniform linear array (ULA) radars, respectively.Comment: Accepted in EUSIPCO 2019, 5 page
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