1,517 research outputs found
Radar Interferometry using Two Images with Different Resolutions
Radar interferometry usually exploits two complex-valued radar images with
the same resolution to extract terrain elevation information. This paper
considers the interferometry using two radar images with different resolutions,
which we refer to as dual-resolution radar interferometry. We find that it is
feasible to recover a high-resolution interferogram from a high-resolution
image and a low-resolution one. We formulate the dual-resolution interferometry
into a compressive sensing problem, and exploit the wavelet-domain sparsity of
the interferogram to solve it. Due to the speckle effect in coherent radar
imaging, the sensing matrix of our model is expected to have small mutual
coherence, which guarantees the performance of our method. In comparison with
the conventional radar interferometry methods, the proposed method reduces the
resolution requirement of radar image acquisition. It therefore can promote
wide coverage, low sampling/data rate and storage cost. Numerical experiments
on Sentinel-1 data are made to validate our method.Comment: 5 pages, 3 figure
Recovery guarantees for multifrequency chirp waveforms in compressed radar sensing
Radar imaging systems transmit modulated wideband waveform to achieve high
range resolution resulting in high sampling rates at the receiver proportional
to the bandwidth of the transmit waveform. Analog processing techniques can be
used on receive to reduce the number of measurements to N, the number of
potential delay bins. If the scene interrogated by the radar is assumed to be
sparse consisting of K point targets, results from compressive sensing suggest
that number of measurements can be further reduced to scale with K logN for
stable recovery of a sparse scene from measurements with additive noise. While
unstructured random projectors guarantee successful recovery under sparsity
constraints, they cannot be implemented in the radar hardware in practice.
Recently, structured random Toeplitz and Circulant matrices that result from
using stochastic waveforms in time delay estimation setting have been shown to
yield recovery guarantees similar to unstructured sensing matrices. However,
the corresponding transmitter and receiver structures have high complexity and
large storage requirements. In this paper, we propose an alternative low
complexity compressive wideband radar sensor which combines multitone signal
chirp waveform on transmit with a receiver that utilizes an analog mixer
followed with a uniform sub-Nyquist sampling stage. We derive the recovery
guarantees for the resulting structured measurement matrix and sufficient
conditions for the number of tones. The only random component of our design is
the sparse tone spectrum implementable efficiently in hardware. Our analytical
and empirical results show that the performance of our scheme is in par with
unstructured random sensing matrices and structured Toeplitz and Circulant
matrices with random entries
LAMP: A Locally Adapting Matching Pursuit Framework for Group Sparse Signatures in Ultra-Wide Band Radar Imaging
It has been found that radar returns of extended targets are not only sparse
but also exhibit a tendency to cluster into randomly located, variable sized
groups. However, the standard techniques of Compressive Sensing as applied in
radar imaging hardly considers the clustering tendency into account while
reconstructing the image from the compressed measurements. If the group
sparsity is taken into account, it is intuitive that one might obtain better
results both in terms of accuracy and time complexity as compared to the
conventional recovery techniques like Orthogonal Matching Pursuit (OMP). In
order to remedy this, techniques like Block OMP have been used in the existing
literature. An alternate approach is via reconstructing the signal by
transforming into the Hough Transform Domain where they become point-wise
sparse. However, these techniques essentially assume specific size and
structure of the groups and are not always effective if the exact
characteristics of the groups are not known, prior to reconstruction. In this
manuscript, a novel framework that we call locally adapting matching pursuit
(LAMP) have been proposed for efficient reconstruction of group sparse signals
from compressed measurements without assuming any specific size, location, or
structure of the groups. The recovery guarantee of the LAMP and its superiority
compared to the existing algorithms has been established with respect to
accuracy, time complexity and flexibility in group size. LAMP has been
successfully used on a real-world, experimental data set.Comment: 14 pages,22 figures, Draft to be submitted to journa
Structure-Aware Bayesian Compressive Sensing for Frequency-Hopping Spectrum Estimation with Missing Observations
In this paper, we address the problem of spectrum estimation of multiple
frequency-hopping (FH) signals in the presence of random missing observations.
The signals are analyzed within the bilinear time-frequency (TF) representation
framework, where a TF kernel is designed by exploiting the inherent FH signal
structures. The designed kernel permits effective suppression of cross-terms
and artifacts due to missing observations while preserving the FH signal
auto-terms. The kernelled results are represented in the instantaneous
autocorrelation function domain, which are then processed using a re-designed
structure-aware Bayesian compressive sensing algorithm to accurately estimate
the FH signal TF spectrum. The proposed method achieves high-resolution FH
signal spectrum estimation even when a large portion of data observations is
missing. Simulation results verify the effectiveness of the proposed method and
its superiority over existing techniques.Comment: 14 pages, 11 figures, to appear in IEEE Transactions on Signal
Processin
Distributed Compressed Estimation for Wireless Sensor Networks Based on Compressive Sensing
This letter proposes a novel distributed compressed estimation scheme for
sparse signals and systems based on compressive sensing techniques. The
proposed scheme consists of compression and decompression modules inspired by
compressive sensing to perform distributed compressed estimation. A design
procedure is also presented and an algorithm is developed to optimize
measurement matrices, which can further improve the performance of the proposed
distributed compressed estimation scheme. Simulations for a wireless sensor
network illustrate the advantages of the proposed scheme and algorithm in terms
of convergence rate and mean square error performance.Comment: 5 pages, 7 figures; IEEE Signal Processing Letters, 201
Non-Local Compressive Sensing Based SAR Tomography
Tomographic SAR (TomoSAR) inversion of urban areas is an inherently sparse
reconstruction problem and, hence, can be solved using compressive sensing (CS)
algorithms. This paper proposes solutions for two notorious problems in this
field: 1) TomoSAR requires a high number of data sets, which makes the
technique expensive. However, it can be shown that the number of acquisitions
and the signal-to-noise ratio (SNR) can be traded off against each other,
because it is asymptotically only the product of the number of acquisitions and
SNR that determines the reconstruction quality. We propose to increase SNR by
integrating non-local estimation into the inversion and show that a reasonable
reconstruction of buildings from only seven interferograms is feasible. 2)
CS-based inversion is computationally expensive and therefore barely suitable
for large-scale applications. We introduce a new fast and accurate algorithm
for solving the non-local L1-L2-minimization problem, central to CS-based
reconstruction algorithms. The applicability of the algorithm is demonstrated
using simulated data and TerraSAR-X high-resolution spotlight images over an
area in Munich, Germany.Comment: 10 page
Compressive Sensing for MIMO Radar
Multiple-input multiple-output (MIMO) radar systems have been shown to
achieve superior resolution as compared to traditional radar systems with the
same number of transmit and receive antennas. This paper considers a
distributed MIMO radar scenario, in which each transmit element is a node in a
wireless network, and investigates the use of compressive sampling for
direction-of-arrival (DOA) estimation. According to the theory of compressive
sampling, a signal that is sparse in some domain can be recovered based on far
fewer samples than required by the Nyquist sampling theorem. The DOA of targets
form a sparse vector in the angle space, and therefore, compressive sampling
can be applied for DOA estimation. The proposed approach achieves the superior
resolution of MIMO radar with far fewer samples than other approaches. This is
particularly useful in a distributed scenario, in which the results at each
receive node need to be transmitted to a fusion center for further processing
Xampling: Compressed Sensing of Analog Signals
Xampling generalizes compressed sensing (CS) to reduced-rate sampling of
analog signals. A unified framework is introduced for low rate sampling and
processing of signals lying in a union of subspaces. Xampling consists of two
main blocks: Analog compression that narrows down the input bandwidth prior to
sampling with commercial devices followed by a nonlinear algorithm that detects
the input subspace prior to conventional signal processing. A variety of analog
CS applications are reviewed within the unified Xampling framework including a
general filter-bank scheme for sparse shift-invariant spaces, periodic
nonuniform sampling and modulated wideband conversion for multiband
communications with unknown carrier frequencies, acquisition techniques for
finite rate of innovation signals with applications to medical and radar
imaging, and random demodulation of sparse harmonic tones. A hardware-oriented
viewpoint is advocated throughout, addressing practical constraints and
exemplifying hardware realizations where relevant. It will appear as a chapter
in a book on "Compressed Sensing: Theory and Applications" edited by Yonina
Eldar and Gitta Kutyniok.Comment: 58 pages, 26 figure
Sub-Nyquist Radar: Principles and Prototypes
In the past few years, new approaches to radar signal processing have been
introduced which allow the radar to perform signal detection and parameter
estimation from much fewer measurements than that required by Nyquist sampling.
These systems - referred to as sub-Nyquist radars - model the received signal
as having finite rate of innovation and employ the Xampling framework to obtain
low-rate samples of the signal. Sub-Nyquist radars exploit the fact that the
target scene is sparse facilitating the use of compressed sensing (CS) methods
in signal recovery. In this chapter, we review several pulse-Doppler radar
systems based on these principles. Contrary to other CS-based designs, our
formulations directly address the reduced-rate analog sampling in space and
time, avoid a prohibitive dictionary size, and are robust to noise and clutter.
We begin by introducing temporal sub-Nyquist processing for estimating the
target locations using less bandwidth than conventional systems. This paves the
way to cognitive radars which share their transmit spectrum with other
communication services, thereby providing a robust solution for coexistence in
spectrally crowded environments. Next, without impairing Doppler resolution, we
reduce the dwell time by transmitting interleaved radar pulses in a scarce
manner within a coherent processing interval or "slow time". Then, we consider
multiple-input-multiple-output array radars and demonstrate spatial sub-Nyquist
processing which allows the use of few antenna elements without degradation in
angular resolution. Finally, we demonstrate application of sub-Nyquist and
cognitive radars to imaging systems such as synthetic aperture radar. For each
setting, we present a state-of-the-art hardware prototype designed to
demonstrate the real-time feasibility of sub-Nyquist radars.Comment: 51 pages, 26 figures, 2 tables, Book chapter. arXiv admin note: text
overlap with arXiv:1611.0644
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