81 research outputs found
Generation and Analysis of Constrained Random Sampling Patterns
Random sampling is a technique for signal acquisition which is gaining
popularity in practical signal processing systems. Nowadays, event-driven
analog-to-digital converters make random sampling feasible in practical
applications. A process of random sampling is defined by a sampling pattern,
which indicates signal sampling points in time. Practical random sampling
patterns are constrained by ADC characteristics and application requirements.
In this paper authors introduce statistical methods which evaluate random
sampling pattern generators with emphasis on practical applications.
Furthermore, the authors propose a new random pattern generator which copes
with strict practical limitations imposed on patterns, with possibly minimal
loss in randomness of sampling. The proposed generator is compared with
existing sampling pattern generators using the introduced statistical methods.
It is shown that the proposed algorithm generates random sampling patterns
dedicated for event-driven-ADCs better than existed sampling pattern
generators. Finally, implementation issues of random sampling patterns are
discussed.Comment: 29 pages, 12 figures, submitted to Circuits, Systems and Signal
Processing journa
Compressed Sensing Based Direct Conversion Receiver With Interference Reducing Sampling
This paper describes a direct conversion receiver applying compressed sensing
with the objective to relax the analog filtering requirements seen in the
traditional architecture. The analog filter is cumbersome in an \gls{IC} design
and relaxing its requirements is an advantage in terms of die area, performance
and robustness of the receiver. The objective is met by a selection of sampling
pattern matched to the prior knowledge of the frequency placement of the
desired and interfering signals. A simple numerical example demonstrates the
principle. The work is part of an ongoing research effort and the different
project phases are explained.Comment: 3 pages, 5 figures, submitted to IEEE International Conference On
Sensing Communication and Networking 2014 (poster
Model-Based Calibration of Filter Imperfections in the Random Demodulator for Compressive Sensing
The random demodulator is a recent compressive sensing architecture providing
efficient sub-Nyquist sampling of sparse band-limited signals. The compressive
sensing paradigm requires an accurate model of the analog front-end to enable
correct signal reconstruction in the digital domain. In practice, hardware
devices such as filters deviate from their desired design behavior due to
component variations. Existing reconstruction algorithms are sensitive to such
deviations, which fall into the more general category of measurement matrix
perturbations. This paper proposes a model-based technique that aims to
calibrate filter model mismatches to facilitate improved signal reconstruction
quality. The mismatch is considered to be an additive error in the discretized
impulse response. We identify the error by sampling a known calibrating signal,
enabling least-squares estimation of the impulse response error. The error
estimate and the known system model are used to calibrate the measurement
matrix. Numerical analysis demonstrates the effectiveness of the calibration
method even for highly deviating low-pass filter responses. The proposed method
performance is also compared to a state of the art method based on discrete
Fourier transform trigonometric interpolation.Comment: 10 pages, 8 figures, submitted to IEEE Transactions on Signal
Processin
Compressed Sensing with Linear Correlation Between Signal and Measurement Noise
Existing convex relaxation-based approaches to reconstruction in compressed
sensing assume that noise in the measurements is independent of the signal of
interest. We consider the case of noise being linearly correlated with the
signal and introduce a simple technique for improving compressed sensing
reconstruction from such measurements. The technique is based on a linear model
of the correlation of additive noise with the signal. The modification of the
reconstruction algorithm based on this model is very simple and has negligible
additional computational cost compared to standard reconstruction algorithms,
but is not known in existing literature. The proposed technique reduces
reconstruction error considerably in the case of linearly correlated
measurements and noise. Numerical experiments confirm the efficacy of the
technique. The technique is demonstrated with application to low-rate
quantization of compressed measurements, which is known to introduce correlated
noise, and improvements in reconstruction error compared to ordinary Basis
Pursuit De-Noising of up to approximately 7 dB are observed for 1 bit/sample
quantization. Furthermore, the proposed method is compared to Binary Iterative
Hard Thresholding which it is demonstrated to outperform in terms of
reconstruction error for sparse signals with a number of non-zero coefficients
greater than approximately 1/10th of the number of compressed measurements.Comment: 37 pages, 5 figures. Accepted for publication in EURASIP Signal
Processing Accompanying Matlab code available at:
https://github.com/ThomasA/cs-correlated-nois
Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals
Generalised approximate message passing (GAMP) is an approximate Bayesian
estimation algorithm for signals observed through a linear transform with a
possibly non-linear subsequent measurement model. By leveraging prior
information about the observed signal, such as sparsity in a known dictionary,
GAMP can for example reconstruct signals from under-determined measurements -
known as compressed sensing. In the sparse signal setting, most existing signal
priors for GAMP assume the input signal to have i.i.d. entries. Here we present
sparse signal priors for GAMP to estimate non-i.d.d. signals through a
non-uniform weighting of the input prior, for example allowing GAMP to support
model-based compressed sensing.Comment: 3 pages, 1 figure, presented at iTWIST 2018, Marseill
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