1,422 research outputs found
Sub-Nyquist Sampling: Bridging Theory and Practice
Sampling theory encompasses all aspects related to the conversion of
continuous-time signals to discrete streams of numbers. The famous
Shannon-Nyquist theorem has become a landmark in the development of digital
signal processing. In modern applications, an increasingly number of functions
is being pushed forward to sophisticated software algorithms, leaving only
those delicate finely-tuned tasks for the circuit level.
In this paper, we review sampling strategies which target reduction of the
ADC rate below Nyquist. Our survey covers classic works from the early 50's of
the previous century through recent publications from the past several years.
The prime focus is bridging theory and practice, that is to pinpoint the
potential of sub-Nyquist strategies to emerge from the math to the hardware. In
that spirit, we integrate contemporary theoretical viewpoints, which study
signal modeling in a union of subspaces, together with a taste of practical
aspects, namely how the avant-garde modalities boil down to concrete signal
processing systems. Our hope is that this presentation style will attract the
interest of both researchers and engineers in the hope of promoting the
sub-Nyquist premise into practical applications, and encouraging further
research into this exciting new frontier.Comment: 48 pages, 18 figures, to appear in IEEE Signal Processing Magazin
Onboard multichannel demultiplexer/demodulator
An investigation performed for NASA LeRC by COMSAT Labs, of a digitally implemented on-board demultiplexer/demodulator able to process a mix of uplink carriers of differing bandwidths and center frequencies and programmable in orbit to accommodate variations in traffic flow is reported. The processor accepts high speed samples of the signal carried in a wideband satellite transponder channel, processes these as a composite to determine the signal spectrum, filters the result into individual channels that carry modulated carriers and demodulate these to recover their digital baseband content. The processor is implemented by using forward and inverse pipeline Fast Fourier Transformation techniques. The recovered carriers are then demodulated using a single digitally implemented demodulator that processes all of the modulated carriers. The effort has determined the feasibility of the concept with multiple TDMA carriers, identified critical path technologies, and assessed the potential of developing these technologies to a level capable of supporting a practical, cost effective on-board implementation. The result is a flexible, high speed, digitally implemented Fast Fourier Transform (FFT) bulk demultiplexer/demodulator
Compressive Parameter Estimation for Sparse Translation-Invariant Signals Using Polar Interpolation
We propose new compressive parameter estimation algorithms that make use of
polar interpolation to improve the estimator precision. Our work extends
previous approaches involving polar interpolation for compressive parameter
estimation in two aspects: (i) we extend the formulation from real non-negative
amplitude parameters to arbitrary complex ones, and (ii) we allow for mismatch
between the manifold described by the parameters and its polar approximation.
To quantify the improvements afforded by the proposed extensions, we evaluate
six algorithms for estimation of parameters in sparse translation-invariant
signals, exemplified with the time delay estimation problem. The evaluation is
based on three performance metrics: estimator precision, sampling rate and
computational complexity. We use compressive sensing with all the algorithms to
lower the necessary sampling rate and show that it is still possible to attain
good estimation precision and keep the computational complexity low. Our
numerical experiments show that the proposed algorithms outperform existing
approaches that either leverage polynomial interpolation or are based on a
conversion to a frequency-estimation problem followed by a super-resolution
algorithm. The algorithms studied here provide various tradeoffs between
computational complexity, estimation precision, and necessary sampling rate.
The work shows that compressive sensing for the class of sparse
translation-invariant signals allows for a decrease in sampling rate and that
the use of polar interpolation increases the estimation precision.Comment: 13 pages, 5 figures, to appear in IEEE Transactions on Signal
Processing; minor edits and correction
Spectral estimation using nonuniform sampling
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (leaves 98-99).by James Martin Nohrden.M.S
Accelerating gravitational wave parameter estimation with multi-band template interpolation
Parameter estimation on gravitational wave signals from compact binary
coalescence (CBC) requires the evaluation of computationally intensive waveform
models, typically the bottleneck in the analysis. This cost will increase
further as low frequency sensitivity in later second and third generation
detectors motivates the use of longer waveforms.
We describe a method for accelerating parameter estimation by exploiting the
chirping behaviour of the signals to sample the waveform sparsely for portions
where the full frequency resolution is not required. We demonstrate that the
method can reproduce the original results with a waveform mismatch of , but with a waveform generation cost up to times
lower for computationally costly frequency-domain waveforms starting from below
8 Hz
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