24 research outputs found
Compressive Time Delay Estimation Using Interpolation
Time delay estimation has long been an active area of research. In this work,
we show that compressive sensing with interpolation may be used to achieve good
estimation precision while lowering the sampling frequency. We propose an
Interpolating Band-Excluded Orthogonal Matching Pursuit algorithm that uses one
of two interpolation functions to estimate the time delay parameter. The
numerical results show that interpolation improves estimation precision and
that compressive sensing provides an elegant tradeoff that may lower the
required sampling frequency while still attaining a desired estimation
performance.Comment: 5 pages, 2 figures, technical report supporting 1 page submission for
GlobalSIP 201
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
Reliable Identification of RFID Tags Using Multiple Independent Reader Sessions
Radio Frequency Identification (RFID) systems are gaining momentum in various
applications of logistics, inventory, etc. A generic problem in such systems is
to ensure that the RFID readers can reliably read a set of RFID tags, such that
the probability of missing tags stays below an acceptable value. A tag may be
missing (left unread) due to errors in the communication link towards the
reader e.g. due to obstacles in the radio path. The present paper proposes
techniques that use multiple reader sessions, during which the system of
readers obtains a running estimate of the probability to have at least one tag
missing. Based on such an estimate, it is decided whether an additional reader
session is required. Two methods are proposed, they rely on the statistical
independence of the tag reading errors across different reader sessions, which
is a plausible assumption when e.g. each reader session is executed on
different readers. The first method uses statistical relationships that are
valid when the reader sessions are independent. The second method is obtained
by modifying an existing capture-recapture estimator. The results show that,
when the reader sessions are independent, the proposed mechanisms provide a
good approximation to the probability of missing tags, such that the number of
reader sessions made, meets the target specification. If the assumption of
independence is violated, the estimators are still useful, but they should be
corrected by a margin of additional reader sessions to ensure that the target
probability of missing tags is met.Comment: Presented at IEEE RFID 2009 Conferenc
Compressive Sensing for Spread Spectrum Receivers
With the advent of ubiquitous computing there are two design parameters of
wireless communication devices that become very important power: efficiency and
production cost. Compressive sensing enables the receiver in such devices to
sample below the Shannon-Nyquist sampling rate, which may lead to a decrease in
the two design parameters. This paper investigates the use of Compressive
Sensing (CS) in a general Code Division Multiple Access (CDMA) receiver. We
show that when using spread spectrum codes in the signal domain, the CS
measurement matrix may be simplified. This measurement scheme, named
Compressive Spread Spectrum (CSS), allows for a simple, effective receiver
design. Furthermore, we numerically evaluate the proposed receiver in terms of
bit error rate under different signal to noise ratio conditions and compare it
with other receiver structures. These numerical experiments show that though
the bit error rate performance is degraded by the subsampling in the CS-enabled
receivers, this may be remedied by including quantization in the receiver
model. We also study the computational complexity of the proposed receiver
design under different sparsity and measurement ratios. Our work shows that it
is possible to subsample a CDMA signal using CSS and that in one example the
CSS receiver outperforms the classical receiver.Comment: 11 pages, 11 figures, 1 table, accepted for publication in IEEE
Transactions on Wireless Communication
Spectral Compressive Sensing with Polar Interpolation
Existing approaches to compressive sensing of frequency-sparse signals
focuses on signal recovery rather than spectral estimation. Furthermore, the
recovery performance is limited by the coherence of the required sparsity
dictionaries and by the discretization of the frequency parameter space. In
this paper, we introduce a greedy recovery algorithm that leverages a
band-exclusion function and a polar interpolation function to address these two
issues in spectral compressive sensing. Our algorithm is geared towards line
spectral estimation from compressive measurements and outperforms most existing
approaches in fidelity and tolerance to noise.Comment: v1: 5 pages, 2 figures, accepted for publication at ICASSP 2013.
v2,v3: This version corrects minor typos in Algorithm 1 from the published
versio
Demodulating Subsampled Direct Sequence Spread Spectrum Signals using Compressive Signal Processing
We show that to lower the sampling rate in a spread spectrum communication
system using Direct Sequence Spread Spectrum (DSSS), compressive signal
processing can be applied to demodulate the received signal. This may lead to a
decrease in the power consumption or the manufacturing price of wireless
receivers using spread spectrum technology. The main novelty of this paper is
the discovery that in spread spectrum systems it is possible to apply
compressive sensing with a much simpler hardware architecture than in other
systems, making the implementation both simpler and more energy efficient. Our
theoretical work is exemplified with a numerical experiment using the IEEE
802.15.4 standard's 2.4 GHz band specification. The numerical results support
our theoretical findings and indicate that compressive sensing may be used
successfully in spread spectrum communication systems. The results obtained
here may also be applicable in other spread spectrum technologies, such as Code
Division Multiple Access (CDMA) systems.Comment: 5 pages, 2 figures, presented at EUSIPCO 201