Transcript UWB Echo Signal Detection With Ultra

Contents
 1. Introduction
 2. UWB Signal processing
 3. Compressed Sensing Theory
 3.1 Sparse representation of signals
 3.2 AIC (analog to information converter)
 3.3 Waveform Matched dictionary for UWB signal
 4. Eco detection sysytem
 5. Experimental results
 6. References
Introduction
 ultra-wide-band (UWB) signal processing is the requirement
for very high sampling rate. This is major challenge.
 The recently emerging compressed sensing (CS) theory
makes processing UWB signal at a low sampling rate possible
if the signal has a sparse representation in a certain space.
 Based on the CS theory, a system for sampling UWB echo
signal at a rate much lower than Nyquist rate and performing
signal detection is proposed in this paper.
2. UWB Signal processing
 ULTRA-WIDE-BAND (UWB) signal processing system
is characterized by its very high bandwidth that is up
to several gigahertzes. To digitize a UWB signal, a very
high sampling rate is required according to ShannonNyquist sampling theorem,
 but it is difficult to implement with a single analog-todigital converter(ADC) chip.
 To address this problem, some parallel ADCs are
developed. Based on hybrid filter banks (HFBs), the
use of a parallel ADCs system to sample and
reconstruct UWB signal.
UWB Signal processing
 But this parallel ADCs system faces the following
difficulty.
 The digital filters for signal synthesis require the exact
transfer functions of the analog filters for signal
analysis. This may not be possible in practice because
of various uncertainties in the system.so an advance
CS theory introduced.
3. Compressed Sensing Theory
 Traditional sampling theorem requires a band-limited signal to be
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sampled at the Nyquist rate. CS theory suggested that, if a signal
has a sparse representation in a certain space, one can sample the
signal at a rate significantly lower than Nyquist rate and
reconstruct it with overwhelming probability by optimization
techniques.
There are three key elements that are needed to be addressed in
the use of CS theory.
1) How to find a space in which signals have sparse
representation?
2) How to obtain random measurements as samples of sparse
signal?
3) How to reconstruct the original signal from the samples by
optimization techniques.
Sparse representation of signals
 Sparse representations are representations that account
for most or all information of a signal with a linear
combination of a small number of elementary signals
called atoms. Often, the atoms are chosen from a so
called over-complete dictionary. Formally, an overcomplete dictionary is a collection of atoms such that the
number of atoms exceeds the dimension of the signal
space, so that any signal can be represented by more
than one combination of different atoms.
 Sparseness is one of the reasons for the extensive use of
popular transforms such as the Discrete Fourier
Transform, the wavelet transform and the Singular Value
Decomposition.
AIC (analog to information
converter)
• AIC offers a feasible technique to implement low-rate
“information” sampling.
•It consists of three main components: a wideband
pseudorandom modulator , a filter and a low-rate ADC .
•The goal of pseudorandom sequence is to spread the
frequency of signal and provide randomness necessary
for successful signal recovery.
3.3 Waveform Matched dictionary
for UWB signal
 To obtain a sparse representation of signal in a certain space,
many rules were proposed to match the signal in question and the
basis functions of the space.
 the use of waveform-matched rules to design a dictionary
forUWBsignal.
 The receiver is aware of the exact model of transmitted signal. To
achieve very sparse representation of echo signals, the a priori
knowledge of transmitted signal and the echo signal model should
be taken into account in the design of basis or dictionary. Without
regard to other interferences, such as Doppler shift, an echo signal
without noise can be simply modeled as the sum of various scaled,
time-shifted versions of the transmitted signal. Based on above
considerations, we can construct a matched dictionary for echo
signal
4. Eco detection sysytem
 There are four main components in this system: the AIC for random sampling;
the waveform-matched dictionary for sparse signal representation; the
optimizator for signal reconstruction; the detector for echo detection. For signal
recovery in this paper, linear programming and quadratic programming
optimization techniques are used in the optimizator for clean signal and noisy
signal, respectively For noise-free signal, target echoes can be detected directly
according to the reconstructed coefficients with respect to the matched
dictionary. But for noisy signal, more reconstructed coefficients are nonzero
because of the influence of noise, which necessitates a threshold scheme for eco d.
5. Experimental results
References
 [1] S. R. Velazquez, T. Q. Nguyen, and S. R. Broadstone, “Design of hybrid filter
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banks for analog/digital conversion,” IEEE Trans. Signal Process., vol. 46, no.
4, pp. 956–967, Apr. 1998.
[2] L. Feng and W. Namgoong, “An adaptive maximally decimated channelized
uwb receiver with cyclic prefix,” IEEE Trans. Circuits Syst. I,
Reg. Papers, vol. 52, no. 10, pp. 2165–2172, Oct. 2005.
[3] P. Lowenborg, H. Johansson, and L. Wanhammar, “Two-channel digital and
hybrid analog digital multirate filter banks with very low-complexity analysis or
synthesis filters,” IEEE Trans. Circuits Syst. II, Analog Digital Signal Process.,
vol. 50, no. 7, pp. 355–367, Jul. 2003.
[4] M. Vetterli, P. Marziliano, and T. Blu, “Sampling signals with finite rate of
innovation,” IEEE Trans. Signal Process., vol. 50, no. 6, pp. 1417–1428, Jun. 2002.
[5] E. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact
signal reconstruction from highly incomplete frequency information,” IEEE
Trans. Inf. Theory, vol. 52, no. 2, pp. 489–509, Feb. 2006.
[6] D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory, vol. 52, no. 4,
pp. 1289–1306, Apr. 2006.
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