PPT On UWB Echo Signal Detection With Ultra-Low Rate
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UWB Echo Signal Presentation Transcript:
1.UWB Echo Signal Detection With Ultra-Low Rate Sampling Based on Compressed Sensing
2.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
3.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.
4.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 Shannon-Nyquist sampling theorem,
but it is difficult to implement with a single analog-to-digital 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.
5.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.
6.Compressed Sensing Theory
Traditional sampling theorem requires a band-limited signal to be 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.
7.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 over-complete 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.
8.AIC (analog to information converter)
9.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.
10.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
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UWB Echo Signal Presentation Transcript:
1.UWB Echo Signal Detection With Ultra-Low Rate Sampling Based on Compressed Sensing
2.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
3.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.
4.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 Shannon-Nyquist sampling theorem,
but it is difficult to implement with a single analog-to-digital 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.
5.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.
6.Compressed Sensing Theory
Traditional sampling theorem requires a band-limited signal to be 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.
7.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 over-complete 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.
8.AIC (analog to information converter)
9.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.
10.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
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