Non uniform digitizer for alias-free sampling of wideband analog signals

Michael, Navin; Shah, Sukruti; Das, Joydeep; Sandeep, M.M.; Vijaykumar, C.

doi:10.1109/tencon.2007.4429036

Cited by 5 publications

(6 citation statements)

References 2 publications

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“…Moreover, bandpass filters cannot sample SAR echoes randomly enough in frequency domain and thus often generate bad sensing matrices, as observed in the following simulations. As for non-uniform sampling, it often imposes strict timing requirements on the digital circuits to preserve timing information [8,30], which makes it difficult to interface with synchronous digital circuits. In fact, it is easier to build a high-rate modulator via mixer/chipping sequence combination than a specially designed non-uniform sampler [31].…”

Section: Remarksmentioning

confidence: 99%

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Sub-Nyquist SAR via Quadrature Compressive Sampling with Independent Measurements

Yang

Chen

et al. 2019

Remote Sensing

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This paper presents an efficient sampling system for the acquisition of synthetic aperture radar (SAR) data at sub-Nyquist rate. The system adopts a quadrature compressive sampling architecture, which uses modulation, filtering, sampling and digital quadrature demodulation to produce sub-Nyquist or compressive measurements. In the sequential transmit-receive procedure of SAR, the analog echoes are modulated by random binary chipping sequences to inject randomness into the measurement projection, and the chipping sequences are independent from one observation to another. As a result, the system generates a sequence of independent structured measurement matrices, and then the resulting sensing matrix has better restricted isometry property, as proved by theoretical analysis. As a standard recovery problem in compressive sensing, image formation from the sub-Nyquist measurements has significantly improved performance, which in turn promotes low sampling/data rate. Moreover, the resulting sensing matrix has structures suitable for fast matrix-vector products, based on which we provide a first-order fast image formation algorithm. The performance of the proposed sampling system is assessed by synthetic and real data sets. Simulation results suggest that the proposed system is a valid candidate for sub-Nyquist SAR.

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Section: Remarksmentioning

confidence: 99%

“…It is easy to see that the linear system (31) is equivalent to y = Dx with proper reshape operations. Furthermore, since the involved FFTs and Hadamard products in (30) and (31) are unitary operators, the matrix D is also unitary [5].…”

Section: Fast Matrix-vector Productsmentioning

confidence: 99%

Sub-Nyquist SAR via Quadrature Compressive Sampling with Independent Measurements

Yang

Chen

et al. 2019

Remote Sensing

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“…Practically, it is reasonable to assume for all the interested w that (7) 3.3. Alias suppression ratio of nonuniform sampling Due to finite and quantized sampling, the first alias product of a practical alias-free signal will not appear at infinity but at some integer multiple of a basic frequency [7]. Hence, in practice, not an alias-free signal but the one with the first alias component at enough high frequency is realistic and useful.…”

Section: Identifiability Criteria With Nonuniform Sparse Datamentioning

confidence: 99%

“…Hence, a tedious expansion equation is obtained and is equivalent to~6), where only the very first term is analyzed, namely IGs(wW . Given (4), (7) and ' Y small enough, it gives Given the coefficients Ak = 0 (for all k i:-0), it results in an alias-free signal. Otherwise, aliased signals are obtained and alias suppression ratio is defined [5].…”

Section: Gs(w) = (Ys(w) -Hs(w)vs(w)) Jus(w)mentioning

confidence: 99%

System identifiability for sparse and nonuniform samples via spectral analysis

Xiao

2009

2009 IEEE Youth Conference on Information, Computing and Telecommunication

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The system identifiability for sparse and nonuniform measurements is addressed. For uniformly sampled data, spectral information is only available below the Nyquist rate. Hence, it is not necessarily "informative enough", which is a prerequisite for system identifiability. Spectral analysis is carried out to reassess this issue. The result shows that nonuniform sampling pattern with some random distributions can keep alias-free and reproduce the spectrum from sparse samples, so that identifiability is still guaranteed. The model error bounds for aliased signal and finite data sets are also demonstrated.

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“…Many researchers are now interested in this subject, in order to profit from the important spectral proprieties of non uniformly sampled signals, either in telecommunication domain ([2]- [4]) or in other domains like instrumentation, large band spectrum analysis [5], Radars [6], high speed analog-to-digital converters ( [7], [8]), etc.…”

Section: Introductionmentioning

confidence: 99%

Software embedded implementation of real time non uniform sampling filter

Dammak

Ben-Romdhane

Grati

2009

2009 4th International Conference on Design &Amp; Technology of Integrated Systems in Nanoscal Era

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The use of digital filters is widely spread in the field of radio receivers. In literature, there is no practical realization of digital filters for non uniformly sampled data in the radio communication context. In this paper we are interested on real time filtering of non uniformly sampled signals using a Blackfin 533 Digital Signal Processor (DSP) as embedded software target. The filtering stage is not preceded by a reconstruction algorithm. As input, the filter requires signal samples and their corresponding non uniform sampling sequence. Obtained filtered data, are analyzed using specific spectrum analysis method based on zero padding and Fast Fourier Transform (FFT).Keywords-Radio communication receiver, non uniform sampling, non uniform digital filters, non uniform spectrum analysis.

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Non uniform digitizer for alias-free sampling of wideband analog signals

Cited by 5 publications

References 2 publications

Sub-Nyquist SAR via Quadrature Compressive Sampling with Independent Measurements

Sub-Nyquist SAR via Quadrature Compressive Sampling with Independent Measurements

System identifiability for sparse and nonuniform samples via spectral analysis

Software embedded implementation of real time non uniform sampling filter

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