Design and implementation of a fully integrated compressed-sensing signal acquisition system

Yoo, Juhwan; Becker, Stephen; Monge, Manuel; Loh, Matthew; Candès, Emmanuel J.; Emami, Azita

doi:10.1109/icassp.2012.6289123

Cited by 62 publications

(35 citation statements)

References 18 publications

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“…Our D.C. coupled system is complementary to the radio frequency architectures recently reported in [17,18]. We expect that further optimization of the circuit components combined with fabrication in a state-of-the-art process will allow our system to span both ranges of operating frequencies.…”

Section: Discussionmentioning

confidence: 65%

“…From a hardware perspective, compressive sampling is desirable because the modulation of the input signal with a binary sequence is simpler and easier to implement at high sampling rates as compared with the sample and hold circuits necessary for interleaved architectures. A single chip sub-Nyquist sampling receiver architecture operating between 100 MHz and 2 GHz was recently reported in the literature [17,18].…”

Section: Analog To Information Convertermentioning

confidence: 99%

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Design of a Parallel Sampling Encoder for Analog to Information (A2I) Converters: Theory, Architecture and CMOS Implementation

2013

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Abstract:We discuss the architecture and design of parallel sampling front ends for analog to information (A2I) converters. As a way of example, we detail the design of a custom 0.5 µm CMOS implementation of a mixed signal parallel sampling encoder architecture. The system consists of configurable parallel analog processing channels, whose output is sampled by traditional analog-to-digital converters (ADCs). The analog front-end modulates the signal of interest with a high-speed digital chipping sequence and integrates the result prior to sampling at a low rate. An FPGA is employed to generate the chipping sequences and process the digitized samples.

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Section: Discussionmentioning

confidence: 65%

Section: Analog To Information Convertermentioning

confidence: 99%

Design of a Parallel Sampling Encoder for Analog to Information (A2I) Converters: Theory, Architecture and CMOS Implementation

2013

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“…i.e. This algorithm takes the advantages of sub-Nyquist method and using multichannel to sample the input signal at low rate, based on the RD concept [6].…”

Section: Related Workmentioning

confidence: 99%

DCSR: A dynamic channel and resolution sampling for a Compressive Sensing receiver to acquire GPS signals

Albu-Rghaif

Lami

2013

2013 IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS 2013)

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Abstract-The use of Compressive Sensing (CS) technique toacquire GPS signals saves processing time when compared with software FFT based receivers. The computational complexity is reduced by up to 60%. Such CS solution uses a fixed size measurement matrix chosen to offer a compromise between the processing overhead and signals acquisition success level. This paper proposes a dynamic acquisition algorithm for GPS signals based on CS. Instead of using a fixed size sampling channels and fixed correlators in the measurement matrix, our DSCR dynamically changes the number and size of the required Channels/Correlators according to the received GPS signals power during acquisition. This adaptive solution offers better fix capability when the GPS receiver is located in harsh signal environment or it will save valuable processing/decoding time (battery power, especially for Smartphones) when the receiver outdoors. A feedback loop is devised to control the sampling channels number and resize the measurement matrix.

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“…Unlabeled sensing is similar to compressed sensing in the sense that they both deal with partially revealed information in a linear measurement system. Compressed sensing [7,8] states that if we have some prior knowledge about the sparsity of the input x, we might be able to reconstruct it uniquely when N < K. The sparsity assumption enables us to model real life applications using compressed sensing [9,10]. On the other hand, in unlabeled sensing there is no specific assumption on the sparsity of data x and the missing part of the information is the correct order of sample values in y.…”

Section: Introductionmentioning

confidence: 99%

Unlabeled sensing: Reconstruction algorithm and theoretical guarantees

Elhami

Scholefield

Haro

et al. 2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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It often happens that we are interested in reconstructing an unknown signal from partial measurements. Also, it is typically assumed that the location (temporal or spatial) of each sample is known and that the only distortion present in the observations is due to additive measurement noise. However, there are some applications where such location information is lost. In this paper, we consider the situation in which the order of noisy samples, taken from a linear measurement system, is missing. Previous work on this topic has only considered the noiseless case and exhaustive search combinatorial algorithms. We propose a much more efficient algorithm based on a geometrical viewpoint of the problem. We also study the uniqueness of the solution under different choices of the sampling matrix and its robustness to noise for the case of two-dimensional signals. Finally we provide simulation results to confirm the theoretical findings of the paper.

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Design and implementation of a fully integrated compressed-sensing signal acquisition system

Cited by 62 publications

References 18 publications

Design of a Parallel Sampling Encoder for Analog to Information (A2I) Converters: Theory, Architecture and CMOS Implementation

Design of a Parallel Sampling Encoder for Analog to Information (A2I) Converters: Theory, Architecture and CMOS Implementation

DCSR: A dynamic channel and resolution sampling for a Compressive Sensing receiver to acquire GPS signals

Unlabeled sensing: Reconstruction algorithm and theoretical guarantees

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