Random Sampling for Analog-to-Information Conversion of Wideband Signals

Laska, Jason N.; Kirolos, S.; Massoud, Yehia; Baraniuk, R.G.; Gilbert, Anna C.; Iwen, Mark A.; Strauss, Martin J.

doi:10.1109/dcas.2006.321048

Cited by 199 publications

(141 citation statements)

References 5 publications

(5 reference statements)

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“…FH signals are sparse in a time-frequency representation as short-time Fourier transform, and they are always wideband when there is no prior restriction on the frequencies of the local sinusoid [16]. Therefore, the measurements obtained with the traditional Nyquist-rate sampling could be excessive and hard to meet with the present ability of hardware instrument.…”

Section: Compressive Identification Problem Setupmentioning

confidence: 99%

The Identification of Frequency Hopping Signal Using Compressive Sensing

Jia¹,

Tian²,

Yu³

2009

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Abstract:Compressive sensing (CS) creates a new framework of signal reconstruction or approximation from a smaller set of incoherent projection compared with the traditional Nyquist-rate sampling theory. Recently, it has been shown that CS can solve some signal processing problems given incoherent measurements without ever reconstructing the signals. Moreover, the number of measurements necessary for most compressive signal processing application such as detection, estimation and classification is lower than that necessary for signal reconstruction. Based on CS, this paper presents a novel identification algorithm of frequency hopping (FH) signals. Given the hop interval, the FH signals can be identified and the hopping frequencies can be estimated with a tiny number of measurements. Simulation results demonstrate that the method is effective and efficient.

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Section: Compressive Identification Problem Setupmentioning

confidence: 99%

The Identification of Frequency Hopping Signal Using Compressive Sensing

Jia¹,

Tian²,

Yu³

2009

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“…The measurement process is summarized in Figure 3. In practice the AIC sampling described by (7) is implemented using analog circuits, where the multiplication of Φ andĩ is performed using mixers and integrators [4]. The compressed measurement i m is used for further digital signal processing, e.g., performing a disaggregation algorithm to derive the individual appliance usage information.…”

Section: Compressed Measurements/samplesmentioning

confidence: 99%

“…Compressive sampling (CS) is a method of acquiring and reconstructing sparse signals [2]. One particular CS application of interest here is the analog-to-information convertor (AIC) [4] that can sample below the Nyquist-rate required by the conventional analog-to-digital convertor (ADC).…”

Section: Introductionmentioning

confidence: 99%

Compressive Sampling for Non-Intrusive Appliance Load Monitoring (NALM) using Current Waveforms

Wang¹,

Filippi²,

Rietman³

et al. 2012

Signal Processing, Pattern Recognition and Applications / 779: Computer Graphics and Imaging

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We propose a NALM technique by exploiting the compressive sampling and sparse reconstruction framework. We estimate the contribution of the individual appliances by measuring the current of the total load. We further assume to know the steady-state current waveform of each appliance. We exploit the sparsity of the current signal to compress the measurement via random sampling, which lowers significantly the processing complexity, the storage and the communication burden. Using the proposed sparse reconstruction approach, we can still identify the on/off status of each appliance from the compressed measurement as if the original non-compressed measurement is used.

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“…In the full analog implementation, analog mixers can be used to simulate φ to obtain the compressive data y in (4) [11], followed by a simple zero-crossing detector. In this case, the data messages are y = ±1.…”

Section: Implementation Detailsmentioning

confidence: 99%

Compressive wireless arrays for bearing estimation

Cevher

Gürbüz

McClellan

et al. 2008

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

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Joint processing of sensor array outputs improves the performance of parameter estimation and hypothesis testing problems beyond the sum of the individual sensor processing results. When the sensors have high data sampling rates, arrays are tethered, creating a disadvantage for their deployment and also limiting their aperture size. In this paper, we develop the signal processing algorithms for randomly deployable wireless sensor arrays that are severely constrained in communication bandwidth. We focus on the acoustic bearing estimation problem and show that when the target bearings are modeled as a sparse vector in the angle space, functions of the low dimensional random projections of the microphone signals can be used to determine multiple source bearings as a solution of an ℓ1-norm minimization problem. Field data results are shown where only 10bits of information is passed from each microphone to estimate multiple target bearings.

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Random Sampling for Analog-to-Information Conversion of Wideband Signals

Cited by 199 publications

References 5 publications

The Identification of Frequency Hopping Signal Using Compressive Sensing

The Identification of Frequency Hopping Signal Using Compressive Sensing

Compressive Sampling for Non-Intrusive Appliance Load Monitoring (NALM) using Current Waveforms

Compressive wireless arrays for bearing estimation

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