An Efficient FPGA Implementation of Orthogonal Matching Pursuit With Square-Root-Free QR Decomposition

Ge, Xiang; Yang, Fan; Zhu, Hengliang; Zeng, Xuan; Zhou, Dian

doi:10.1109/tvlsi.2018.2879884

Cited by 24 publications

(6 citation statements)

References 25 publications

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“…11 Based on compressive sensing (CS) theory, 9 the DSI can be estimated by OMP algorithm with under-sampled measurement, which can help to reduce the computational complexity. Indeed, OMP implementation on FPGA has been demonstrated already [12][13][14] with reasonable computational power requirement with respect to those provided by the Zynq 7010 system on chip fitted on the Redpitaya platform we are using on our experiment.…”

Section: Orthogonal Matching Pursuit For Direct Signal Interference Rmentioning

confidence: 98%

Passive radar for measuring passive sensors: direct signal interference suppression on FPGA using orthogonal matching pursuit and stochastic gradient descent

Friedt

Feng

Chrétien

et al. 2019

Multimodal Sensing: Technologies and Applications

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Passive wireless sensors provide an attractive technology in niche applications where battery powered sensors are not applicable. Surface acoustic wave technology provides an optimum implementation of passive wireless transducers acting as cooperative targets to short range radar systems: the signal carrying the information is delayed beyond clutter, with a piezoelectric substrate converting the electromagnetic wave to an acoustic wave whose properties are dependent on the environment. We have tackled the issue of short range radar certification by considering a passive radar approach in which existing non-cooperative radiofrequency sources are used to power the sensor, and the reader is made solely of passive receivers aimed at recording the reference signal generated by the non-cooperative source and the backscattered signal returned by the sensor. A passive radar approach only requires a cross-correlation between the reference and surveillance signals to identify the time delay between the incoming and backscattered signals and hence the recovery of the physical quantity sensed by the acoustic transducer through acoustic velocity modulation. Practical sources do exhibit some short term correlation. Hence, strong copies of the reference signal with delays shorter than the one introduced by the sensor must be canceled by the receiver to allow for the weak sensor response to be extracted. This classical problem is called Direct Signal Interference (DSI) suppression. In a post-processing approach with little computation time limitation, this problem is solved using a least square error optimization approach. In the context of real time sensor measurement using a Field Programmable Gate Array (FPGA) implementation, data recording, frequency transposition and decimation are readily implemented in the gate array matrix. DSI removal appears as the limiting factor for a full FPGA implementation of the short range passive radar reader. We address the challenge by the iterative process of DSI suppression using Orthogonal Matching Pursuit (OMP) algorithm, and additionnally consider the FPGAfriendly Stochastic Gradient Descent (SGD) approach to try to recover DSI coefficients from a pipelined algorithm using streams of measurement data.

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Section: Orthogonal Matching Pursuit For Direct Signal Interference Rmentioning

confidence: 98%

Passive radar for measuring passive sensors: direct signal interference suppression on FPGA using orthogonal matching pursuit and stochastic gradient descent

Friedt

Feng

Chrétien

et al. 2019

Multimodal Sensing: Technologies and Applications

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show abstract

“…For correlation optimisation, the suggested MMU supported two (2) types of multiplications: vector-vector multiplication and scalar-vector multiplication. Square-root-free QR decomposition was introduced in [19]. The proposed algorithm adopted an incremental quick response decomposition (QRD), and hence it was further optimised by eliminating the square-root operation to ease hardware implementation.…”

Section: Fpga-based Omp Implementationsmentioning

confidence: 99%

“…HSL eases the process and increases design productivity but it may affect in terms of time and usage of area. [6], [15], [17], (c) Improved OMP in [13], [18], [19], [21], (d) Improved OMP in [14], (e) Improved OMP in [16], and (f) Improved OMP in [20]…”

Section: Fpga-based Omp Implementationsmentioning

confidence: 99%

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A review of optimisation and least-square problem methods on field programmable gate array-based orthogonal matching pursuit implementations

Nadzri

Ahmad

2022

IJEECS

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Orthogonal matching pursuit (OMP) is the most efficient algorithm used for the reconstruction of compressively sampled data signals in the implementation of compressive sensing. OMP operates in an iteration-based nature, which involves optimisation and least-square problem (LSP) as the main processes. However, optimisation and LSP processes comprise complex mathematical operations that are computationally demanding, and software-based implementations are slow, power-consuming, and unfit for real-time applications. To fill the research gap, we reviewed the optimisation and LSP techniques implemented on the FPGA platform as the hardware accelerator. Aspects that contributed to the performance, algorithm, and methods involved in the implemented works were discussed and compared. The methods were found to be improved when modified or combined. However, the best approach still depends on the requirement of the system to be developed, and this review is significant as a reference.

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“…Although an interesting approach, where efficient architectures have been proposed such as [29,30], OMP typically assumes that the number of non-zero entries of the vector β-and, thus, the number of candidates that compose the signal of interest-is known.…”

Section: State-of-the-art Hardware Implementations Of Sparse Estimatimentioning

confidence: 99%

FPGA-embedded Linearized Bregman Iteration algorithm for trend break detection

Calliari

Amaral

Lunglmayr

2020

J Wireless Com Network

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Detection of level shifts in a noisy signal, or trend break detection, is a problem that appears in several research fields, from biophysics to optics and economics. Although many algorithms have been developed to deal with such a problem, accurate and low-complexity trend break detection is still an active topic of research. The Linearized Bregman Iterations have been recently presented as a low-complexity and computationally efficient algorithm to tackle this problem, with a formidable structure that could benefit immensely from hardware implementation. In this work, a hardware architecture of the Linearized Bregman Iteration algorithm is presented and tested on a Field Programmable Gate Array (FPGA). The hardware is synthesized in different-sized FPGAs, and the percentage of used hardware, as well as the maximum frequency enabled by the design, indicate that an approximately 100 gain factor in processing time, concerning the software implementation, can be achieved. This represents a tremendous advantage in using a dedicated unit for trend break detection applications. The proposed architecture is compared with a state-of-the-art hardware structure for sparse estimation, and the results indicate that its performance concerning trend break detection is much more pronounced while, at the same time, being the indicated solution for long datasets.

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An Efficient FPGA Implementation of Orthogonal Matching Pursuit With Square-Root-Free QR Decomposition

Cited by 24 publications

References 25 publications

Passive radar for measuring passive sensors: direct signal interference suppression on FPGA using orthogonal matching pursuit and stochastic gradient descent

Passive radar for measuring passive sensors: direct signal interference suppression on FPGA using orthogonal matching pursuit and stochastic gradient descent

A review of optimisation and least-square problem methods on field programmable gate array-based orthogonal matching pursuit implementations

FPGA-embedded Linearized Bregman Iteration algorithm for trend break detection

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