Compensation Module Design for Overlapping Band in Band-Interleaved Data Acquisition Systems Based on Hybrid Particle Swarm Optimization Algorithm

Zhao, Yu; Ye, Peng; Meng, Jie; Yang, Kun; Gao, Jian; Pan, Zhixiang; Huang, Wuhuang; Song, Jiaqing; Dai, Xuefeng

doi:10.1109/access.2020.3027310

Cited by 13 publications

(8 citation statements)

References 29 publications

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“…And then, the nonlinear phase error of FI-DAC system can be estimated. According to [5], the nonlinear phase error can be compensated by digital all-pass filter. We compensated the built FI-DAC system according to the phase error estimated by the proposed method, and used a square wave signal with a frequency of 45 MHz to verify the performance of system.…”

Section: University Of Electronic Science and Technology Of Chinamentioning

confidence: 99%

A comprehensive estimation method of phase error in the frequency-interleaving digital-to-analog converter

Zhao

Tian

Liu

et al. 2022

Preprint

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This letter studied the phase error in the frequency-interleaving digital-to-analog converter (FI-DAC) and proposed a comprehensive phase estimation method. Firstly, the model of FI-DAC system was established, and the phase error were considered that consists of three parts: time delay error, initial phase error and nonlinear phase error. By analyzing the phase function characteristics of the system, the least squares (LS) was used to linearly fit the phase functions of the non-overlapping bands to estimate both the time delay and initial phase of each sub-band channel. Then, the nonlinear phase error of the system was estimated by the difference between the system phase function and the estimated linear phase function. Finally, the effectiveness of the proposed estimation method was verified by the built FI-DAC test bench.

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Section: University Of Electronic Science and Technology Of Chinamentioning

confidence: 99%

A comprehensive estimation method of phase error in the frequency-interleaving digital-to-analog converter

Zhao

Tian

Liu

et al. 2022

Preprint

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“…Furthermore, the total delay of the low frequency channel is shorter, F 1 e jω and F 2a e jω have eliminated the alias. Therefore, in [19], let G 1 e jω be a cascade of a fixed delay and digital infinite impulse response (IIR) all-pass filter and G 2 e jω = 1 to simplify the implementation. In addition, the frequency-independent factors in Equations ( 9) and ( 10) are compensated by adjusting the ADC drive amplifier, which adds less of a noise floor than the digital compensation.…”

Section: ωmentioning

confidence: 99%

“…Most gigahertz converters do not have this capability. Therefore, a series of frequency-interleaved architectures that use mixers to down-convert the input signal have been proposed [3,[13][14][15][16][17][18][19]. They can be divided into two categories: one is similar to time-interleaved ADCs, where the input signal is first distributed to each channel by a power divider and then down-converted by a mixer, which is generally a complex mixer [13,15,17,18]; the other is similar to the HFB system, where the analog analysis filter bank allocates the input signal to the sub-channel, followed by down-conversion.…”

Section: Introductionmentioning

confidence: 99%

“…They can be divided into two categories: one is similar to time-interleaved ADCs, where the input signal is first distributed to each channel by a power divider and then down-converted by a mixer, which is generally a complex mixer [13,15,17,18]; the other is similar to the HFB system, where the analog analysis filter bank allocates the input signal to the sub-channel, followed by down-conversion. Due to the high operating frequency, the analog filter bank is passive, and the mixer is also a real mixer with a simple structure [3,14,16,19].…”

Section: Introductionmentioning

confidence: 99%

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Synchronous Mixing Architecture for Digital Bandwidth Interleaving Sampling System

Jiang

2021

Electronics

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By using a mixer to down-convert the high frequency components of a signal, digital bandwidth interleaving (DBI) technology can simultaneously increase the sampling rate and bandwidth of the sampling system, compared to the time-interleaved and hybrid filter bank. However, the software and hardware of the classical architecture are too complicated, which also leads to poor performance. In particular, the pilot tone used to synchronize the analog and digital local oscillators (LO) of mixers intermodulates with the high frequency components of the signal, resulting in larger spurs. This paper proposes a synchronous mixing architecture for the DBI system, where the LO of the analog mixer is synchronized with the sampling clock of the analog-to-digital converter. Its hardware and software are simplified—the pilot tone used to synchronize the LOs can also be removed. An evaluation platform with a sampling rate of 250 MSPS is implemented to illustrate the performance of the new architecture. The result shows that the spurious free dynamic range (SFDR) of the new architecture is more than 20 dB higher than the classical one in a high frequency range. The rise time of a step signal of the new architecture is 0.578 ± 0.070 ns faster than the classical one with the same bandwidth (90 MHz).

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“…Analytics 3.0 deployment presents a unique challenge for the acquisition and development of SOS capabilities, as each of the contributing organisations employs a different set of predictive and prescriptive analytics tools for their respective systems (the Literature Review of Machine Learning Techniques and Applications in the DOD provides details on predictive machine learning techniques as applied primarily in the DOD application space) [7]. All SOS stakeholders rarely have access to these analyses and the underlying data sets [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Design of System‐of‐System Acquisition Analysis Using Machine Learning

Alshammari

2022

Complexity

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A system of system’s ability to function is derived from the integration of systems from different sources. An SOS’s systems serve two purposes: first, to accomplish their own specific aims, and second, to provide resources to the SOS as a whole. In the last few decades, machine learning and data analytics have been widely used in system design and acquisitions. Every organisation that acquires a sophisticated system employs some type of data analytics to evaluate the system’s independent objectives, which is universally accepted. Data analytics and decision-making regarding the independent system is rarely shared across SOS stakeholders, even though the systems contribute to and benefit from the larger SOS. The goal of this research is to determine how the exchange of data sets and the corresponding analytics by SOS stakeholders can improve SOS capacity. Predicting SOS capabilities by exchanging relevant data sets and prescribing information connections between systems, we propose to use machine learning techniques. This article serves as an intermediate analysis of the above research work and aims to estimate the benefit of information sharing among the SOS stakeholders. In this research, we have applied different machine learning models to the IBM HR analytics data set to determine the corresponding analytics by SOS stakeholders that can improve SOS capacity. We propose using machine learning techniques to forecast SOS capabilities through the sharing of relevant data sets, and we prescribe the information linkages across systems to make this possible. This paper provides an update on the progress being made toward the aforementioned research project, and its primary focus is on developing a method to put a dollar amount on the benefits of information sharing among the many parties involved in the SOS.

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Compensation Module Design for Overlapping Band in Band-Interleaved Data Acquisition Systems Based on Hybrid Particle Swarm Optimization Algorithm

Cited by 13 publications

References 29 publications

A comprehensive estimation method of phase error in the frequency-interleaving digital-to-analog converter

A comprehensive estimation method of phase error in the frequency-interleaving digital-to-analog converter

Synchronous Mixing Architecture for Digital Bandwidth Interleaving Sampling System

Design of System‐of‐System Acquisition Analysis Using Machine Learning

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