A novel particle filter-based digital phase-locked loop robust against quantization error

Chung, Jun Ho; You, Sung Hyun; Pak, Jung Min; Kim, Jeong Hoon; Lim, Myo Taeg; Song, Moon Kyou

doi:10.1007/s12555-016-0212-6

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…The threshold value is chosen as a chi-square value from the given confidence level and the chi-square table. If the threshold value is d th , the confidence interval, a new measurement range indicating that the MC estimation is normal is expressed as (y * k − √ Rd th , y * k + √ Rd th ) by (5). In a case where failure is not detected, the MC estimatex MC = N MC i=1 w i x i becomes the final estimation result; otherwise, the following form of estimatex FM,k can be represented as the final estimation result:…”

Section: The Hybrid Mcfmdpllmentioning

confidence: 99%

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Robust Phase Estimation of a Hybrid Monte Carlo/Finite Memory Digital Phase-Locked Loop

Lee

You

Kim

2019

IEICE Trans. Inf. & Syst.

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Digital phase-locked loops (DPLLs) have been designed in a number of ways to correctly generate pulse signals in various systems. However, the existing DPLLs have poor acquisition performance or are prone to the divergence phenomenon when modeling and/or round-off errors exist and the noise statistics are incorrect. In this paper, we propose a novel DPLL whose phase estimator is designed in hybrid form that utilizes the advantages of Monte Carlo estimation, which is robust to nonlinear effects such as measurement quantization, and a finite memory estimator, which is robust against incorrect noise information and system model mismatch. The robustness of the proposed hybrid Monte Carlo/finite memory DPLL is demonstrated by comparing its phase estimation performance via a numerical example. key words: digital phase-locked loop, Monte Carlo estimation, finite memory estimation, hybrid estimation

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Section: The Hybrid Mcfmdpllmentioning

confidence: 99%

“…Moreover, it is vulnerable to mismatch between the actual process and the system model. Particle filter (PF)-based DPLL has been designed to overcome the effects of any form of noise [5]. The PF performs a Monte Carlo (MC) estimation based on a defined number of particles [6].…”

Section: Introductionmentioning

confidence: 99%

Robust Phase Estimation of a Hybrid Monte Carlo/Finite Memory Digital Phase-Locked Loop

Lee

You

Kim

2019

IEICE Trans. Inf. & Syst.

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“…A modified Kalman-like PF has been established in [20] with a new likelihood function calculated based on the quantization process. With quantized measurements, PF approaches have also been developed to estimate the phase information in a digital phase-locked loop [3] and identify parameters for controlled auto-regressive systems [6]. Note that the likelihood functions in these papers have been revised in the presence of the quantization errors.…”

Section: Introductionmentioning

confidence: 99%

A Novel Algorithm for Quantized Particle Filtering With Multiple Degrading Sensors: Degradation Estimation and Target Tracking

Liu

Wang

Liu

et al. 2023

IEEE Trans. Ind. Inf.

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This paper addresses the particle filtering problem for a class of nonlinear/non-Gaussian systems with quantized measurements and multiple degrading sensors. A degradation variable described by the Wiener process is proposed to describe the phenomenon of sensor degradation that is often encountered in engineering practice. The measurement output of each sensor is quantized by a uniform quantizer before being sent to the remote filter. An augmented system is constructed which aggregates the original system state and the degradation variables. In the presence of the sensor degradation and the quantization errors, a new likelihood function at the remote filter is calculated by resorting to all the transmitted measurements. According to the mathematical characterization of the likelihood function, a novel particle filtering algorithm is developed where the parameters of both the degradation processes and the quantization functions are exploited to obtain the modified importance weights. Finally, the effectiveness of the proposed method is shown via a target tracking example with bearing measurements.

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Square Root Unscented Digital Phased-locked Loop

You

Kang

Song

2018

Int. J. Control Autom. Syst.

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A novel particle filter-based digital phase-locked loop robust against quantization error

Cited by 7 publications

References 27 publications

Robust Phase Estimation of a Hybrid Monte Carlo/Finite Memory Digital Phase-Locked Loop

Robust Phase Estimation of a Hybrid Monte Carlo/Finite Memory Digital Phase-Locked Loop

A Novel Algorithm for Quantized Particle Filtering With Multiple Degrading Sensors: Degradation Estimation and Target Tracking

Square Root Unscented Digital Phased-locked Loop

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