Multipath parameters estimation of weak GPS signal based on new colored noise unscented Kalman filter

Yuan, Gannan; Xie, Yisong; Yang, Song; Liang, Haibo

doi:10.1109/icinfa.2010.5512240

Cited by 11 publications

(12 citation statements)

References 4 publications

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“…parameter estimation problem of a weak global position satellite (GPS) signal [10], the measurement noise can be also regarded as the colored one owing to error accumulation and/or measurementbased feedback control. Therefore, on the one hand, there is a great need to develop the nonlinear estimation method with the colored measurement noise.…”

Section: Motivationmentioning

confidence: 99%

“…The reasons resulting in the noise correlation in mainly lie in (i) the measurement frequency is high enough so that the correlation between the successive samples of the measured noise cannot be ignored and (ii) the measurement noise can be regarded as the output excited by white noises through integral and feedback parts. For example, the colored noise model has been used in multipath parameter estimation problem of weak GPS signal . In general, the aforementioned practical engineering requirements motivate the theoretical research in estimation and identification for nonlinear stochastic systems with the colored noise.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…For example, the glint noise in radar tracking system and the gyro random slow drift in inertial navigation system are all colored because the sampling frequency is too high to ignore the noise correlation in the time domain. Moreover, in the multipath parameter estimation problem of a weak global position satellite (GPS) signal , the measurement noise can be also regarded as the colored one owing to error accumulation and/or measurement‐based feedback control. Therefore, on the one hand, there is a great need to develop the nonlinear estimation method with the colored measurement noise.…”

Section: Introductionmentioning

confidence: 99%

“…But the considered measurement model was limited to linear. Yuan et al and Xiong et al modeled the measurement noise as the colored one with the first‐order AR process and developed the variants of the UKF applied to the multipath parameter estimation of weak signal from GPS and multi‐sensor target tracking, respectively. However, the improved UKFs still used the first‐order linearization to decompose the correlation in the colored measurement noise, so that they had to face similar shortcomings of the linearized error and the non‐trivial Jacobian matrix computation in the extended Kalman filter .…”

Section: Introductionmentioning

confidence: 99%

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EM‐based adaptive divided difference filter for nonlinear system with multiplicative parameter

Wang

Song

Liang

2016

Intl J Robust & Nonlinear

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Summary This paper is concerned with application of expectation maximization (EM) algorithm for deriving an adaptive version of divided difference filter for joint state estimation and multiplicative parameter identification of nonlinear system with the colored measurement noise. Owing to the fact that there exist a mutual coupling and interaction of state and parameter on each other, it requires a joint or simultaneous estimation of both state and parameter by a mutual iteration, and justly, EM iterates Expectation (E‐)step and Maximization (M‐)step to meet such requirement. Firstly, E‐step involves state filtering and smoothing issues under knowing the previous parameter identification results, which is well solved by resorting to the Gaussian approximation with a trade‐off between accuracy and complexity. Further, such Gaussian approximation estimators are applied for evaluating the condition expectation of complete‐data likelihood function, nonlinearly characterized by the multiplicative parameter needed to be optimized. Secondly, M‐step deals with the maximization of the condition expectation by directly making its derivative as zero to obtain the current general parameter identification equation as the nonlinear integral. Thirdly, by iteratively operating E‐step and M‐step, an adaptive divided difference filter is proposed for joint state estimation and parameter identification by using the second‐order Stirling interpolation to compute the associated nonlinear integral. Finally, the robust performance of the EM‐based adaptive version of divided difference filter to the unknown or time‐varying multiplicative parameter, as compared with the standard augmentation method, is demonstrated by a maneuvering target tracking example. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Motivationmentioning

confidence: 99%

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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EM‐based adaptive divided difference filter for nonlinear system with multiplicative parameter

Wang

Song

Liang

2016

Intl J Robust & Nonlinear

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“…First-order AR model formulated in (3) has been widely accepted and used to represent the colored noise in many practical applications, such as target tracking [29], multipath parameters estimation of weak GPS signal [34], inertial navigation [35], channel and spectral estimation [36,37], and speech processing [38].…”

Section: Remarkmentioning

confidence: 99%

Gaussian approximate filter for stochastic dynamic systems with randomly delayed measurements and colored measurement noises

Zhang

Huang

2016

Sci. China Inf. Sci.

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In this paper, a new Gaussian approximate (GA) filter for stochastic dynamic systems with both one-step randomly delayed measurements and colored measurement noises is presented. For linear systems, a Kalman filter can be obtained to include one-step randomly delayed measurements and colored measurement noises. On the other hand, for nonlinear stochastic dynamic systems, different GA filters can be developed which exploit numerical methods to compute Gaussian weighted integrals involved in the proposed Bayesian solution. Existing GA filter with one-step randomly delayed measurements and existing GA filter with colored measurement noises are special cases of the proposed GA filter. The efficiency and superiority of the proposed method are illustrated in a numerical example concerning a target tracking problem.

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Joint time–frequency mask and convolutional neural network for real-time separation of multipath in GNSS deformation monitoring

Yuan

Liu

et al. 2021

GPS Solut

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Multipath parameters estimation of weak GPS signal based on new colored noise unscented Kalman filter

Cited by 11 publications

References 4 publications

EM‐based adaptive divided difference filter for nonlinear system with multiplicative parameter

EM‐based adaptive divided difference filter for nonlinear system with multiplicative parameter

Gaussian approximate filter for stochastic dynamic systems with randomly delayed measurements and colored measurement noises

Joint time–frequency mask and convolutional neural network for real-time separation of multipath in GNSS deformation monitoring

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