Particle Smoother for Nonlinear Systems With One‐Step Randomly Delayed Measurements

Huang, Yulong; Zhang, Yonggang

doi:10.1002/asjc.1394

Cited by 8 publications

(3 citation statements)

References 16 publications

(53 reference statements)

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“…The same group of authors extended the random delay in measurements from single step to two steps [21]. Yu‐Long and Yong‐Gang [22] proposed particle smoother for estimation of states with randomly delayed measurements. Later, in 2013, Wang et al [23] presented a general algorithm of nonlinear estimation based on Gaussian approximation of probability density function (PDF) with 1‐RD measurements and subsequently presented CKF with random delay [23].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Gaussian filters for nonlinear state estimation with one‐step randomly delayed measurements

Poluri

Dey

2022

Asian Journal of Control

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This paper proposes new algorithms of adaptive Gaussian filters for nonlinear state estimation with maximum one‐step randomly delayed measurements. The unknown random delay is modeled as a Bernoulli random variable with the latency probability known a priori. However, a contingent situation has been considered in this work when the measurement noise statistics remain partially unknown. Due to unavailability of the complete knowledge of measurement noise statistics, the unknown measurement noise covariance matrix is estimated along with states following: (i) variational Bayesian approach, (ii) maximum likelihood estimation. The adaptation algorithms are mathematically derived following both of the above approaches. Subsequently, a general framework for adaptive Gaussian filter is presented with which variants of adaptive nonlinear filters can be formulated using different rules of numerical approximation for Gaussian integrals. This paper presents a few of such filters, viz., adaptive cubature Kalman filter, adaptive cubature quadrature Kalman filter with their higher degree variants, adaptive unscented Kalman filter, and adaptive Gauss–Hermite filter, and demonstrates the comparative performance analysis with the help of a nontrivial Bearing only tracking problem in simulation. Additionally, the paper carries out relative performance comparison between maximum likelihood estimation and variational Bayesian approaches for adaptation using Monte Carlo simulation. The proposed algorithms are also validated with the help of an off‐line harmonics estimation problem with real data.

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Section: Introductionmentioning

confidence: 99%

Adaptive Gaussian filters for nonlinear state estimation with one‐step randomly delayed measurements

Poluri

Dey

2022

Asian Journal of Control

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“…In addition, although the sequential importance sample PF uses 500 particles, its performance is not as good as the CKF. Although the PF can be improved by increasing the number of particles or using some advanced techniques [26,27], the computational complexity will significantly increase. The PF was not studied further, because it is out of the scope of this study.…”

Section: Bearings-only Trackingmentioning

confidence: 99%

“…In this section, the NCKF was compared with the CKF, SCKF and PF in a manoeuvring target tracking application; this has been used as a benchmark problem to evaluate the performance of CKF algorithm [11,15]. The dynamic equation of the target tracking can be modelled as follows: þ n kÀ1 (27) where ξ k and _ ξ k are the position and velocity in Xdirection, respectively; & k and _ & k are the position and velocity in Ydirection, respectively; Dtrepresents the sampling interval; c k represents the unknown turn rate; v kÀ1 is the white Gaussian noise with zero mean and covariance Q kÀ1:…”

Section: Manoeuvring Target Trackingmentioning

confidence: 99%

Nonlinear state estimation using neural-cubature Kalman filter

Zhang

Zhao

et al. 2017

Automatika

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The cubature Kalman filter (CKF) has been widely used in solving nonlinear state estimation problems because of many advantages such as satisfactory filtering accuracy and easy implementation compared to extended Kalman filter and unscented Kalman filter. However, the performance of CKF may degrade due to the uncertainty of the nonlinear dynamic system model. To solve this problem, a neural-cubature Kalman filter (NCKF) algorithm containing a multilayer feed-forward neural network (MFNN) in CKF is proposed to further improve the estimation accuracy and enhance the robustness of CKF. In the proposed NCKF algorithm, the MFNN was used to modify the nonlinear state estimation of CKF as the measurements were processed, and the CKF was used as both a state estimator and an online training paradigm simultaneously. The experimental results show that the estimation accuracy and robustness of the proposed method are better than those of the CKF, square-root CKF and particle filter.

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A novel particle filtering for nonlinear systems with multi-step randomly delayed measurements

Chen

Yan

Zhang

2021

Applied Mathematical Modelling

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Particle Smoother for Nonlinear Systems With One‐Step Randomly Delayed Measurements

Cited by 8 publications

References 16 publications

Adaptive Gaussian filters for nonlinear state estimation with one‐step randomly delayed measurements

Adaptive Gaussian filters for nonlinear state estimation with one‐step randomly delayed measurements

Nonlinear state estimation using neural-cubature Kalman filter

A novel particle filtering for nonlinear systems with multi-step randomly delayed measurements

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