An Improved Kalman Filter With Adaptive Estimate of Latency Probability

Wang, Zhao; Huang, Yulong; Zhang, Yonggang; Jia, Guangle; Chambers, Jonathon A.

doi:10.1109/tcsii.2019.2952090

Cited by 21 publications

(22 citation statements)

References 16 publications

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“…where t represents the discrete time series, and F t−1 ∈ R n×n denotes the state transition function, and G t ∈ R m×n denotes the observation function, and f t−1 ∈ R n and g t ∈ R m represent white Gaussian process noise with known distribution parameters and white NSHTMN, respectively, and x t ∈ R n represents the system state vector, and y t ∈ R m represents ideal measurement vector. Due to the measurement random delay with unknown delay rate exists in the data transmission process of the communication network, the actual data y a t which received by estimator is formulated as follows 9,22 :…”

Section: Problem Formulationmentioning

confidence: 99%

“…where Δk = 1 s represents sampling interval. The true process noise covariance matrix Q t and the nominal measurement noise covariance matrix R t are given as follows 22 :…”

Section: Target Tracking Simulationmentioning

confidence: 99%

“…The NSHTMN and time-varying UMRDR are divided into four stages, and the details are lists in Table 1 The initial system state vector x 0 and the corresponding error covariance P 0|0 are set as 22 :…”

Section: Target Tracking Simulationmentioning

confidence: 99%

“…However, this delay rate is usual be time-varying and unknown, and the performances of abovementioned algorithms will be degraded in this situation. 22 Aiming at addressing the estimating problem with such unknown measurement random delay rate (UMRDR), some improved algorithms have been proposed. By resorting to expectation maximization approach, Wang et al proposed improved smoother to identify UMRDR and achieved the rapid computation of the expectation in E-step.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, an improved variational Bayesian-based delayed Kalman filter (VBDKF) is proposed to achieve the online estimation of UMRDR, and the system state and unknown variables are jointly estimated by utilizing variational Bayesian technique. 22 In the scenarios of Gaussian measurement noise with known statistical characteristics, the VBDKF is a good solution. However, in practical applications, the sensors will receive outliers that appear at random times, which will cause the measurement noise to form a non-stationary heavy-tailed distribution, that is, if the system is operating normally, the measurement noise is Gaussian distributed, and if the measurement has outliers, the measurement noise distribution is heavy-tailed with unknown distribution parameters.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A new Gaussian–Student's t mixing distribution‐based Kalman filter with unknown measurement random delay rate

Shan

Zhou

Yang

et al. 2022

Adaptive Control & Signal

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In this article, a new Gaussian-Student's t mixing distribution-based Kalman filter is presented to investigate the filtering issue for linear stochastic system with unknown measurement random delay rate and non-stationary heavy-tailed measurement noise. Firstly, by employing a Bernoulli distributed variable and introducing system state extension method, the form of measurement likelihood function of double measurement noise distributions is converted from the weighted sum to an exponential product. Secondly, the non-stationary heavy-tailed measurement noises of current time and last time are modeled as Gaussian-Student's t mixing distributions by introducing extra Bernoulli distributed variables. Thirdly, the variational Bayesian technique is utilized to jointly infer the system state, the Bernoulli distributed variables of measurement noises, distribution mixing probabilities, the intermediate random variables, the Bernoulli distributed variable of measurement delay and the unknown measurement random delay rate. Finally, the effectiveness of the proposed Kalman filter is demonstrated by a target tracking simulation experiment.

show abstract

Section: Problem Formulationmentioning

confidence: 99%

“…where Δk = 1 s represents sampling interval. The true process noise covariance matrix Q t and the nominal measurement noise covariance matrix R t are given as follows 22 :…”

Section: Target Tracking Simulationmentioning

confidence: 99%

Section: Target Tracking Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new Gaussian–Student's t mixing distribution‐based Kalman filter with unknown measurement random delay rate

Shan

Zhou

Yang

et al. 2022

Adaptive Control & Signal

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show abstract

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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An improved unscented Kalman filter for nonlinear systems with one-step randomly delayed measurement and unknown latency probability

Tong

Zheng

Wang

et al. 2022

Digital Signal Processing

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An Improved Kalman Filter With Adaptive Estimate of Latency Probability

Cited by 21 publications

References 16 publications

A new Gaussian–Student's t mixing distribution‐based Kalman filter with unknown measurement random delay rate

A new Gaussian–Student's t mixing distribution‐based Kalman filter with unknown measurement random delay rate

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

An improved unscented Kalman filter for nonlinear systems with one-step randomly delayed measurement and unknown latency probability

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