Tobit Kalman filter with channel fading and dead-zone-like censoring

Li, Shuhui; Feng, Xiaoxue; Deng, Zhihong; Pan, Feng

doi:10.1080/00207721.2020.1859160

Cited by 6 publications

(3 citation statements)

References 43 publications

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“…In recent years, the Tobit model has been used more and more widely. The trajectory roll rate is estimated using the Tobit Kalman filter (TKF) with the aid of the enhancement technique and orthogonal projection principle [ 20 , 21 ]. The TKF is used to estimate the hidden state vectors in human skeletal tracking [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

The Tobit-Unscented-Kalman-Filter-Based Attitude Estimation Algorithm Using the Star Sensor and Inertial Gyro Combination

et al. 2023

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For the orbit operation of spacecraft, due to environmental factors, a star sensor installed on the spacecraft must have data censoring, which greatly reduces the attitude determination ability of the traditional combined-attitude-determination algorithm. To address this problem, this paper proposes an algorithm for high-precision attitude estimation based on a Tobit unscented Kalman filter. This is on the basis of establishing the nonlinear state equation of the integrated star sensor and gyroscope navigation system. The measurement update process of the unscented Kalman filter is improved. The Tobit model is used to describe the gyroscope drift when the star sensor fails. The latent measurement values are calculated using the probability statistics, and the measurement error covariance expression is derived. The proposed design is verified via computer simulations. When the star sensor fails for 15 min, the accuracy of the Tobit unscented Kalman filter based on the Tobit model is improved by approximately 90% compared to the unscented Kalman filter. Based on the results, the proposed filter can effectively estimate the error caused by the gyro drift, and the method is effective and feasible, provided there is theoretical support for the engineering practice.

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Section: Introductionmentioning

confidence: 99%

The Tobit-Unscented-Kalman-Filter-Based Attitude Estimation Algorithm Using the Star Sensor and Inertial Gyro Combination

et al. 2023

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“…In the context of measurement censoring, there have been mainly two kinds of censoring models reported in the literature, namely, the dead-band-like censoring [30], [51] and the saturation-like censoring [1]. In order to attenuate the effect from censored measurements on system performance, some elegant results have been obtained on the filtering issues subject to measurement censoring, see [16], [25] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Protocol-Based Particle Filtering for Nonlinear Complex Networks: Handling Non-Gaussian Noises and Measurement Censoring

Song

Wang

et al. 2023

IEEE Trans. Netw. Sci. Eng.

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In this paper, the particle filtering problem is investigated for a class of discrete-time nonlinear complex networks with stochastic perturbations under the scheduling of random access protocol. The stochastic perturbations stem from the onoff stochastic coupling, non-Gaussian noises and measurement censoring. The random occurrence of the on-off node coupling is governed by a set of Bernoulli distributed white sequences, and two kinds of measurement censoring models (i.e. deadband-like model and saturation-like model) are characterized by the predetermined left-and right-end censoring thresholds. To alleviate data collision over the networks, the so-called random access protocol is elaborately exploited to orchestrate the process of measurement transmission. Moreover, two expressions of the modified likelihood function are established to weaken the adverse effects from the measurement censoring. Accordingly, a protocol-based filter is designed in the auxiliary particle filtering framework, where the new particles are generated from a mixture distribution and the associated weights are assigned based on the derived likelihood function. Finally, a multi-target tracking application is taken into account to demonstrate the practicability and effectiveness of the developed filtering scheme.

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“…[1][2][3] Generally speaking, the core idea of state estimation is to design an appropriate estimation algorithm to reconstruct system states by using output measurements containing only partial state information. During the past decades, a great number of representative results have appeared on different state estimation approaches for achieving particular engineering specifications such as the Kalman filtering and its variants, [4][5][6][7][8] particle filtering, 9,10 H ∞ estimation, [11][12][13] interval observer, 14 set-valued filtering, [15][16][17] and so forth. Most of the existing state estimation methods aforementioned are built on an implicit assumption that system parameters are required to be known, which is somehow restrictive in applications.…”

Section: Introductionmentioning

confidence: 99%

Joint estimation of state, parameter, and unknown input for nonlinear systems: A composite estimation scheme

Wang

Luo

2021

Intl J Robust & Nonlinear

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This study is concerned with the joint estimation problem for a class of nonlinear systems with the simultaneous unknown of the system state, the parameter, as well as the input signal. A composite estimation scheme is proposed where the estimator consists of both linear and nonlinear components, under which the estimation performance is improved. The analysis and synthesis issues of the developed estimation algorithm are addressed for both the continuous-time case and the discrete-time case. By utilizing the Lyapunov stability theory combined with the singular value decomposition technique, sufficient conditions are established for both continuous-time and discrete-time cases to guarantee the convergence of the estimation error, rather than the boundedness in most of the existing literature. To facilitate the algorithm implementation in practical engineering, the Newton-Raphson method is adopted to deal with the feasibility issue for the discrete-time case. Numerical simulations are provided for both the continuous-and discrete-time cases to demonstrate the effectiveness of the proposed joint estimation strategies.

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Tobit Kalman filter with channel fading and dead-zone-like censoring

Cited by 6 publications

References 43 publications

The Tobit-Unscented-Kalman-Filter-Based Attitude Estimation Algorithm Using the Star Sensor and Inertial Gyro Combination

The Tobit-Unscented-Kalman-Filter-Based Attitude Estimation Algorithm Using the Star Sensor and Inertial Gyro Combination

Protocol-Based Particle Filtering for Nonlinear Complex Networks: Handling Non-Gaussian Noises and Measurement Censoring

Joint estimation of state, parameter, and unknown input for nonlinear systems: A composite estimation scheme

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