Sequential unbiased converted measurement non‐linear filter with range rate in ECEF coordinates

Li, Song; Chen, Yongmei; Gou, Bin; Gao, Shibo

doi:10.1049/iet-rsn.2019.0631

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…However, the attitude errors diverge with time going, thereby indicating that the variances of the attitude errors are time-varying. Therefore, the method in [31] using fixed attitude error covariance of INS is inaccurate, which can be further improved. Since the INS error model is developed in section 4, the attitude error variances at time k can be obtained approximately from the predicted covariance of the INS error model in equation ( 26):…”

Section: Unbiased Conversion Model Of the Tasmentioning

confidence: 99%

“…To validate the superiority of the proposed method in the converted measurement, the simulations of three methods are conducted for comparison, i.e. CM [23], E-UCM [31], and IEM-UCM proposed in this paper.…”

Section: The Evaluation Of the Converted Measurementmentioning

confidence: 99%

“…Nevertheless, they only focused on the conversion from spherical frame to body frame, not to navigation frame. In [31], a UCMs in Earth-centred Earth-fixed coordinate (E-UCM) method for target tracking is proposed, which resolves the task of unbiased measurement conversion from body frame to navigation frame. However, due to lack of INS error model, it assumes that the error covariance of INS attitude is fixed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel INS/ADS integrated navigation method based on INS error model-aided unbiased converted measurement

Li¹,

Chen²,

Zhang³

et al. 2022

Meas. Sci. Technol.

Self Cite

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In the inertial navigation system (INS)/ air data system (ADS) integrated navigation system, the measurements of INS and ADS need to be converted to a unified coordinate for fusion. However, the sensor noises cause serious conversion bias in the direct measurement conversion method. In this paper, a novel INS/ADS integrated navigation method based on INS error model-aided unbiased converted measurement (IEM-UCM) is proposed. First, the IEM-UCM model of ADS data from spherical frame to navigation frame is developed, which exploits the INS error model to calculate the compensation factors. Subsequently, the noise covariance of the converted measurement is derived. Finally, a standard Kalman filter (KF) is implemented for INS/ADS integrated navigation. The simulation results validate the effectiveness of the proposed method in terms of the unbiasedness and consistency of the converted measurement. The root mean square error (RMSE) values indicate that the proposed method outperforms the existing methods in navigation accuracy.

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Section: Unbiased Conversion Model Of the Tasmentioning

confidence: 99%

Section: The Evaluation Of the Converted Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel INS/ADS integrated navigation method based on INS error model-aided unbiased converted measurement

Li¹,

Chen²,

Zhang³

et al. 2022

Meas. Sci. Technol.

Self Cite

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show abstract

“…When the motion model is also linear, the Kalman filter (KF), which is computationally more efficient and achieves the optimal performance in a linear system, can be applied. Several effective methods have been proposed to convert measurements in polar coordinates, that is range, one or two angles and Doppler, into Cartesian coordinates [12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Nearly constant acceleration model for state estimation in the range‐Doppler plane

Guo

Zhou

2021

IET Radar Sonar & Navi

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The problem of motion modelling in the range-Doppler (R-D) plane as well as range and Doppler estimation for the Cartesian nearly constant acceleration motion, which is a common manoeuvering motion, is investigated. The temporal evolution equation is derived based on the state vector consisting of target range, Doppler and derivatives of the product of range and range rate versus time. In this way, the measurement equation of range and Doppler measurements can be maintained in a desirable linear-Gaussian structure. Based on the non-linear state equation and the linear measurement equation, the unscented Kalman filter is adopted to tackle the non-linear filtering problem. The corresponding filter initialisation method is developed based on the two-point differencing method. Explicit expressions of the initial state estimates and the initial covariance matrix are presented in analytic forms where the correlation among the state components is handled properly. The posterior Cramer-Rao lower bound (PCRLB) is provided for state estimation in the R-D plane. Comprehensive comparisons of the proposed method against the existing R-D state estimation methods using approximate models, Cartesian state estimator and PCRLB are carried out in simulations to demonstrate the validity and correctness of the proposed motion model and estimation method.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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An EM algorithm for target tracking with an unknown correlation coefficient of measurement noise

He¹,

Wu²,

Peng³

et al. 2022

Meas. Sci. Technol.

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In this paper, an expectation maximization based sequential modified unbiased converted measurement Kalman filter is proposed for target tracking with an unknown correlation coefficient of measurement noise between the range and the range rate. Firstly, a pseudo measurement is constructed by multiplying the range and the range rate to reduce the strong nonlinearity between the measurement and the target state. The mean and covariance of converted errors are subsequentlsubsequently derived by modified unbiased converted measurement to weaken the error caused by the linearization of the measurement equation, which is effectively to improve the dynamic accuracy of target tracking. Then, the converted errors of the position and the pseudo measurement are decorrelated by the Cholesky factorization and thus to obtain the posterior probability distribution of the state by using the sequential filtering in the Bayesian framework. Finally, the expectation maximization is introduced in the updating procedure of the pseudo measurement to jointly estimate the target state and the correlation coefficient. The target tracking scenario with an unknown correlation coefficient is built to demonstrate the validness and feasibility of the proposed algorithm. Simultaneously, the results of the normalized error squared validate the consistency of the modified unbiased converted measurement.

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Sequential unbiased converted measurement non‐linear filter with range rate in ECEF coordinates

Cited by 4 publications

References 22 publications

A novel INS/ADS integrated navigation method based on INS error model-aided unbiased converted measurement

A novel INS/ADS integrated navigation method based on INS error model-aided unbiased converted measurement

Nearly constant acceleration model for state estimation in the range‐Doppler plane

An EM algorithm for target tracking with an unknown correlation coefficient of measurement noise

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