The robust residual-based adaptive estimation Kalman filter method for strap-down inertial and geomagnetic tightly integrated navigation system

Zhai, Hong-Qi; Wang, Lihui

doi:10.1063/5.0019305

Cited by 8 publications

(10 citation statements)

References 30 publications

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“…In the complicated platform such as manipulating unmanned aerial vehicles, state estimation is a challenge due to inherent couplings, nonlinearities, and uncertainties, so that the extended Kalman filter might not be applicable. The other form of this filter that includes two options of the unscented Kalman filter, is employed to address the state estimation problem [16]. The improvement in estimation accuracy, overall control performance, and algorithm execution time compared to the other extended Kalman filters is solidly confirmed.…”

Section: Problem Statementmentioning

confidence: 97%

Dynamical analysis and validation of motion control by filtering performance for aerial robotic system

Tran¹,

Vo²,

Tu³

et al. 2021

J. vibroeng.

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Although drone appears in different applications, such as environmental inspection, agriculture or transportation, some aspects require more studies to clarify the efficient outcomes. One of them is to investigate the filtering performance such as Kalman and Complementary filters when the autonomous aerial system (AAS) handles its mission. However, it lacks the systematic research about these filters to provide the proper evaluation. Therefore, in this paper, the research topic related to AAS model to indicate the filtering effects in the agricultural application for making an alternative solution is presented. Firstly, the mathematical representation of system model is established in order to describe the dynamical performance and motion constraints. Then, the theory of filter structure is implemented to estimate the system state. The proposed design is validated in both numerical simulation and experiments. The system parameters that are monitored, include angular values of roll, pitch and yaw in three axes, motion parameters and its trajectories. By utilizing various sensing devices such as gyroscope, accelerometer and compass in real-world hardware, the experimental results could evaluate more precise and efficient design. The findings of this study are to (1) propose the model of AAS and proper filters, (2) launch the verified process and calibration, and (3) demonstrate the competitive performance among filters. From these results of our work, it could be clearly seen that the AAS plays an important role in daily applications and the related topics are still attractive.

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Section: Problem Statementmentioning

confidence: 97%

Dynamical analysis and validation of motion control by filtering performance for aerial robotic system

Tran¹,

Vo²,

Tu³

et al. 2021

J. vibroeng.

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show abstract

“…) −1 s lj k+1 (20) ξ k+1 follows the Chi-square distribution with degree of freedom m if the assumption holds. The significance level is set as γ, which indicates the probability threshold that the null hypothesis below this threshold will be rejected.…”

Section: Measurement Anomaly Detectionmentioning

confidence: 99%

An Adaptive Filtering Method for Cooperative Localization in Leader–Follower AUVs

Zhao

Hirata

Lang

et al. 2022

Sensors

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In the complex and variable marine environment, the navigation and localization of autonomous underwater vehicles (AUVs) are very important and challenging. When the conventional Kalman filter (KF) is applied to the cooperative localization of leader–follower AUVs, the outliers in the sensor observations will have a substantial adverse effect on the localization accuracy of the AUVs. Meanwhile, inaccurate noise covariance matrices may result in significant estimation errors. In this paper, we proposed an improved Sage–Husa adaptive extended Kalman filter (improved SHAEKF) for the cooperative localization of multi-AUVs. Firstly, the measurement anomalies were evaluated by calculating the Chi-square test statistics based on the innovation. The detection threshold was determined according to the confidence level of the Chi-square test, and the Chi-square test statistics exceeding the threshold were regarded as measurement abnormalities. When measurement anomalies occurred, the Sage–Husa adaptive extended Kalman filter algorithm was improved by suboptimal maximum a posterior estimation using weighted exponential fading memory, and the measurement noise covariance matrix was adjusted online. The numerical simulation of leader–follower multi-AUV cooperative localization verified the effectiveness of the improved SHAEKF and demonstrated that the average root mean square and the average standard deviation of the localization errors based on the improved SHAEKF were significantly reduced in the case of the presence of measurement abnormalities.

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“…Since the innovation vectorz i (k i ) obeys zero-mean Gaussian distribution, we can obtain from (24) that…”

Section: Fault Detectionmentioning

confidence: 99%

“…In [23], a new adaptive Kalman filter has studied for multi-sensor integrated navigation system. Recently, a new technique has been designed in [24] to handle unknown noise statistical characteristics of system and outliers in the measurement information.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Multi-Sensor State Estimation for Systems Subject to Multiplicative and Cross-Correlated Noises With Measurement Packet Dropping

Zhang

Wang

et al. 2021

IEEE Access

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This paper is concerned with the optimal state estimation problem for linear discrete-time systems with both multiplicative and cross-correlated noises. The measurement outputs for state estimation are collected from multiple sensors whose sampling rates are different that provide asynchronous data. In addition, the noises that affect the measurement information are correlated among different sensors and also coupled with the process noises as well. The aim of the addressed problem is to propose an optimal state estimation algorithm such that the estimation error is minimized in the mean-square sense with the existence of asynchronous data, possible sensor faults and correlated noises. In order to mitigate the impact of measurement missing, this paper utilizes neural networks to compensate the state estimation when measurement packets are dropping. Then, a fault detection mechanism that utilizes normalized innovation test is adopted to ensure that the abnormal data would be detected and removed. By resorting to the projection theorem and mathematical induction approach, sufficient conditions are derived for the existence of the desired optimal state estimator where the optimized estimation gains are formulated and can be computed iteratively at each time step. The proposed theoretical results are demonstrated via an illustrative numerical example. INDEX TERMS Correlated noises, multiplicative noises, asynchronous data, multi-sensors, multi-rate systems, neural networks.

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The robust residual-based adaptive estimation Kalman filter method for strap-down inertial and geomagnetic tightly integrated navigation system

Cited by 8 publications

References 30 publications

Dynamical analysis and validation of motion control by filtering performance for aerial robotic system

Dynamical analysis and validation of motion control by filtering performance for aerial robotic system

An Adaptive Filtering Method for Cooperative Localization in Leader–Follower AUVs

Asynchronous Multi-Sensor State Estimation for Systems Subject to Multiplicative and Cross-Correlated Noises With Measurement Packet Dropping

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