An Asynchronous Adaptive Direct Kalman Filter Algorithm to Improve Underwater Navigation System Performance

Davari, Narjes; Gholami, Asghar

doi:10.1109/jsen.2016.2637402

Cited by 58 publications

(30 citation statements)

References 22 publications

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“…To implement above equation,

usually approximated by means of averaging

over time using a windowing method. Instead of using moving window methods (like in works [ 15 , 19 , 23 , 29 ]), this paper adaptively adjusts

by utilizing a weighting factor

to balance the last noise covariance value and current estimation. The weighting factor

is set with a lower limit

to ensure the update strength and will increase as the rise of the

if the

exceeds a preset threshold.…”

Section: Robust Adaptive Unscented Kalman Filtermentioning

confidence: 99%

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance

Zheng

2018

Sensors

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The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results.

show abstract

“…To implement above equation,

usually approximated by means of averaging

over time using a windowing method. Instead of using moving window methods (like in works [ 15 , 19 , 23 , 29 ]), this paper adaptively adjusts

by utilizing a weighting factor

to balance the last noise covariance value and current estimation. The weighting factor

is set with a lower limit

to ensure the update strength and will increase as the rise of the

if the

exceeds a preset threshold.…”

Section: Robust Adaptive Unscented Kalman Filtermentioning

confidence: 99%

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance

Zheng

2018

Sensors

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show abstract

“…On the other hand, if the uncertain environment results in improper configuration, the parameters cannot be adaptive to the change of the environment. When the noise mismatch emerging in the tracking problem, adaptive KF (AKF), adaptive UKF (AUKF) and some other adaptive covariancematching algorithms were developed to estimate the statistical properties of the system noise and solve the problem in an uncertain noise scenario [16], such as the methods based on multiple model [17]- [18], the methods based on innovation or residual, and the windowing methods [19]- [21]. Moreover, an adaptive UKF extended the windowing concept from the linear KF to the nonlinear UKF [22].…”

Section: Introductionmentioning

confidence: 99%

Robust Cognitive Radar Tracking Based on Adaptive Unscented Kalman Filter in Uncertain Environments

Zhong

Cheng

et al. 2020

IEEE Access

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This paper presents an approach for cognitive radar to track the target state despite unknown statistics or sudden changes in noise exposed to uncertain environments. Given the prior knowledge or accurate estimation of the noise, cognitive radar can adaptively adjust the waveform in the transmitter by perceiving the environment. However, the measurement noise usually contains a priori unknown parameters which are difficult to be estimated, or the process noise without empirical model might be improperly configured. Besides, they may change abruptly or time-varying in practice. These issues are prone to occur in cognitive radar, leading to performance decline or failure in tracking ultimately. To alleviate this problem, a robust cognitive radar tracking method based on adaptive unscented Kalman filter (AUKF) is proposed in this paper. Specifically, UKF is used to derive the cognitive mathematical model and estimate the states in nonlinear systems. Moreover, a robust adaptive mechanism is designed in the cognitive framework, which is independent of prior information. When the mismatch between the noise in the model and the noise in the environment emerges, the noise covariance can be corrected adaptively. To verify the efficiency of the scheme, maneuvering target tracking experiments are carried out in three uncertain noise scenarios. Simulation results show that the scheme outperforms the existing adaptive UKF and cognitive radar algorithms in terms of intelligence and robustness. INDEX TERMS cognitive radar, adaptive filtering, robust estimation, target tracking, uncertain noise.

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“…The navigation system has been extensively studied and improved. For example, a reduced relative root mean square error of an estimated position (22) and improvements to the reliability and fault-tolerant capability are some of the improvements made. (23) In addition, several navigation systems have been proposed, including an indoor navigation system, (24,25) a pedestrian dead reckoning system, (26) and a navigation system of an unmanned aerial vehicle.…”

Section: Introductionmentioning

confidence: 99%

Cloud-based Dialog Navigation Agent System for Service Robots

Chen¹,

Wu²,

Wang³

2019

Sensors and Materials

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The conventional human-robot interactions of robotic navigation systems must rely on strict language instructions and numerous button operations. In this study, a cloud-based dialog navigation agent (CDNA) system was designed for a campus navigation robot (CNR) that provides navigation services to students, school visitors, and people with impaired vision. The CDNA is based on a lightweight belief-desire-intention (BDI) software architecture (i.e., CellS, a cell-inspired efficient software framework), which is a goal-oriented and dynamic parallel framework. The proposed CDNA system has the following three primary functions: (1) conversational navigation service, (2) immediate path planning and path modification, and (3) location guide and place evaluation. The system can be applied to regional navigation guidance services such as campus tours. The CellS-based CDNA uses a natural language processing (NLP) technology to analyze the semantics of user statements and uses dialog to eliminate ambiguity in language to improve interaction with users. In this study, 15 items for three different navigation systems were evaluated, which demonstrated that the CDNA is advantageous in terms of interactivity and usability. The CellS-based CDNA can achieve an average speedup of 1.75 times in seven data sets. Therefore, the CDNA possesses the following advantages: high interactivity, high usability, and high performance.

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An Asynchronous Adaptive Direct Kalman Filter Algorithm to Improve Underwater Navigation System Performance

Cited by 58 publications

References 22 publications

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance

Robust Cognitive Radar Tracking Based on Adaptive Unscented Kalman Filter in Uncertain Environments

Cloud-based Dialog Navigation Agent System for Service Robots

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