Square-Root Unscented Information Filter and Its Application in SINS/DVL Integrated Navigation

Guo, Yan; Wu, Meiping; Tang, Kanghua; Zhang, Lu

doi:10.3390/s18072069

Cited by 13 publications

(7 citation statements)

References 19 publications

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“…According to [18,34], the process of the CUIF is summarized in Algorithm 1. In which, L is the number of consensus iterations; θ is the consensus rate and 0<θ<1/Δmax, where Δmax is the maximum degree of the graph.…”

Section: Fundamentals Of the Proposed Filtermentioning

confidence: 99%

Adaptive Consensus-Based Unscented Information Filter for Tracking Target with Maneuver and Colored Noise

Wang

Zheng

2019

Sensors

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Distributed state estimation plays a key role in space situation awareness via a sensor network. This paper proposes two adaptive consensus-based unscented information filters for tracking target with maneuver and colored measurement noise. The proposed filters can fulfill the distributed estimation for non-linear systems with the aid of a consensus strategy, and can reduce the impact of colored measurement noise by employing the state augmentation and measurement differencing methods. In addition, a fading factor that shrinks the predicted information state and information matrix can suppress the impact of dynamical model error induced by target maneuvers. The performances of the proposed algorithms are investigated by considering a target tracking problem using a space-based radar network. This shows that the proposed algorithms outperform the traditional consensus-based distributed state estimation method in aspects of tracking stability and accuracy.

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Section: Fundamentals Of the Proposed Filtermentioning

confidence: 99%

Adaptive Consensus-Based Unscented Information Filter for Tracking Target with Maneuver and Colored Noise

Wang

Zheng

2019

Sensors

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“…In 2018, aiming at the problem of the low precision of the conventional filtering algorithm in the strap down inertial navigation/Doppler integrated positioning system, Guo proposed a square root unscented information filter, which reduces the computational complexity of the system while obtaining higher positioning accuracy. The results show that the tracking accuracy of the integrated positioning system is much higher than that of the SINS alone [28]. Different from previous works, in this paper, considering the parameter uncertainties and model uncertainties, we take full advantage of an organic fusion of strong tracking filtering and Sage–Husa adaptive filtering to achieve robust Kalman filtering for the SINS/DVL integrated positioning system.…”

Section: Related Workmentioning

confidence: 99%

A SINS/DVL Integrated Positioning System through Filtering Gain Compensation Adaptive Filtering

Yan

Yang

Luo

et al. 2019

Sensors

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Because of the complex task environment, long working distance, and random drift of the gyro, the positioning error gradually diverges with time in the design of a strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated positioning system. The use of velocity information in the DVL system cannot completely suppress the divergence of the SINS navigation error, which will result in low positioning accuracy and instability. To address this problem, this paper proposes a SINS/DVL integrated positioning system based on a filtering gain compensation adaptive filtering technology that considers the source of error in SINS and the mechanism that influences the positioning results. In the integrated positioning system, an organic combination of a filtering gain compensation adaptive filter and a filtering gain compensation strong tracking filter is explored to fuse position information to obtain higher accuracy and a more stable positioning result. Firstly, the system selects the indirect filtering method and uses the integrated positioning error to model the navigation parameters of the system. Then, a filtering gain compensation adaptive filtering method is developed by using the filtering gain compensation algorithm based on the error statistics of the positioning parameters. The positioning parameters of the system are filtered and information on errors in the navigation parameters is obtained. Finally, integrated with the positioning parameter error information, the positioning parameters of the system are solved, and high-precision positioning results are obtained to accurately position autonomous underwater vehicles (AUVs). The simulation results show that the SINS/DVL integrated positioning method, based on the filtering gain compensation adaptive filtering technology, can effectively enhance the positioning accuracy.

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“…To solve this problem, the interacting multiple model algorithm which uses more than one model is proposed in [11], where a variable model set based on the model probability weighted average of the model parameter is generated to pursue the real model. Considering the nonlinearity of the model of SINS/DVL, the square root unscented information filter is designed in [12], the randomly weighted cubature Kalman filter is discussed in [13], and the unscented Kalman filter (UKF) is employed in [14][15][16]. Furthermore, considering the influence of unknown environment and the inexact error model caused by model simplification, various adaptive Kalman filters are proposed in [17][18][19][20], where statistical characteristics of noises are online estimated in [17][18][19] and the recursive filtering gain is adaptively adjusted in [20].…”

Section: Introductionmentioning

confidence: 99%

Modified Sage‐Husa Adaptive Kalman Filter‐Based SINS/DVL Integrated Navigation System for AUV

Liu

Liu³

et al. 2021

Journal of Sensors

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This paper presents a modified Sage-Husa adaptive Kalman filter-based SINS/DVL integrated navigation system for the autonomous underwater vehicle (AUV), where DVL is employed to correct the navigation errors of SINS that accumulate over time. When negative definite items are large enough, different from the positive definiteness of noise matrices which cannot be guaranteed for the conventional Sage-Husa adaptive Kalman filter, the proposed modified Sage-Husa adaptive Kalman filter deletes the negative definite items of adaptive update laws of the noise matrix to ensure the convergence of the Sage-Husa adaptive Kalman filter. In other words, this method sacrifices some filtering precision to ensure the stability of the filter. The simulation tests are implemented to verify that expected navigation accuracy for AUV can be obtained using the proposed modified Sage-Husa adaptive Kalman filter.

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Square-Root Unscented Information Filter and Its Application in SINS/DVL Integrated Navigation

Cited by 13 publications

References 19 publications

Adaptive Consensus-Based Unscented Information Filter for Tracking Target with Maneuver and Colored Noise

Adaptive Consensus-Based Unscented Information Filter for Tracking Target with Maneuver and Colored Noise

A SINS/DVL Integrated Positioning System through Filtering Gain Compensation Adaptive Filtering

Modified Sage‐Husa Adaptive Kalman Filter‐Based SINS/DVL Integrated Navigation System for AUV

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