Adaptive Kalman Filtering for Vehicle Navigation

Hu, Chengjin; Chen, Wu; Chen, Yongqi; Liu, Dajie

doi:10.5081/jgps.2.1.42

Cited by 180 publications

(81 citation statements)

References 5 publications

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“…For actual MMR systems, this is difficult to satisfy because of the vast workload. It is well known that the adaptive filter can be utilized to estimate and correct the model parameters and the noise characteristics at the same time [20]. As a result, the adaptive filter has been considered to enhance the estimation accuracy in robotics [21,22].…”

Section: Problem Statementsmentioning

confidence: 99%

Cooperative Localization Algorithm for Multiple Mobile Robot System in Indoor Environment Based on Variance Component Estimation

Sun

Diao²,

Zhang³

et al. 2017

Symmetry

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Abstract:The Multiple Mobile Robot (MMR) cooperative system is becoming a focus of study in various fields due to its advantages, such as high efficiency and good fault tolerance. However, the uncertainty and nonlinearity problems severely limit the cooperative localization accuracy of the MMR system. Thus, to solve the problems mentioned above, this manuscript presents a cooperative localization algorithm for MMR systems based on Cubature Kalman Filter (CKF) and adaptive Variance Component Estimation (VCE) methods. In this novel algorithm, a nonlinear filter named CKF is used to enhance the cooperative localization accuracy and reduce the computational load. On the other hand, the adaptive VCE method is introduced to eliminate the effects of unknown system noise. Furthermore, the performance of the proposed algorithm is compared with that of the cooperative localization algorithm based on normal CKF by utilizing the real experiment data. In addition, the results demonstrate that the proposed algorithm outperforms the CKF cooperative localization algorithm both in accuracy and consistency.

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Section: Problem Statementsmentioning

confidence: 99%

Cooperative Localization Algorithm for Multiple Mobile Robot System in Indoor Environment Based on Variance Component Estimation

Sun

Diao²,

Zhang³

et al. 2017

Symmetry

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“…It was found that four-level wavelet transform was best suited for this study where first two PC covered more than 99% of information variation [14]. Individual stage of decomposition process provided detailed j+1 coefficients where as j th coefficient after the final stage of wavelet was eventually used as representative feature of that class or month in this case [14][15][16][17]. …”

Section: Discrete Wavelet Transform: Feature Extractionmentioning

confidence: 99%

Environmental Sensor Drift Correction using Wavelet-mFCM-FIS Architecture: An Unsupervised Machine Learning Approach

Dutta¹

2013

IJCA

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A novel hybrid sensor informatics architecture based on Discrete Wavelet Transform (DWT), Fuzzy logic based clustering (FCM) and Fuzzy Rule based Inference system (FIS) has been investigated and proposed to estimate dynamic generic sensor drift during physical sampling. DWT has been used for sensor pre-processing; data dimension reduction and feature extraction from sensor time series, where as DWT-FCM based approach has been used to estimate the cumulative drift in our sensory system. An intelligent generalized algorithm using multiple FCM maps (m-FCM) has been proposed to implement the drift correction on any future unknown data sets. In the next stage a newly proposed DWT-FCM-FIS architecture has been tested on multisensory environmental data sets covering three consecutive years (2009 -11) to predict probable month (environmental time of the year) with up to 93% accuracy. This novel hybrid data processing architecture have been implemented and tested upon a real time estuary sensory platform of temperature, conductivity and salinity sensors that have been deployed to monitor the Derwent Estuary in Hobart, Australia. This newly proposed approach could be an adaptive solution to tackle drifts in the sensor networks and improve overall monitoring quality.

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“…Loebis et al (Loebis et al, 2004) proposed an adaptive EKF algorithm, which adjusted the measurement noise covariance matrix by fuzzy logic. Other works can also be seen in references (Noriega & Pasupathy, 1997;Mehra, 1970;Hu et al, 2003;Chaer et al, 1997;Garcia-Velo, 1997). As far as the adaptive UKF (AUKF) is concerned, the most-oftenmentioned scheme was proposed by Lee and Alfriend (Lee & Alfriend, 2004), where the Maybeck's method (Maybeck, 1979) was modified by maximum-likelihood principle to estimate the error covariance matrix, and this estimator was further integrated into the normal UKF as the adaptive mechanism.…”

mentioning

confidence: 99%