A Constrained Total Extended Kalman Filter for Integrated Navigation

Mahboub, Vahid; Mohammadi, Dorsa

doi:10.1017/s0373463318000012

Cited by 13 publications

(4 citation statements)

References 28 publications

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“…The results showed that the robot performed well at low weed density, with compensation accuracy of less than 2.5 • and an average error from the target path of 4.59 cm. Mahboub and Mohammadi [34] proposed a combined positioning method that integrated BDS and visual navigation, providing accurate and real-time obstacle information in agricultural fields. The position deviation of the tractor was within ±0.1 m, resulting in high accuracy of autonomous navigation.…”

Section: Sensing Technology 321 Field Environment Perceptionmentioning

confidence: 99%

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

Qu,

Zhang,

Qin

et al. 2024

Machines

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The development of unmanned agricultural tractors (UAT) represents a significant step towards intelligent agricultural equipment. UAT technology is expected to lighten the workload of laborers and enhance the accuracy and efficiency of mechanized operations. Through the investigation of 123 relevant studies in the literature published in recent years, this article reviews three aspects of autonomous navigation technologies for UATs: perception, path planning and tracking, and motion control. The advantages and deficiencies of these technologies in the context of UATs are clarified by analyzing technical principles and the status of current research. We conduct summaries and analyses of existing unmanned navigation solutions for different application scenarios in order to identify current bottleneck issues. Based on the analysis of the applicability of autonomous navigation technologies in UATs, it can be seen that fruitful research progress has been achieved. The review also summarizes the common problems seen in current UAT technologies. The application of research to the sharing and integrating of multi-source data for autonomous navigation has so far been relatively weak. There is an urgent need for high-precision and high-stability sensing equipment. The universality of path planning methods and the efficiency and precision of path tracking need to be improved, and it is also necessary to develop highly reliable electrical control modules to enhance motion control performance. Overall, advanced sensors, high-performance intelligent algorithms, and reliable electrical control hardware are key factors in promoting the development of UAT technology.

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Section: Sensing Technology 321 Field Environment Perceptionmentioning

confidence: 99%

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

Qu,

Zhang,

Qin

et al. 2024

Machines

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“…In addition, due to the existence of various noises, such as ocean currents, salt cliffs, and surrounding ships, the marine environment is complicated and time-varying, resulting in the loss of measurement information [6,7]. To eliminate the influence of complex measurement noise and the measurement information randomly lost, several Bayesian-theory-based filtering algorithms have been proposed and applied, such as the extended Kalman filter (EKF) [8,9], unscented Kalman filter (UKF) [10], Cubature Kalman filter (CKF) [11], and so on. The performance of the filtering algorithms is crucial in judging whether the AUV can properly work and safely return [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Switched Variational Estimation Algorithm for Continuous Discrete Measurement Information Loss in Underwater Navigation: Linear versus Nonlinear

2022

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In various applications of automatic underwater vehicles (AUVs), it is necessary to acquire the real-time location and speed information of the AUV. However, the complicated and fluctuating marine environment leads to measurement information loss. The accuracy of noise measurement is vital for accurate state estimation, but it is difficult for traditional algorithms to acquire time-varying noise measurements. Due to inaccurate process models and measurement noise, the filtering performance becomes poor or even diverges. To address the problems above, a switched variational estimation filtering (SVEF) algorithm, which combines the advantages of both Gaussian filtering and the variational estimation (VE) method, is proposed. In SVEF, VE is embedded into a linear or nonlinear filtering algorithm. Owing to the continuous discrete underwater navigation model, the state vector is estimated by the SVEF in the case of measurement information loss, and the accurate position and velocity of the AUV are determined. The experimental results prove that the SVEF achieves better positioning precision and is more robust than other conventional algorithms for AUV applications.

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“…A new algorithm for an integrated system, the constrained total extended Kalman filter (CTEKF) algorithm, makes full use of state equations and is equipped with quadratic constraints. It has been used to fuse a GPS receiver, an INS, and remote sensors [37]. The adaptive complementary filtering (ACF) was used for improving the accuracy of the orientation during the indoor positioning.…”

Section: Introductionmentioning

confidence: 99%

WiFi/PDR integrated navigation with robustly constrained Kalman filter

Long

Qin

et al. 2020

Meas. Sci. Technol.

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Nowadays, global navigation satellite systems (GNSSs) are widely used in location-based services (LBSs) as they can provide high-accuracy position services continuously. However, their performance deteriorates in indoor scenarios, in which GNSS signal reception is limited or completely impossible. In this paper, an enhanced constrained Kalman filter is presented to enhance the indoor positioning performance of a LBS and for use in a WiFi/pedestrian dead reckoning (PDR) integrated navigation algorithm. A robust scheme for computing the gross error in constrained conditions is suggested to make the performance of the constrained condition model in the WiFi/PDR integrated system more robust. The results of simulation analysis indicate that the robustly constrained Kalman filter can reliably determine gross errors in constrained conditions. An indoor field experiment was conducted to test the performance of the proposed filter algorithm, and the results show that the improved filter can eliminate the effect of gross error from constrained conditions in the WiFi/PDR system.

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A Constrained Total Extended Kalman Filter for Integrated Navigation

Cited by 13 publications

References 28 publications

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

A Switched Variational Estimation Algorithm for Continuous Discrete Measurement Information Loss in Underwater Navigation: Linear versus Nonlinear

WiFi/PDR integrated navigation with robustly constrained Kalman filter

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