Predictive Adaptive Kalman Filter and Its Application to INS/UWB-integrated Human Localization with Missing UWB-based Measurements

Xu, Yuan; Shen, Tao; Chen, Xiuyuan; Bu, Lili; Feng, Ning

doi:10.1007/s11633-018-1157-4

Cited by 22 publications

(19 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method was found to rapidly drop the estimated error, compared to the extended Kalman filter. Kalman filter approaches have been developed to combine IMU and RSSI data and deal with scenarios with missing data [ 23 ]. RSSI and IMU-based location have been calculated independently in the work of [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

Combining RSSI and Accelerometer Features for Room-Level Localization

Tsanousa

Xefteris

Meditskos

et al. 2021

Sensors

View full text Add to dashboard Cite

The continuing advancements in technology have resulted in an explosion in the use of interconnected devices and sensors. Internet-of-Things (IoT) systems are used to provide remote solutions in different domains, like healthcare and security. A common service offered by IoT systems is the estimation of a person’s position in indoor spaces, which is quite often achieved with the exploitation of the Received Signal Strength Indication (RSSI). Localization tasks with the goal to locate the room are actually classification problems. Motivated by a current project, where there is the need to locate a missing child in crowded spaces, we intend to test the added value of using an accelerometer along with RSSI for room-level localization and assess the performance of ensemble learning methods. We present here the results of this preliminary approach of the early and late fusion of RSSI and accelerometer features in room-level localization. We further test the performance of the feature extraction from RSSI values. The classification algorithms and the fusion methods used to predict the room were evaluated using different protocols applied to a public dataset. The experimental results revealed better performance of the RSSI extracted features, while the accelerometer’s individual performance was poor and subsequently affected the fusion results.

show abstract

Section: Related Workmentioning

confidence: 99%

Combining RSSI and Accelerometer Features for Room-Level Localization

Tsanousa

Xefteris

Meditskos

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…An indoor positioning algorithm using the unweighted Kalman filter was developed to obtain the final positioning solution. The results show that the proposed integrated VLC and IMU algorithm reduced the errors of the positioning solution to less than 0.23 m. Shen et al also combined the inertial system with RFID tags, which provide RSSI values, via Extended Kalman filter (EKF) [119]. Li, Poulose, and Correa described other examples of hybrid systems [9,45,120].…”

Section: Rssi-imu Hybrid Systemsmentioning

confidence: 99%

A Review of Technologies and Techniques for Indoor Navigation Systems for the Visually Impaired

Simões

Machado

Sales

et al. 2020

Sensors

View full text Add to dashboard Cite

Technologies and techniques of location and navigation are advancing, allowing greater precision in locating people in complex and challenging conditions. These advances have attracted growing interest from the scientific community in using indoor positioning systems (IPSs) with a higher degree of precision and fast delivery time, for groups of people such as the visually impaired, to some extent improving their quality of life. Much research brings together various works that deal with the physical and logical approaches of IPSs to give the reader a more general view of the models. These surveys, however, need to be continuously revisited to update the literature on the features described. This paper presents an expansion of the range of technologies and methodologies for assisting the visually impaired in previous works, providing readers and researchers with a more recent version of what was done and the advantages and disadvantages of each approach to guide reviews and discussions about these topics. Finally, we discuss a series of considerations and future trends for the construction of indoor navigation and location systems for the visually impaired.

show abstract

“…Almagbile et al proved that with the same filter performance RAE is more reliable than IAE (Almagbile et al, 2010). To simplify computation and improve estimation accuracy, the moving windows (MW) method was proposed to estimate Q and R (Yang and Xu, 2003), and the exponential weighted moving average (EWMA) method was also introduced (Narasimhappa et al, 2018;Franzen and Fingscheidt, 2019;Xu et al, 2019). However, the optimal window width in MW and the forgetting factor in EWMA have to be determined empirically.…”

Section: Introductionmentioning

confidence: 99%

“…The exploration of the vast ocean has become a momentous issue for humans. Underwater navigation technology, as the core technology of ocean exploration, is the most difficult challenge to tackle (Zhang et al., 2019). In the domain of underwater navigation, the strapdown inertial navigation system (SINS), with the performance characteristics of high autonomy and anti-interference, is widely utilised.…”

Section: Introductionmentioning

confidence: 99%

Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

Hou

Yao

et al. 2020

J. Navigation

View full text Add to dashboard Cite

The strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.

show abstract

Predictive Adaptive Kalman Filter and Its Application to INS/UWB-integrated Human Localization with Missing UWB-based Measurements

Cited by 22 publications

References 34 publications

Combining RSSI and Accelerometer Features for Room-Level Localization

Combining RSSI and Accelerometer Features for Room-Level Localization

A Review of Technologies and Techniques for Indoor Navigation Systems for the Visually Impaired

Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

Contact Info

Product

Resources

About