Adaptive robust INS/UWB-integrated human tracking using UFIR filter bank

Xu, Yuan; Ahn, Choon Ki; Shmaliy, Yuriy S.; Chen, Xiyuan; Li, Yueyang

doi:10.1016/j.measurement.2018.03.043

Cited by 78 publications

(42 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The tracking performance is compared to existing works that applied UWB and INS fusion and is summarized in Table 4, in terms of mean position error, the total traveled distance of the evaluation experiment, the number of UWB anchors used and path characteristics including the number and type of the turns made. Regarding mean position error, best performance is seen in [16] and [18] where the error is within 0.33 m. However, it is noted that the length of the experiment path is relatively short without repetitive loops and the performance for a longer path is not explored. An experiment path traveled over 1 km is used in [26] by repeating a rectangular path 19 times and the achieved mean position error is 1.05 m. The experiment path used in this paper repeats the U-shaped path 16 times and contained significantly more U-turns.…”

Section: Resultsmentioning

confidence: 99%

“…The two locations are subsequently fused for a final estimation. An unbiased finite impulse response filter is designed in [16] to correct the difference between the INS-measured position and UWB-measured position. By using anti-magnetic ring to eliminate the outliers from the UWB distance measurements, a double-state adaptive KF is proposed in [17] to improve the positioning accuracy during information fusion.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

“…The distance is calculated based on ToF by implementing a two-way ranging algorithm [15]. A number of works design INS and UWB fusion IPSs (FIPSs) for more reliable tracking performance [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking

Tian

Wang

Salčić

2020

Sensors

View full text Add to dashboard Cite

Information fusion combining inertial navigation and radio frequency (RF) technologies, is commonly applied in indoor positioning systems (IPSs) to obtain more accurate tracking results. The performance of the inertial navigation system (INS) subsystem is affected by sensor drift over time and the RF-based subsystem aims to correct the position estimate using a fusion filter. However, the inherent sensor drift is usually not corrected during fusion, which leads to increasingly erroneous estimates over a short period of time. Among the inertial sensor drifts, gyroscope drift has the most significant impact in determining the correct orientation and accurate tracking. A gyroscope drift correction approach is proposed in this study and is incorporated in an INS and ultra-wideband (UWB) fusion IPS where only distance measurements from UWB subsystem are used. The drift correction approach is based on turn detection to account for the fact that gyroscope drift is accumulated during a turn. Practical pedestrian tracking experiments are conducted to demonstrate the accuracy of the drift correction approach. With the gyroscope drift corrected, the fusion IPS is able to provide more accurate tracking performance and achieve up to 64.52% mean position error reduction when compared to the INS only tracking result.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Preliminaries and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking

Tian

Wang

Salčić

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The accelerometer has higher sensitivity and suppresses temperature drift significantly [4]. Xu proposed an INS/uwb integrated system based on uffb to effectively reduce the drift error of the sensor [5]. Yang proposed that architecture use an on-chip temperature sensor to achieve on-chip temperature compensation, and a micro-heater to realize on-chip temperature control of the sensor [6].…”

Section: Introductionmentioning

confidence: 99%

Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method

Zhu

Pang²,

Zhang

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

In this paper, the method for compensating the temperature drift of high-G MEMS accelerometer (HGMA) is proposed, including radial basis function neural network (RBF NN), RBF NN based on genetic algorithm (GA), RBF NN based on GA with Kalman filter (KF), and the RBF NN + GA + KF method compensated by the temperature drift model. First, this paper introduces an HGMA structure working principle, conducts a finite element analysis, and produces the results. The simulation results show that the HGMA working mode is the 1st order mode, and its resonant frequency is 408 kHz. The 2nd order mode resonant frequency is 667 kHz, and the gap with the first mode is 260 kHz, indicating that the coupling movement between the two modes is tiny, so the HGMA has good linearity. Then, a temperature experiment is performed to obtain the output value of HGMA. The output values of HGMA are analyzed and optimized by using the algorithms proposed in this paper. The processing results show that the RBF NN + GA + KF method compensated by the temperature drift model achieves the best denoing consequent. The processing results show that the temperature drift of the HGMA is effectively compensated. The final results show that acceleration random walking improved from 17130 g/h/Hz0.5 to 765.3 g/h/Hz0.5, and bias stability improved from 4720 g/h to 57.27 g/h, respectively. The results show that after using the RBF NN + GA + KF method, combined with the temperature drift model, the temperature drift trend and noise characteristics of HGMA are well optimized.

show abstract

“…However, despite great progress in the development of the KF approach, the recursive KF-based algorithms demonstrate good performance mostly when the noise statistics are known exactly, and the model perfectly matches the process; otherwise, the KF-based estimators often demonstrate poor performance [18][19][20]. In order to provide the robust estimation, the unbiased finite impulse response (UFIR) filter has been proposed in [21][22][23]. Then, there are some improving UFIR filters proposed to improve the performance [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Robust Self-Contained Pedestrian Navigation by Fusing the IMU and Compass Measurements via UFIR Filtering

Hou

Liu

2018

Journal of Electrical and Computer Engineering

Self Cite

View full text Add to dashboard Cite

In order to overcome the poor observability of yaw measurement for foot-mounted inertial measurement unit (IMU), an integrated IMU+Compass scheme for self-contained pedestrian navigation is presented. In this mode, the compass measurement is used to provide the accurate yaw to improve the accuracy of the attitude transformation matrix for the foot-mounted IMU solution. And then, when the person is in a stance phase during walk, a unbiased finite impulse response (UFIR) filter based on the self-contained pedestrian navigation scheme is investigated, which just needs the state vector size 푀 and the filtering horizon size 푁 , while ignoring the noise statistics compared with the Kalman filter (KF). Finally, a real test has been done to verify the performance of the proposed self-contained pedestrian navigation using the IMU and compass measurements via UFIR filter. The test results show that the proposed filter has robust performance compared with the KF.

show abstract

Adaptive robust INS/UWB-integrated human tracking using UFIR filter bank

Cited by 78 publications

References 41 publications

An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking

An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking

Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method

Robust Self-Contained Pedestrian Navigation by Fusing the IMU and Compass Measurements via UFIR Filtering

Contact Info

Product

Resources

About