Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization

An improved UKF (Unscented Kalman Filter) algorithm is proposed to solve the problem of radar azimuth mutation. Since the radar azimuth angle will restart to count after each revolution of the radar, and when the aircraft just passes the abrupt angle change, the radar observation measurement will have a sudden change, which has serious consequences and is solved by the proposed novel UKF based on SVD. In order to improve the tracking accuracy and stability of the radar tracking system further, the SVD-MUKF (Singular Value Decomposition-based Memory Unscented Kalman Filter) based on multiple memory fading is constructed. Furthermore, several simulation results show that the SVD-MUKF algorithm proposed in this paper is better than the SVD-UKF (Singular Value Decomposition of Unscented Kalman Filter) algorithm and classical UKF algorithm in accuracy and stability. Last but not the least, the SVD-MUKF can achieve stable tracking of targets even in the case of angle mutation.

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Development of Unscented Kalman filter algorithm for 3-D point based rigid registration

2008 International Symposium on Telecommunications ◽

10.1109/istel.2008.4651367 ◽

2008 ◽

Author(s):

Farnaz Zamani ◽

Ali Asghar Beheshti

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

Rigid Registration ◽

Kalman Filter Algorithm

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Integrated WiFi/PDR/Smartphone Using an Adaptive System Noise Extended Kalman Filter Algorithm for Indoor Localization

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi5020008 ◽

2016 ◽

Vol 5 (2) ◽

pp. 8 ◽

Cited By ~ 37

Author(s):

Xin Li ◽

Jian Wang ◽

Chunyan Liu ◽

Liwen Zhang ◽

Zhengkui Li

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Indoor Localization ◽

Adaptive System ◽

System Noise ◽

Kalman Filter Algorithm

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Enhanced PDR-BLE Compensation Mechanism Based on HMM and AWCLA for Improving Indoor Localization

Sensors ◽

10.3390/s21216972 ◽

2021 ◽

Vol 21 (21) ◽

pp. 6972

Author(s):

Harun Jamil ◽

Faiza Qayyum ◽

Faisal Jamil ◽

Do-Hyeun Kim

Keyword(s):

Kalman Filter ◽

Indoor Environment ◽

Indoor Localization ◽

Unscented Kalman Filter ◽

Sensor Data ◽

Compensation Mechanism ◽

Localization Algorithm ◽

High Position ◽

Zero Velocity ◽

Conflicting Information

This paper presents an enhanced PDR-BLE compensation mechanism for improving indoor localization, which is considerably resilient against variant uncertainties. The proposed method of ePDR-BLE compensation mechanism (EPBCM) takes advantage of the non-requirement of linearization of the system around its current state in an unscented Kalman filter (UKF) and Kalman filter (KF) in smoothing of received signal strength indicator (RSSI) values. In this paper, a fusion of conflicting information and the activity detection approach of an object in an indoor environment contemplates varying magnitude of accelerometer values based on the hidden Markov model (HMM). On the estimated orientation, the proposed approach remunerates the inadvertent body acceleration and magnetic distortion sensor data. Moreover, EPBCM can precisely calculate the velocity and position by reducing the position drift, which gives rise to a fault in zero-velocity and heading error. The developed EPBCM localization algorithm using Bluetooth low energy beacons (BLE) was applied and analyzed in an indoor environment. The experiments conducted in an indoor scenario shows the results of various activities performed by the object and achieves better orientation estimation, zero velocity measurements, and high position accuracy than other methods in the literature.

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Underwater Bearing-Only Multitarget Tracking in Dense Clutter Environment Based on PMHT

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20203820359 ◽

2020 ◽

Vol 38 (2) ◽

pp. 359-365

Author(s):

Xiaohua Li ◽

Ya'an Li ◽

Xiaofeng Lu ◽

Chenxu Zhao ◽

Jing Yu

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

Data Association ◽

Estimation Accuracy ◽

Multitarget Tracking ◽

Single Observation ◽

Multiple Observations ◽

Multiple Hypothesis Tracking ◽

Kalman Filter Algorithm ◽

Present Algorithm

Underwater bearing-only multitarget tracking in clutter environment is challenging because of the measurement nonlinearity, range unobservability, and data association uncertainty. In terms of the principle of expectation maximization, combining the extended Kalman filter (EKF) and unscented Kalman filter algorithm(UKF), a new bearing-only multi-sensor multitarget tracking via probabilistic multiple hypothesis tracking(PMHT) algorithm is proposed. The PMHT algorithm introduces an association variable to deal with the data association uncertainty problem between the measurements and the targets. Furthermore, the EKF-based PMHT for multi-sensor multitarget system is simplified, which obviate the need to "stack" the synthetic measurements and can reduce the computation cost. The estimation accuracy of the EKF based on PMHT approach and UKF based on PMHT approach in simulation experiments for underwater bearing-only cross-moving targets and closely spaced targets for the case of stationary multiple observations and maneuvering single observation under dense clutter environment is analyzed. The experimental results demonstrate that the present algorithm is very well in a highly clutter environment and its computational load is low, which confirms the effectiveness of the algorithm to the bearing-only multitarget tracking in dense clutter.

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