Scaled Unscented Kalman Filter for RSSI-based Indoor Positioning and Tracking

2015 ◽  
Author(s):  
Laith Khalil ◽  
Peter Jung
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Indoor Positioning

scholarly journals An integrity monitoring algorithm for WiFi/PDR/smartphone-integrated indoor positioning system based on unscented Kalman filter

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Haiyun Yao ◽  
Hong Shu ◽  
Hongxing Sun ◽  
B. G. Mousa ◽  
Zhenghang Jiao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Computational Complexity ◽  
Unscented Kalman Filter ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Integrity Monitoring ◽  
Observation Vector ◽  
Indoor Positioning System ◽  
Monitoring Algorithm

AbstractIndoor positioning navigation technologies have developed rapidly, but little effort has been expended on integrity monitoring in Pedestrian Dead Reckoning (PDR) and WiFi indoor positioning navigation systems. PDR accuracy will drift over time. Meanwhile, WiFi positioning accuracy decreases in complex indoor environments due to severe multipath propagation and interference with signals when people move about. In our research, we aimed to improve positioning quality with an integrity monitoring algorithm for a WiFi/PDR-integrated indoor positioning system based on the unscented Kalman filter (UKF). The integrity monitoring is divided into three phases. A test statistic based on the innovation of UKF determines whether the positioning system is abnormal. Once a positioning system abnormality is detected, a robust UKF (RUKF) is triggered to achieve higher positioning accuracy. Again, the innovation of RUKF is used to judge the outliers in observations and identify positioning system faults. In the last integrity monitoring phase, users will be alerted in time to reduce the risk from positioning fault. We conducted a simulation to analyze the computational complexity of integrity monitoring. The results showed that it did not substantially increase the overall computational complexity when the number of dimensions in the state vector and observation vector in the system is small (< 20). In practice, the number of dimensions of state vector and observation vector in an indoor positioning system rarely exceeds 20. The proposed integrity monitoring algorithm was tested in two field experiments, showing that the proposed algorithm is quite robust, yielding higher positioning accuracy than the traditional method, using only UKF.

A Novel Infrared Landmark Indoor Positioning Method Based on Improved IMM-UKF

2014 ◽  
Vol 511-512 ◽  
pp. 880-885 ◽  
Author(s):  
Yi Zhang ◽  
Hong Song Chen ◽  
Yuan Luo
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Robot Navigation ◽  
Infrared Camera ◽  
Indoor Positioning ◽  
Multiple Models ◽  
The Novel ◽  
Fusion Algorithm ◽  
Time Performance ◽  
Positioning Method

In structured environment, according to the requirement of indoor robot navigation for accuracy and real-time performance, On the basis of a novel positioning method using infrared landmarks, another novel infrared landmark indoor positioning method which uses high power infrared tube as landmarks, infrared camera as receiving sensor ,and combines track deduction is proposed in this paper. An improved Interacting Multiple Models Unscented Kalman Filter (IMM-UKF) data fusion algorithm for the two positioning scheme is used to improve the precision. Experimental results show that the novel infrared landmark indoor positioning method can increase the location speed and precision effectively.

scholarly journals A Federated Derivative Cubature Kalman Filter for IMU-UWB Indoor Positioning

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3514 ◽  
Author(s):  
Chengyang He ◽  
Chao Tang ◽  
Chengpu Yu
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Unscented Kalman Filter ◽  
Wide Band ◽  
Linear Measurement ◽  
Indoor Positioning ◽  
State Equation ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Cubature Kalman Filter

The inertial measurement unit and ultra-wide band signal (IMU-UWB) combined indoor positioning system has a nonlinear state equation and a linear measurement equation. In order to improve the computational efficiency and the localization performance in terms of the estimation accuracy, the federated derivative cubature Kalman filtering (FDCKF) method is proposed by combining the traditional Kalman filtering and the cubature Kalman filtering. By implementing the proposed FDCKF method, the observations of the UWB and the IMU can be effectively fused; particularly, the IMU can be continuously calibrated by UWB so that it does not generate cumulative errors. Finally, the effectiveness of the proposed algorithm is demonstrated through numerical simulations, in which FDCKF was compared with the federated cubature Kalman filter (FCKF) and the federated unscented Kalman filter (FUKF), respectively.

scholarly journals An Enhanced Smartphone Indoor Positioning Scheme with Outlier Removal Using Machine Learning

2021 ◽  
Vol 13 (6) ◽  
pp. 1106
Author(s):  
Zhenbing Zhang ◽  
Jingbin Liu ◽  
Lei Wang ◽  
Guangyi Guo ◽  
Xingyu Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Outlier Detection ◽  
Unscented Kalman Filter ◽  
Dead Reckoning ◽  
Maximum Error ◽  
Indoor Positioning ◽  
Fusion Scheme ◽  
Hybrid Fusion ◽  
Pedestrian Dead Reckoning

In smartphone indoor positioning, owing to the strong complementarity between pedestrian dead reckoning (PDR) and WiFi, a hybrid fusion scheme of them is drawing more and more attention. However, the outlier of WiFi will easily degrade the performance of the scheme, to remove them, many researches have been proposed such as: improving the WiFi individually or enhancing the scheme. Nevertheless, due to the inherent received signal strength (RSS) variation, there still exist some unremoved outliers. To solve this problem, this paper proposes the first outlier detection and removal strategy with the aid of Machine Learning (ML), so called WiFi-AGNES (Agglomerative Nesting), based on the extracted positioning characteristics of WiFi when the pedestrian is static. Then, the paper proposes the second outlier detection and removal strategy, so called WiFi-Chain, based on the extracted positioning characteristics of WiFi, PDR, and their complementary characteristics when the pedestrian is walking. Finally, a hybrid fusion scheme is proposed, which integrates the two proposed strategies, WiFi, PDR with an inertial-navigation-system-based (INS-based) attitude heading reference system (AHRS) via Extended Kalman Filter (EKF), and an Unscented Kalman Filter (UKF). The experiment results show that the two proposed strategies are effective and robust. With WiFi-AGNES, the minimum percentage of the maximum error (MaxE) is reduced by 66.5%; with WiFi-Chain, the MaxE of WiFi is less than 4.3 m; further the proposed scheme achieves the best performance, where the root mean square error (RMSE) is 1.43 m. Moreover, since characteristics are universal, the proposed scheme integrated the two characteristic-based strategies also possesses strong robustness.

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