Study on UWB/INS integration techniques

Zwirełło, Łukasz; Ascher, Christian; Trommer, Gert F.; Zwick, Thomas

doi:10.1109/wpnc.2011.5961007

Cited by 11 publications

(10 citation statements)

References 4 publications

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“…End (24) heading ( variance, the array of particle states, the array of particle scores, the weight array, the numerical fusion positioning result, and the array of static beacon coordinates. Line 3 utilizes the first 5 groups of UWB data to estimate the pedestrian's initial position through the triangle method.…”

Section: (:T) � Vel_n(:t) − Vel_error; Pos_n(:t) � Pos_n(:t) − Pomentioning

confidence: 99%

“…Meanwhile, the adoption of UWB technology for error correction had obtained a full sixDoF pose of the drone. In [24], the authors studied the EKF loosely/tightly coupled UWB/INS integration based on the PDF algorithm, but they utilized the ray-launching simulations to generate UWB data. In [25], the authors presented an improving tightly-coupled navigation model for indoor pedestrian navigation.…”

Section: Introductionmentioning

confidence: 99%

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Research on Extended Kalman Filter and Particle Filter Combinational Algorithm in UWB and Foot-Mounted IMU Fusion Positioning

Wang

Liu

2018

Mobile Information Systems

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As UWB high-precision positioning in NLOS environment has become one of the hot topics in the research of indoor positioning, this paper firstly presents a method for the smoothing of original range data based on the Kalman filter by the analysis of the range error caused by UWB signals in LOS and NLOS environment. en, it studies a UWB and foot-mounted IMU fusion positioning method with the integration of particle filter with extended Kalman filter. is method adopts EKF algorithm in the kinematic equation of particle filters algorithm to calculate the position of each particle, which is like the way of running N (number of particles) extended Kalman filters, and overcomes the disadvantages of the inconformity between kinematic equation and observation equation as well as the problem of sample degeneration under the nonlinear condition of the standard particle filters algorithm. e comparison with the foot-mounted IMU positioning algorithm, the optimization-based UWB positioning algorithm, the particle filter-based UWB positioning algorithm, and the particle filter-based IMU/UWB fusion positioning algorithm shows that our algorithm works very well in LOS and NLOS environment. Especially in an NLOS environment, our algorithm can better use the foot-mounted IMU positioning trajectory maintained by every particle to weaken the influence of range error caused by signal blockage. It outperforms the other four algorithms described as above in terms of the average and maximum positioning error.

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Section: (:T) � Vel_n(:t) − Vel_error; Pos_n(:t) � Pos_n(:t) − Pomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Extended Kalman Filter and Particle Filter Combinational Algorithm in UWB and Foot-Mounted IMU Fusion Positioning

Wang

Liu

2018

Mobile Information Systems

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“…Through the IMU integral data, the observations of speed, direction, and position can be obtained. To a certain extent, it can not only eliminate the CKF has three advantages [20,30,31]: first, there is no need to define a motion model, that is, the algorithm can be used for both vehicle and pedestrian applications. Secondly, the error of the state is stored rather than the state itself.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive UWB/MEMS-IMU Complementary Kalman Filter for Indoor Location in NLOS Environment

Liu

Jian

et al. 2019

Remote Sensing

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High precision positioning of UWB (ultra-wideband) in NLOS (non-line-of-sight) environment is one of the hot issues in the direction of indoor positioning. In this paper, a method of using a complementary Kalman filter (CKF) to fuse and filter UWB and IMU (inertial measurement unit) data and track the errors of variables such as position, speed, and direction is presented. Based on the uncertainty of magnetometer and acceleration, the noise covariance matrix of magnetometer and accelerometer is calculated dynamically, and then the weight of magnetometer data is set adaptively to correct the directional error of gyroscope. Based on the uncertainty of UWB distance observations, the covariance matrix of UWB measurement noise is calculated dynamically, and then the weight of UWB data observations is set adaptively to correct the position error. The position, velocity and direction errors are corrected by the fusion of UWB and IMU. The experimental results show that the algorithm can reduce the gyroscope deviation with magnetic noise and motion noise, so that the orientation estimates can be improved, as well as the positioning accuracy can be increased with UWB ranging noise.

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“…Therefore, this paper adopts a fusion method by mounting the UWB on the head and IMU on a foot. Based on the choice of the IMU as the core, many researchers take the UWB data as positional observed values and add them into the EKF algorithm, which is based on zero velocity, to realize the fusion [5,17]. Nevertheless, as the EKF is, in essence, the linear approximation of the observation equation, it can hardly achieve a good approximation of the UWB sensor [16] based on a highly nonlinear observation model.…”

Section: Introductionmentioning

confidence: 99%

The IMU/UWB Fusion Positioning Algorithm Based on a Particle Filter

Wang

2017

IJGI

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This paper integrates UWB (ultra-wideband) and IMU (Inertial Measurement Unit) data to realize pedestrian positioning through a particle filter in a non-line-of-sight (NLOS) environment. After the acceleration and angular velocity are integrated by the ZUPT-based algorithm, the velocity and orientation of the feet are obtained, and then the velocity and orientation of the whole body are estimated by a virtual odometer method. This information will be adopted as the prior information for the particle filter, and the observation value of UWB will act as the basis for weight updating. According to experimental results, the prior information provided by an IMU can be used to restrain the observation error of UWB under an NLOS condition, and the positioning precision can be improved from the positioning error of 1.6 m obtained using the pure UWB-based algorithm to approximately 0.7 m. Moreover, with high computational efficiency, this algorithm can achieve real-time computing performance on ordinary embedded devices.

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Study on UWB/INS integration techniques

Cited by 11 publications

References 4 publications

Research on Extended Kalman Filter and Particle Filter Combinational Algorithm in UWB and Foot-Mounted IMU Fusion Positioning

Research on Extended Kalman Filter and Particle Filter Combinational Algorithm in UWB and Foot-Mounted IMU Fusion Positioning

An Adaptive UWB/MEMS-IMU Complementary Kalman Filter for Indoor Location in NLOS Environment

The IMU/UWB Fusion Positioning Algorithm Based on a Particle Filter

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