Synthesis and Experimental Accuracy Assessment of Kalman Filter Algorithm for UWB ToA Local Positioning System

2021 ◽  
Author(s):  
Nikita Petukhov ◽  
Alexander Chugunov ◽  
Vladimir Zamolodchikov ◽  
Dmitry Tsaregorodtsev ◽  
Ilya Korogodin
Keyword(s):  
Kalman Filter ◽  
Accuracy Assessment ◽  
Positioning System ◽  
Experimental Accuracy ◽  
Local Positioning System ◽  
Kalman Filter Algorithm

An implementation of the cubature Kalman filter for estimating trajectory parameters and air data of a hypersonic vehicle

2019 ◽  
Vol 233 (12) ◽  
pp. 4554-4570
Author(s):  
Zhijian Ding ◽  
Huan Zhou ◽  
Feng Wang ◽  
Dongsheng Wu ◽  
Yingchuan Wu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Inertial Navigation System ◽  
Hypersonic Vehicle ◽  
Positioning System ◽  
System State ◽  
Sensing System ◽  
Cubature Kalman Filter ◽  
Global Positioning ◽  
Kalman Filter Algorithm

Trajectory parameters (including the position, velocity, and attitude angles of a vehicle) and air data (consisting of the flow angles, the Mach number, and the freestream static pressure) are vital data for the analysis and evaluation process in the hypersonic flight tests. This paper describes a data fusion estimation algorithm for a flush air data sensing system/inertial navigation system/global positioning system integrated system, which is used to estimate the trajectory parameters and air data for an unpowered hypersonic vehicle. In the approach, the raw outputs of flush air data sensing system (i.e. the surface pressure measurements) are integrated with global positioning system results (the vehicle’s position and velocity) and inertial navigation system measurements (including the acceleration and the angular velocity measurements) by using a nonlinear Kalman filter algorithm. Firstly, the system state vector is defined with the trajectory parameters, the biases of the inertial sensors and the winds. Then, the system dynamic models are built based on the motion equations of an unpowered hypersonic vehicle, the inertial sensor error models and the wind model. Besides, the system measurement vector is designed with the global positioning system results and the flush air data sensing system raw outputs. Based on these works, the system state is directly estimated by using the cubature Kalman filter algorithm. After that, the air data is calculated based on the estimated values and a high-fidelity model of atmosphere. Simulation cases are implemented to assess the performance of the proposed algorithm. The results show that the proposed method could estimate the trajectory parameters and air data for hypersonic vehicle with a high precision.

scholarly journals Implementation of a Differential Geometric Filter for Spacecraft Formation Orbit Estimation

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Shu Ting Goh ◽  
Ossama Abdelkhalik ◽  
Seyed A. (Reza) Zekavat
Keyword(s):  
Kalman Filter ◽  
Convergence Rate ◽  
Extended Kalman Filter ◽  
Positioning System ◽  
Absolute Position ◽  
Spacecraft Formation ◽  
Stability Performance ◽  
Local Positioning System ◽  
The Absolute ◽  
Orbit Estimation

The differential geometric filter is implemented to estimate the absolute and relative positions of the spacecraft in a formation. The extended Kalman Filter is also implemented as a benchmark for the differential geometric estimation. Only relative positions between the spacecraft are measured. Relative positions are measured using a wireless local positioning system (WLPS) installed in all spacecraft. Two different scenarios are studied: (1) the observations include WLPS measurements only and (2) the observations include WLPS measurements in addition to measurements for the absolute position of one spacecraft made by a radar that takes measurements from the earth’s surface. Results show that the differential geometric estimation has better stability performance and a faster convergence rate compared to the extended Kalman filter.

scholarly journals Acceleration Feature Extraction of Human Body Based on Wearable Devices

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 924
Author(s):  
Zhenzhen Huang ◽  
Qiang Niu ◽  
Ilsun You ◽  
Giovanni Pau
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Kalman Filter ◽  
Human Body ◽  
Wearable Devices ◽  
Human Motion ◽  
Data Set ◽  
Feature Extraction Method ◽  
Motion Data ◽  
Kalman Filter Algorithm

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

scholarly journals Evaluation of Multi-Sensor Fusion Methods for Ultrasonic Indoor Positioning

2021 ◽  
Vol 11 (15) ◽  
pp. 6805
Author(s):  
Khaoula Mannay ◽  
Jesús Ureña ◽  
Álvaro Hernández ◽  
José M. Villadangos ◽  
Mohsen Machhout ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Experimental Tests ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Smart Devices ◽  
Final Position ◽  
Positioning Systems ◽  
Local Positioning System ◽  
Tightly Coupled ◽  
Position Estimate

Indoor positioning systems have become a feasible solution for the current development of multiple location-based services and applications. They often consist of deploying a certain set of beacons in the environment to create a coverage volume, wherein some receivers, such as robots, drones or smart devices, can move while estimating their own position. Their final accuracy and performance mainly depend on several factors: the workspace size and its nature, the technologies involved (Wi-Fi, ultrasound, light, RF), etc. This work evaluates a 3D ultrasonic local positioning system (3D-ULPS) based on three independent ULPSs installed at specific positions to cover almost all the workspace and position mobile ultrasonic receivers in the environment. Because the proposal deals with numerous ultrasonic emitters, it is possible to determine different time differences of arrival (TDOA) between them and the receiver. In that context, the selection of a suitable fusion method to merge all this information into a final position estimate is a key aspect of the proposal. A linear Kalman filter (LKF) and an adaptive Kalman filter (AKF) are proposed in that regard for a loosely coupled approach, where the positions obtained from each ULPS are merged together. On the other hand, as a tightly coupled method, an extended Kalman filter (EKF) is also applied to merge the raw measurements from all the ULPSs into a final position estimate. Simulations and experimental tests were carried out and validated both approaches, thus providing average errors in the centimetre range for the EKF version, in contrast to errors up to the meter range from the independent (not merged) ULPSs.

scholarly journals Measuring Gait Velocity and Stride Length with an Ultrawide Bandwidth Local Positioning System and an Inertial Measurement Unit

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2896
Author(s):  
Pratham Singh ◽  
Michael Esposito ◽  
Zach Barrons ◽  
Christian A. Clermont ◽  
John Wannop ◽  
...  
Keyword(s):  
Gold Standard ◽  
Gait Speed ◽  
Inertial Measurement Unit ◽  
Stride Length ◽  
Measurement Unit ◽  
Gait Velocity ◽  
Positioning System ◽  
Inertial Measurement ◽  
Measurement Systems ◽  
Local Positioning System

One possible modality to profile gait speed and stride length includes using wearable technologies. Wearable technology using global positioning system (GPS) receivers may not be a feasible means to measure gait speed. An alternative may include a local positioning system (LPS). Considering that LPS wearables are not good at determining gait events such as heel strikes, applying sensor fusion with an inertial measurement unit (IMU) may be beneficial. Speed and stride length determined from an ultrawide bandwidth LPS equipped with an IMU were compared to video motion capture (i.e., the “gold standard”) as the criterion standard. Ninety participants performed trials at three self-selected walk, run and sprint speeds. After processing location, speed and acceleration data from the measurement systems, speed between the last five meters and stride length in the last stride of the trial were analyzed. Small biases and strong positive intraclass correlations (0.9–1.0) between the LPS and “the gold standard” were found. The significance of the study is that the LPS can be a valid method to determine speed and stride length. Variability of speed and stride length can be reduced when exploring data processing methods that can better extract speed and stride length measurements.

Sign in / Sign up

Export Citation Format

Share Document