scholarly journals Strapdown Sculling Velocity Algorithms Using Novel Input Combinations

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Huang ◽  
Zhaochun Li ◽  
Fei Xie ◽  
Kai Feng
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Angular Rate ◽  
Dynamic Environment ◽  
Navigation Systems ◽  
Specific Force ◽  
The Novel ◽  
Inertial Navigation Systems ◽  
Carrier Velocity ◽  
Digital Simulations

Sculling motion is a standard input to evaluate the performance of the velocity algorithm in a highly dynamic environment. Conventional sculling algorithms usually adopt incremental angle/specific force increments or angular rate/specific force as algorithm inputs. However modern inertial sensors have different output types now, which do not correspond to the inputs of those traditional algorithms. For example, some inertial sensors have the integrated angular rate (incremental angle)/specific force outputs or angular rate/specific force increments outputs. Hence the conventional sculling algorithms cannot be easily applied to these situations. A novel sculling algorithm using incremental angle/specific force inputs or angular rate/specific force increments inputs is developed in this paper. The advantage of the novel algorithm is that it can calculate the carrier velocity directly without converting the dimension of inertial sensor outputs values. Theoretical analysis, digital simulations, and a trial study are carried out to verify our algorithm. The results demonstrate that for corresponding types of strapdown inertial navigation systems (SINS) the novel sculling algorithm exhibits better performance than the conventional sculling velocity algorithms.

scholarly journals A Hybrid Framework for Mitigating Heading Drift for a Wearable Pedestrian Navigation System through Adaptive Fusion of Inertial and Magnetic Measurements

2021 ◽  
Vol 11 (4) ◽  
pp. 1902
Author(s):  
Liqiang Zhang ◽  
Yu Liu ◽  
Jinglin Sun
Keyword(s):  
Magnetic Measurements ◽  
Inertial Sensor ◽  
Angular Rate ◽  
Human Motion ◽  
Navigation Systems ◽  
Pedestrian Navigation ◽  
Hybrid Framework ◽  
Outdoor Environments ◽  
Adaptive Fusion ◽  
Indoor And Outdoor Environments

Pedestrian navigation systems could serve as a good supplement for other navigation methods or for extending navigation into areas where other navigation systems are invalid. Due to the accumulation of inertial sensing errors, foot-mounted inertial-sensor-based pedestrian navigation systems (PNSs) suffer from drift, especially heading drift. To mitigate heading drift, considering the complexity of human motion and the environment, we introduce a novel hybrid framework that integrates a foot-state classifier that triggers the zero-velocity update (ZUPT) algorithm, zero-angular-rate update (ZARU) algorithm, and a state lock, a magnetic disturbance detector, a human-motion-classifier-aided adaptive fusion module (AFM) that outputs an adaptive heading error measurement by fusing heuristic and magnetic algorithms rather than simply switching them, and an error-state Kalman filter (ESKF) that estimates the optimal systematic error. The validation datasets include a Vicon loop dataset that spans 324.3 m in a single room for approximately 300 s and challenging walking datasets that cover large indoor and outdoor environments with a total distance of 12.98 km. A total of five different frameworks with different heading drift correction methods, including the proposed framework, were validated on these datasets, which demonstrated that our proposed ZUPT–ZARU–AFM–ESKF-aided PNS outperforms other frameworks and clearly mitigates heading drift.

scholarly journals Analysis and Verification of Rotation Modulation Effects on Inertial Navigation System based on MEMS Sensors

2013 ◽  
Vol 66 (5) ◽  
pp. 751-772 ◽  
Author(s):  
Xueyun Wang ◽  
Jie Wu ◽  
Tao Xu ◽  
Wei Wang
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Cost Effective ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Mems Sensors ◽  
Micro Electro Mechanical Systems ◽  
Position Accuracy

Inertial Navigation Systems (INS) were large, heavy and expensive until the development of cost-effective inertial sensors constructed with Micro-electro-mechanical systems (MEMS). However, the large errors and poor error repeatability of MEMS sensors make them inadequate for application in many situations even with frequent calibration. To solve this problem, a systematic error auto-compensation method, Rotation Modulation (RM) is introduced and detailed. RM does no damage to autonomy, which is one of the most important characteristics of an INS. In this paper, the RM effects on navigation performance are analysed and different forms of rotation schemes are discussed. A MEMS-based INS with the RM technique applied is developed and specific calibrations related to rotation are investigated. Experiments on the developed system are conducted and results verify that RM can significantly improve navigation performance of MEMS-based INS. The attitude accuracy is improved by a factor of 5, and velocity/position accuracy by a factor of 10.

Pedestrian navigation using inertial sensors and altitude error correction

Sensor Review ◽  
2015 ◽  
Vol 35 (1) ◽  
pp. 68-75 ◽  
Author(s):  
Wen Liu ◽  
Yingjun Zhang ◽  
Xuefeng Yang ◽  
Shengwei Xing
Keyword(s):  
Error Correction ◽  
Inertial Sensors ◽  
Angular Rate ◽  
Gait Recognition ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Content Type ◽  
Positioning Errors ◽  
Walking Motion ◽  
Dynamic Time

Purpose – The aim of this article is to present a PIN (pedestrian inertial navigation) solution that incorporates altitude error correction, which eliminates the altitude error accurately without using external sensors. The main problem of PIN is the accumulation of positioning errors due to the drift caused by the noise in the sensors. Experiment results show that the altitude errors are significant when navigating in multilayer buildings, which always lead to localization to incorrect floors. Design/methodology/approach – The PIN proposed is implemented over an inertial navigation systems (INS) framework and a foot-mounted IMU. The altitude error correction idea is identifying the most probable floor of each horizontal walking motion. To recognize gait types, the walking motion is described with angular rate measured by IMU, and the dynamic time warping algorithm is used to cope with the different dimension samples due to the randomness of walking motion. After gait recognition, the altitude estimated with INS of each horizontal walking is checked for association with one of the existing in a database. Findings – Experiment results show that high accuracy altitude is achieved with altitude errors below 5 centimeters for upstairs and downstairs routes in a five floors building. Research limitations/implications – The main limitations of the study is the assumption that accuracy floor altitude information is available. Originality/value – Our PIN system eliminates altitude errors accurately and intelligently, which benefits from the new idea of combination of gait recognition and map-matching. In addition, only one IMU is used which is different from other approach that use external sensors.

scholarly journals Particle-Filter-Based WiFi-Aided Reduced Inertial Sensors Navigation System for Indoor and GPS-Denied Environments

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
M. M. Atia ◽  
M. J. Korenberg ◽  
A. Noureldin
Keyword(s):  
Particle Filter ◽  
Inertial Sensors ◽  
Low Cost ◽  
Indoor Navigation ◽  
Feature Reduction ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Error Accumulation ◽  
Non Gaussian ◽  
Multipath Errors

Indoor navigation is challenging due to unavailability of satellites-based signals indoors. Inertial Navigation Systems (INSs) may be used as standalone navigation indoors. However, INS suffers from growing drifts without bounds due to error accumulation. On the other side, the IEEE 802.11 WLAN (WiFi) is widely adopted which prompted many researchers to use it to provide positioning indoors using fingerprinting. However, due to WiFi signal noise and multipath errors indoors, WiFi positioning is scattered and noisy. To benefit from both WiFi and inertial systems, in this paper, two major techniques are applied. First, a low-cost Reduced Inertial Sensors System (RISS) is integrated with WiFi to smooth the noisy scattered WiFi positioning and reduce RISS drifts. Second, a fast feature reduction technique is applied to fingerprinting to identify the WiFi access points with highest discrepancy power to be used for positioning. The RISS/WiFi system is implemented using a fast version of Mixture Particle Filter for state estimation as nonlinear non-Gaussian filtering algorithm. Real experiments showed that drifts of RISS are greatly reduced and the scattered noisy WiFi positioning is significantly smoothed. The proposed system provides smooth indoor positioning of 1 m accuracy 70% of the time outperforming each system individually.

scholarly journals Tuning of the Wavelet Filters for the IMU Data Based on the PDC Method and the GPS Solution in a Bi-Dimensional Navigation Application

2019 ◽  
Vol 11 (3) ◽  
pp. 3-14
Author(s):  
Ioana Raluca ADOCHIEI ◽  
Teodor Lucian GRIGORIE ◽  
Felix Constantin ADOCHIEI
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Wavelet Filters ◽  
Time Frequency ◽  
Tuning Method ◽  
Coupling Dynamics ◽  
Navigation Accuracy ◽  
Sensor Errors

The degradation of navigation accuracy and integrity of GPS in the presence of radio frequency interference, hostile jamming and high dynamical situations, when the satellite signals may get lost due to signal blockage, led to the development of MEMS-INS/GPS integrated navigation systems for various applications of the positioning and navigation technologies. Unfortunately, the short-term advantages brought by the INS systems are overshadowed by their imprecise operation over the long term, mainly due to inertial sensor errors. A critical component of the inertial sensors errors is the noise. To improve the quality of the inertial sensors data, many denoising techniques have been used. Wavelet method has been proven as a useful tool for signal analysis, and it is widely used in signal processing and denoising applications. The here proposed technique is based on a time-frequency approach previously applied in bio-signals processing. In the proposed mechanism, the inertial sensors signals are processed analysed by using an extended version of the Wavelet transform. The optimal levels of decomposition are established for the wavelet filters, based on the evaluation of a parameter called coupling level (CL). It characterizes the coupling dynamics information between the reference signals, provided by a GPS, and the perturbed signal, which are the outputs of the inertial navigation system (INS). The proposed tuning method is experimentally tested in a bi-dimensional navigation application.

scholarly journals Robust Special Strategies Re Sampling for Mobile Inertial Navigation Systems

2019 ◽  
Vol 9 (2) ◽  
pp. 3196-3204
Keyword(s):  
Particle Filtering ◽  
Inertial Sensor ◽  
Low Cost ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Computing Platform ◽  
Mobile Navigation ◽  
Global Positioning ◽  
Particle Measurement ◽  
Special Strategy

The mobile navigation services in an obstructed area can be extremely challenging especially if the Global Positioning System (GPS) is blocked. In such conditions, users will find it difficult to navigate directly on-site. This needs to use inertial sensor in order to determine the location as standalone, low cost and ubiquity. However, the usage of accurate inertial sensor and fast localization module in the system would lead the phenomenon of sample impoverishment, which it is contribute computation burden to the system. There are different situation of the sample impoverishment, and the solution by using special strategies resampling algorithm cannot be used or fitted in different cases in altogether. Adaptations relating to particle filtering attribute need to be made to the algorithm in order to make resampling more intelligent, reliable and robust. In this paper, we are proposes a robust special strategy resampling algorithm by adapting particle filtering attribute such as; noise and particle measurement. This adaptation is used to counteract sample impoverishment in different cases in altogether. Finally, the paper presents the proposed solution can survive in three (3) types of sample impoverishment situation inside mobile computing platform.

scholarly journals IMPROVEMENT OF NAVIGATION SYSTEMS OF VEHICLES BY MEANS OF INERTIAL SENSORS AND INFORMATION PROCESSING USING PROBABILITY-GEOMETRIC METHODS

2020 ◽  
Vol 1 (46) ◽  
pp. 353-364
Author(s):  
Topolskov E ◽  
◽  
Beljaevskiy L L ◽  
Serdjuke A ◽  
◽  
...  
Keyword(s):  
Information Processing ◽  
Moving Objects ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Satellite Navigation ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Ground Vehicle ◽  
Geometric Methods ◽  
Urgent Task

Providing high accuracy of the coordinates and trajectories of objects by measurements conducted in navigation systems and complexes is an urgent task, which improves safety and efficiency of different modes of transport. However difficult environmental conditions, where vehicles are commonly used, stipulate influence of different factors on performance of onboard satellite navigation receivers, which are used as basic navigation devices for ground vehicle nowadays. Setting on cars used for common purposes additional navigation devices, which provide better performance, in most cases is economically unreasonable. Economically reasonable ways to improve onboard navigation complexes of vehicles, which are used for common purposes, are examined in this article. Functional diagram and principles of work of navigational complex, which uses the satellite navigation receiver and simplified variant of inertial navigation system is pointed as well. Also, the justification of methods for minimizing the error formats of coordinates and trajectories of moving objects based on information processing in multipositional, in particular satellite-inertial navigation systems and complexes, is presented. The obtained research results give an opportunity to develop an algorithm for coordinate refinement, which can be implemented in the improved on-board navigational complex of vehicle. KEY WORDS: NAVIGATION SYSTEMS AND COMPLEXES, INERTIAL SENSORS, NAVIGATION DEFINITIONS, ACCURACY AND RELIABILITY OF COORDINATES AND TRAJECTORIES OF MOVING OBJECTS, ELLIPS OF ERRORS, PROBABILISTIC-GEOMETRIC METHODS.

scholarly journals Dual MIMU Pedestrian Navigation by Inequality Constraint Kalman Filtering

2016 ◽  
Author(s):  
Wei Shi ◽  
Yang Wang ◽  
Yuanxin Wu
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Inertial Navigation ◽  
Inequality Constraint ◽  
Time Range ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Pedestrian Navigation ◽  
External Assistance ◽  
Constraint Method

The foot-mounted inertial navigation system is an important application of pedestrian navigation as it in principle does not rely any external assistance. A real-time range decomposition constraint method is proposed in this paper to combine the information of dual foot-mounted inertial navigation systems. It is well known that low-cost inertial sensors with ZUPT (zero-velocity update) and range decomposition constraint perform better than in either single way. This paper recommends that the distance of separation between the position estimates of feet-mounted inertial navigation systems be restricted in the ellipsoidal constraint which relates to the maximum step and leg height. The performance of the proposed method is studied utilizing experimental data. The results indicate that the method can effectively correct the dual navigation systems’ position over the existing spherical constraint.

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