scholarly journals Distributed Kalman filter for UWB/INS integrated pedestrian localization under colored measurement noise

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Yuan Xu ◽  
Jing Cao ◽  
Yuriy S. Shmaliy ◽  
Yuan Zhuang
Keyword(s):  
Kalman Filter ◽  
Wide Band ◽  
Measurement Noise ◽  
Indoor Environments ◽  
Factor Matrix ◽  
Distance Measurements ◽  
Localization Accuracy ◽  
The Difference ◽  
Local Filters ◽  
Human Position

AbstractColored Measurement Noise (CMN) has a great impact on the accuracy of human localization in indoor environments with Inertial Navigation System (INS) integrated with Ultra Wide Band (UWB). To mitigate its influence, a distributed Kalman Filter (dKF) is developed for Gauss–Markov CMN with switching Colouredness Factor Matrix (CFM). In the proposed scheme, a data fusion filter employs the difference between the INS- and UWB-based distance measurements. The main filter produces a final optimal estimate of the human position by fusing the estimates from local filters. The effect of CMN is overcome by using measurement differencing of noisy observations. The tests show that the proposed dKF developed for CMN with CFM can reduce the localization error compared to the original dKF, and thus effectively improve the localization accuracy.

scholarly journals Measurement Noise Covariance-Adapting Kalman Filters for Varying Sensor Noise Situations

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8304
Author(s):  
Anirudh Chhabra ◽  
Jashwanth Rao Venepally ◽  
Donghoon Kim
Keyword(s):  
Autonomous Agents ◽  
Wide Band ◽  
Measurement Noise ◽  
Indoor Environments ◽  
Simulation Studies ◽  
Distance Measurements ◽  
Localization Accuracy ◽  
Aerospace Applications ◽  
Measurement Noise Covariance ◽  
Noise Covariance

An accurate and reliable positioning system (PS) is a significant topic of research due to its broad range of aerospace applications, such as the localization of autonomous agents in GPS-denied and indoor environments. The PS discussed in this work uses ultra-wide band (UWB) sensors to provide distance measurements. UWB sensors are based on radio frequency technology and offer low power consumption, wide bandwidth, and precise ranging in the presence of nominal environmental noise. However, in practical situations, UWB sensors experience varying measurement noise due to unexpected obstacles in the environment. The localization accuracy is highly dependent on the filtering of such noise, and the extended Kalman filter (EKF) is one of the widely used techniques. In varying noise situations, where the obstacles generate larger measurement noise than nominal levels, EKF cannot offer precise results. Therefore, this work proposes two approaches based on EKF: sequential adaptive EKF and piecewise adaptive EKF. Simulation studies are conducted in static, linear, and nonlinear scenarios, and it is observed that higher accuracy is achieved by applying the proposed approaches as compared to the traditional EKF method.

scholarly journals Neural network assisted Kalman filter for INS/UWB integrated seamless quadrotor localization

2021 ◽  
Vol 7 ◽  
pp. e630
Author(s):  
Shuhui Bi ◽  
Liyao Ma ◽  
Tao Shen ◽  
Yuan Xu ◽  
Fukun Li
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Wide Band ◽  
Indoor Environments ◽  
Position Information ◽  
Localization Accuracy ◽  
The Neural Network ◽  
Measurement Vector ◽  
Localization Information ◽  
Real Test

Due to some harsh indoor environments, the signal of the ultra wide band (UWB) may be lost, which makes the data fusion filter can not work. For overcoming this problem, the neural network (NN) assisted Kalman filter (KF) for fusing the UWB and the inertial navigation system (INS) data seamlessly is present in this work. In this approach, when the UWB data is available, both the UWB and the INS are able to provide the position information of the quadrotor, and thus, the KF is used to provide the localization information by the fusion of position difference between the INS and the UWB, meanwhile, the KF can provide the estimation of the INS position error, which is able to assist the NN to build the mapping between the state vector and the measurement vector off-line. The NN can estimate the KF’s measurement when the UWB data is unavailable. For confirming the effectiveness of the proposed method, one real test has been done. The test’s results demonstrate that the proposed NN assisted KF is effective to the fusion of INS and UWB data seamlessly, which shows obvious improvement of localization accuracy. Compared with the LS-SVM assisted KF, the proposed NN assisted KF is able to reduce the localization error by about 54.34%.

Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

2020 ◽  
pp. 1-21
Author(s):  
Lanhua Hou ◽  
Xiaosu Xu ◽  
Yiqing Yao ◽  
Di Wang ◽  
Jinwu Tong
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation System ◽  
Moving Average ◽  
Integrated System ◽  
Measurement Noise ◽  
Noise Estimation ◽  
Doppler Velocity ◽  
Forgetting Factor ◽  
Exponential Weighted Moving Average ◽  
Weighted Moving Average

Abstract The strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.

scholarly journals A Novel Robust Kalman Filter With Non-stationary Heavy-tailed Measurement Noise

2020 ◽  
Vol 53 (2) ◽  
pp. 368-373
Author(s):  
Guangle Jia ◽  
Yulong Huang ◽  
Mingming B. Bai ◽  
Yonggang zhang
Keyword(s):  
Kalman Filter ◽  
Measurement Noise ◽  
Robust Kalman Filter ◽  
Heavy Tailed

scholarly journals Induced Magnetic Field-Based Indoor Positioning System for Underwater Environments

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2218
Author(s):  
Sizhen Bian ◽  
Peter Hevesi ◽  
Leif Christensen ◽  
Paul Lukowicz
Keyword(s):  
Magnetic Field ◽  
Low Cost ◽  
Autonomous Underwater Vehicles ◽  
Wide Band ◽  
Propagation Delay ◽  
Positioning System ◽  
Indoor Environments ◽  
Coverage Area ◽  
Underwater Positioning ◽  
Similar Accuracy

Autonomous underwater vehicles (AUV) are seen as an emerging technology for maritime exploration but are still restricted by the availability of short range, accurate positioning methods necessary, e.g., when docking remote assets. Typical techniques used for high-accuracy positioning in indoor use case scenarios, such as systems using ultra-wide band radio signals (UWB), cannot be applied for underwater positioning because of the quick absorption of the positioning medium caused by the water. Acoustic and optic solutions for underwater positioning also face known problems, such as the multi-path effects, high propagation delay (acoustics), and environmental dependency. This paper presents an oscillating magnetic field-based indoor and underwater positioning system. Unlike those radio wave-based positioning modalities, the magnetic approach generates a bubble-formed magnetic field that will not be deformed by the environmental variation because of the very similar permeability of water and air. The proposed system achieves an underwater positioning mean accuracy of 13.3 cm in 2D and 19.0 cm in 3D with the multi-lateration positioning method and concludes the potential of the magnetic field-based positioning technique for underwater applications. A similar accuracy was also achieved for various indoor environments that were used to test the influence of cluttered environment and of cross environment. The low cost and power consumption system is scalable for extensive coverage area and could plug-and-play without pre-calibration.

scholarly journals A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3809 ◽  
Author(s):  
Yushi Hao ◽  
Aigong Xu ◽  
Xin Sui ◽  
Yulei Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Measurement Noise ◽  
Model Errors ◽  
State Process ◽  
Stability And Accuracy ◽  
The Impact

Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.

Proportional–Integral Controller Design for Combustion-Timing Feedback, From n-Heptane to Iso-Octane in Compression–Ignition Engines

2017 ◽  
Vol 140 (5) ◽  
Author(s):  
Gabriel Ingesson ◽  
Lianhao Yin ◽  
Rolf Johansson ◽  
Per Tunestål
Keyword(s):  
Ignition Delay ◽  
Controller Design ◽  
Negative Temperature ◽  
Operating Conditions ◽  
Measurement Noise ◽  
Noise Sensitivity ◽  
Proportional Integral ◽  
Primary Reference ◽  
The Difference ◽  
Primary Reference Fuels

The problem of designing robust and noise-insensitive proportional–integral (PI) controllers for pressure-sensor-based combustion-timing control was studied through simulation. Different primary reference fuels (PRF) and operating conditions were studied. The simulations were done using a physics-based, control-oriented model with an empirical ignition-delay correlation. It was found that the controllable region in between the zero-gain region for early injection timings and the misfire region for late injection timings is strongly PRF dependent. As a result, it was necessary to adjust intake temperature to compensate for the difference in fuel reactivity prior to the controller design. With adjusted intake temperature, PRF-dependent negative-temperature coefficient (NTC) behavior gave different system characteristics for the different fuels. The PI controller design was accomplished by solving the optimization problem of maximizing disturbance rejection and tracking performance subject to constraints on robustness and measurement-noise sensitivity. Optimal controller gains were found to be limited by the high system gain at late combustion timings and high-load conditions; furthermore, the measurement-noise sensitivity was found to be higher at the low-load operating points where the ignition delay is more sensitive to variations in load and intake conditions. The controller-gain restrictions were found to vary for the different PRFs; the optimal gains for higher PRFs were lower due to a higher system gain, whereas the measurement-noise sensitivity was found to be higher for lower PRFs.

Localization and Map Building of Laser Sensor AGV Based on Kalman Filter

2013 ◽  
Vol 712-715 ◽  
pp. 1938-1943
Author(s):  
Li Xiao Guo ◽  
Fan Kun ◽  
Wen Jun Yan
Keyword(s):  
Kalman Filter ◽  
Sensor Data ◽  
Laser Sensor ◽  
Map Building ◽  
Localization Accuracy ◽  
Key Technology ◽  
Navigation Algorithm ◽  
Automatic Guided Vehicle ◽  
Kalman Filter Algorithm ◽  
Navigation Method

Localization and navigation algorithm is the key technology to determine whether or not an AGV (automatic guided vehicle) can run normally. In this paper, we summarize the popular navigation technologies first and then focus on the positioning principle of Nav200 which is adopted in our AGV system. Besides that, the map building method and the layout of the reflective board is also introduced briefly. This paper introduced two navigation methods. The traditional navigation method only uses the sensor data and the electronic map to guide AGV. To improve positioning accuracy, we use the Kalman filter to minimize the error of localization sensor. At last some simulation work was done, the results shows that the localization accuracy was improved by adopting Kalman filter algorithm.

scholarly journals Discrete EKF with pairwise Time Correlated Measurement Noise for Image-Aided Inertial Integrated Navigation

2014 ◽  
Vol II-2 ◽  
pp. 61-66 ◽  
Author(s):  
N. S. Gopaul ◽  
J. G. Wang ◽  
B. Hu
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation ◽  
Measurement Noise ◽  
Integrated Navigation ◽  
Process Noise ◽  
Shaping Filter ◽  
Egomotion Estimation ◽  
Simulation Results ◽  
The One ◽  
Better Than

An image-aided inertial navigation implies that the errors of an inertial navigator are estimated via the Kalman filter using the aiding measurements derived from images. The standard Kalman filter runs under the assumption that the process noise vector and measurement noise vector are white, i.e. independent and normally distributed with zero means. However, this does not hold in the image-aided inertial navigation. In the image-aided inertial integrated navigation, the relative positions from optic-flow egomotion estimation or visual odometry are <i>pairwise</i> correlated in terms of time. It is well-known that the solution of the standard Kalman filter becomes suboptimal if the measurements are colored or time-correlated. Usually, a shaping filter is used to model timecorrelated errors. However, the commonly used shaping filter assume that the measurement noise vector at epoch <i>k</i> is not only correlated with the one from epoch <i>k</i> &ndash; 1 but also with the ones before epoch <i>k</i> &ndash; 1 . The shaping filter presented in this paper uses Cholesky factors under the assumption that the measurement noise vector is pairwise time-correlated i.e. the measurement noise are only correlated with the ones from previous epoch. Simulation results show that the new algorithm performs better than the existing algorithms and is optimal.

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