scholarly journals Online Estimation of Allan Variance Coefficients Based on a Neural-Extended Kalman Filter

Sensors ◽  
2015 ◽  
Vol 15 (2) ◽  
pp. 2496-2524 ◽  
Author(s):  
Zhiyong Miao ◽  
Feng Shen ◽  
Dingjie Xu ◽  
Kunpeng He ◽  
Chunmiao Tian
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Allan Variance ◽  
Online Estimation

scholarly journals Extended Kalman Filter Enhanced by Neural Network to Solve the SLAM Problem

2021 ◽  
Author(s):  
Bruno França Coelho ◽  
João Viana Fonseca Neto
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Multilayer Perceptron ◽  
Online Estimation ◽  
Holonomic Robot

This work presents a way for online estimation of the location and mapping of a non-holonomic robot by means of an algorithm that uses EKF and in the output of this algorithm, a multilayer perceptron neural network (MLP) has been added that aims to improve the estimation of the robot pose in an unfamiliar environment. The effectiveness was proven through the comparison between the EKF-SLAM and the EKFMLP-SLAM, where it was evidenced a significant improvement in relation to the location of the poses of the robot.

Tyre–road grip coefficient assessment – Part II: online estimation using instrumented vehicle, extended Kalman filter, and neural network

2013 ◽  
Vol 51 (12) ◽  
pp. 1872-1893 ◽  
Author(s):  
Pablo Luque ◽  
Daniel A. Mántaras ◽  
Eloy Fidalgo ◽  
Javier Álvarez ◽  
Paolo Riva ◽  
...  
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Online Estimation ◽  
Instrumented Vehicle

scholarly journals Online Estimation of ARW Coefficient of Fiber Optic Gyro

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yang Li ◽  
Baiqing Hu ◽  
Fangjun Qin ◽  
Kailong Li
Keyword(s):  
Kalman Filter ◽  
Fiber Optic ◽  
Noise Analysis ◽  
Digital Simulation ◽  
State Space Model ◽  
Allan Variance ◽  
Measurement Noise ◽  
Fiber Optic Gyro ◽  
Online Estimation ◽  
Measurement Noise Covariance

As a standard method for noise analysis of fiber optic gyro (FOG), Allan variance has too large offline computational burden and data storages to be applied to online estimation. To overcome the barriers, the state space model is firstly established for FOG. Then the Sage-husa adaptive Kalman filter (SHAKF) is introduced in this field. Through recursive calculation of measurement noise covariance matrix, SHAKF can avoid the storage of large amounts of history data. However, the precision and stability of this method are still the primary matters that needed to be addressed. Based on this point, a new online method for estimation of the coefficient of angular random walk is proposed. In the method, estimator of measurement noise is constructed by the recursive form of Allan variance at the shortest sampling time. Then the estimator is embedded into the SHAKF framework resulting in a new adaptive filter. The estimations of measurement noise variance and Kalman filter are independent of each other in this method. Therefore, it can address the problem of filtering divergence and precision degrading effectively. Test results of both digital simulation and experimental data of FOG verify the validity and feasibility of the proposed method.

Cooperative Navigation of UAVs in GPS-denied area using an extended Kalman filter with colored RSSI measurements

2020 ◽  
Author(s):  
Ouyang Xiaofeng ◽  
Lyu Daqian ◽  
Dong Tianbao
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Flicker Noise ◽  
Gaussian White Noise ◽  
Measurement Model ◽  
Peer To Peer ◽  
Allan Variance ◽  
Quantization Noise ◽  
The Difference ◽  
Navigation Mode

<p>In this paper, we focus on UAVs (Unmanned Aerial Vehicles) positioning in GPS-denied environments and proposes a navigation mode of “track reckoning + relative ranging + heading constraint”. Internal sensors (gyros, accelerometer, barometer, etc.) measure the self-motion to obtain the flight path and attitude, and the external sensors identify and measure the relative ranging to achieve peer-to-peer constraint for UAVs. In order to guide the swarm to the intended destination when GPS is denied, the ground anchor nodes are set to provide relative heading constraints to the UAVs for target and trajectory guidance. We propose a hybrid centralized-distributed scheme including 20 UAVs, as well as its dynamic motion model and measurement model. To improve the ranging accuracy in the actual RSSI measurement, we analyze the influence of antenna pattern inhomogeneity and channel variation, respectively. The former mainly determines an antenna radiation function related to the yaw angle and relative position between the two measuring UAVs. The latter uses overlapping Allan variance to analyze and identify the measurement noises from outfield tests, that is, quantization noise, flicker noise, random walk noise and Gaussian white noise, which to some extent bridges the difference between the theoretical model and the practical measurement of RSSI. In this way, an improved extended Kalman filter is to predict and correct the colored noise by adaptively integrating the current peer-to-peer radio ranging performance and its Allan variance. To prove the effectiveness of this approach, simulation results base on practical noise modeling are demonstrated.</p>

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