scholarly journals A System for Tracking an Autonomously Controlled Canine

2012 ◽  
Vol 65 (3) ◽  
pp. 427-444 ◽  
Author(s):  
Jeff Miller ◽  
George Flowers ◽  
David Bevly
Keyword(s):  
Fuzzy Logic Controller ◽  
Gps Receiver ◽  
Process Noise ◽  
Embedded Control ◽  
Noise Covariance Matrix ◽  
Adaptive Tuning ◽  
Noise Characteristics ◽  
Noise Covariance ◽  
Motion Characteristics ◽  
Tone Generation

This paper presents an approach for outdoor navigation of an autonomously guided canine using an embedded command module with vibration and tone generation capabilities and an embedded control suite comprised of a microprocessor, wireless radio, GPS receiver, and an Attitude and Heading Reference System. In order to determine the canine's motions, which inherently contain non-conventional noise characteristics, the sensor measurements were integrated using a specialized Extended Kalman Filter (EKF), equipped with a Fuzzy Logic controller for adaptive tuning of the Process Noise Covariance Matrix. This allowed for rejection of un-modelled canine motion characteristics which tend to corrupt accelerometer bias tracking in a standard EKF. The EKF solution provided an optimized estimate of the canine position and velocity and also proved to be effective in tracking the canine's position (within 7·5 m) and velocity (within 1·2 m/s) during simulated 10 second GPS outages.

Online evaluation of the process noise covariance matrix for event‐based state estimators

2019 ◽  
Vol 120 (2) ◽  
pp. 195-208
Author(s):  
Miguel Martínez‐Rey ◽  
Carlos Santos ◽  
Rubén Nieto ◽  
Cristina Losada ◽  
Felipe Espinosa
Keyword(s):  
Covariance Matrix ◽  
Process Noise ◽  
Noise Covariance Matrix ◽  
Online Evaluation ◽  
State Estimators ◽  
Noise Covariance ◽  
Event Based

scholarly journals Effect of Strapdown Integration Order and Sampling Rate on IMU-Based Attitude Estimation Accuracy

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2775 ◽  
Author(s):  
Jung Lee ◽  
Mi Choi
Keyword(s):  
Low Cost ◽  
Sampling Rate ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Third Order ◽  
Process Noise ◽  
Noise Covariance Matrix ◽  
First Order ◽  
Noise Covariance ◽  
Integration Order

This paper deals with the strapdown integration of attitude estimation Kalman filter (KF) based on inertial measurement unit (IMU) signals. In many low-cost wearable IMU applications, a first-order is selected for strapdown integration, which may degrade attitude estimation performance in high-speed angular motions. The purpose of this research is to provide insights into the effect of the strapdown integration order and sampling rate on the attitude estimation accuracy for low-cost IMU applications. Experimental results showed that the effect of integration order was small when the angular velocity was low and the sampling rate was large. However, as the angular velocity increased and the sampling rate decreased, the effect of integration order increased, i.e., obviously, the third-order KF resulted in better estimations than the first-order KF. When comparing the case where both transient matrix and process noise covariance matrix are applied to the corresponding order and the case where only the transient matrix is applied to the corresponding order but the process noise covariance matrix for the first-order is still used, both cases had almost equivalent estimation accuracy. However, in terms of the calculation cost, the latter case was more economical than the former, particularly for the third-order KF (i.e., the ratio of the former to the latter is 1.22 to 1).

scholarly journals RL-AKF: An Adaptive Kalman Filter Navigation Algorithm Based on Reinforcement Learning for Ground Vehicles

2020 ◽  
Vol 12 (11) ◽  
pp. 1704
Author(s):  
Xile Gao ◽  
Haiyong Luo ◽  
Bokun Ning ◽  
Fang Zhao ◽  
Linfeng Bao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Navigation System ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter ◽  
Process Noise ◽  
Noise Covariance Matrix ◽  
Integrated Navigation System ◽  
Navigation Algorithm ◽  
Noise Covariance

Kalman filter is a commonly used method in the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation system, in which the process noise covariance matrix has a significant influence on the positioning accuracy and sometimes even causes the filter to diverge when using the process noise covariance matrix with large errors. Though many studies have been done on process noise covariance estimation, the ability of the existing methods to adapt to dynamic and complex environments is still weak. To obtain accurate and robust localization results under various complex and dynamic environments, we propose an adaptive Kalman filter navigation algorithm (which is simply called RL-AKF), which can adaptively estimate the process noise covariance matrix using a reinforcement learning approach. By taking the integrated navigation system as the environment, and the opposite of the current positioning error as the reward, the adaptive Kalman filter navigation algorithm uses the deep deterministic policy gradient to obtain the most optimal process noise covariance matrix estimation from the continuous action space. Extensive experimental results show that our proposed algorithm can accurately estimate the process noise covariance matrix, which is robust under different data collection times, different GNSS outage time periods, and using different integration navigation fusion schemes. The RL-AKF achieves an average positioning error of 0.6517 m within 10 s GNSS outage for GNSS/INS integrated navigation system and 14.9426 m and 15.3380 m within 300 s GNSS outage for the GNSS/INS/Odometer (ODO) and the GNSS/INS/Non-Holonomic Constraint (NHC) integrated navigation systems, respectively.

A New Process Noise Covariance Matrix Tuning Algorithm for Kalman Based State Estimators

2009 ◽  
Vol 42 (11) ◽  
pp. 572-577
Author(s):  
Nina P.G. Salau ◽  
Jorge O. Trierweiler ◽  
Argimiro R. Secchi ◽  
Wolfgang Marquardt
Keyword(s):  
Covariance Matrix ◽  
Process Noise ◽  
Noise Covariance Matrix ◽  
State Estimators ◽  
New Process ◽  
Noise Covariance

scholarly journals Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1126
Author(s):  
Zhentao Hu ◽  
Linlin Yang ◽  
Yong Jin ◽  
Han Wang ◽  
Shibo Yang
Keyword(s):  
Real Time ◽  
Gaussian Mixture ◽  
Measurement Noise ◽  
Tracking Accuracy ◽  
Process Noise ◽  
Probability Hypothesis Density ◽  
Measurement Noise Covariance ◽  
Multi Target Tracking ◽  
Noise Covariance ◽  
Fading Factor

Assuming that the measurement and process noise covariances are known, the probability hypothesis density (PHD) filter is effective in real-time multi-target tracking; however, noise covariance is often unknown and time-varying for an actual scene. To solve this problem, a strong tracking PHD filter based on Variational Bayes (VB) approximation is proposed in this paper. The measurement noise covariance is described in the linear system by the inverse Wishart (IW) distribution. Then, the fading factor in the strong tracking principle uses the optimal measurement noise covariance at the previous moment to control the state prediction covariance in real-time. The Gaussian IW (GIW) joint distribution adopts the VB approximation to jointly return the measurement noise covariance and the target state covariance. The simulation results show that, compared with the traditional Gaussian mixture PHD (GM-PHD) and the VB-adaptive PHD, the proposed algorithm has higher tracking accuracy and stronger robustness in a more reasonable calculation time.

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