scholarly journals Five-State Extended Kalman Filter for Estimation of Speed over Ground (SOG), Course over Ground (COG) and Course Rate of Unmanned Surface Vehicles (USVs): Experimental Results

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7910
Author(s):  
Sindre Fossen ◽  
Thor I. Fossen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Experimental Testing ◽  
Low Cost ◽  
Magnetic Compass ◽  
Doppler Velocity ◽  
Safety Critical ◽  
Gnss Receiver ◽  
Heading Angle ◽  
Electromagnetic Disturbances

Small USVs are usually equipped with a low-cost navigation sensor suite consisting of a global navigation satellite system (GNSS) receiver and a magnetic compass. Unfortunately, the magnetic compass is highly susceptible to electromagnetic disturbances. Hence, it should not be used in safety-critical autopilot systems. A gyrocompass, however, is highly reliable, but it is too expensive for most USV systems. It is tempting to compute the heading angle by using two GNSS antennas on the same receiver. Unfortunately, for small USV systems, the distance between the antennas is very small, requiring that an RTK GNSS receiver is used. The drawback of the RTK solution is that it suffers from dropouts due to ionospheric disturbances, multipath, interference, etc. For safety-critical applications, a more robust approach is to estimate the course angle to avoid using the heading angle during path following. The main result of this article is a five-state extended Kalman filter (EKF) aided by GNSS latitude-longitude measurements for estimation of the course over ground (COG), speed over ground (SOG), and course rate. These are the primary signals needed to implement a course autopilot system onboard a USV. The proposed algorithm is computationally efficient and easy to implement since only four EKF covariance parameters must be specified. The parameters need to be calibrated for different GNSS receivers and vehicle types, but they are not sensitive to the working conditions. Another advantage of the EKF is that the autopilot does not need to use the COG and SOG measurements from the GNSS receiver, which have varying quality and reliability. It is also straightforward to add complementary sensors such as a Doppler Velocity Log (DVL) to the EKF to improve the performance further. Finally, the performance of the five-state EKF is demonstrated by experimental testing of two commercial USV systems.

scholarly journals A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Heikki Hyyti ◽  
Arto Visala
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Direction Cosine ◽  
Estimation Algorithm ◽  
Calibration Method ◽  
Attitude Estimation ◽  
Mems Sensors ◽  
Acceleration Sensors ◽  
Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

scholarly journals Performance Enhancement of Pedestrian Navigation Systems Based on Low-Cost Foot-Mounted MEMS-IMU/Ultrasonic Sensor

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 364 ◽  
Author(s):  
Ming Xia ◽  
Chundi Xiu ◽  
Dongkai Yang ◽  
Li Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Ultrasonic Sensor ◽  
Measurement Unit ◽  
Positioning Error ◽  
Process Noise ◽  
Pedestrian Navigation ◽  
Mems Imu

The pedestrian navigation system (PNS) based on inertial navigation system-extended Kalman filter-zero velocity update (INS-EKF-ZUPT or IEZ) is widely used in complex environments without external infrastructure owing to its characteristics of autonomy and continuity. IEZ, however, suffers from performance degradation caused by the dynamic change of process noise statistics and heading estimation errors. The main goal of this study is to effectively improve the accuracy and robustness of pedestrian localization based on the integration of the low-cost foot-mounted microelectromechanical system inertial measurement unit (MEMS-IMU) and ultrasonic sensor. The proposed solution has two main components: (1) the fuzzy inference system (FIS) is exploited to generate the adaptive factor for extended Kalman filter (EKF) after addressing the mismatch between statistical sample covariance of innovation and the theoretical one, and the fuzzy adaptive EKF (FAEKF) based on the MEMS-IMU/ultrasonic sensor for pedestrians was proposed. Accordingly, the adaptive factor is applied to correct process noise covariance that accurately reflects previous state estimations. (2) A straight motion heading update (SMHU) algorithm is developed to detect whether a straight walk happens and to revise errors in heading if the ultrasonic sensor detects the distance between the foot and reflection point of the wall. The experimental results show that horizontal positioning error is less than 2% of the total travelled distance (TTD) in different environments, which is the same order of positioning error compared with other works using high-end MEMS-IMU. It is concluded that the proposed approach can achieve high performance for PNS in terms of accuracy and robustness.

scholarly journals Experimentally Validated Extended Kalman Filter for UAV State Estimation Using Low-Cost Sensors

2018 ◽  
Vol 51 (15) ◽  
pp. 43-48 ◽  
Author(s):  
S.P.H. Driessen ◽  
N.H.J. Janssen ◽  
L. Wang ◽  
J.L. Palmer ◽  
H. Nijmeijer
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Low Cost

Design and Implementation of Sensing and Estimation Software in AggieNav, a Small UAV Navigation Platform

2009 ◽  
Author(s):  
Calvin Coopmans ◽  
Haiyang Chao ◽  
YangQuan Chen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
High Accuracy ◽  
Level System ◽  
Design And Implementation ◽  
Small Uav ◽  
High Level ◽  
Navigational Systems ◽  
Uav Navigation

Small UAV performance is limited by the sensors and software filters used in the navigational systems. Several solutions of various complexity and cost exist, however no ready-made solutions exist for a high-accuracy, low-cost UAV system. Presented is the design (low-level system as well as high-level extended Kalman filter) for a specifically designed small-UAV navigation platform, AggieNav.

Extended Kalman Filter Based In-Cylinder Temperature Estimation for Diesel Engines With Thermocouple Lag Compensation

2014 ◽  
Vol 136 (5) ◽  
Author(s):  
Song Chen ◽  
Fengjun Yan
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Diesel Engines ◽  
Measurement Errors ◽  
Low Cost ◽  
Estimation Method ◽  
Measured Temperature ◽  
Temperature Estimation ◽  
Engine Model ◽  
Auto Ignition

The in-cylinder temperature information is critical for auto-ignition combustion control in diesel engines, but difficult to be directly accessed at low cost in production engines. Through investigating the thermodynamics of Tivc, cycle-by-cycle models are proposed in this paper for the estimation of in-cylinder temperature at the crank angle of intake valve closing (IVC), referred to as Tivc. An extended Kalman filter (EKF) based method was devised by utilizing the measurable temperature information from the intake and exhaust manifolds. Due to the fact that measured temperature signals by typical thermocouples have slow responses which can be modeled as first-order lags with varying time-constants, temperature signals need to be reconstructed in transient conditions. In the proposed EKF estimation method, this issue can be effectively addressed by analyzing the measurement errors and properly selecting the noises covariance matrices. The proposed estimation method was validated through a high-fidelity GT-power engine model.

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