Enhanced Kalman Filter Navigation Algorithm Based on Correntropy and Fixed-Point Update

Abstract

The accuracy of position estimation plays a key role in many of the precise positioning applications such as category I (CAT-I) aircraft landings, survey work, etc. To improve the accuracy of position estimation, a novel kinematic positioning algorithm designated as correntropy Kalman filter (CKF) is proposed in this study. Instead of minimum mean square error (MMSE), correntropy criterion (CC) is used as the optimality criterion of CKF. The prior estimates of the state and covariance matrix are computed in CKF and a novel fixed-point algorithm is then used to update the posterior estimates. The data of a dual-frequency global positioning system (GPS) receiver located at Indian Institute of Science (IISc), Bangalore (13.021°N/77.5°E) is collected from Scripps Orbit and Permanent Array Centre (SOPAC) to implement the proposed algorithm. The results of the proposed CKF algorithm are promising and exhibit significant improvement in position estimation compared to the conventional methods.

GPS Receiver Position Estimation and DOP Analysis Using a New Form of the Observation Matrix Approximations

“…Thus, the equation ( 14) shows that all parameters in H are a function of true and estimated values. If true and estimated values of all parameters are equal in equation ( 14), then the first three parameters of all rows H will be the same as the parameters H in equation (15). But the fourth parameter will be zero, and hence all values of the fourth column are zero, which not only contradicts H but also makes H a singular or degenerate matrix.…”

“…The GDOP computation formula includes receiver clock bias along with north, east, and up components. In [12][13][14][15], the direct difference method is used only for position estimation, but the receiver clock bias is not included either in the modified gain matrix 'g' or in the observation matrix, without which it is impossible to obtain the GDOP and the accuracy enhancement in the GPS receiver position estimate. But the proposed H in this article includes the receiver clock bias.…”

Monitoring of Single-Phase Induction Motor through IoT Using ESP32 Module

“…Prediction of faults and its classification of industrial machines can also be possible. The real time data from motors can be given to trained artificial neural network, and the output of the ANN model will be the prediction of fault and its type [27]. If the fault is of incipient stage, machine can be scheduled for maintenance.…”