Three State Estimation Fusion Methods Based on the Characteristic Function Filtering

There are three state estimation fusion methods for a class of strong nonlinear measurement systems, based on the characteristic function filter, namely the centralized filter, parallel filter, and sequential filter. Under ideal communication conditions, the centralized filter can obtain the best state estimation accuracy, and the parallel filter can simplify centralized calculation complexity and improve feasibility; in addition, the performance of the sequential filter is very close to that of the centralized filter and far better than that of the parallel filter. However, the sequential filter can tolerate non-ideal conditions, such as delay and packet loss, and the first two filters cannot operate normally online for delay and will be invalid for packet loss. The performance of the three designed fusion filters is illustrated by three typical cases, which are all better than that of the most popular Extended Kalman Filter (EKF) performance.

A covariance shaping filtering method for tightly-coupled MIMU/GNSS of UAV

Purpose Regarding the important roles of accuracy and robustness of tightly-coupled micro inertial measurement unit (MIMU)/global navigation satellite system (GNSS) for unmanned aerial vehicle (UAV). This study aims to explore the efficient method to improve the real-time performance of the sensors. Design/methodology/approach A covariance shaping adaptive Kalman filtering method is developed. For optimal performance of multiple gyros and accelerometers, a distribution coefficient of precision is defined and the data fusion least square method is applied with fault detection and identification using the singular value decomposition. A dual channel parallel filter scheme with a covariance shaping adaptive filter is proposed. Findings Hardware-in-the-loop numerical simulation was adopted, the results indicate that the gain of the covariance shaping adaptive filter is self-tuning by changing covariance weighting factor, which is calculated by minimizing the cost function of Frobenius norm. With the improved method, the positioning accuracy with tightly-coupled MIMU/GNSS of the adaptive Kalman filter is increased obviously. Practical implications The method of covariance shaping adaptive Kalman filtering is efficient to improve the accuracy and robustness of tightly-coupled MIMU/GNSS for UAV in complex and dynamic environments and has great value for engineering applications. Originality/value A covariance shaping adaptive Kalman filtering method is presented and a novel dual channel parallel filter scheme with a covariance shaping adaptive filter is proposed, to improve the real-time performance in complex and dynamic environments.

The impact of the larger number of non-linear consumers on the quality of electricity

Theoretically this paper will explain the formation of higher harmonic components in the electricity network, their causes, consequences on consumers and the ways of their elimination. Transformer role in the Dyg connection will be explained on the concrete example. For a specific example the waveform of primary (R) phase at 10 kV voltage level, the current of the secondary (r) phase and the neutral conductor at the 0.4 kV voltage level will be determined as shown in the concrete example in the work. Harmonic content will be determined up to 15 harmonics and the effective value of all these currents (phases R and r). THD for current of primary (R) phase and secondary (r) fase will be calculated. In this paper, the dimensional three-phase filter is set to eliminate the maximum harmonic component of current of the primary (R) phase on the 10 kV side of the transformer. The waveform, the corresponding harmonic content for the current and THD of primary (R) phase will be determined. Additional measures have been proposed to reduce the THD. Another parallel filter has been realized to eliminate the second by size harmonic components of primary (R) phase current. It will also compare THD for primary (R) phase as in the previous cases. For the total duration of the simulation, the used time is Tstop = 0.1 sec. All of the above simulations will be realized in the MATLAB/PSB program package and simulation models will be displayed.