High-precision Position Estimation Research Articles

Speed Sensorless Control Method of Synchronous Reluctance Motor Based on Resonant Kalman Filter

When estimating the active back electromotive force (AEMF) of synchronous reluctance motor (SynRM) at medium and high speed, the traditional Kalman Filter (KF) will produce large phase delay due to the low-pass filter characteristics. A novel speed sensorless control scheme of SynRM based on resonant Kalman filter (RKF) is proposed in this paper. Firstly, the AEMF observer based on KF is designed and the bandwidth performance is analyzed. To solve the phase delay problem caused by KF, a generalized integral resonance perturbation estimator (GIRPE) is introduced to the framework of KF and the proposed RKF is formed. The estimation error of AEMF is compensated through GIRPE in the current error feedback loop of KF observer. In this way, the high-precision position estimation at high speed range is obtained. Finally, an improved dynamic position compensator (IDPC) is proposed to enhance the position estimation performance under dynamic case. The effectiveness of proposed scheme is validated at a 1.5kW SynRM drive, and the computational burden of the proposed method is assessed quantitatively.

Monitoring of Possible Activities of Yangsan Fault Zone Using GNSS

After the Gyeongju earthquake in 2016 and the subsequent one in Pohang the following year, there is an imminent necessity to evaluate the risk of earthquakes accurately as well as respond to the risks on the Korean peninsula. For this purpose, the existence and movement of a fault should be investigated in the area. In this study, we calculated the displacement of the crust around the mass production fault using GNSS (Global Navigation Satellite System) data and analyzed the deformation characteristics by applying the method of stress calculation. The Yangsan Fault Zone has been analyzed with a total of 24 GNSS stations between 2018 and 2021. Data processing was conducted with Bernese GNSS Software, which requires high-precision orbit, satellite clock, ionosphere information for high-precision position estimation. By accumulating daily solutions over the three years to produce the final solution with the velocity of the stations, the Yangsan Fault Zone moved about 32 mm per year southeast on average. Based on the results, the movements of the stations on either side of the Faults are almost the same. Stress analysis of the Yangsan Fault Zone showed a large east–west expansion during 2018–2019 but decreased in stress afterwards, thus it is evaluated to be relatively stable compared to the past. However, due to the nature of crustal variation continuous monitoring research with long-term data processing should be followed, which will be discussed in further research.

Error Constraint Enhanced Particle Filter Using Quantum Particle Swarm Optimization

The position is one of the foremost imperative properties of an object. The Inertial Measurement Unit (IMU) development has enabled the system to continuously decrease in size, reduce power consumption, and become more and more universal. Although IMU meets the real-time needs of the localization system, it faces accumulative error and drifting problems. This paper presents an error constraint enhanced particle filter algorithm using quantum particle swarm optimization (EC-QPF), which achieves high-precision position estimation of target tracking with IMU. First, we proposed a quantum particle swarm optimization-based resampling method taking the place of the traditional weight-based resampling method in the particle filter, which avoids the particle impoverishment problem and keeps the particles’ diversity. The theoretical foundation of error constraint was raised applied to enhance the performance of the proposed particle filter. The error constraint is established utilizing the known estimated center and confidence scale to achieve particle screening based on the geometrical position of particles. Numerical experimental results show that the proposed EC-QPF has a 67% improvement instead of other modified particle filters and more efficiently eliminates the cumulative error. Furthermore, higher accuracy and stability are obtained under the exact condition, demonstrating better performance than traditional particle filter methods.

(Invited) A Novel Self-Localization Method for USV Based on GPS and Visual-Map

With the vigorous development of computer technology, information technology and intelligent transportation, their combination in the maritime field is becoming more and more extensive. At present, unmanned surface vehicle (USV) has been widely applied in the water transportation environment, especially for dangerous and time-consuming tasks. However, the intellectualization of USV is still far from actually being “unmanned”. Among them, USV uses its own sensors to perceive the surrounding environment and obtain high-precision position estimation is a prerequisite for its intelligence. In response to the demand, a novel two-step approach for USV self-localization is proposed in this paper. In the first step, the coarse-to-fine strategy is used to obtain the nearest node. Firstly, coarse image node is got by matching the current GPS information and the DGPS information of the visual-map. Then, the fine image node is obtained by the multi-vector fusion algorithm. In the second step, the pose of the current image is gained with respect to the nearest node by the epipolar geometry constraint, so as to achieve high-precision self-localization for USV. This method only needs an ordinary camera and GPS module in positioning, which can reduce the cost of high-precision positioning to the greatest extent. Pool experiments show that this method can achieve centimeter-level positioning accuracy.

High-precision position estimation in PET using artificial neural networks

Traditionally, the most popular technique to predict the impact position of gamma photons on a PET detector has been Anger's logic. However, it introduces nonlinearities that compress the light distribution, reducing the useful field of view and the spatial resolution, especially at the edges of the scintillator crystal. In this work, we make use of neural networks to address a bias-corrected position estimation from real stimulus obtained from a 2D PET system setup. The preprocessing and data acquisition were performed by separate custom boards, especially designed for this application. The results show that neural networks yield a more uniform field of view while improving the systematic error and the spatial resolution. Therefore, they stand as a better performing and readily available alternative to classic positioning methods.