Position Estimation Technique Research Articles

A Solution to Ambiguities in Position Estimation for Solenoid Actuators by Exploiting Eddy Current Variations.

Position estimation techniques for solenoid actuators are successfully used in a wide field of applications requiring monitoring functionality without the need for additional sensors. Most techniques, which also include standstill condition, are based on the identification of the differential inductance, a parameter that exhibits high sensitivity towards position variations. The differential inductance of some actuators shows a non-monotonic dependency over the position. This leads to ambiguities in position estimation. Nevertheless, a unique position estimation in standstill condition without prior knowledge of the actuator state is highly desired. In this work, the eddy current losses inside the actuator are identified in terms of a parallel resistor and are exploited in order to solve the ambiguities in position estimation. Compared to other state-of-the-art techniques, the differential inductance and the parallel resistance are estimated online by approaches requiring low implementation and computation effort. Furthermore, a data fusion algorithm for position estimation based on a neural network is proposed. Experimental results involving a use case scenario of an end-position detection for a switching solenoid actuator prove the uniqueness, the precision and the high signal-to-noise ratio of the obtained position estimate. The proposed approach therefore allows the unique estimation of the actuator position including standstill condition suitable for low-cost applications demanding low implementation effort.

Sensorless Predictive Current Control of PMSM EV Drive Using DSOGI-FLL Based Sliding Mode Observer

Conventional back electromotive force (EMF) estimation based active flux concept for sensorless control has various limitations due to pure integrator problems and performance deterioration at low speeds. The fundamental back EMF component extraction with a conventional low pass filter, is difficult due to the presence of lower order harmonics, dc offset, saturation, etc., and causes significant phase delay in the estimated position and speed. This article deals with a sensorless speed and position estimation technique for a surface mounted permanent magnet synchronous motor (SPMSM) based electric vehicle (EV) drive using a sliding mode observer (SMO) with a dual second order generalized integrator frequency locked loop (DSOGI-FLL). A novel method of utilizing the DSOGI-FLL, along with feedforward term is shown to achieve better harmonic elimination of estimated back EMF components and faster dynamic speed estimation. A delay compensated predictive current controller-based PMSM EV drive with the presented estimation technique is used for improved dynamic response. This system is simulated using Simulink and its efficacy is experimentally validated using dSPACE 1202. Test results show that the DSOGI-FLL-based SMO has better performance compared to the conventional technique.

Design of a Compact, Multifrequency, Multiconstellation GNSS Precise Point Positioning Correction Format

High-accuracy, high-precision global navigation satellite systems (GNSSs) have been utilized by specialized user markets for over two decades. Traditional high-accuracy GNSS systems are not scalable as they require local infrastructure. Precise point positioning (PPP) is a GNSS position estimation technique that allows positioning and navigation with centimetre-level accuracy without local infrastructure. The caveat of PPP is the timely transmission of corrections to errors in the GNSS signals. Next-generation GNSS satellites such as Galileo, BeiDou, QZSS, and other local systems have plans to broadcast corrections for PPP services broadcast corrections for PPP services. However, since these services are expected to have limited channel capacity, the development of efficient formats for PPP corrections is needed. In this article, a messaging strategy for PPP corrections and testing its performance, in terms of data rate and obtained positioning accuracy, is proposed. Message formats in the literature require 522 bps or more to transmit corrections for 70 satellites. By contrast, the message format proposed in this article can transmit information corrections for more than 70 satellites using 52% of the standard satellite-based augmentation system channel's 212 bps capacity, while incurring less than 1 cm of positioning accuracy degradation.

A Novel Roll and Pitch Estimation Approach for a Ground Vehicle Stability Improvement Using a Low Cost IMU.

Onboard attitude estimation for a ground vehicle is persuaded by its application in active anti-roll bar design. Conventionally, the attitude estimation problem for a ground vehicle is a complex one, and computationally, its solution is very intensive. Lateral load transfer is an important parameter which should be taken in account for all roll stability control systems. This parameter is directly related to vehicle roll angle, which can be measured using devices such as dual antenna global positioning system (GPS) which is a costly technique, and this led to the current work in which we developed a simple and robust attitude estimation technique that is tested on a ground vehicle for roll mitigation. In the first phase Luenberger and Sliding mode observer is implemented using simplest roll dynamics model to measure the roll angle of a vehicle and the validation of results is carried using commercial software, CarSim® (CarSim, Ann Arbor, MI, USA). In the second phase of research, complementary and Kalman filters have been designed for attitude estimation. In the third phase, a low-cost inertial measurement unit (IMU) is mounted on a vehicle, and both the complementary filter (CF) and Kalman filter (KF) are applied independently to measure the data for both smooth and uneven terrains at four different frequencies. We compared the simulated and real-time results of roll and pitch angles obtained using the complementary and Kalman filters. Using the proposed method, the achieved root mean square error (RMSE) is less than 0.73 degree for pitch and 0.68 degree for roll, with a sample time of 2 ms. Thus, a warning signal can be generated to mitigate roll over. Hence, we claim that our proposed method can provide a low-cost solution to the roll-over problem for a road vehicle.

High-Performance Selective and Output Filter Techniques for Sensorless Direct Position and Speed Estimation

This article introduces high-performance filter techniques for direct position and speed estimation to augment its robustness against disturbances. The direct estimation concept provides an independent position and speed estimate at each sampling instant by solving an optimization problem parameterized with the current, current derivative, and voltage of the same sample. It can operate at any speed employing a voltage injection at low speed or pulsewidth modulated current derivative. A selective filter concept is proposed that discards samples lacking robustness based on cost function properties. The concept is most effective in removing worst case errors and should always be applied. Also, output filter techniques are investigated to improve the estimates. A finite impulse response (FIR) structure is proposed that filters estimates according to a least-square criterion and is effective in reducing average estimation errors. The FIR filter is benchmarked against an enhanced dual phase-locked loop (PLL) filter, which is enabled by direct estimation. The FIR and dual-PLL filters are found to have a 6.8 and 1 kHz practical bandwidth, respectively, while achieving a $ 1% absolute mean position and speed estimation error. Hence, they perform one to two orders of magnitude better than traditional estimation schemes, which typically achieve $ 100 Hz bandwidth at similar errors.

RBPPFF for robust TAN

Terrain-aided navigation (TAN) is a technique for position estimation based on altimeter and digital elevation model measurements. To perform TAN, non-linear filters such as a particle filter (PF) or a point mass filter (PMF) are mainly used owing to the non-linearity of measurements and systems. In particular, owing to the degradation of the global positioning system or sensors and the non-linearity of maps and measurements, an initial state error or sudden state change may occur. The authors propose a Rao-Blackwellised particle-point mass fusion filter (RBPPFF) that performs robustly in the presence of such initial state errors or sudden state changes with low computational complexity in real time. The RBPPFF combines the advantages of a PF and PMF, and it employs Rao-Blackwellisation to efficiently increase the number of states. To develop the RBPPFF, the formulae of the RBPF and Rao-Blackwellised point mass fusion filter (RBPMF) are derived using the same conventions, and a method is presented for an effective fusion of the PF and the PMF. Finally, the performance of the proposed algorithm is demonstrated through Monte Carlo simulation across various environments with filters such as PF, PMF, RBPF, and RBPMF.

A Hybrid Sensorless Controller of an Interior Permanent Magnet Synchronous Machine Using Current Derivative Measurements and a Sliding Mode Observer

The ability of current-derivative based speed and position estimation of IPMSMs over a full speed range has recently been demonstrated. However, at high-speeds, the modulation index becomes limited because the extension of the zero-voltage vectors and current waveforms become distorted. This article proposes a hybrid position and speed estimation technique which uses current derivatives at the first active-voltage vector during each pulsewidth modulation (PWM) switching period for enhancing the performance of a sliding mode observer at low and very low speeds. This article presents the theoretical analysis and experimental evaluation of the proposed sensorless method for an IPMSM over a full speed range from zero to rated speed. The experimental results have shown a significant improvement in speed and position estimation accuracy at low speeds and a considerable reduction of the current distortion over the full speed range, compared to the existing fundamental PWM excitation sensorless control methods.

관성 항법 센서와 도킹 유도 센서를 융합한 무인잠수정의 도킹 유도 항법 알고리즘에 대한 연구

This paper describes a docking guidance navigation algorithm for autonomous underwater vehicles (AUVs). To dock an AUV to a docking station installed underwater, a set of inertial navigation sensors used for conventional AUV navigation and a set of docking guidance sensors for docking missions are used. A docking guidance sensor typically measures the relative distance and attitude between the AUV and the docking station. As many different sensors are used, a precise position and attitude estimation technique, considering both types of sensors, is needed. Therefore, we designed a serial filter chain for estimating the orientation, position, and relative distance. Considering the non-holonomic characteristics of an AUV in the underwater environment, we first estimated the attitude by separating the position and the attitude. Then, the position of the AUV was estimated on the basis of the estimated attitude. Finally, we estimated the relative distance from the docking station to the estimated AUV position. This paper contributes to the following topics: i) position and attitude estimation considering non-holonomic characteristics of AUVs: and ii) precision estimation algorithm for docking guidance using docking guidance sensors.

Position estimation technique for a flexible pneumatic linear actuator and its application to motion generation of an in-pipe robot

Position estimation technique for a flexible pneumatic linear actuator and its application to motion generation of an in-pipe robot

Face Recognition Using Location Averaging and Intensity’s Position Estimation Techniques for Human Authentication

Face recognition is an effective tool in the biometric human recognition system. In this competitive world, several techniques and systems are emerging to satisfy the needs of the face recognition system’s performance. To obtain the high-performance ratio novel techniques are combined and created a new face recognition system. Spatial domain techniques like Gray averaging technique, Location averaging technique and Intensity’s position estimation technique are united with frequency domain technique like Discrete Cosine Transform. Intensity’s position estimation is a novel feature extraction and classification technique proposed in this work. Three standard face databases are tested using this system. Accuracy and runtime are major parameters used to validate the obtained results. The maximum accuracy rate of about 86% is obtained.

Full-Speed Range Encoderless Control for Salient-Pole PMSM with a Novel Full-Order SMO

For salient-pole permanent magnet synchronous motor (PMSM), the amplitude of extended back electromotive force (EEMF) is determined by rotor speed, stator current and its derivative value. Theoretically, even at extremely low speed, the back EEMF can be detected if the current in q-axis is changing. However, it is difficult to detect the EEMF precisely due to the current at low speed. In this paper, novel full-order multi-input and multi-output discrete-time sliding mode observer (SMO) is built to detect the rotor position. With the proposed rotor position estimation technique, the motor can start up from standstill and reverse between positive and negative directions without a position sensor. The proposed method was evaluated by experiment.

A New Fast In-Motion Coarse Alignment Method for GPS-Aided Low-Cost SINS

In this paper, a new fast in-motion coarse alignment method is proposed for a low-cost strap-down inertial navigation system (SINS) aided by the global positioning system (GPS). As compared with existing dynamic attitude estimation based initial alignment methods, in the proposed method, the constant attitude matrix from initial body frame to initial navigation frame is rapidly estimated, which results in improved alignment speed. Moreover, the time-varying attitude matrix from current body frame to initial body frame and gyroscope bias are jointly estimated based on a new constructed state-space model using the dynamic attitude estimation technique, and the resultant estimate and closed-loop calculation are utilized in the construction of vector observations, which improves the accuracy of coarse alignment. Experimental results illustrate that the proposed method has faster alignment speed and better alignment accuracy than existing state-of-the-art methods for GPS-aided low-cost SINS.

Attitude estimation for indoor navigation and augmented reality with smartphones

We investigate the precision of attitude estimation algorithms in the particular context of pedestrian navigation with commodity smartphones and their inertial/magnetic sensors. We report on an extensive comparison and experimental analysis of existing algorithms. We focus on typical motions of smartphones when carried by pedestrians. We use a precise ground truth obtained from a motion capture system. We test state-of-the-art and built-in attitude estimation techniques with several smartphones, in the presence of magnetic perturbations typically found in buildings. We discuss the obtained results, analyze advantages and limits of current technologies for attitude estimation in this context. Furthermore, we propose a new technique for limiting the impact of magnetic perturbations with any attitude estimation algorithm used in this context. We show how our technique compares and improves over previous works. A particular attention was paid to the study of attitude estimation in the context of augmented reality motions when using smartphones.

Géolocalisation des DM : principes et applications

Position location usually relies on direct observation to/from conventional landmarks with known positions from/to a Node of Interest (NOI). Nonetheless, in an Ad-Hoc Wireless Sensor Network (AHWSN), nodes are often unable to establish a direct connection with the available Access Points (APs). In such a scenario, neighboring nodes may supply cooperative information to enable inference of the location of a given NOI in a network. In this paper we examine the feasibility of relational techniques in multihop environments to estimate position location. Two novel position estimation techniques are presented: the Relative Proximity Algorithm (RPA) and the Enhanced Relative Proximity Algorithm (ERPA). RPA and ERPA can operate as range-based or as range-free techniques, which makes them both attractive and flexible solutions for position estimation in AHWSNs. The performance of these techniques is characterized and found to be related to the number of cooperating nodes, the number of APs available in the network, and the presence of measurement noise. RPA and ERPA are also compared to several known position location methods reported in the literature, and it is shown that they achieve adequate location estimation accuracy with some advantages in terms of the number of access points required and network traffic overhead.

Simultaneous Projection and Positioning of Laser Projector Pixels.

This paper presents a novel projected pixel localization principle for online geometric registration in dynamic projection mapping applications. We propose applying a time measurement of a laser projector raster-scanning beam using a photosensor to estimate its position while the projector displays meaningful visual information to human observers. Based on this principle, we develop two types of position estimation techniques. One estimates the position of a projected beam when it directly illuminates a photosensor. The other localizes a beam by measuring the reflection from a retro-reflective marker with the photosensor placed in the optical path of the projector. We conduct system evaluations using prototypes to validate this method as well as to confirm the applicability of our principle. In addition, we discuss the technical limitations of the prototypes based on the evaluation results. Finally, we build several dynamic projection mapping applications to demonstrate the feasibility of our principle.

Adaptive Wireless Biomedical Capsule Tracking Based on Magnetic Sensing

This paper studies wireless biomedical capsule (WBC) tracking, for magnetic sensing and actuation settings, where an embedded permanent magnet is used inside a passive WBC together with magnetic sensors outside the human body, producing a magnetic field around the WBC. First, a 2D WBC localization scheme is developed based on only magnetic sensing and adaptive recursive least squares (RLS) on-line parameter estimation with forgetting factor. Next, we propose a hybrid localization technique for simultaneous position and orientation estimation with high accuracy. The proposed hybrid localization technique is based on data fusion of magnetic measurements and electromagnetic signals emitted by the WBC for image transmission and other medical information using a similar adaptive RLS parameter estimation scheme. Later, the proposed localization techniques are integrated with an adaptive tracking law to construct an adaptive capsule tracking controller. The simulations demonstrate promising results for the efficiency and accuracy level of the proposed adaptive localization and tracking control schemes.

Single-point position estimation in interplanetary trajectories using star trackers

This study provides a single-point position estimation technique for interplanetary missions by observing visible planets using star trackers. Closed-form least-squares solution is obtained by minimizing the sum of the expected object-space squared distance errors. A weighted least-squares solution is provided by an iterative procedure. The weights are evaluated using the distances to the planets estimated by the least-squares solution. It is shown that the weighted approach only requires one iteration to converge and results in significant accuracy gains compared to simple least squares approach. The light-time correction is taken into account while the star-light aberration cannot be implemented in single-point estimation as it requires knowledge of the observer velocity. The proposed method is numerically validated through a statistical scenario as follows. A three-dimensional grid of test cases is generated: two dimensions sweep through the ecliptic plane and the third dimension sweeps through time from January 1, 2018 to January 1, 2043 in 5-year increments. The observer position is estimated at each test case and the estimate error is recorded. The results obtained show that a large majority of positions are well suited to position estimation by using star trackers pointing to visible planets, and reliable and accurate single-point position estimations can be provided in interplanetary missions. The proposed approach is suitable to be used to initiate a filtering technique to increase the estimation accuracy.

A Novel Techniques for Speed and Rotor Position Estimation of Brushless DC Motor with an Extended Kalman Filter by using Matlab Simulation

A Novel Techniques for Speed and Rotor Position Estimation of Brushless DC Motor with an Extended Kalman Filter by using Matlab Simulation

Attitude Estimation of A Rocking Ship with The Angle of Arrival Measurements Using Beacons

Abstract: To improve the accuracy of navigation solutions such as position, velocity and attitude, various navigation techniques have been developed. For example, the global positioning system (GPS) and inertial navigation system (INS) have been used in general navigation systems. Nowadays, to provide more accurate navigation solution, extended Kalman filter (EKF) based GPS/INS coupled systems, which calibrate the navigation solution of INS by using the GPS, are also widely used for military systems. However, with the ordinary GPS/INS coupled systems the attitude cannot be calibrated, since the GPS is able to calibrate position and velocity only, and this feature can be vulnerable to certain applications where attitude information is needed. To overcome this drawback, an angle-based attitude estimation technique using beacons is proposed in this paper. The proposed technique uses the angle of arrival (AOA) measurements of the received signals using an array antenna, and estimates the own-ship attitude. The proposed technique is also coupled with INS and the performance of the proposed technique is demonstrated through simulation.

A solar PV augmented hybrid scheme for enhanced wind power generation through improved control strategy for grid connected doubly fed induction generator

AbstractIn this paper, a wind power generation scheme using a grid connected doubly fed induction generator (DFIG) augmented with solar PV has been proposed. A reactive power-based rotor speed and position estimation technique with reduced machine parameter sensitivity is also proposed to improve the performance of the DFIG controller. The estimation algorithm is based on model reference adaptive system (MRAS), which uses the air gap reactive power as the adjustable variable. The overall generation reliability of the wind energy conversion system can be considerably improved as both solar and wind energy can supplement each other during lean periods of either of the sources. The rotor-side DC-link voltage and active power generation at the stator terminals of the DFIG are maintained constant with minimum storage battery capacity using single converter arrangement without grid-side converter (GSC). The proposed scheme has been simulated and experimentally validated with a practical 2.5 kW DFIG using dSPACE...