Improved estimation of rotor position for sensorless control of a PMSM based on a sliding mode observer

This work proposes a new strategy to improve the rotor position estimation of a permanent magnet synchronous motor(PMSM) over wide speed range. Rotor position estimation of a PMSM is performed by using sliding mode observer(SMO). An adaptive observer gain was designed based on Lyapunov function and applied to solve the chattering problem caused by the discontinuous function of the SMO in the wide speed range. The cascade low-pass filter(LPF) with variable cut-off frequency was proposed to reduce the chattering problem and to attenuate the filtering capability of the SMO. In addition, the phase shift caused by the filter was counterbalanced by applying the variable phase delay compensation for the whole speed area. High accuracy estimation result of the rotor position was obtained in the experiment by applying the proposed estimation strategy.

Real-Time Position and Attitude Estimation for Homing and Docking of an Autonomous Underwater Vehicle Based on Bionic Polarized Optical Guidance

As an important tool for marine exploration, the autonomous underwater vehicle(AUV) must home in and dock at a docking station(DS) to be recharged, repaired, or to exchange information at set intervals. However, the complex and hostile underwater environment makes this process challenging. This study proposes a real-time method based on polarized optical guidance for determining the position and attitude of the AUV relative to its DS. Four polarized artificial underwater landmarks are positioned at the DS, which are recognized by the AUV vision system. Compared with light intensity, the polarization of a light beam is known to be better maintained at greater propagation distances, especially in underwater environments. The proposed method, which is inspired by the ability of marine animals to communicate, calculates the pose parameters in less than 10 ms without any other navigational information. The simulation results reveal that the angle errors are small and the position errors are no more than 0.116 m within 100 m in the coastal ocean. The results of underwater experiments further demonstrate the feasibility of the proposed method, which extends the operating distance of the AUV beyond what is currently possible while maintaining the precision of traditional optical guidance.

Accuracy Analysis of Position Estimation Based on Measurements of Received Signal Strength Difference

The performance of a cellular location system based on received signal strength difference （RSSD） is investigated. In the cellular location system, each mobile station needs to measure the signal strength transmitted by surrounding base stations, and sends its measurements to the service base station. Using the strength difference between the service base station and neighboring base stations, the position of a mobile station is estimated. The related Cramer-Rao lower bound （CRLB） on the location error of this method was derived, and numerical simulations are made to discuss the influences of the number of base stations, correlation coefficient of shadowing attenuation, and cell radius on CRLB. The results show that the CRLB is positively correlated with the standard deviation of shadowing attenuation and cell radius, but negatively correlated with the number of base stations and the correlation coefficient of shadowing attenuation. In addition, the CRLB results obtained in this paper were compared with those of the cellular location system based on received signal strength （RSS） measurements, which reveals that the former is more tight.

A Stochastic Approach for Cooperative Position Estimation of Multiple Mobile Robots

This paper proposes the cooperative position estimation of a group of mobile robots, which pertbrms disaster relief tasks in a wide area. When searching the wide area, it becomes important to know a robot＇s position correctly. However, for each mobile robot, it is impossible to know its own position correctly. Therefore, each mobile robot estimates its position from the data of sensor equipped on it. Generally, the sensor data is incorrect since there is sensor noise, etc. This research considers two types of the sensor data errors from omnidirectional camera. One is the error of white noise of the image captured by omnidirectional camera and so on. Another is the error of position and posture between two omnidirectional cameras. To solve the error of latter case, we proposed a self-position estimation algorithm for multiple mobile robots using two omnidirectional cameras and an accelerometer. On the other hand, to solve the error of the former case, this paper proposed an algorithm of cooperative position estimation for multiple mobile robots. In this algorithm, each mobile robot uses two omnidirectional cameras to observe the surrounding mobile robot and get the relative position between mobile robots. Each mobile robot estimates its position with only measurement data of each other mobile robots. The algorithm is based on a Bayesian filtering. Simulations of the proposed cooperative position estimation algorithm for multiple mobile robots are performed. The results show that position estimation is possible by only using measurement value from each other robot.

Simultaneous Position Estimation and Omnidirectional Camera Parameter Calibration for Multiple Mobile Robots

This paper proposed an algorithm on simultaneous position estimation and calibration of omnidirectional camera parameters for a group of multiple mobile robots. It is aimed at developing of exploration and information gathering robotic system in unknown environment. Here, each mobile robot is not possible to know its own position. It can only estimate its own position by using the measurement value including white noise acquired by two omnidirectional cameras mounted on it. Each mobile robot is able to obtain the distance to those robots observed from the images of two omnidirectional cameras while making calibration during moving but not in advance. Simulation of three robots moving straightly shows the effectiveness of the proposed algorithm.

Mobile Station Position Estimation using Remote Radio Head with Adaptive Power Control

The adaptive power control of CDMA （Code Division Multiple Access） communications between multiple MSs （Mobile Stations） with a link-budget based SIR （Signal-to-lnterference Ratio） estimate is applied to inner loop power control algorithms. CTR （Consecutive Transmit-Power-Control Ratio） calculated from these algorithms can estimate MS speed, together with MS moving distance, but cannot estimate MS position. In this paper, RRH （Remote Radio Head） is introduced and it is concluded that BS （Base Station） calculates MS distance with CTR from one of RRHs, in addition BS that has RSSIs （Received Signal Strength Indicators） information from other two RRHs can estimate MS position.

基于三维测量的无人艇自动靠泊引导方法研究

Automatic berthing guidance is an important aspect of automated ship technology to obtain the ship-shore position relationship.The current mainstream measurement methods for ship-shore position relationships are based on radar,multisensor fusion,and visual detection technologies.This paper proposes an automated ship berthing guidance method based on three-dimensional(3D)target measurement and compares it with a single-target recognition method using a binocular camera.An improved deep object pose estimation(DOPE)network is used in this method to predict the pixel coordinates of the two-dimensional(2D)keypoints of the shore target in the image.The pixel coordinates are then converted into 3D coordinates through the camera imaging principle,and an algorithm for calculating the relationship between the ship and the shore is proposed.Experiments were conducted on the improved DOPE network and the actual ship guidance performance to verify the effectiveness of the method.Results show that the proposed method with a monocular camera has high stability and accuracy and can meet the requirements of automatic berthing.

BLE beacons for sample position estimation in a life science automation laboratory

Estimation of the sample position is essential for working process monitoring and management in the life science automation laboratory.Bluetooth low-energy(BLE)beacons have the advantages of low price,small size and low energy consumption,which make them a promising solution for sample position estimation in the automated laboratory.Several fingerprinting models have been proposed to achieve indoor localization with the received signal strength(RSS)data.However,most of the research depends on intensive beacon installation.Proximity estimation,which depends entirely on one beacon,is more suitable for sample position estimation in large automated laboratories.The complexity of the life science automation laboratory environment brings challenges to the traditional path loss model(PLM),which is a widely used radio wave propagation model-based proximity estimation method.In this paper,BLE sensing devices for sample position estimation are proposed.The BLE beacon-based proximity estimation is discussed in the framework of machine learning,in which the support vector regression(SVR)is utilized to model the nonlinear relationship between the RSS data and distance,and the Kalman filter is utilized to decrease the RSS data deviation.The experimental results over different environments indicate that the SVR outperforms the PLM significantly,and provides 1 m absolute errors for more than 95%of the testing samples.The Kalman filter brings benefits to stable distance predictions.Apart from proximity-based sample position estimation,the proposed framework turned out to be effective in position estimation between parallel workbenches and position estimation on an automated workstation.

Distributed localization for anchor-free sensor networks

Geographic location of nodes is very useful in a sensor network. Previous localization algorithms assume that there exist some anchor nodes in this kind of network, and then other nodes are estimated to create their coordinates. Once there are not anchors to be deployed, those localization algorithms will be invalidated. Many papers in this field focus on anchor-based solutions. The use of anchors introduces many limitations, since anchors require external equipments such as global position system, cause additional power consumption. A novel positioning algorithm is proposed to use a virtual coordinate system based on a new concept--virtual anchor. It is executed in a distributed fashion according to the connectivity of a node and the measured distances to its neighbors. Both the adjacent member information and the ranging distance result are combined to generate the estimated position of a network, one of which is independently adopted for localization previously. At the position refinement stage the intermediate estimation of a node begins to be evaluated on its reliability for position mutation; thus the positioning optimization process of the whole network is avoided falling into a local optimal solution. Simulation results prove that the algorithm can resolve the distributed localization problem for anchor-free sensor networks, and is superior to previous methods in terms of its positioning capability under a variety of circumstances.

DeepPOSE:Detecting GPS spoofing attack via deep recurrent neural network

The Global Positioning System(GPS)has become a foundation for most location-based services and navigation systems,such as autonomous vehicles,drones,ships,and wearable devices.However,it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing tools.Pervasive tools,such as Fake GPS,Lockito,and software-defined radio,enable ordinary users to hijack and report fake GPS coordinates and cheat the monitoring server without being detected.Furthermore,it is also a challenge to get accurate sensor readings on mobile devices because of the high noise level introduced by commercial motion sensors.To this end,we propose DeepPOSE,a deep learning model,to address the noise introduced in sensor readings and detect GPS spoofing attacks on mobile platforms.Our design uses a convolutional and recurrent neural network to reduce the noise,to recover a vehicle's real-time trajectory from multiple sensor inputs.We further propose a novel scheme to map the constructed trajectory from sensor readings onto the Google map,to smartly eliminate the accumulation of errors on the trajectory estimation.The reconstructed trajectory from sensors is then used to detect the GPS spoofing attack.Compared with the existing method,the proposed approach demonstrates a significantly higher degree of accuracy for detecting GPS spoofing attacks.

An Automatic Segmentation of Kidney in Serial Abdominal CT Scans Using Region Growing Approach

Automatic kidney segmentation from abdominal CT images is a key step in computer-aided diagnosis for kidney CT as well as computeraided surgery. However, kidney segmentation from CT images is generally performed manually or semi-autornatically because of gray levels similarities of adjacent organs/tissues in abdominal CT images. This paper presents an efficient algorithm for segmenting kidney from serials of abdominal CT images. First, we extracted estimated kidney position （EKP） according to the statistical geometric location of kidney within the abdomen. Second, we analyzed the intensity distribution of EKP for several abdominal CT images and exploit an adaptive threshold searching algorithm to eliminate many other organs/tissues in the EKP. Finally, a novel region growing approach based on labeling is used to obtain the fine kidney regions. Experimental results are comparable to those of manual tracing radiologist and shown to be efficient.

An Enhanced Sensorless Control Scheme for PMSM Drives Considering Self-inductance Asymmetry

Inductance asymmetry,which is brought by inherent asymmetric parameters,manufacture tolerance,winding fault,cables with unequal lengths,etc.,of permanent-magnet synchronous machines(PMSMs)can cause current harmonics and inaccurate position estimation.This paper proposes an enhanced fundamental model based sensorless control strategy for PMSMs with asymmetric inductances.The proportional-integral-resonant current regulator is introduced to reduce the second-order harmonics of currents,but there are still negative sequence components in the estimated back-electromotive forces(EMFs),which can cause the position estimated error.Differing from conventional methods in which negative sequences are filtered out before the phase-locked loop(PLL)module,the proposed method directly applies the estimated back-EMF with negative sequences as the reference input of PLL.An improved PLL with a bi-quad filter is proposed to attenuate the arising second harmonic position error and heighten the steady-state accuracy.Then,this position error is used for asymmetric inductance identification and its result is utilized to update the observer model.Furthermore,the dynamic performance is improved by the output limitation on the bi-quad filter as well as the implementation of a fast-locking technique in the PLL.The effectiveness of the proposed scheme is verified by experimental results.

Indoor 3D Reconstruction Using Camera, IMU and Ultrasonic Sensors

The recent advances in sensing and display technologies have been transforming our living environments drastically. In this paper, a new technique is introduced to accurately reconstruct indoor environments in three-dimensions using a mobile platform. The system incorporates 4 ultrasonic sensors scanner system, an HD web camera as well as an inertial measurement unit (IMU). The whole platform is mountable on mobile facilities, such as a wheelchair. The proposed mapping approach took advantage of the precision of the 3D point clouds produced by the ultrasonic sensors system despite their scarcity to help build a more definite 3D scene. Using a robust iterative algorithm, it combined the structure from motion generated 3D point clouds with the ultrasonic sensors and IMU generated 3D point clouds to derive a much more precise point cloud using the depth measurements from the ultrasonic sensors. Because of their ability to recognize features of objects in the targeted scene, the ultrasonic generated point clouds performed feature extraction on the consecutive point cloud to ensure a perfect alignment. The range measured by ultrasonic sensors contributed to the depth correction of the generated 3D images (the 3D scenes). Experiments revealed that the system generated not only dense but precise 3D maps of the environments. The results showed that the designed 3D modeling platform is able to help in assistive living environment for self-navigation, obstacle alert, and other driving assisting tasks.

Global Classical Solution and Asymptotic Behavior to a Kind of Linearly Degenerate Quasilinear Hyperbolic System

This paper is concerned with the global classical solution and the asymptotic behavior to a kind of linearly degenerate quasilinear hyperbolic system in several space variables.When the semilinear terms contain at least two waves with different propagation speeds,we can prove that the system considered admits a global classical solution by the weighted energy estimate under the small and suitable decay assumptions on the initial data.Furthermore,we can show that the solution converges to a solution of the linearized system based on the decay property of the nonlinearties.

The possibility of isolated target 3-D position estimation and optimal receiver position determination in SS-BSAR

Starting from the generalized ambiguity function of bistatic SAR （BSAR）, it is shown that 3-D point target estimation can be carried out in space-surface bistatic SAR （SS-BSAR）. Appropriate analytical equations, based on maximum likelihood estimation （MLE）, are derived and confirmed via computer simulation. Furthermore, the performance of the estimate using the Crammer-Rao bound is analyzed for the case in question, thus further revealing the possibility and potential of target 3-D position estimation. Setting the determinant maximum of the information matrix as the criterion, the optimal receiver position and multi-receiver configuration are analytically determined in the SS-BSAR system. Simulation results also validate the correctness of the analytical calculation.

Estimation of Vehicle Pose and Position with Monocular Camera at Urban Road Intersections

With the rapid development of urban, the scale of the city is expanding day by day. The road environment is becoming more and more complicated. The vehicle ego-localization in complex road environment puts forward imperative requirements for intelligent driving technology. The reliable vehicle ego-localization, including the lane recognition and the vehicle position and attitude estimation, at the complex traffic intersection is significant for the intelligent driving of the vehicle. In this article, we focus on the complex road environment of the city, and propose a pose and position estimation method based on the road sign using only a monocular camera and a common GPS （global positioning system）. Associated with the multi-sensor cascade system, this method can be a stable and reliable alternative when the precision of multi-sensor cascade system decreases. The experimental results show that, within 100 meters distance to the road signs, the pose error is less than 2 degrees, and the position error is less than one meter, which can reach the lane-level positioning accuracy. Through the comparison with the Beidou high-precision positioning system L202, our method is more accurate for detecting which lane the vehicle is driving on.

A New Approach to Estimate True Position of Unmanned Aerial Vehicles in an INS/GPS Integration System in GPS Spoofing Attack Conditions

This paper presents a new approach to estimate the true position of an unmanned aerial vehicle （UAV） in the conditions of spoofing attacks on global positioning system （GPS） receivers. This approach consists of two phases, the spoofing detection phase which is accomplished by hypothesis test and the trajectory estimation phase which is carried out by applying the adapted particle filters to the integrated inertial navigation system （INS） and GPS. Due to nonlinearity and unfavorable impacts of spoofing signals on GPS receivers, deviation in position calculation is modeled as a cumulative uniform error. This paper also presents a procedure of applying adapted particle swarm optimization filter （PSOF） to the INS/GPS integration system as an estimator to compensate spoofing attacks. Due to memory based nature of PSOF and benefits of each particle＇s experiences, application of PSOF algorithm in the INS/GPS integ- ration system leads to more precise positioning compared with general particle filter （PF） and adaptive unscented particle filer （AUPF） in the GPS spoofing attack scenarios. Simulation results show that the adapted PSOF algorithm is more reliable and accurate in estim- ating the true position of UAV in the condition of spoofing attacks. The validation of the proposed method is done by root mean square error （RMSE） test.

Initial position estimation strategy for a surface permanent magnet synchronous motor used in hybrid electric vehicles

A novel nonlinear model for surface permanent magnet synchronous motors（SPMSMs） is adopted to estimate the initial rotor position for hybrid electric vehicles（HEVs）. Usually, the accuracy of initial rotor position estimation for SPMSMs relies on magnetic saturation. To verify the saturation effect, the transient finite element analysis（FEA） model is presented first. Hybrid injection of a static voltage vector（SVV） superimposed with a high-frequency rotating voltage is proposed. The magnetic polarity is roughly identified with the aid of the saturation evaluation function, based on which an estimation of the position is performed. During this procedure, a special demodulation is suggested to extract signals of iron core saturation and rotor position. A Simulink/MATLAB platform for SPMSMs at standstill is constituted, and the effectiveness of the proposed strategy is verified. The proposed method is also validated by experimental results of an SPMSM drive.

A New Monocular Vision Measurement Method to Estimate 3D Positions of Objects on Floor

A new visual measurement method is proposed to estimate three-dimensional （3D） position of the object on the floor based on a single camera. The camera fixed on a robot is in an inclined position with respect to the floor. A measurement model with the camera＇s extrinsic parameters such as the height and pitch angle is described. Single image of a chessboard pattern placed on the floor is enough to calibrate the camera＇s extrinsic parameters after the camera＇s intrinsic parameters are calibrated. Then the position of object on the floor can be computed with the measurement model. Furthermore, the height of object can be calculated with the paired-points in the vertical line sharing the same position on the floor. Compared to the conventional method used to estimate the positions on the plane, this method can obtain the 3D positions. The indoor experiment testifies the accuracy and validity of the proposed method.

Vision algorithms for fixed-wing unmanned aerial vehicle landing system

Autonomous landing has become a core technology of unmanned aerial vehicle（UAV）guidance,navigation and control system