Method of improving pedestrian navigation performance based on chest card

With the development of positioning technology,loca-tion services are constantly in demand by people.As a primary location service pedestrian navigation has two main approaches based on radio and inertial navigation.The pedestrian naviga-tion based on radio is subject to environmental occlusion lead-ing to the degradation of positioning accuracy.The pedestrian navigation based on micro-electro-mechanical system inertial measurement unit(MIMU)is less susceptible to environmental interference,but its errors dissipate over time.In this paper,a chest card pedestrian navigation improvement method based on complementary correction is proposed in order to suppress the error divergence of inertial navigation methods.To suppress atti-tude errors,optimal feedback coefficients are established by pedestrian motion characteristics.To extend navigation time and improve positioning accuracy,the step length in subsequent movements is compensated by the first step length.The experi-mental results show that the positioning accuracy of the pro-posed method is improved by more than 47%and 44%com-pared with the pure inertia-based method combined with step compensation and the traditional complementary filtering com-bined method with step compensation.The proposed method can effectively suppress the error dispersion and improve the positioning accuracy.

Under-determined blind source separation using complementary filter based sub-band division

This paper considers the blind source separation in under-determined case,when there are more sources than sensors.So many algorithms based on sparse in some signal representation domain,mostly in Time-Frequency(T-F) domain,are proposed in recent years.However,constrained by window effects and T-F resolution,these algorithms cannot have good performance in many cases.Considering most of signals in real world are band-limited signals,a new method based on sub-band division is proposed in this paper.Sensing signals are divided into different sub-bands by complementary filter firstly.Then,classical Independent Component Analysis(ICA) algorithms are applied in each sub-band.Next,based on each sub-band's estimation of mixing matrix,the mixing matrix is estimated with cluster analysis algorithms.After that,the sub-band signals are recovered using the estimation mixing matrix,and then,the resource signals are reconstructed by combining the related sub-band signals together.This method can recover the source signals if active sources at any sub-band do not exceed that of sensors.This is also a well mixing matrix estimating algorithm.Finally,computer simulation confirms the validity and good separation performance of this method.

Complementary Kalman Filter as a Baseline Vector Estimator for GPS-Based Attitude Determination

The Global Positioning System(GPS)offers the interferometer for attitude determination by processing the carrier phase observables.By using carrier phase observables,the relative positioning is obtained in centimeter level.GPS interferometry has been firstly used in precise static relative positioning,and thereafter in kinematic positioning.The carrier phase differential GPS based on interferometer principles can solve for the antenna baseline vector,defined as the vector between the antenna designated master and one of the slave antennas,connected to a rigid body.Determining the unknown baseline vectors between the antennas sits at the heart of GPS-based attitude determination.The conventional solution of the baseline vectors based on least-squares approach is inherently noisy,which results in the noisy attitude solutions.In this article,the complementary Kalman filter(CKF)is employed for solving the baseline vector in the attitude determination mechanism to improve the performance,where the receiver-satellite double differenced observable was utilized as the measurement.By using the carrier phase observables,the relative positioning is obtained in centimeter level.Employing the CKF provides several advantages,such as accuracy improvement,reliability enhancement,and real-time assurance.Simulation results based on the conventional method where the least-squares approach is involved,and the proposed method where the CKF is involved are compared and discussed.

Quaternions Complementary Filter for Attitude Algorithm Research

Quatemions complementary filter attitude algorithm was conducted on the unmanned aerial vehicle （UAV） platform. This introduces traditional attitude algorithm and attitude quaternion complementary filter algorithm difference, and the attitude quatemion complementary filter algorithm realization are introduced in details

Personnel Localization of Real-Time Kinematic Based on Pedestrian Heading Projection Compensation in Substation Signal Interference Environment

To address the intermittent positioning and drift of personnel positioning RTK in the high-frequency signal interference environment of substations, we propose to use IMU as the positioning compensation module of RTK and adopt the joint RTK/PDR positioning method to solve the positioning results. The heading angle is easily scattered in the pedestrian heading projection (PDR) process and the heading angles calculated from the output data of the gyroscope, accelerometer and magnetometer after denoising are input into the complementary filter for fusion. To improve the accuracy of step estimation in the PDR process, an improved step estimation model is used. For RTK/PDR data fusion, the extended Kalman filter (EKF) method is used, which helps to achieve outdoor full-scene high-accuracy positioning. The final simulation results show that RTK can be effectively compensated by PDR under the interference of high-frequency signals, and the positioning accuracy reaches 0.02 m.

Building up an Inertial Navigation System Using Standard Mobile Devices

This paper presents the development ofa PNS （Pedestrian Navigation System）, which utilizes accelerometer, gyroscope and magnetometer data to enable accurate positioning. Therefore, the sensor basics as well as the mathematics regarding reference frames and coordinate transformations are introduced initially. Particular focus is given to quaternions, since they provide a performance-effective means to execute rotations. In great detail the two distinct approaches for a PNS are introduced, i.e. INS （inertial navigation systems） and PDR （pedestrian dead reckoning）. For each, a comparison of state-of-the-art techniques is presented. Special attention is paid to orientation estimation and stance phase detection. Our system combines the most promising techniques and describes improvements, whose usefulness becomes obvious in our experiments. We have applied our PNS in five different test scenarios. For the most complex rectangular-shaped use case, we achieve on average error of 1.66% with regard to the total travelled distance, which is superior to other recent PNS utilizing comparable sensors.

Mathematical Modelling of a Two Degree of Freedom Platform Using Accelerometers and Gyro Sensors

This paper demonstrates the assembly of a servo-controlled platform with two degrees of freedom, empirical methods and a developed closed-loop control found in the system mathematical model. This control aims to stabilize and hold small objects on the platform. We parsed the step response in X and Y axes, hence we found the first and second-order models for each one. We did some further analyses to decide which one would better represent the behavior of the system. The MATLAB software provided step response for the model empirically obtained and latter compared it to experimental data acquired in the trials. Accelerometers and gyro sensors from the MPU-6050 sensor measured the angular position of platform on X and Y axes. In order to improve measurements accuracy and eliminate noise effects, we implemented the complementary filter to the firmware system. We used Arduino to control servomotors through PWM pulses and perform data acquisition.

An error-based micro-sensor capture system for real-time motion estimation

A wearable micro-sensor motion capture system with 16 IMUs and an error-compensatory complementary filter algorithm for real-time motion estimation has been developed to acquire accurate 3D orientation and displacement in real life activities.In the proposed filter algorithm,the gyroscope bias error,orientation error and magnetic disturbance error are estimated and compensated,significantly reducing the orientation estimation error due to sensor noise and drift.Displacement estimation,especially for activities such as jumping,has been the challenge in micro-sensor motion capture.An adaptive gait phase detection algorithm has been developed to accommodate accurate displacement estimation in different types of activities.The performance of this system is benchmarked with respect to the results of VICON optical capture system.The experimental results have demonstrated effectiveness of the system in daily activities tracking,with estimation error 0.16 ± 0.06m for normal walking and 0.13 ± 0.11m for jumping motions.

Design of Ethernet Based Data Acquisition System for Yaw Rate and Longitudinal Velocity Measurement in Automobiles

Design of an Ethernet network compatible data acquisition system for the measurement of yaw rate and longitudinal velocity in automobiles is presented.The data acquisition system includes a base node and a remote node.The remote node consists of a micro electro mechanical system(MEMS)accelerometer,an MEMS gyroscope,an advanced RISC machines(ARM)CORTEX M3 microcontroller and an Ethernet PHY device.The remote node measures the yaw rate and the longitudinal velocity of an automobile and sends the measured values to the base node using Ethernet communication.The base node consists of an ARM CORTEX M3 microcontroller and an Ethernet PHY device.The base node receives the measured values and saves in a microSD card for further analysis.The characteris tics of the net work and the measurement system are stu died and repor ted.