Ao Peng

A 3D indoor positioning system based on low-cost MEMS sensors

Abstract A positioning system in the absence of GPS is important in establishing indoor directional guidance and localization. Inertial Measuring Units (IMUs) can be used to detect the movement of a pedestrian. In this paper, we present a three-dimensional (3D) indoor positioning system using foot mounted low cost Micro-Electro-Mechanical System (MEMS) sensors to locate the position and attitude of a person in 3D view, and to plot the path travelled by the person. The sensors include accelerometers, gyroscopes, and a barometer. The pedestrians motion information is collected by accelerometers and gyroscopes to achieve Pedestrian Dead-Reckoning (PDR) which is used to estimate the pedestrian’s rough position. A zero velocity update (ZUPT) algorithm is developed to detect the standing still moment. A Kalman filter is combined with the ZUPT to eliminate non-linear errors in order to obtain accurate positioning information of a pedestrian. The information collected by the barometer is integrated with the accelerometer data to detect the altitude changes and to obtain accurate height information. The main contribution of this research is that the approach proposed fuses barometer and accelerometer in Kalman filter to obtain accurate height information, which has improved the accuracy at x axis and y axis. The proposed system has been tested in several simulated scenarios and real environments. The distance errors are around 1%, and the positioning errors are less than 1% of the total travelled distance. Results indicate that the proposed system performs better than other similar systems using the same low-cost IMUs.

A Novel Energy-Efficient Approach for Human Activity Recognition

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper.

An INS/WiFi Indoor Localization System Based on the Weighted Least Squares

For smartphone indoor localization, an INS/WiFi hybrid localization system is proposed in this paper. Acceleration and angular velocity are used to estimate step lengths and headings. The problem with INS is that positioning errors grow with time. Using radio signal strength as a fingerprint is a widely used technology. The main problem with fingerprint matching is mismatching due to noise. Taking into account the different shortcomings and advantages, inertial sensors and WiFi from smartphones are integrated into indoor positioning. For a hybrid localization system, pre-processing techniques are used to enhance the WiFi signal quality. An inertial navigation system limits the range of WiFi matching. A Multi-dimensional Dynamic Time Warping (MDTW) is proposed to calculate the distance between the measured signals and the fingerprint in the database. A MDTW-based weighted least squares (WLS) is proposed for fusing multiple fingerprint localization results to improve positioning accuracy and robustness. Using four modes (calling, dangling, handheld and pocket), we carried out walking experiments in a corridor, a study room and a library stack room. Experimental results show that average localization accuracy for the hybrid system is about 2.03 m.

Evolutionary Particle Filter for Indoor Navigation and Location

Indoor positioning technology can provide accurate location services for pedestrians. MEMS inertial sensors are inexpensive and small in size. Therefore, inertial navigation and positioning become popular research direction. The inertial sensor, which contains 3-axis accelerometer and 3-axis gyroscope, collects the acceleration and angular velocity information. The relative position of the pedestrian is calculated by integrating the acceleration and the angular velocity. The extended Kalman filter estimates attitude, angular velocity, position, velocity and acceleration system state errors. The system state error is updated when the foot touches the ground. Directional drift is the main problem of inertial navigation. Correcting heading by adding auxiliary basic information is one of the more common methods, such as GPS, geomagnetism, and Wi-Fi, but the additional basic information adds to the extra cost. We propose a novel algorithm based on the fact that pedestrians cannot cross the wall during walking. After the extended Kalman filter, the step size and the azimuth change are used as the observed state to establish the walking motion model. Considering the map information, the particle filter estimates the pedestrian position. For the particle impoverishment problem, the mutation operation of the genetic algorithm is used. A healthy male participates in the experiment. The results show an absolute error of 1.6 m.

Human Activity Recognition with Smart Watch based on H-SVM

Activity recognition allows ubiquitous wearable device like smart watch to simplify the study and experiment. It is very convenient and extensibility that we do study with the accelerometer sensor of a smart watch. In this paper, we use Samsung GEAR smart watch to collect data, then extract features, classify with H-SVM (Hierarchical Support Vector Machine) classifier and identify human activities classification. Experiment results show great effect at low sampling rate, such as 10 and 5 Hz, which will give us the energy saving. In most cases, the accuracies of activity recognition experiment are above 99%.

A Gyroscope Bias Estimation Algorithm Based on Map Specific Information

In an inertial navigation system, especially in a pedestrian dead-reckoning system, gyroscope bias can demonstrably reduce positioning accuracy. A novel gyroscope bias estimation algorithm is proposed, which estimates the bias of a gyroscope under any set of angle observations. Moreover, a method for obtaining Euler angles using map corridor information is proposed. The heading information obtained from a map is used to estimate the bias, and the estimated bias is used to correct the trajectories. Experimental results show that it is feasible for the algorithm to estimate the bias of the gyroscope.

An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter

Location-based services for smartphones are becoming more and more popular. The core of location-based services is how to estimate a user’s location. An INS/floor-plan indoor localization system, using the Firefly Particle Filter (FPF), is proposed to estimate a user’s location. INS includes an attitude angle module, a step length module and a step counting module. In the step length module, we propose a hybrid step length model. The proposed step length algorithm reasonably calculates a user’s step length. Because of sensor deviation, non-orthogonality and the user’s jitter, the main bottleneck for INS is that the error grows over time. To reduce the cumulative error, we design cascade filters including the Kalman Filter (KF) and FPF. To a certain extent, KF reduces velocity error and heading drift. On the other hand, the firefly algorithm is used to solve the particle impoverishment problem. Considering that a user may not cross an obstacle, the proposed particle filter is proposed to improve positioning performance. Results show that the average positioning error in walking experiments is 2.14 m.

Research on multiple gait and 3D indoor positioning system

High accuracy in indoor navigation with foot-mounted sensors attracts a lot of researchers in the last decades. Most indoor positioning schemes based on strap-down inertial navigation can only be used for normal walking. This paper present a 3D foot-mounted inertial navigation system, which can meet the challenge of the multi-gaits. During walking, the foot will have a contact with the ground in every step, in which time, the velocity of foot is zero. The correctness of zero velocity detection is important for drift removing in pedestrian dead-reckoning based inertial pedestrian indoor position systems. Previous algorithm of zero velocity detection is hard to handle the gaits variety. In this paper, by analyzing the inertial data from different modes of motion, a heuristic zero-velocity detection algorithm is designed. The algorithm can accurately detect the zero-velocity time of pedestrians among a variety of gaits. Then the speed and the displacement are updated in the Kalman Filter. Moreover, the barometer is fused with accelerometer for the calculation of height and achievement the 3D trajectory tracking. The experimental results show that the average distance error is 2.59%, the average distance error is 5.78% during running and the average height error is about 0.2m when the pedestrian is going stairs.

A smart-phone based hand-held indoor tracking system

A smart-phone based hand-held indoor positioning system is presented in this paper. The system collects data using the accelerometers, gyroscopes, barometers and gravity sensors embedded in the smart-phone. The accelerometer and gravity data are used for zero-velocity detection and calculating the vertical displacement of each walking step, and then the inverted pendulum model is applied to calculate the step length of every step. The angle of direction is estimated by processing gyroscope data with the quaternion method. The step length and the direction angle of each step are combined to determine the coordinates of each step. The barometer is used for measuring the height information. A Kalman filter is used in zero-velocity-update (ZUPT) to reduce the vertical speed offset caused by accelerometer drift errors. Wifi is also fused in our system. In order to guarantee the accuracy, map information and magnetic field information are used in the navigation systems. The experiment results show that we obtained high precision results with common hand-held smart-phone often seen on market.

Heading Judgement for Indoor Position Based on the Gait Pattern

In the inertial sensing unit based indoor positioning systems, the gyroscope drift is the primary source of heading error. To reduce this error, we proposed that the heading drift and the real heading change can be distinguished by the similarity of the gait pattern in the same movement model. It use the curve fitting method to find out the gait pattern in walking straightly. The Frechet distance is used to discriminate the gait of walking in turn and walking straightly. Experiments show that this method can recognize the walking in turn successfully with no mistake and the rate of mismatch walking in straight to walking in turn is less than 17.39%. Although there are some mistakes of match the walking in straight to walking in turn model, it will have few impact because heading drift is little in a short time. The result of test two shows that it get the best result compared with the other two methods when doing heading correction. It indicates that the proposal can promote the performance of heading correction and reduce the effect of sensor drift.