Ruizhi Chen

A Hybrid Smartphone Indoor Positioning Solution for Mobile LBS

Smartphone positioning is an enabling technology used to create new business in the navigation and mobile location-based services (LBS) industries. This paper presents a smartphone indoor positioning engine named HIPE that can be easily integrated with mobile LBS. HIPE is a hybrid solution that fuses measurements of smartphone sensors with wireless signals. The smartphone sensors are used to measure the user’s motion dynamics information (MDI), which represent the spatial correlation of various locations. Two algorithms based on hidden Markov model (HMM) problems, the grid-based filter and the Viterbi algorithm, are used in this paper as the central processor for data fusion to resolve the position estimates, and these algorithms are applicable for different applications, e.g., real-time navigation and location tracking, respectively. HIPE is more widely applicable for various motion scenarios than solutions proposed in previous studies because it uses no deterministic motion models, which have been commonly used in previous works. The experimental results showed that HIPE can provide adequate positioning accuracy and robustness for different scenarios of MDI combinations. HIPE is a cost-efficient solution, and it can work flexibly with different smartphone platforms, which may have different types of sensors available for the measurement of MDI data. The reliability of the positioning solution was found to increase with increasing precision of the MDI data.

Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning

The paper presents an indoor navigation solution by combining physical motion recognition with wireless positioning. Twenty-seven simple features are extracted from the built-in accelerometers and magnetometers in a smartphone. Eight common motion states used during indoor navigation are detected by a Least Square-Support Vector Machines (LS-SVM) classification algorithm, e.g., static, standing with hand swinging, normal walking while holding the phone in hand, normal walking with hand swinging, fast walking, U-turning, going up stairs, and going down stairs. The results indicate that the motion states are recognized with an accuracy of up to 95.53% for the test cases employed in this study. A motion recognition assisted wireless positioning approach is applied to determine the position of a mobile user. Field tests show a 1.22 m mean error in “Static Tests” and a 3.53 m in “Stop-Go Tests”.

Human Behavior Cognition Using Smartphone Sensors

This research focuses on sensing context, modeling human behavior and developing a new architecture for a cognitive phone platform. We combine the latest positioning technologies and phone sensors to capture human movements in natural environments and use the movements to study human behavior. Contexts in this research are abstracted as a Context Pyramid which includes six levels: Raw Sensor Data, Physical Parameter, Features/Patterns, Simple Contextual Descriptors, Activity-Level Descriptors, and Rich Context. To achieve implementation of the Context Pyramid on a cognitive phone, three key technologies are utilized: ubiquitous positioning, motion recognition, and human behavior modeling. Preliminary tests indicate that we have successfully achieved the Activity-Level Descriptors level with our LoMoCo (Location-Motion-Context) model. Location accuracy of the proposed solution is up to 1.9 meters in corridor environments and 3.5 meters in open spaces. Test results also indicate that the motion states are recognized with an accuracy rate up to 92.9% using a Least Square-Support Vector Machine (LS-SVM) classifier.

Two-channel Hyperspectral LiDAR with a Supercontinuum Laser Source

Recent advances in nonlinear fiber optics and compact pulsed lasers have resulted in creation of broadband directional light sources. These supercontinuum laser sources produce directional broadband light using cascaded nonlinear optical interactions in an optical fibre framework. This system is used to simultaneously measure distance and reflectance to demonstrate a technique capable of distinguishing between a vegetation target and inorganic material using the Normalized Difference Vegetation Index (NDVI) parameters, while the range can be obtained from the waveform of the echoes. A two-channel, spectral range-finding system based on a supercontinuum laser source was used to determine its potential application of distinguishing the NDVI for Norway spruce, a coniferous tree, and its three-dimensional parameters at 600 nm and 800 nm. A prototype system was built using commercial components.

An effective Pedestrian Dead Reckoning algorithm using a unified heading error model

Nowadays, navigation is an important application in mobile phones. However, locating a mobile user anytime anywhere is still a demanding task, because the GPS signal is easily corrupted or unavailable in urban canyons and indoor environments. Integrating GPS and self-contained dead reckoning sensors is an autonomous method to obtain a seamless positioning solution by means of Pedestrian Dead Reckoning (PDR) algorithms. A low-cost Multi-Sensor Positioning (MSP) platform has been developed by the Finnish Geodetic Institute, which includes a GPS receiver, a 2-axis digital compass and a 3-axis accelerometer. To construct a trajectory in GPS degraded environments, step length and the heading of each step are two key issues in PDR. In this paper, three typical estimation models of step length are presented and compared to demonstrate that in most cases, step length is not as critical as the determination of heading. Therefore, a unified heading error model is proposed, which includes all predictable errors from the navigation platform and the pedestrians walking behavior, and applies to calibrating 2-axis magnetic compasses without tedious and complicated calibration procedures. Then the corresponding PDR algorithm is introduced, which integrates the step length estimated from a nonlinear model and the heading compensated by the unified model suggested through an Extended Kalman Filter (EKF). Several tests were conducted to validate the effectiveness of the heading error model and evaluate the positioning performance of this PDR algorithm. The results demonstrated that the heading error model is applicable for calibrating the 2-axis compass, and based on the PDR algorithm, the typical positioning performance of MSP can reach an accuracy of below 1.5% of the travelled distance during 10 minutes of continuous walking when GPS outages occur.

Photorealistic Building Reconstruction from Mobile Laser Scanning Data

Abstract: Nowadays, advanced real-time visualization for location-based applications, such as vehicle navigation or mobile phone navigation, requires large scale 3D reconstruction of street scenes. This paper presents methods for generating photorealistic 3D city models from raw mobile laser scanning data, which only contain georeferenced XYZ coordinates of points, to enable the use of photorealistic models in a mobile phone for personal navigation. The main focus is on the automated processing algorithms for noise point filtering, ground and building point classification, detection of planar surfaces, and on the key points (e.g., corners) of building derivation. The test site is located in the Tapiola area, Espoo, Finland. It is an area of commercial buildings, including shopping centers, banks, government agencies, bookstores, and high-rise residential buildings, with the tallest building being 45 m in height. Buildings were extracted by comparing the overlaps of X and Y coordinates of the point clouds between the cutoff-boxes at different and transforming the top-view of the point clouds of each overlap into a binary image and applying standard image processing technology to remove the non-building points, and finally transforming this image back into point clouds. The purpose for using points from cutoff-boxes instead of all points for building detection is to reduce the influence of tree points close to the building facades on building extraction. This method can also be extended to transform point clouds in different views into binary images for various other object extractions. In order to ensure the building geometry completeness, manual check and correction are needed after the key points of building derivation by automated algorithms. As our goal is to obtain photorealistic 3D models for walk-through views, terrestrial images were captured and used for texturing building facades. Currently, fully

Inquiry-Based Bluetooth Indoor Positioning via RSSI Probability Distributions

This paper presents an inquiry-based Bluetooth indoor positioning solution via RSSI probability distributions. A practical system architecture is designed after the Bluetooth protocol and profiles are studied. Weibull function is applied for approximating the Bluetooth signal strength distribution in the data training phase. The Histogram Maximum Likelihood position estimation based on Bayesian theory is utilized in the location determination phase. The results show the possibility of indoor positioning through inquiring the Bluetooth-enabled handsets in range. Weibull distribution improves the performance of fingerprinting. The practicality of the system architecture is also proved by the outcome of a test campaign.

iParking: An Intelligent Indoor Location-Based Smartphone Parking Service

Indoor positioning technologies have been widely studied with a number of solutions being proposed, yet substantial applications and services are still fairly primitive. Taking advantage of the emerging concept of the connected car, the popularity of smartphones and mobile Internet, and precise indoor locations, this study presents the development of a novel intelligent parking service called iParking. With the iParking service, multiple parties such as users, parking facilities and service providers are connected through Internet in a distributed architecture. The client software is a light-weight application running on a smartphone, and it works essentially based on a precise indoor positioning solution, which fuses Wireless Local Area Network (WLAN) signals and the measurements of the built-in sensors of the smartphones. The positioning accuracy, availability and reliability of the proposed positioning solution are adequate for facilitating the novel parking service. An iParking prototype has been developed and demonstrated in a real parking environment at a shopping mall. The demonstration showed how the iParking service could improve the parking experience and increase the efficiency of parking facilities. The iParking is a novel service in terms of cost- and energy-efficient solution.

Indoor/Outdoor Seamless Positioning Technologies Integrated on Smart Phone

The correlation analysis of telemetry data plays a significant role in satellite performance analysis. However, the existing methods cannot be well applied, because the telemetry data is large and high-dimensional. In this paper, an efficient algorithm named QARC Apriori is proposed. First, to reduce the redundant attributes and lower the problem complexity, grey relational analysis method is applied. Second, each filtered attribute is partitioned into several subintervals, combining with K-Means clustering algorithm. During clustering, the outliers are removed to improve the accuracy of clustering results. Due to different distributions and scopes of attributes, the clustering centers are automatically adjusted. Moreover, the statistical information of each attribute is used to avoid repeatedly scanning database. Finally, all quantitative association rules are mined by an improved Apriori algorithm. In order to improve the mining efficiency, two pruning strategies are used. The experiments are conducted with the power supply data of a Chinas satellite from 2011.6.1 to 2011.9.1. It indicates that the proposed algorithm is suitable for quantitative association rules mining and is important for satellite on-orbit performance analysis.This paper presents a flexible approach to ubiquitous positioning technologies on smart phone. It deploys three optional indoor/outdoor locating solutions based on Multi-sensor, Satellite, and Terrestrial positioning techniques. The solutions cover six locators including Integrated GPS (Global Positioning System), Bluetooth GPS, AGPS (Assisted GPS), Network based, Multi-sensors, and Wireless LAN (WLAN). In order to merge multi positioning techniques on smart phone, a five-layer software architecture is developed on Symbian S60 platform. Moreover, the implementations of the solutions are described in the paper. Finally, we evaluate the performances of time consumption and positioning accuracy of the solutions according to the experiments on Nokia N95.Both Xilinx and Altera have released SoCs that tightly couple programmable logic with a dual core Cortex A9 ARM processor. These SoCs show promise in accelerating applications that exploit both the FPGAs parallel processing architecture and the CPUs sequential processing. %Spectrum sensing in cognitive radios is one such application. For example, before accessing a wireless channel, a cognitive radio does spectrum sensing to detect channel occupancy and then makes a decision based on spectrum policies. Spectrum sensing maps well to FPGA fabric, while spectrum decision can be implemented with a CPU. Both algorithms are highly sensitive to latency as a faster decision improves spectrum utilization. This paper introduces CRASH: Cognitive Radio Accelerated with Software and Hardware -- a new software and programmable logic framework for Xilinxs Zynq SoC targeting cognitive radio. We implement spectrum sensing and the spectrum decision in three configurations: both algorithms in the FPGA, both in software only, and spectrum sensing on the FPGA and spectrum decision on the CPU. We measure the end-to-end latency to detect and acquire unoccupied spectrum for these configurations. Results show that CRASH can successfully partition algorithms between FPGA and CPU and reduce processing latency.

Accelerometer assisted robust wireless signal positioning based on a hidden Markov model

Reliable and accurate indoor positioning remains nowadays as one of the greatest challenges in the area of personal navigation and location based services (LBS). This manuscript proposes methods to improve the accuracy and robustness of indoor positioning using signal strength measurements of Wireless Local Area Networks (WLAN), and presents three aspects of contributions. First, the Weibull function is employed to represent the distribution of the signal strength over time. Thus, the impact of the signal strength variation on the fingerprinting database is mitigated, and fewer samples are required for training the database. Second, the accelerometer sensor is utilized to provide the pedestrian dynamics information, which is used to improve the positioning accuracy and reliability. Lastly, hidden Markov model (HMM) based particle filters are performed to compute the positioning solution through combining the signal strength measurements with the pedestrian dynamics information. Through the experimental evaluation of three scenarios, the proposed methods were found to improve significantly the accuracy and robustness of WLAN positioning. Due to their affordable computational load, the positioning methods proposed can be implemented for indoor navigation on mass-market mobile devices without any extra cost requirements.