Robert Guinness

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.

The evaluation of WiFi positioning in a Bluetooth and WiFi coexistence environment

An increasing number of WiFi and Bluetooth terminals, tags, and other mobile devices drives a growing demand for integration and coexistence of these two technologies. This paper gives the preliminary results of WiFi positioning in a WiFi and Bluetooth coexistence environment. The received signal strength indication is introduced as an observation applied to our WiFi positioning. Then, we present the basis of a fingerprinting approach to WiFi positioning and analyze the characters and protocols of WiFi and Bluetooth networks. Furthermore, we present the interference in the WiFi and Bluetooth coexistence environments.

Beyond Where to How: A Machine Learning Approach for Sensing Mobility Contexts Using Smartphone Sensors

This paper presents the results of research on the use of smartphone sensors (namely, GPS and accelerometers), geospatial information (points of interest, such as bus stops and train stations) and machine learning (ML) to sense mobility contexts. Our goal is to develop techniques to continuously and automatically detect a smartphone users mobility activities, including walking, running, driving and using a bus or train, in real-time or near-real-time (<5 s). We investigated a wide range of supervised learning techniques for classification, including decision trees (DT), support vector machines (SVM), naive Bayes classifiers (NB), Bayesian networks (BN), logistic regression (LR), artificial neural networks (ANN) and several instance-based classifiers (KStar, LWLand IBk). Applying ten-fold cross-validation, the best performers in terms of correct classification rate (i.e., recall) were DT (96.5%), BN (90.9%), LWL (95.5%) and KStar (95.6%). In particular, the DT-algorithm RandomForest exhibited the best overall performance. After a feature selection process for a subset of algorithms, the performance was improved slightly. Furthermore, after tuning the parameters of RandomForest, performance improved to above 97.5%. Lastly, we measured the computational complexity of the classifiers, in terms of central processing unit (CPU) time needed for classification, to provide a rough comparison between the algorithms in terms of battery usage requirements. As a result, the classifiers can be ranked from lowest to highest complexity (i.e., computational cost) as follows: SVM, ANN, LR, BN, DT, NB, IBk, LWL and KStar. The instance-based classifiers take considerably more computational time than the non-instance-based classifiers, whereas the slowest non-instance-based classifier (NB) required about five-times the amount of CPU time as the fastest classifier (SVM). The above results suggest that DT algorithms are excellent candidates for detecting mobility contexts in smartphones, both in terms of performance and computational complexity.

Sound positioning using a small-scale linear microphone array

Microphone arrays, also known as acoustic antennas, have been extensively used for sound localization. Small-scale microphone arrays have especially been used in teleconferences and game consoles due to their small dimension and easy deployment. In this article, we present an approach to locating a sound source using a small linear microphone array. We describe the fundamentals of linear microphone arrays and analyze the impact of geometry in terms of positioning accuracy using the dilution of precision (DOP) concept. The generalized cross-correlation (GCC) based on the phase transform (PHAT) weighting function is used to estimate the time difference of arrivals in a microphone array. Given the time differences, we use both closed-form and iterative optimization solutions to calculate the coordinates of the sound source. In order to evaluate the performances of the solutions applied in this paper, simulations and field tests were conducted. Simulation results show that the closed-form algorithm gives a positioning error of less than 5 cm in a 10-by-10 meter room when the geometry of a microphone array is good and the signal to noise ratio (SNR) is high. Linear small microphone arrays have lower performances compared to a non-linear distributed array. When the scale of a linear array is reduced, the positioning accuracy decreases dramatically. With a small linear array, the iterative optimization algorithm gives much better performance compared to the closed-form algorithm. Field tests were conducted in an 11-by-5.6 meter room using a linear array with a length of 0.23 meters. Positioning results show an average error of 0.25 meters along the axis parallel to the linear array and 0.53 meters error along the axis which is perpendicular to the linear array.

Monocular visual SLAM for tactical situational awareness

Tactical situational awareness for military applications should be based on infrastructure-free systems and should be able to form knowledge of the previously unknown environment. Simultaneous Localization and Mapping (SLAM) is a key technology for providing an accurate and reliable infrastructure-free solution for indoor situational awareness. However, indoor environments and the requirements , especially the size and weight limits of the system, make the implementation of SLAM using existing algorithms challenging. In particular, we aim to implement SLAM using a monocular camera, due to size limitations, whereas most existing algorithms use stereo images. The two major obstacles to be overcome are the unknown scale of translation observed using a monocular camera and the shortage of features indoors, complicating visual perception. Herein, a Kalman filter based SLAM solution is discussed, utilizing a concept called visual odometry that provides absolute translation information with a reduced number of features. The results show that our solution is feasible for performing SLAM indoors using a monocular camera.

Maritime Safety – Stakeholders in Information Exchange Process

This paper presents the methodology and research results on identification of potential users of the ESABALT system, which is targeted towards improving the situational awareness in the Baltic Sea region. The authors describe the technique of analysing the stakeholders involved in maritime sector processes, especially in maritime transport processes, while also taking into account their different classification criteria. The resulting list of stakeholders is used to identify system users and their classification into user profiles groups. This study will form the basis for the identification of user requirements of the ESABALT system.

ESABALT Improvement of Situational Awareness in the Baltic with the Use of Crowdsourcing

This paper presents the key assumptions and preliminary research on an integrated system called ESABALT, for enhancing maritime safety, which incorporates the latest technological advances in positioning, e-Navigation, Earth observation systems and multi-channel cooperative communications. The most novel part of the ESABALT concept, however, is a focus on user-driven crowdsourcing techniques for information gathering and integration. The system will consist of a situational awareness solution for real-time maritime traffic monitoring via utilizing various positioning technologies; an observation system of the marine environment relevant to transportation and accidents including assessing the sea ice, oil spread, waves, wind etc.; and a methodology for context-aware maritime communication with cooperative, multi-channel capabilities. The paper presents the intelligent, novel, user-driven solution and associated services developed in ESABALT for enhancing the maritime safety in the whole Baltic area.

P3: A Public Precise Positioning Service based on a national GNSS network

In this article we present the P3 (Public Precise Positioning) Service project where network GNSS data are combined with a consumer-grade receiver. P3 utilizes the Finnish national FinnRef GNSS network which is operated by the Finnish government and available free of charge to the general public. The project investigates the use of network real-time kinematics (RTK) and real-time precise point positioning (PPP) techniques in order to achieve a horizontal positioning accuracy better than 0.5 meters using a low-cost GNSS receiver. An online survey was conducted to identify potential users and applications of the service, and initial positioning results presented in the paper support the feasibility of the goal.