In many personal navigation applications accurate altitude information is required. Finding a correct floor in a tall building or precise guidance in multi-layer intersection is not possible with two-dimensional navigation system. Satellite navigation systems provide altitude information, but accuracy is not necessarily sufficient for this kind of situations. Barometers can be used to improve the accuracy and availability of the altitude solution. The objective of this study was to find relevant error sources of barometer based altitude in the context of personal navigation. MEMS barometer data were collected in several scenarios where different disturbances affect the pressure reading. To separate error sources, reference barometer at known altitude was used. The results show that barometers in differential mode provide highly accurate altitude solution, but local disturbances in pressure need to be taken into account in the application design.

Commonly used Global Navigation Satellite Systems (GNSS) are inappropriate as Location Based Services (LBS) in indoor environment. Therefore research teams are developing different systems, which can be used as a suitable alternative. One of options is to use Inertial Navigation System (INS) which consists of inertial sensors and mathematic procedures. This concept has been known for a long time, but with arrival of Microelectro Mechanical System (MEMS) INS found wide use. Smartphones with inertial sensors, such as accelerometers and gyroscopes, allow us to use them as input devices for Pedestrian Dead Reckoning (PDR). In this paper we present PDR by using smartphone sensors. They can be classified as low-cost Inertial Measurement Unit (IMU), and have been compared with more precise and expensive Xsens IMU. Accuracy of inertial sensors has increased in the past few years, but they still cannot alone provide proper accuracy because of many negative effects, such as heading drift due to gyroscope bias. Particle Filter (PF) has been successfully used with map constraints to increase the accuracy of proposed location system. Presented results show that low-cost smartphone IMU combined with PF can be applicable as proper navigation system.

In this paper, an adaptive activity and environment recognition algorithm running on a mobile phone is presented. The algorithm makes inferences based on sensor and radio receiver data provided by the phone. A wide set of features that can be extracted from these data sources were investigated, and a Bayesian maximum a posteriori classifier was used for classifying between several user activities and environments. The accuracy of the method was evaluated on a dataset collected in a real-life trial. In addition, comparison to other state-of-the-art classifiers, namely support vector machines and decision trees, was performed. To make the system adaptive for individual user characteristics, an adaptation algorithm for context model parameters was designed. Moreover, a confidence measure for the classification correctness was designed. The proposed adaptation algorithm and confidence measure were evaluated on a second dataset obtained from another real-life trial, where the users were requested to provide binary feedback on the classification correctness. The results show that the proposed adaptation algorithm is effective at improving the classification accuracy.

This paper presents the development of a land vehicle navigation system that provides accurate and uninterrupted positioning. Ground speed Doppler radar and one MEMS gyroscope are used to augment differential GPS (DGPS) and provide accurate navigation during DGPS outages. Using Doppler radar has advantages of easy assembling and lowcost maintenance compared to wheel encoders. The Doppler radar and gyro are calibrated when DGPS is available. Loosely coupled Kalman filter gives optimally tuned navigation solution. Field tests were carried out to evaluate the performance of the system. The results show that position accuracy of 1.5 meters can be achieved during 15 seconds DGPS outages.

A way is proposed of improving the time to first fix (TTFF) in Global Navigation Satellite System (GNSS) positioning by combining pseudo-range and Doppler positioning with external altitude information. This way the position of a stationary receiver can be resolved using two satellites only. Test results obtained using authentic satellite data demonstrate that the method is sensitive to Doppler measurement errors but can improve the TTFF significantly if the sky view is constrained as, e.g., in urban canyons.

In this paper we present a concept for automated crash detection to motorcycles. In this concept, three different inertial measurement units are attached to head of the motorist, torso of the motorist and to the rear of the motor cycle. Crash dummy tests are done by throwing the dummy with different altitudes to simulate the effect of crash to the motorist and real data is collected by driving the motorcycle. A maximum a posteriori classifier is trained to classify the crash and normal driving. The implemented prototype system shows promising results for automatic crash detection.