Jussi Parviainen

Differential barometry in personal navigation

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.

Adaptive Activity and Environment Recognition for Mobile Phones

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.

Using Doppler radar and MEMS gyro to augment DGPS for land vehicle navigation

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.

Pedestrian Dead Reckoning with Particle Filter for handheld smartphone

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.

Energy consumption analysis for green routing - Data collection from electric vehicles

The current state in navigation and route calculations is mostly concentrated offering two route options for drivers: shortest and quickest route. With the environmental awareness and fuel prices raising the energy consumption of a route should be taken into consideration. The problem in finding the route consuming the least energy is not the lack of minimization algorithms but finding correct energy values for different parts of the path. This article introduces the equipment and methods for collecting the necessary data in an efficient manner, providing base values for route parts. It also presents examples, which can later be used for algorithm development.

Automatic crash detection for motor cycles

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.

Doppler radar and MEMS gyro augmented DGPS for large vehicle navigation

This paper presents the development of a land vehicle navigation system that provides accurate and uninterrupted positioning. A ground speed Doppler radar and one MEMS gyroscope are used to augment differential GPS (DGPS) and provide accurate navigation during GPS outages. The goal is to maintain a position accuracy of 2 meters or better for 15 seconds when an accurate GPS solution is not available. The Doppler radar and gyro are calibrated when DGPS is available, and a loosely coupled Kalman filter gives an optimally tuned navigation solution. Field tests were carried out in a harbor environment using straddle carriers.

Robust Misalignment Handling in Pedestrian Dead Reckoning

Modern mobile devices consists various sensors such as accelerometers and gyroscopes that can be used to aid and complement Global Navigation Satellite System position and velocity updates. Especially these sensors are now capable of providing good navigation solutions for pedestrians with competitive battery power consumption when compared to radio navigation systems. In order to achieve this energy efficiency, the navigation solutions could primarily be based on onboard inertial sensors alone, and additional aiding systems such as GNSS or indoor map used only when requested, such as to initialize the starting position. One of the most critical problems in such mobile device navigation solutions is the inability to accurately estimate the misalignment between the pedestrian walking direction and the mobile device orientation, eventually causing the navigation algorithm to output wrong location solution. In this paper we propose methods to detect such situations and means to reduce the errors due to the misalignment estimation error.

Pedestrian detection with high resolution inertial measurement unit

Inertial sensors are used widely for detecting different contexts. However, noise components at high frequencies can disturb the recognition. In this paper, the measurements are made with multi IMU (MIMU) which combines the results of 32 inertial sensors. Averaging of individual sensor outputs reduce the noise level significantly and enables higher resolution. As an example case, we present application for passenger detection in two environments; hallway corridor and public city bus. The results show that accuracy can be increased when MIMU is used compared to single IMU.

Improving TTFF by Two-Satellite GNSS Positioning

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.