Pavel Davidson

Application of particle filters for indoor positioning using floor plans

This paper presents a numerical approach to the pedestrian map-matching problem using building plans. The proposed solution is based on a sequential Monte Carlo method, so called particle filtering. This algorithm can be adapted for implementation on real-time pedestrian navigation systems using low-cost MEMS gyroscopes and accelerometers as dead-reckoning sensors. The algorithm reliability and accuracy performance was investigated using simulated data typical for pedestrians walking inside building. The results show that this map-aided dead reckoning system is able to provide accurate indoor positioning for long periods of time without using GPS data.

A Survey of Selected Indoor Positioning Methods for Smartphones

This paper provides an overview of the most significant existing methods for indoor positioning on a contemporary smartphone. The approaches include Wi-Fi and Bluetooth based positioning, magnetic field fingerprinting, map aided navigation using building floor plans, and aiding from self-contained sensors. Wi-Fi and Bluetooth based positioning methods considered in this survey are fingerprint approaches that determine a user’s position using a database of radio signal strength measurements that were collected earlier at known locations. Magnetic field fingerprinting can be used in an information fusion algorithm to improve positioning. The map-matching algorithms include application of wall constraints, topological indoor maps, and building geometry for heading correction. Finally, methods for step counting, step length and direction estimation, orientation tracking, motion classification, transit mode detection, and floor change detection in multi-storey buildings are discussed.

Distributed Indoor Positioning System With Inertial Measurements and Map Matching

Accurate position information is nowadays very important in many applications. For instance, maintaining the situation awareness in command center in emergency operations is very crucial. Due to signal strength attenuation and multipath, Global Navigation Satellite Systems are not suitable for indoor navigation purposes. Radio network-based positioning techniques, such as wireless local area network, require local infrastructure that is often vulnerable in emergency situations. We propose here a distributed system for personal positioning based on inertial sensors. The system consists of an inertial measurement unit (IMU) connected to a radio carried by a person and the server connected to another radio. Step length and heading estimation is computed in the IMU and sent to the server. On the server side, the position is estimated using particle filter-based map matching. The benefit of the distributed architecture is that the computational capacity can be kept very low on the user side, which leads to long operation time as power consumption also remains very low.

Application of particle filters to a map-matching algorithm

This paper presents a numerical probabilistic approach to the map-matching problem within the framework of the Bayesian theory. The proposed solution is based on the sequential Monte Carlo method—the so-called particle filtering. This algorithm can be adapted for implementation on real-time portable car navigation systems equipped with GPS or dead reckoning sensors. The reliability and accuracy of this algorithm were investigated using simulated data and data from real-world driving tests in urban environments.

Uninterrupted portable car navigation system using GPS, map and inertial sensors data

This paper presents the development of car navigation system for portable navigation devices and car telematics applications. The objective was to develop a system that can provide uninterrupted reliable navigation even when GPS signals are not available. The approach uses digital maps, 3D accelerometer and one gyro for directional measurements to improve positioning availability and reliability in weak signal environment and during short GPS signal outages. This system does not require vehicle installation and can be easily transferred between vehicles. Loosely coupled extended Kalman filter and probabilistic map-matching algorithm provide optimally tuned navigation solution and continuous auto calibration of inertial sensors. A real-time prototype was built. The system accuracy performance was investigated using field tests in an urban environment.

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.

Algorithm for pedestrian navigation combining IMU measurements and gait models

This paper presents a novel approach to INS velocity aiding in autonomous pedestrian navigation systems with body-mounted IMU. The proposed solution uses a kinetic model of human gait as a virtual velocity sensor. In this paper we show how an understanding of INS error dynamics and knowledge of human motion help to curb the divergence of INS computed horizontal velocity and tilt errors. Heading and heading gyro drift cannot be corrected with this method and require some additional procedures. This algorithm is based on Kalman filter and can be adapted for implementation on real-time pedestrian navigation systems equipped with 6 DOF IMU. The algorithm accuracy performance was investigated using data from indoor walking tests.

Inertial Sensors and Their Applications

Due to the universal presence of motion, vibration, and shock, inertial motion sensors can be applied in various contexts. Development of the microelectromechanical (MEMS) technology opens up many new consumer and automotive applications for accelerometers and gyroscopes. The large variety of application creates different requirements to inertial sensors in terms of accuracy, size, power consumption and cost. It makes it difficult to choose sensors that are suited best for the particular application. Signal processing methods depend on the application and should reflect on the physical principles behind this application. This chapter describes the principles of operation of accelerometers and gyroscopes, different applications involving the inertial sensors. It also gives examples of signal processing algorithms for pedestrian navigation and motion classification.

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.

Navigation algorithm combining building plans with autonomous sensor data

This paper presents an approach to navigation system’s position and heading correction using building floor plans. The algorithm includes three steps: (a) autonomous sensors data processing to obtain position and heading, (b) map-matching correction, and (c) navigation system errors estimation. A particle filter is used to incorporate the building plan information and a Kalman filter estimates the dead reckoning error states. This algorithm was designed for vehicle navigation systems operating inside buildings with known floor plans and can be adapted for implementation on real-time navigation systems using low-cost MEMS gyroscope and speed sensor as dead reckoning instruments. The real-world data collected from the vehicle indoor tests has shown that the proposed algorithm is able to correct significant errors in dead reckoning position and heading by applying the map constraints.