ABSTRACT
This paper presents a cognitive approach for a reliable yet battery-friendly personal positioning. A user's position is learned from both historical log and possible measurements. Firstly, user's past activities recorded in the log are summarized into an activity map. Accordingly, a user-habit guided particle filtering algorithm is presented for position prediction. Specifically, our algorithm makes reference to the map to determine the most probable correct position, smoothed with occasional measurement. User's current position is modeled probabilistically by a collection of particles and her future moves are modeled with a tendency to follow a familiar path on the map; The estimate is then smoothed by Bayesian filtering. We also allow the number of particles to vary according to user's position in the map. Thus, along with better insights about user's movement experience, our approach can learn from the past and potentially improve the quality of estimates. Our experiments show that this adaptive filtering model using the activity map can deal with non-linear behaviors rather effectively. The new cognitive scheme can indeed track the user's position with a high degree of accuracy. Moreover, the algorithms exhibit low computational complexities, making them well suited for applications on wearable computers.
- M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Trans. on Signal Proc., 50(2):174--188, 2002. Google ScholarDigital Library
- F. Aurenhammer. Voronoi diagrams - a survey of a fundamental geometric data structure. ACM Comput. Surv., 23(3):345--405, 1991. Google ScholarDigital Library
- M. Bolic, S. Hong, and P. Djuric. Performance and complexity analysis of adaptive particle filtering for tracking applications. In 36th Conf. on Signals, Sys. and Comput., pages 853--857, 2002.Google ScholarCross Ref
- J. Carpenter, P. Clifford, and P. Fearnhead. An improved particle filter for non-linear problems. In IEEE Proc. of Radar, Sonar and Navigation, number 146, pages 2--7, 1999.Google ScholarCross Ref
- A. Doucet, N. Freitas, and N. Gordon. Sequential Monte Carlo Methods in Practice. Springer, 2001.Google ScholarCross Ref
- H. Durrant-Whyte and T. Bailey. Simultaneous localisation and mapping (SLAM): The essential algorithms. Robotics and Automation Magazine, 13:99--110, 2006.Google ScholarCross Ref
- H. Fang. Cognitive personal positioning based on activity map and bayesian filters. Technical Report TR-09-FH03, Singapore-MIT Alliance, 2009.Google Scholar
- R. Kalman. A new approach to linear filtering and prediction problems. Trans. of the ASME - Journal of Basic Engineering, 82:35--45, 1960.Google Scholar
- T. Manesis and N. Avouris. Survey of position location techniques in mobile systems. In the 7th Int. Conf. on Human Computer Interaction with Mobile Devices and Services, pages 291--294, Austria, 2005. Google ScholarDigital Library
- S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach, 2nd Edition. Prentice Hall, 2003. Google ScholarDigital Library
Index Terms
- Cognitive personal positioning based on activity map and adaptive particle filter
Recommendations
Edge computing-enabled green multisource fusion indoor positioning algorithm based on adaptive particle filter
AbstractEdge computing enables portable devices to provide smart applications, and the indoor positioning technique offers accurate location-based indoor navigation and personalized smart services. To achieve the high positioning accuracy, an indoor ...
ZigBee-based indoor localization system with the personal dynamic positioning method and modified particle filter estimation
We introduce a portable Wireless Sensor Network; which characterized by its great precision, fast detection, real time-monitoring and cheapness. The received signal strength indication (RSSI) is used for estimating the location of the target based on ...
An Enhanced Particle Filter Algorithm with Map Information for Indoor Positioning System
2019 IEEE Global Communications Conference (GLOBECOM)Recently, the demand for indoor positioning has gradually increased. Considering that people walk indoors with a serious restriction, the map information is extremely significant, which can be used as an aid in indoor positioning. In order to exploit map ...
Comments