Particle Filters for Location Estimation in Ubiquitous Computing: A Case Study Jeffrey Hightower and Gaetano Borriello Intel Research and University of Washingon, CS and Eng.

The point Location is a big part of context Ubiquitous computers need to guess location without direct input Several different types sensors for one application is more effective in guessing location

Current Technology Sensing technologies: GPS, infrared, ultrasound, WiFi, vision, etc… Each has its own strengths and weaknesses Most systems designed to a specific type of sensor, rigid algorithms

Particle Filters “Probabilistic approximations” to track location of robots Not modeled on any one type of sensor Combines data from various objects in the environment Machine learning capabilities increase accuracy but diminish generality

Experiment Procedure Building equipped with a combination of infrared, ultrasound, and WiFi tracking systems “Ground truth” collected by highly accurate robot traveling through the building making human-like motions while wearing badges, RFID tags and other devices Tracked for 15 minutes ultrasound and 537 infrafed measurements

Experiment Results Compared accuracy of deterministic position algorithms (Point, Centroid, Smooth Centroid, Smooth Weighted Centroid) with particle filters Particle filter as accurate as any other algorithm, much more accurate when different sensors were combined

What Does This Mean? Using multiple location-sensing technologies produces more accurate results because they each have different strengths/capabilities Particle filters designed to be non- specific, so they’re a good choice of algorithm

Tradeoffs Particle filters require more computation time and memory…but many devices can handle it and processor speeds keep going up Accuracy greatly increases with repetition…but might take a while to learn

Implications Seamless location-sensing between outdoor and indoor environments Applications/devices unaware of the sensing technologies they depend upon Potential ability to infer human activity, situational context other than location

Discussion Do we really want to be weighed down with all these badges, or are we going to wait until the sensing technologies are combined? Why does a device need to know where we are? What are the advantages? What if it’s wrong? Do we want our computers to stalk us? How do we turn it off? Minority Report -- cut out our eyes…