Indoor Localization Carick Wienke Advisor: Dr. Nicholas Kirsch University of New Hampshire ECE 791H Using a Modern Smartphone

Problem Description A firefighter is trying to navigate through a burning building but the route is blocked and he needs to find another… System for precise indoor localization Other applications as well – Find a route or determine your position inside an unknown building Current systems do not work efficiently – GPS fails without line of sight to the sky – WLAN Trilateration is not precise enough for most applications

Possible Solutions No Infrastructure – Can be implemented in any building – Greater error than other systems New Infrastructure – Can be designed and installed to provide precise localization – System only works in certain buildings – High cost, especially in larger buildings

Possible Solutions (cont.) Common Infrastructure – Builds upon the infrastructure that is commonly found in buildings, e.g. Wireless LAN Access Points (APs) – Can be applied to most buildings – Only cost due to the receiver – Error will not increase over time – Infrastructure is not designed for localization, leading to a less precise position estimation

Our Solution Compliment WLAN trilateration with direction of movement Modern Smartphone for measuring, calculating, and displaying information – WLAN Connectivity – Accelerometer – Compass / Magnetic Sensor – Dell Streak

WLAN Trilateration Inputs – Location of the APs – Received signal strength (RSS) from each of the AP Output – An area of probable location – Selecting a single point

Direction of Movement Input – Orientation of device relative to the ground Provided by Android API – Direction of movement relative to the phone 3 accelerometers provide data relative to the device Assuming the device is parallel to the ground reduces the complexity, using only 2 accelerometers – Orientation of phone relative to the building Compass Output – Velocity vector

Synthesis Comparing the direction of movement with the geometry of the floor plan can decrease the estimated area. If the device is moving right, it is reasonable to assume that is moving toward the hallway and not the wall. Maximum Likelihood Function WLAN Trilateration and Direction of Movement

Budget Dell Streak $550 Image from Dell Streak product webpage

Project Timeline

Basic Application Become familiar with programming, compiling, and loading applications for the Streak Create a “Hello World!” application which will simply display text on the device. Extend to display the WLAN RSS for all the APs within range Phase 1: 1 – 2 weeks

Display Map Storing a digital map on the device that allows – To draw to screen easily – To check for collisions between the path and walls State Machine Phase 2: 5 – 8 weeks

Display Map (cont.) Ability to zoom, translate, and rotate the map on the screen – Complexity can be reduced by using constant zoom and rotation Testing – Does it display properly? – Hardcode different zooms, translations, and rotations. Do they display properly? Phase 2: 5 – 8 weeks

WLAN Trilateration Calculates a probable range of distance that the device is from each AP Create an algorithm to approximate location based on these distances Testing – Check approximate location under a number of circumstances Stationary vs. moving Different locations, rooms – How is the response time? Does it occur in real time? Phase 3: 8 – 10 weeks

Accelerometer and Compass Data Mining Store/Print information about the readings from the sensors Compare human movements like walking, running, turning, etc. to the sensor reading Phase 4: 2 – 3 weeks

Compute Direction of Movement Create an algorithm to determine how the person is moving from the sensor readings Draw an arrow/symbol on the screen Testing – Move around in various ways – Compare computed movements to actual movements made – How is the response time? Does it occur in real time? Phase 5: 3 – 4 weeks

Synthesize Information Calculate a location from both trilateration and movement information – Determine WLAN trilateration heat map – Reduce map based direction of movement – How heavily are the two parts weighed to calculate the one point? Testing – Same as Phase 3: WLAN Trilateration Phase 6: 6 – 8 weeks

Compare Methods Gather enough data to compare the precision and accuracy of the two methods How does including direction of movement improve localization? Is error reduced? How does the system perform compared to other systems that have been created? What other information provided by the device can be incorporated into the algorithm? Phase 7: 2 – 3 weeks

Questions?