Tracking Mobile Nodes Using RF Doppler Shifts

1. Tracking Mobile Nodes Using RF Doppler Shifts Branislav Kusy Computer Science Department Stanford University Akos Ledeczi, Xenofon Koutsoukos Institute for Software Integrated Systems Vanderbilt University

2. Tracking Mobile Objects Problem definition: keep track of location and velocity of “cooperating” moving objects continuously over time.

3. Our Contributions • We propose a novel tracking algorithm that utilizes RF Doppler shifts • We develop a technique allowing us to measure RF Doppler shifts using low cost hardware • Mica2: 8MHz CPU and 9kHz sampling rate • We evaluate our algorithm both experimentally and in simulation

4. Utilizing Doppler Effect • Single receiver allows us to measure relative speed.

5. Utilizing Doppler Effect • Multiple receivers allow us to calculate location and velocity of the tracked node.

6. f’ = f + Δf Δf = - v / λf v is relative speed of source and receiver λfis wavelength of the transmitted signal Doppler Effect • Assume a mobile source transmits a signal with frequency f, and f’ is the frequency of received signal source Jose Wudka, physics.ucr.edu

7. Can we Measure Doppler Shifts? Intriguing option: if we can utilize radio signals, no extra HW is required Solution: radio interfereometry

8. 430MHz A 430MHz+300Hz 300Hz Measuring Doppler shift We use radio interferometry to measure Doppler frequency shifts with 0.21 Hz accuracy. • 2 nodes T, A transmit sine waves @430 MHz • fT, fA • Node Si receives interference signal (in stationary case) • fi = fT – fA • T is moving, fi is Doppler shifted • fi = fT – fA + Δfi,T • (one problem: we don’t know the value fT-fA accurately) T Si + Δfi,T Beat frequency is estimated using the RSSI signal.

9. f4 = fT – fA + Δf4 = fT – fA + v4/λT Non-linear system of equations! Formalization We want to calculate both location and velocity of node T from the measured Doppler shifts. Unknowns: • Location, velocity of T, and fT-fA x=(x,y,vx,vy,f^) Knowns (constraints): • Locations (xi,yi) of nodes Si • Doppler shifted frequencies fi c=(f1,…,fn) Function H(x)=c:

10. Experiment: • 1 mobile transmitter • 8 nodes measure fi • Figure shows objective function for fixed (x,y) coordinates Tracking as Optimization Problem • Non-linear Least Squares (NLS) • Minimize objective function ||H(x) – c|| • What’s the effect of measurement errors?

11. Experiment: • tracked node moves on a line and then turns • KF requires 6 rounds to converge back. Improving Accuracy • State Estimation: Kalman Filter • Measurement error is Gaussian • Model dynamics of the tracked node (constant speed) • Accuracy improves, but maneuvers are a problem

12. Resolving EKF Problems • Combine Least Squares and Kalman Filter • Run standard KF algorithm • Detect maneuvers of the tracked node • Update KF state with NLS solution • Dilemma: how much to trust our measurements

13. Calculate location and velocity using Kalman filter. Extended Kalman filter Location & Velocity Maneuver detection Non-linear least squares Run a simple maneuver detection algorithm. Yes No NLS Location & Velocity Location & Velocity If maneuver is detected, calculate NLS solution and update EKF state. Update EKF Show location on the screen. Updated Location & Velocity Tracking Algorithm Infrastructure nodes record Doppler shifted beat frequency. Doppler shifted frequencies

14. Experimental Evaluation • Vanderbilt football stadium • 50 x 30 m area • 9 infrastructure XSM nodes • 1 XSM mote tracked • position fix in 1.5 seconds Non-maneuvering case 14

15. Experimental Evaluation • Vanderbilt football stadium • 50 x 30 m area • 9 infrastructure XSM nodes • 1 XSM mote tracked • position fix in 1.5 seconds Maneuvering case Only some of the tracks are shown for clarity. 15

16. Conclusions • Introduced novel tracking algorithm that utilizes Doppler shift measurements only • Doppler shifts can be accurately measured using radio interferometry • Improved EKF performance in maneuvering case • Feasibility of our approach shown experimentally