Tutorial on Exploiting Rich Information in WSNs: A Case for Low Power Radar

1. Tutorial on Exploiting Rich Information in WSNs:A Case for Low Power Radar Anish Arora The Samraksh Company samraksh.com

2. Main motivation • People sensing and activity monitoring is of broad and growing interest • Attempt to address false alarm challenge at scale • Using robust motion detection, tracking, classification, counting building blocks vs

3. Need for Information Rich Sensors • People monitoring applications need information rich sensors • Traditional WSN sensors are inadequate • Point sensors (e.g., temperature) • Tripwire sensors (e.g., PIR) • Pressure wave sensors (e.g., acoustic) • Video image analysis sort of or mostly works • But high power & high cost • Not really WSN, wired power and high bandwidth • WSN community has spent lots of time on networking and not enough on sensing • We focus on low-cost, low-power PDRs

4. Outline Video overview Radar concepts, BumbleBee, relative resolution via phase Research results Displacement detection Fine-grain and Coarse-grain Tracking Gait classification People counting Conclusions

5. Generated Pulse 1 0.5 Relative Signal Strength 0 -0.5 -1 -20 0 20 40 60 Time in Nanoseconds Pulsed Radar (versus Continuous Wave) Concepts: Pulse Width/Length Pulse Power Pulse Repetition Frequency Duty Cycle Average Power Cont. Wave=100% Duty Cycle

6. Complex Output PDRs Generate two pulses 90 degrees out of phase Correlate them with the same reference pulse Produce in phase and quadratureresponses I & Q Treat as one complex measurement

7. Coherent Radars • When signals are the same at each time they add coherently • noise typically is not coherent • integration over N pulses increases SNR by N • useful when signal buried in the noise, i.e. SNR<0 • For ground radars the background is as large as the returns from a human • unlike traditional aerial radars, so coherent radars suit

8. Phase is a Function of Range We are measuring range Measurement has high local precision Measurement has no global information Range measurement has high information: But is ambiguous Phase determines range plus or minus integer multiple of the wavelength With range gating, the set of multiples has cardinality of 10 to 100 (not millions)

9. Phase Unwrapping A temporal sequence of the phase reveals the relativerange Converting the “wrapped” phase to relative range is known as “phase unwrapping” Equivalent to tracking phase changes

10. Phase Unwrapping Errors But noise will cause unwrapping errors “Wrap” the origin when you shouldn’t Didn’t “wrap” the origin when you should Key problem: errors have permanent effect But errors are relatively rare Phase → Unwrap → Curve Fit → Differentiate → Velocity Profile 2 Unwrapped Phase Ideal Phase 1.5 1 0.5 Rotations 0 -0.5 -1 0 10 20 30 40 50 60 70 80 90 100

11. 5 Weak Sig. Strong Sig. In Phase Component Comb. Sig. 0 -5 0 0.2 0.4 0.6 0.8 1 0.5 Weak Sig. Strong Sig. Rotation Comb. Sig. 0 -0.5 0 0.2 0.4 0.6 0.8 1 Multiple Targets

12. Multiple Targets (cont.) • Returns from multiple targets are mixed • Returns tends to vary greatly • 1/R4effect makes slightly closer targets significantly stronger • Wide range of RCS • As a result one of the targets tends to be dominant • Lesser targets introduce only modest wobble about the dominant target • Only slight dominance is required • A human: • A collection of several returns moving in close proximity • A complex non-static formation • Still looks like a single smoothly moving target

13. The BumbleBee Radar • A coherent, complex output Doppler radar • Not a ranging radar; only one range bin • Provides complex Doppler returns (e.g., separates positive and negative frequencies) • WB but not quite UWB • ~100 MHz of bandwidth • UWB requires significant computing power for the receiver, or expensive electronics • Short range, low power, low cost • 10 m range • $100 in quantity one

14. Displacement Detection Brush blowing in the wind causes serious false alarm problems Ground based radars tend to be looking up at the trees Often large cross sections; may be larger than the targets Trees move back and forth, but stay in one place Targets of interest don’t stay in one place Detect displacements larger than a few meters Use phase unwrapping

15. Pendulum Tracking Place two radars 90º apart and track a 2d pendulum

16. Network Tracking

17. Gait Classification How to look at motion in the frame of reference of the target: Track the main return, using phase unwrapping Demodulate the signal using this “main” return The residual is the “Doppler” with respect to main motion Motion of human legs exhibits a characteristic pattern Two pendulums exactly out of phase with each other What we call a “butterfly” pattern Not present when a dog walks through field of view

18. People “Counting” Really, estimation of people count Spectral pattern and energy level varies significantly with type of activity However, given a kind of activity, total energy scales with number of people in the scene Useful when type of activity (e.g., standing in line) is known Also more people result in spectral fill-in Even if counts are only accurate to 10 or 20%, still useful Ongoing research, maybe better