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

Anish Arora

The Samraksh Company

samraksh.com

Anish Arora

The Samraksh Company

samraksh.com

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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

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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

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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

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Range OffSet in Nanoseconds

Cor

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Correlation

Pulsed Radar (versus Continuous Wave)

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Generated Pulse

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Concepts: Pulse Width/Length Pulse Power Pulse Repetition Frequency

Duty Cycle Average Power Cont. Wave=100% Duty

Cycle

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Complex Output PDRs

• Generate two pulses 90 degrees out of phase

Correlate them with the same reference pulse

• Produce in phase and quadrature responses

I & Q Treat as one complex

measurement

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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

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Rota

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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)

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Phase Unwrapping

• A temporal sequence of the phase reveals the relative range

• Converting the “wrapped” phase to relative range is known as “phase unwrapping”

Equivalent to tracking phase changes

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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

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Multiple Targets

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Multiple Targets (cont.)

• Returns from multiple targets are mixed• Returns tends to vary greatly

1/R4 effect 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

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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

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rsDisplacement 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

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Pendulum Tracking

• Place two radars 90º apart and track a 2d pendulum

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Network Tracking

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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

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Dual Out of Phase

Pendulums

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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

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Power Spectral Density

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