Feasibility and Limits of Wi-Fi Imaging

Donny HuangRajalakshmi Nandakumar Shyamnath Gollakota

(University of Washington)

SenSys-2014

1. Introduction2. Related Work3. Wision’s Design & Implementation4. Evaluation5. Conclusion

Goals

To explore limits of imaging using Wi-Fi signals

To describe and evaluate a prototype – Wision

Approach Overview

2D antena array (4x1 to 8x8 USRP-N210s, 2.4GHz)

Approach Overview

Use of information from Wi-Fi signal (OFDM)

Transform echo of Wi-Fi signal to image visible to humans

Extract the depth information

Line-of-sight and non-line-of-sight scenarious

Static infrastructure with several antennas

Based on prior work in domains

• Wi-Fi localization and device-free sensing (MUSIC etc.)• Motion detection• RF Image Sensing (e.g. radar systems)• Radio Tomography Imaging• Magnetic Resonance Imaging (MRI)• Optics• Microscopy• Computational Photography

Advantages of Wi-Fi over Radar Imaging

Cost of Wi-Fi chipset is lower than a radar device

Bi-static systems have wider range of operation (for the same power level)

Wi-Fi devices needed for “lighting” are already there

Challenges using Wi-Fi for Imaging

Wi-Fi offer much lower bandwidth than is required for radar imaging method (20MHz -> 7.5m resolution)

OFDM-based transmissions are not designed for imaging purposes (OFDM – Orthogonal Frequency Division Multiplexing)

Wision Design

Different approach: Measuring the incoming signal at each azimuth and elevation angle – like in optical imaging systems

Signal is a linear combination of reflections from multiple regions on each antenna

Separation of signals from different directions is done by – multiple antennas and Fourier analysis

Wision’s Imaging Algorithm

1. Mapping directions to basis functions

Wision’s Imaging Algorithm

1. Mapping directions to basis functions

Wision’s Imaging Algorithm

2. Extracting images from basis functions

(2D Fourier Transformation)

Wision’s Imaging Algorithm

Algorithm summary:

1. Initialization. The receiver measures phase and magnitude information from all of its antennas.

2. Step 1. Compute the azimuthal (ψ) and elevation (α) angles for a given region in space.

3. Step 2. Compute the corresponding intensity value by performing 2D FFT.

4. Step 3. Repeat Step 1 and Step 2 for every region in the 2D-space to generate image visible by human eye.

Wision with One-Dimensional Antenna Array

Accounting for the Wi-Fi Transmitter

• Removing direct strong Wi-Fi transmissions using multiple antennas and interference nulling

• Using multiple transmitters

Extracting Depth Information

Beamforming at the transmitter

Wi-Fi Interaction with Objects

Factors:

• Material of the object• Size of the object (~ to the wavelength, 12cm at 2.4GHz)• Diffraction effects

Implementation

Wision prototype:

• USRP-N210 hardware (Unified Software Radio Peripheral)• 2.4 GHz frequency• 10 MHz bandwidth• 8x8, 4x4, 8x1, 4x1 antenna arrays• Linear actuator (8 x 8, 6cm interval)• wa5vjb or hg2415g directional antenna, 0..180o, by step

10o (for beamforming tests)

Imaging Using 1-D Antenna Arrays

Objects at different receiver directions

2 metallic objects (16x20x5cm)

8x1 antenna array at receiver

Imaging Using 1-D Antenna Arrays

Objects at different receiver depths. Experiment 1

2 metallic objects (44x44x18cm) at 80o(2m) and 130o(0.8m) from the receiver

8x1 antenna array at receiver, directional antenna wa5vjb at transmitter

Imaging Using 1-D Antenna Arrays

Objects at different receiver depths. Experiment 2

2 objects – a stack of books and a laptop one behind another (at 1 and 3 meters)

8x1 antenna array at receiver, hg2415g directional antenna at transmitter

Imaging Using 1-D Antenna Arrays

Objects at different receiver depths. Observed limitations

1. Orientation of objects is a key factor (light must reach the receiver)

2. If objects of different materials are used, objects with material with strong reflection will dominate in a image

Imaging Using 2-D Antenna Arrays

T-shaped metallic object8x8 antenna array as a receiver, 1-2 antennas as a transmitter

Imaging Using 2-D Antenna Arrays

Observed limitations

1. 10 minutes per image with 8x8 antenna array (moving antenna by hand)

2. Objects with material with strong reflection will dominate in a image

Imaging in NLOS Scenarios

Imaging in NLOS Scenarios

Observed limitations

1. As the barrier would become thicker and from more reflective material – the less transparent it would be

2. In the experiment setting coach was very near to wall. Its mainly because OFDM implementation on USRP’s did not work good with high power levels (due to hw non-linearities)

Proof-of-Concept Applications

1. Localizing Objects without Tagging Them

• Room dimensions 35m2 with furniture (coach, tables, chairs)• Object to localize - desktop 44x44x18cm• Room does not have other similar objects• At 10 different locations:• Imaging using 2 antenna array receivers• Combine• Search for a high reflection area (accuracy 14cm)• Localization error computed

• Beamforming and nulling algorithms

Proof-of-Concept Applications

1. Localizing Objects without Tagging Them

Proof-of-Concept Applications

2. Static Human Localization

• Human, male, 174cm, 65kg• Setting as above (but no furniture)• 10 different locations (static)• Other humans moved around

Proof-of-Concept Applications

2. Static Human Localization

Proof-of-Concept Applications

3. Imaging Through-the-fabric Scenarios. Detecting laptop in backpack

Proof-of-Concept Applications

4. Imaging Through-the-fabric Scenarios. Detecting phone in the pocket

Micro-Benchmarks

Effect on Size

Micro-Benchmarks

Effect on Material

Micro-Benchmarks

Effect on Orientation

The Limits of Wi-Fi Imaging

Object Size and Material

Imaging Resolution

Orientation of Objects

Questions?