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WiCeNav – UAV Position Estimation via WiFi and Cellular Network Signals

Note

This is a survey of the current solution landscape.

This project is ongoing.

3 ‘solutions’ presented, 2 are not viable (this is explored in detail).

Done this way to document problem areas in available solutions.

The Idea

My phone knows where I am, sometimes when I don’t have GPS. How?

If an app can find me without GPS, can I use that same functionality in a payload?

High Level Overview

WiCeNav provides GPS-like data in the absence of a GPS signal.

It utilizes WiFi and cell network data to do that.

It is a stand alone payload that requires no ground station.

The payload uses COTS products and services, managed by a set of C++ applications running at the same time.

It is plug-and-play into your autopilot, feeding in NMEA sentences.

Section 1: Need To Know

NTK – Previous Work

AFSL

Position estimation via ADS-B Transponder and Local Area Multilateration [15]

Relative position measurement of visually distinct objects for UAV guidance [16]

Other - Specific to UAVs

Zak Kassas – Software Defined Radio, Signals of Opportunity [1]

FaceBook, Google – Mobile base stations [2],[3]

Other - More Generally

Android and IOS location services [4],[5]

NTK – 3GPP

Specification writing organization, defines what location services are available in what networks (among many other things).[6]

NTK – Supported Methods

From the latest LCS spec[14]:- cell coverage based positioning method;- OTDOA positioning method;- A-GNSS based positioning methods;- UTDOA positioning method;- Barometric pressure sensor method;- WLAN method;- Bluetooth method;- Terrestrial Beacon System method.

NTK – The Issue With 3GPP

The delta between spec and reality:

No one knows what portion of the spec is implemented.

The available commands are a bridge to firmware that actually interacts with the network.

No way to get the data needed for the listed methods(tower location, transmitter height).

Section 2: Hardware Overview

HO – Hardware and Signal Flow

Section 3: Software Overview

SO – General Structure

WiCe-Nav is a set of C++ applications running at the same time.

They communicate via sockets, use a publish/subscribe type pattern (~15ms latency).

Each application is simple, main loop plus essential functionality.

Multiple instances of each can be run at any time.

SO – Applications

Section 4: Investigated Methods

Algorithm Dev. Strategy

Develop necessary architecture in C++ to gather flight data needed for position estimation (method dependent).

Test algorithm in Matlab post flight.

If results look promising, implement onboard aircraft.

Experimental Data North Seattle Ground Data Route:

Experimental Data South Seattle Ground Data Route:

Experimental Data Ground Install:

Experimental Data Flight Operational Area:

Experimental Data Flight Install:

IM – TelitHE910D, ATMONI

Overview

Built in AT command ATMONI provides the data[11]

RSSI based. Get power readings for neighboring cells and estimate ranges.

Requires a sensor model to estimate distance.

Requires a database of known cell tower locations in the operational area.

Three towers for an estimate (for 3D).

IM – TelitHE910D, ATMONI Hardware/Software

IM – TelitHE910D, ATMONI Example Data:

#MONI: Cell BSIC LAC CellId ARFCN Power C1 C2 TA RxQual PLMN

#MONI: S 64 A1AF 11F1 689 -61dbm 46 46 0 0 AT&T

#MONI: N1 34 A1AF 12F5 759 -63dbm 44 44

#MONI: N2 33 A1AF FFFF 756 -68dbm 39 39

#MONI: N3 12 A1AF E20F 760 -84dbm 15 15

#MONI: N4 37 A1AF 11FA 687 -85dbm 22 22

#MONI: N5 FF FFFF 0000 691 -111dbm -1 -1

#MONI: N6 FF FFFF 0000 757 -111dbm -1 -1

IM – TelitHE910D, ATMONI Range Estimation(Sensor Model):

Simplified Friis transmission equation[7]: Where: = Power Received (RSSI) = Power Received at 1 meter = Constant = Distance from transmitter (meters)

Given RSSI and true distance the parameters , can be determined experimentally using weighted linear least squares and the Eqs:

Where: W = I = RSSI = = ,

IM – TelitHE910D, ATMONI Range Estimation Cont., Getting Distance:

Location is known via GPS receiver, Cell Tower location is not.

OpenCellID is a free DB with cell locations. [8]

Running OCD DB towers through GoogleMaps Geolocation API[9] increases accuracy (assumed).

Altitude of cell tower is gained via Elevation API.

Once truth ranges can be calculated, a per tower fit is done to get , for each cell.

IM – TelitHE910D, ATMONI Position Estimation Note:

One non-trivial detail is the difference between tower location and cell location:

IM – TelitHE910D, ATMONI Position Estimation[10]:

Once ranges can be estimated from RSSI, non-linear least squares can be used to estimate location on any reading that had at least 3 towers.

Where:

IM – TelitHE910D, ATMONI Analytic solution for ranging applications[R. Rysdyk, Insitu]:

ATMONI - Flight Data

IM – TelitHE910D, ATMONI Sanity Check, Flight Location w/ Truth, No noise:

IM – TelitHE910D, ATMONI Sanity Check, Flight Location w/ Truth, R = 10:

IM – TelitHE910D, ATMONI Solution sensitive to z noise.

Reduce to 2D, use GPS receiver altitude as stand in for altimeter data

Sanity Check, Flight Location w/ Truth, R = 10:

IM – TelitHE910D, ATMONI Results, Flight Location Estimation, 2D analytic solution:

IM – TelitHE910D, ATMONI Range Estimation, Flight Data:

IM – TelitHE910D, ATMONI Flight Data, Towers (5 Total):

ATMONI - Ground Data

IM – TelitHE910D, ATMONI Results, Ground Location Estimation, 2D analytic solution:

IM – TelitHE910D, ATMONI Ground Data, Towers (50 Total):

IM – TelitHE910D, ATMONI Range Estimation, Ground Range MAEs:

9 under 20 meters, 11 over 500

IM – TelitHE910D, ATMONI Range Estimation, Ground Data:


Issues/Sources of error:

Ranges are inaccurate, i.e. sensor model does not describe reality.

Cell = tower location assumption

Obstacles, Multi-path effects on signal

Power assumed constant

RSSI is reported as an integer


Method Conclusions:

There is usually enough towers for multilateration.

RSSI did not improve significantly while in the air (at the altitudes we flew at).

Gathering data in urban areas where noise and obstacles exist has a large impact on signal quality.

Not a viable GPS degraded/denied solution in its current form.

IM – FONA, ATCIPGSMLOC

Overview

IP Based, Built in command [12]

Returns (Lat, Long, Time(GMT)) or error

Times out at 60s

IM – FONA, ATCIPGSMLOC Hardware/Software

IM – FONA, ATCIPGSMLOC Ground Testing

IM – FONA, ATCIPGSMLOC Ground Testing,

Interpolated GPS via data point system time

IM – FONA, ATCIPGSMLOC Ground Testing,

Interpolated GPS plus towers

IM – FONA, ATCIPGSMLOC Flight Testing


Flight Testing


Method Conclusions

This method only returns serving cell location, regardless of number of cells.

Not a viable GPS degraded/denied solution.

Useful for sporadic updates or as a seed estimate for other methods.

IM – WiCe

Overview

Uses a WiFi scraper along with cell modem to get WiFi and cell network data

Sends data to Google Geolocation API.

API returns a position estimate in the form of {lat, long, error}

IM – WiCe

Example Call

IM – WiCe Hardware/Software

IM – WiCe Ground Testing

IM – WiCe Method Conclusions

Stable, updating estimate

Latency needs to be addressed immediately

Ideal version is a good candidate for GPS degraded/denied, assuming WiFi and cell data exists.

Section 5: Future Work

FW – Project Level

Immediate

Identify/Fix latency issues with WiCe

Long Term

Software filter module for upsampling/data fusion

GPS bridge


Step 1: Implement the GPS bridge and test latency and performance.


Step 2: Implement the filtering algorithm and do the same.


Step 3: Add the switch and compare deltas.

Questions? [email protected]

Bibliography [1] http://aspin.eng.uci.edu/research.html

[2] https://www.wired.com/2016/07/facebooks-giant-internet-beaming-drone-finally-takes-flight/

[3] http://mosaic-lab.org/blog-post.aspx?blgp_id=1d4995fc-d2be-4e1a-87ce-53b266974f43

[4] https://developer.android.com/training/location

[5] https://developer.apple.com/documentation/corelocation

[6] https://www.3gpp.org/about-3gpp

[7] https://ieeexplore.ieee.org/document/6184942

[8] https://opencellid.org

[9] https://developers.google.com/maps/documentation/geolocation/intro?hl=en_US

[10] J. L. Crassidis and J. L. Junkins, Optimal estimation of dynamic systems, 2nd ed. Boca Raton, FL: Chapman and Hall/CRC, 2012.

[11] https://www.telit.com/wp-content/uploads/2017/09/Telit_3G_Modules_AT_Commands_Reference_Guide_r11.pdf

[12] https://www.elecrow.com/wiki/images/2/20/SIM800_Series_AT_Command_Manual_V1.09.pdf

[13] https://www.ngs.noaa.gov/PUBS_LIB/inverse.pdf

[14] https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=834

[15] C. W. Lum, H. Rotta, R. Patel, H. Kuni, T. Patana-anake, J. Longhurst, and K. Chen,

“Uas operation and navigation in gps-denied environments using multilateration of avi-

ation transponders,” in Proceedings of the AIAA SciTech 2019 Forum, (San Diego, CA),

January 2019.

Bibliography Cont. [16] R. Svitelskyi, “A gimbal-supported, mono camera, relative position measurement system

of a visually distinct object for uav guidance,” Master’s thesis, University of Washington,

Seattle, WA, June 2019.

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