Next Generation Personal Navigator:

An Enhanced Prototype Personal Inertial Navigation System

Yunqian Ma, PhD

Honeywell Aerospace WPI workshop

August 6, 2012

2

Personal Navigation Overview

• What is personal navigation ?

- Provides localization to humans on foot

Includes GPS denied environments

• Who needs PN?

- Situations that require personnel accountability, situational awareness

Soldiers, first responders, industrial workers

DHS funded GLANSER program focused at ER/firefighter community

• Product differentiators

- Cost, SWaP, accuracy

User community has high expectations based on cell phone /GPS experience

IMU

GPS

Antenna

Radio

3

Measurement Sources

- Visual odometry (Camera, LIDAR, …) – measures position change relative to recognized features

Requires image processing, accurate attitude

Require system calibration

Realized accuracy (stereo camera): 4%

- Zero Velocity Aiding (ZUPT)

Boot mounted IMU can achieve accurate navigation

Zupt occurs at every heel strike – limits free navigation time to ~1 second

Customers resistant to boot mounted hardware

4

Evolution of ePINS: Prior projects

• SUO SAS (DARPA/IR&D 1999)

- HG1700 IMU, walking motion model, DGPS, mag, baro, (MFMU)

- Manual initialization, gyrocompass alignment

- Very large SWaP (full size backpack)

• iPINS (DARPA 2005)

- HG1930 IMU

- Manual heading initialization

- Extended motion model to running, non-forward walking

- Waist pack

5

ePINS Objectives

• Develop a ePINS prototype

- Use iPINS design as baseline

ECTOS Nav/Kalman software

- Update hardware components

Block C HG1930 IMU

ANGIE navigation processor

Commercial GPS receiver

- Redesign package – miniaturize as much as possible

Belt mount

- Incorporate IR&D developments

LPI Heading initialization method

Wavelet based motion model classification

Motion model altitude control

Android mobile display besides PC display

6

ePINS Components

• ePINS - enhanced Personal Inertial Navigation System

Operating Motion Performance at end point

(Closed Path@ 1 Hour)

Walking <2% distance traveled

Combined (walking, running

crawling) < 4% distance traveled

Package

Volume (with battery) 613 cm3

Weight 454 g

Power ~5 watts

Operating life 4 hours

Interfaces

RS-422 Serial

Standard NMEA outputs and

custom NMEA inputs and outputs

Display Interface

Ethernet Data Logging,

Display Interface

USB Data logging

Preliminary Prototype Specification Product component, sub-system,

and package

ePINS Package

HG1930 IMU Microprocessor Board

NovaTel GPS

Receiver

HMC6343 Compass

Pressure Sensor

7

ePINS Assembly

Leather

or cloth belt

loop

GPS

receiver

Switch

Battery

Plastic Housing

Pieces (3 each)

Aluminum Housing

(1 each)

IMU -

HG1930 Circuit Card

Assembly

(Attaches to

plastic

housing)

8

Hardware Architecture

9

Approach

• Heading Initialization

- Developed Low Performance IMU (LPI) heading determination method

User walks a few circles in presence of GPS

Capable of determining heading to 0.6 deg

• Improve distance traveled estimate while allowing individual to move in a more natural manner

- Motion classification using wavelet domain classifier

- Step model algorithm was developed to improve distance traveled estimate

Provides an estimate of the length of each foot step based on delta time between footfalls

• Integration of inertial navigation with the distance traveled estimate to achieve optimal performance

10

Motion Classification Algorithm

• Segment IMU signal based on peak or valley of the IMU data

0 500 1000 1500 2000 2500 3000 3500 4000-0.01

0

0.01

0.02Wes Running [2921-2929]

dr x

(r/

s)

0 500 1000 1500 2000 2500 3000 3500 4000-0.04

-0.02

0

0.02

dr y

(r/

s)

0 500 1000 1500 2000 2500 3000 3500 4000-0.01

0

0.01

0.02

dr z

(r/

s)

0 500 1000 1500 2000 2500 3000 3500 4000-0.5

0

0.5

dv x

(g)

0 500 1000 1500 2000 2500 3000 3500 4000-1

-0.5

0

dv y

(g)

0 500 1000 1500 2000 2500 3000 3500 4000-0.5

0

0.5

dv z

(g)

time (s)

11

Motion Classification Algorithm

• 2 persons walking IMU data (red curve for one person, blue curve for the other person)

0 10 20 30 40 50 60 70 80 90-0.02

0

0.02

dr x

(r/

s)

0 10 20 30 40 50 60 70 80 90-0.02

0

0.02

dr y

(r/

s)

0 10 20 30 40 50 60 70 80 90-0.02

0

0.02

dr z

(r/

s)

0 10 20 30 40 50 60 70 80 90-0.5

0

0.5

dv x

(g)

0 10 20 30 40 50 60 70 80 90-2

-1

0

1

dv y

(g)

0 10 20 30 40 50 60 70 80 90-0.5

0

0.5

1

dv z

(g)

time (s)

roll

.

pitch

yaw

x

y

z ..

..

..

. .

12

Step distance model

• The step-length modeling formulated as a regression over the frequency and user’s biometric information

13

Sensor Integration: ECTOS Nav/EKF

NovaTel

OEMStar

GPSR

14

Benefits of Integration

• Navigation output is based on strapdown inertial navigation, not dead reckoning

- Provides continuous solution that is not dependent on step detection

• Kalman filter residual test is used to reject poor measurements from Step Model

- Step Model will provide erroneous results when individual moves in an “unusual” manner, relative to walking

Side stepping, walking with torso turned relative to direction of travel

- System operates in “free inertial” mode when Step Model measurements are rejected

15

Demonstration

• Startup/Initialization - System heading initialized

from GPS position and

velocity measurement

- Operation in GPS -denied

environments - Real time observation of

performance

• COTS 900 MHz data radio transmits user position to display @ 1 Hz

16

Demonstration Procedure

• Heading calibration

• Step parameter estimation with GPS

• Outdoor GPS denied (compare performance to above step)

• Transition from outside to inside (GPS aided)

• Enter building (GPS denied)

• Walk up stairs

• Walk down stairs

• End at conference room

1

2

4

5

6

7

8

3

17

ePINS Summary

• Benefits - Obtain accurate positioning in GPS denied environments

- Small, light weight unit can be easily carried by first responders, rescue workers or soldiers

- Rugged design and packaging will withstand the most difficult first responder and military environments

• Features - Incorporates state-of-the-art Micro Electromechanical Systems

(MEMS) gyros and accelerometers, 3 axis magnetic compass, barometric altitude sensor and advanced navigation software

- Patented motion classification algorithms accurately identify and measure user activity

- System automatically adapts navigation parameters to the user, achieving outstanding accuracy without calibration

- High performance MEMS gyros eliminate errors caused by magnetic disturbances

18

Future ePINS concept

• Future performance and physical characteristic