Vehicle sensor calibration for determining vehicle dynamics

• US 8,589,015 B2
• Filed: 02/12/2010
• Issued: 11/19/2013
• Est. Priority Date: 02/12/2010
• Status: Active Grant

• Current Assignee: GeoTab Incorporated
• Original Assignee: Webtech Wireless Incorporated (GeoTab Incorporated)
• Inventors: Willis, Richard Ernest, McIntyre, Richard Ian, McConnell, Peter Robert Henderson, West, Gerald Alexander
• Primary Examiner(s): Amin, Bhavesh V
• Application Number:
  US12/656,657
• Publication Number:
  US 20110202225A1
• Time in Patent Office: 1,376 Days
• Field of Search: None
• US Class Current: 701/29.1
• CPC Class Codes: G01C 25/005 initial alignment, calibration or starting-up of inertial devices
• CPC Class Codes: G01P 21/00 Testing or calibrating of apparatus or devices covered by the preceding groups

Abstract

An accelerometer sensor equipped device uses GPS and known alignment data to determine the alignment of the accelerometer sub-system when the vehicle is stationary and in motion. The alignment data is determined from known surface information, measured GPS velocity, and measured GPS Heading.

Claims

1. A method of calibrating a tri-axial accelerometer that provides uncalibrated outputs for X, Y and Z Cartesian axes resident in a locator that is installed on a mobile asset, comprising the steps of:

a) obtaining the uncalibrated X, V, and Z axes accelerometer output data;

b) determining, via a processor, in two phases and in at least one of auto and self calibration modes a coordinate transform T that is appropriate for the tri-axial orientation of the accelerometer relative to the frame of reference of the mobile asset, wherein;

the transform T is a Euler transform comprising a yaw correction angle φ

, a roll correction angle θ

, and a pitch correction angle ψ

;

the mobile asset is static on a plumbed surface in a first of said two phases, in which the roll correction angle θ and

the pitch correction angle ψ

are calculated; and

the mobile asset is moving in a second of said two phases, in which the yaw correction angle φ

is calculated;

c) applying said transform T to said uncalibrated X, Y, and Z axes accelerometer output data to obtain calibrated X, Y, and Z axes accelerometer data; and

d) removing from acceleration data, an offset created by gravity when the mobile asset is moving on a sloped terrain, comprising the steps of;

low pass filtering the Z axis acceleration data to obtain â

z;

calculating the terrain pitch;

p=arc cos(â

z/G);

determining the sign (negative or positive) of the slope; and

correcting said calibrated X axis acceleration data by subtracting therefrom G·

sin(ρ

).

2. The method of claim 1, wherein said transform T is obtained in the auto calibration mode, in which an installer of the locator drives the mobile asset in a prescribed manner during the second phase.

3. The method of claim 1, wherein said transform T is obtained in the self calibration mode, in which the second phase occurs automatically while an owner or regular operator of the mobile asset drives the mobile asset.

4. The method of claim 1, wherein the second phase of obtaining the yaw correction angle φ

has the step of gating A_lx and A_ly acceleration samples according to one or more of the following;

a) variance of the mobile asset heading is less than a preset threshold variance threshold;

b) mobile asset speed greater than a preset threshold speed; and

c) magnitude of accelerations A_lx and of A_ly are greater than preset threshold values.

5. The method of claim 1, further comprising the step of stopping the step of calculating the yaw correction angle φ

when an adjustment thereto is below a preset threshold value.

6. The method of claim 1, wherein the step of obtaining the yaw correction angle φ

uses one of the following algorithms;

Gradient Descent, Least Mean Squares, Recursive Least Squares, Kalman, or Fast Kalman.

7. The method of claim 1, further comprising the step of compensating for slope, the step being performed between said first phase and said second phase.

8. The method of claim 1 for filtering and thereby qualifying events of interest, comprising the steps of:

(a) configuring an envelope of acceleration data to correspond to an event of interest;

(b) evaluating samples of acceleration data and determining if said samples fit over said envelope; and

(c) inferring an event of interest based on said evaluating.

9. The method of claim 1, comprising the use of GPS, GLONASS, or other positioning systems to obtain time, location, velocity, altitude, and heading of said mobile asset.

10. The method of claim 1, comprising the use of wireless modem interfaces for GSM, GPRS, CDMA, HSDPA, HSPA+, HSUPA, UMTS, EDGE, Iridium, Wi-Fi, Bluetooth or Zigbee networks, or other proprietary wireless data networks.

11. The method of claim 1, wherein the tri-axial accelerometer is implemented by the combination of a two axis accelerometer to obtain the roll correction angle θ

and the pitch correction angle ψ and

a single axis accelerometer to obtain the yaw correction angle φ

.

12. The method of claim 1, wherein the tri-axial accelerometer is implemented by the combination of a single axis accelerometer to obtain the roll correction angle θ

, a single axis accelerometer to obtain the pitch correction angle ψ and

a single axis accelerometer to obtain the yaw correction angle φ

.

13. The method of claim 1, wherein the mobile asset is equipped with a gyroscope, and the further step of fusing acceleration data with data from said gyroscope.

14. The method of claim 1, further comprising the step of the calibrated accelerometer data to aggressive driving thresholds in order to identify aggressive driving of the mobile asset.

15. The method of claim 1, the step of removing from acceleration data, the offset created by gravity when the mobile asset is moving on a sloped terrain, further comprising the step of high pass filtering the acceleration data a_x.