# US 9,752,879 B2 · System and method for estimating heading misalignment

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- Number: US 9,752,879 B2
- Document ID: US9752879B2
- Status: Granted
- Kind: B2
- Application number: 14/686,441
- Filed: 2015-04-14
- Published: 2016-10-20
- Granted: 2017-09-05
- Earliest claimed: 2015-04-14
- Assignees: InvenSense, Inc.
- Applicants: Invensense Incorporated
- Inventors: Amr Al-Hamad; Abdelrahman Ali; Jacques Georgy
- Web page: https://patentgrep.com/patent/US9752879B2

## CPC

- G01C 21/183: {Compensation of inertial measurements, e.g. for temperature effects}
- G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S)

## Abstract

This disclosure relates to heading misalignment estimation with a portable device and more specifically to estimating misalignment between a portable device having a sensor assembly and a platform transporting the portable device when the platform is undergoing motion having periodic characteristics. In one aspect, a suitable method includes obtaining inertial sensor data for the portable device and determining an effective frequency characteristic of the inertial sensor data representing the motion having periodic characteristics. The inertial sensor data may be processed using the determined frequency characteristic so that a heading misalignment may be estimated between the heading of the portable device and the platform.

## Claims

### Claim 1

A portable device, the device being within a platform, the device comprising a sensor assembly integrated with the portable device that outputs inertial sensor data and a heading misalignment estimator configured to:
determine an effective frequency characteristic of the inertial sensor data, wherein the effective frequency characteristic of the inertial sensor data represents periodic characteristics of motion of the platform;
process the inertial sensor data based at least in part on the determined frequency characteristic, wherein processing the inertial sensor data comprises reducing signal artifacts; and
estimate a heading misalignment between a heading of the portable device and the platform using the processed inertial sensor data, wherein estimating the heading misalignment is based at least in part on a maximum variance of the processed inertial sensor data.

### Claim 2 (depends on 1)

The device of claim 1, wherein the heading misalignment estimator is configured to determine the effective frequency characteristic of the inertial sensor data by performing a time domain analysis.

### Claim 3 (depends on 1)

The device of claim 1, wherein the heading misalignment estimator is configured to determine the effective frequency characteristic of the inertial sensor data by performing a frequency domain analysis.

### Claim 4 (depends on 1)

The device of claim 1, further comprising a band pass filter, wherein the heading misalignment estimator processes the inertial sensor data by applying the band pass filter.

### Claim 5 (depends on 1)

The device of claim 1, wherein the heading misalignment estimator is further configured to estimate the heading misalignment based at least in part on a maximum variance of the processed inertial sensor data.

### Claim 6 (depends on 1)

The device of claim 1, wherein the heading misalignment estimator is further configured to resolve an ambiguity in the estimated heading misalignment.

### Claim 7 (depends on 1)

The device of claim 1, wherein the sensor assembly comprises an inertial sensor implemented as a Micro Electro Mechanical System (MEMS).

### Claim 8

A method for navigation with a portable device associated with a platform undergoing motion having periodic characteristics, the method comprising:
obtaining inertial sensor data for the portable device;
determining an effective frequency characteristic of the inertial sensor data;
processing the inertial sensor data based at least in part on the determined frequency characteristic, wherein processing the inertial sensor data comprises reducing signal artifacts; and
estimating a heading misalignment between a heading of the portable device and the platform using the processed inertial sensor data, wherein estimating the heading misalignment is based at least in part on a maximum variance of the processed inertial sensor data.

### Claim 9 (depends on 8)

The method of claim 8, wherein processing the inertial sensor data comprises processing accelerometer data.

### Claim 10 (depends on 9)

The method of claim 9, wherein determining the effective frequency characteristic of the inertial sensor data comprises performing a time domain analysis.

### Claim 11 (depends on 10)

The method of claim 10, wherein the time domain frequency analysis is performed on a periodic signal selected from at least one of accelerometer data, leveled accelerometer data, gyroscope data, leveled gyroscope data, roll data for the portable device and pitch data for the portable device.

### Claim 12 (depends on 11)

The method of claim 11, further comprising classifying at least one of a use case and a motion mode for the portable device and selecting the periodic signal for performing the time domain analysis based at least in part on the classification.

### Claim 13 (depends on 11)

The method of claim 11, wherein the time domain analysis calculates peak frequency in the obtained inertial sensor data.

### Claim 14 (depends on 13)

The method of claim 13, comprising processing the sensor data with the calculated peak frequency to reduce the artifacts.

### Claim 15 (depends on 14)

The method of claim 14, further comprising determining coefficients at the calculated peak frequency.

### Claim 16 (depends on 15)

The method of claim 15, wherein processing the inertial sensor data comprises applying the determined coefficients to reconstruct the inertial sensor data to reduce the artifacts.

### Claim 17 (depends on 9)

The method of claim 9, wherein determining the effective frequency characteristic of the inertial sensor data comprises performing a frequency domain analysis.

### Claim 18 (depends on 17)

The method of claim 17, wherein the frequency domain analysis is performed on a periodic signal selected from at least one of accelerometer data, leveled accelerometer data, gyroscope data, leveled gyroscope data, roll data for the portable device and pitch data for the portable device.

### Claim 19 (depends on 18)

The method of claim 18, further comprising classifying at least one of a use case and a motion mode for the portable device and selecting the periodic signal for performing the frequency domain analysis based at least in part on the classification.

### Claim 20 (depends on 18)

The method of claim 18, wherein the frequency domain analysis comprises a Fast Fourier Transform (FFT).

### Claim 21 (depends on 20)

The method of claim 20, further comprising determining a maximum amplitude frequency component corresponding to the effective frequency characteristic.

### Claim 22 (depends on 21)

The method of claim 21, comprising processing the sensor data with the determined maximum amplitude frequency component to reduce the artifacts.

### Claim 23 (depends on 22)

The method of claim 22, further comprising determining coefficients at the maximum amplitude frequency component.

### Claim 24 (depends on 23)

The method of claim 23, wherein processing the inertial sensor data comprises applying the determined coefficients to reconstruct the inertial sensor data to reduce the artifacts.

### Claim 25 (depends on 9)

The method of claim 9, wherein processing the inertial sensor data comprises applying a band pass filter.

### Claim 26 (depends on 25)

The method of claim 25, further comprising classifying at least one of a use case and a motion mode for the portable device and selecting the band pass filter based at least in part on the classification.

### Claim 27 (depends on 8)

The method of claim 8, further comprising determining the maximum variance of the processed inertial sensor data using a Principle Component Analysis (PCA).

### Claim 28 (depends on 8)

The method of claim 8, further comprising determining the maximum variance of the processed inertial sensor data using a binary search.

### Claim 29 (depends on 8)

The method of claim 8, further comprising determining the maximum variance of the processed inertial sensor data by analytically determining an along-track axis representing a minimum sum of squared distances.

### Claim 30 (depends on 8)

The method of claim 8, further comprising resolving an ambiguity in the estimated heading misalignment.

### Claim 31 (depends on 30)

The method of claim 30, wherein resolving the estimated heading misalignment ambiguity comprises assessing a difference between two angles calculated between a vertical acceleration of the portable device and an along-track axis acceleration of the portable device.

### Claim 32 (depends on 31)

The method of claim 31, wherein the difference between two angles calculated between a vertical acceleration of the portable device and the along-track axis acceleration of the portable device is determined as a difference between an angle representing a phase shift between a lagging vertical acceleration signal and a leading along-track axis acceleration signal and an angle representing a phase shift between a leading vertical acceleration signal and a lagging along-track axis acceleration signal.

### Claim 33 (depends on 30)

The method of claim 30, wherein resolving estimated heading misalignment ambiguity comprises assessing phase shift between a vertical acceleration of the portable device and an along-track axis acceleration of the portable device.

### Claim 34 (depends on 30)

The method of claim 30, wherein resolving the estimated heading misalignment ambiguity comprises determining effective motion parameters based at least in part on a maximum variance between a vertical acceleration of the portable device and an along-track axis acceleration of the portable device.

### Claim 35 (depends on 34)

The method of claim 34, further comprising determining the maximum variance between the vertical acceleration of the portable device and the along-track axis acceleration of the portable device using a Principle Component Analysis (PCA).

### Claim 36 (depends on 34)

The method of claim 34, further comprising determining the maximum variance between the vertical acceleration of the portable device and the along-track axis acceleration of the portable device by calculating a slope of a principle axis between the vertical acceleration of the portable device and the along-track axis acceleration of the portable device, wherein the principal axis is obtained using a binary search.

### Claim 37 (depends on 34)

The method of claim 34, further comprising analytically determining the maximum variance between the vertical acceleration of the portable device and the along-track axis acceleration of the portable device by calculating a slope of a principle axis between the vertical acceleration of the portable device and the along-track axis acceleration of the portable device, wherein the principal axis represents a minimum sum of squared distances.