# US 10,371,528 B2 · Pedestrian navigation devices and methods > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,371,528 B2 - Document ID: US10371528B2 - Status: Granted - Kind: B2 - Application number: 14/323,823 - Filed: 2014-07-03 - Published: 2016-01-07 - Granted: 2019-08-06 - Earliest claimed: 2014-07-03 - Assignees: Texas Instruments Incorporated - Applicants: Texas Instruments Incorporated - Inventors: Jayawardan Janardhanan; Jaiganesh Balakrishnan - Web page: https://patentgrep.com/patent/US10371528B2 ## CPC - G01C 21/12: executed aboard the object being navigated; Dead reckoning - A61B 5/112: {Gait analysis} - G01C 21/166: {Mechanical, construction or arrangement details of inertial navigation systems} - G01C 22/006: {Pedometers} - A61B 2562/0219: Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches - G01P 15/18: in two or more dimensions - 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) - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G01P: MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT (measuring or recording blood flow A61B5/02, A61B8/06; monitoring speed or deceleration of electrically-propelled vehicles B60L3/00; vehicle lighting systems adapted to indicate speed B60Q1/54; determining position or course in navigation, measuring ground distance in geodesy or surveying G01C; combined measuring devices for measuring two or more variables of movement G01C23/00; measuring velocity of sound G01H; measuring velocity of light G01J7/00; measuring direction or velocity of solid objects by reception or emission of radiowaves or other waves and based on propagation effects, e.g. Doppler effect, propagation time, direction of propagation, G01S; measuring speed of nuclear radiation G01T; measuring acceleration of gravity G01V; {measuring or recording the speed of trains B61L23/00; speed indicators incorporated in motor vehicles B60K35/00; measuring frequency or phase G01R; traffic control G08G}) ## Abstract Devices and methods for pedestrian navigation are disclosed. In an embodiment, a device includes an accelerometer sensor configured to sense acceleration components associated with a device motion in a plurality of axes of the accelerometer sensor. The acceleration components include stride frequency components and step frequency components. The device includes a processing module communicably associated with the accelerometer sensor. The processing module is configured to process at least a portion of the acceleration components to determine an estimated attitude associated with the device motion with respect to the accelerometer sensor. The processing module is configured to filter out the step frequency components by blocking the stride frequency components. The processing module is further configured to determine the estimated attitude based on the step frequency components to thereby mitigate a bias in the estimated attitude associated with a lateral sway of the device motion. ## Claims ### Claim 1 A pedestrian navigation device comprising: an accelerometer sensor to sense acceleration components associated with a device motion of the device in a plurality of axes of the accelerometer sensor, wherein the acceleration components include stride frequency components and step frequency components; and a processing module coupled to the accelerometer sensor determine an estimated attitude associated with the device motion by: determining tilt measurements associated with the acceleration components; generating de-tilted acceleration components along each of a vertical axis, a first horizontal axis, and a second horizontal axis based on the tilt measurements; filtering the de-tilted acceleration components to remove data corresponding to the stride frequency components to produce filtered acceleration components, wherein the filtered acceleration components include a vertical acceleration component along the vertical axis, a first horizontal acceleration component along the first horizontal axis, and a second horizontal acceleration component along the second horizontal axis; determining a pre-attitude based on an auto-covariance of the first horizontal acceleration component (Rxx), an auto-covariance of the second horizontal acceleration component (Ryy), and a cross-covariance of the first horizontal acceleration component and the second horizontal acceleration component (Rxy); determining a quality metric factor based on a cross-covariance between a forward acceleration component and the vertical acceleration component (Rfv), an auto-covariance of the forward acceleration component (Rff), and an auto-covariance of the vertical acceleration component (Rvv); determining the estimated attitude based on the quality metric factor, the pre-attitude, and a direction of the device motion; and determining a user heading estimation based on the estimated attitude. ### Claim 2 (depends on 1) The pedestrian navigation device of claim 1, comprising a device-heading sensor arranged in a fixed relationship to the accelerometer sensor and configured to determine a direction of the accelerometer sensor with respect to a global reference direction, wherein the determination of the user heading estimation by the processing module is made by combining the estimated attitude with the direction of the accelerometer sensor. ### Claim 3 (depends on 1) The pedestrian navigation device of claim 1, wherein the pre-attitude is determined as: pre ⁢ - ⁢ attitude = 1 2 ⁢ tan - 1 ⁡ ( - 2 ⁢ R xy R xx - R yy ) . ### Claim 4 (depends on 1) The pedestrian navigation device of claim 1, wherein the estimated attitude is determined as: YawFiltn=Yawn*abs(YawQM)+YawFiltn−1*(1−abs(YawQM)); wherein YawFiltn is the estimated attitude at a time tn, YawFiltn−1 an estimated attitude at a time tn−1, YawQM is the quality metric factor, and Yawn is an attitude determined based on the pre-attitude and the direction of the device motion at the time tn. ### Claim 5 (depends on 1) The pedestrian navigation device of claim 1, wherein the quality metric factor is determined as: YawQM = R fv R ff ⁢ R vv . ### Claim 6 A pedestrian navigation device comprising: an accelerometer sensor to sense acceleration components associated with a device motion of the device in a plurality of axes of the accelerometer sensor, wherein the acceleration components include stride frequency components and step frequency components; and a processing module coupled to the accelerometer sensor determine an estimated attitude associated with the device motion by: determining tilt measurements associated with the acceleration components; generating de-tilted acceleration components along each of a vertical axis, a first horizontal axis, and a second horizontal axis based on the tilt measurements; filtering the de-tilted acceleration components to remove data corresponding to the stride frequency components to produce filtered acceleration components, wherein the filtered acceleration components include a vertical acceleration component along the vertical axis, a first horizontal acceleration component along the first horizontal axis, and a second horizontal acceleration component along the second horizontal axis; determining a pre-attitude based on an auto-covariance of the first horizontal acceleration component (Rxx), an auto-covariance of the second horizontal acceleration component (Ryy), and a cross-covariance of the first horizontal acceleration component and the second horizontal acceleration component (Rxy); determining a direction of the device motion; determining a quality metric factor based on a cross-covariance between an acceleration component corresponding to the direction of the device motion and the vertical acceleration component, an auto-covariance of the acceleration component corresponding to the direction of the device motion, and an auto-covariance of the vertical acceleration component; determining the estimated attitude based on the quality metric factor, the pre-attitude, and the direction of the device motion; and determining a user heading estimation based on the estimated attitude. ### Claim 7 (depends on 6) The pedestrian navigation device of claim 6, wherein the direction of the device motion is determined to be either a forward direction of a backward direction. ### Claim 8 (depends on 7) The pedestrian navigation device of claim 7, wherein the processing module is configured to de-rotate the first horizontal acceleration component in response to the pre-attitude to generate a pre-forward acceleration component. ### Claim 9 (depends on 8) The pedestrian navigation device of claim 8, wherein the direction of the device motion is determined as the forward direction or the backward direction by determining whether the pre-forward acceleration and the vertical acceleration component have a leading relation the vertical acceleration component rotated by a 90 degree phase. ### Claim 10 (depends on 9) The pedestrian navigation device of claim 9, wherein: the direction of the device motion is the forward direction if a cross-covariance between the pre-forward acceleration component and the 90 degree phase rotated vertical acceleration component is positive; and the direction of the device motion is the backward direction if the cross-covariance between the pre-forward acceleration component and the 90 degree phase rotated vertical acceleration component is negative. ### Claim 11 (depends on 8) The pedestrian navigation device of claim 8, wherein the processing module is configured to de-rotate the second horizontal acceleration component in response to the pre-attitude to generate a pre-lateral acceleration component. ### Claim 12 (depends on 11) The pedestrian navigation device of claim 11, wherein the pre-attitude is determined as: pre-attitude=tan−1(Rxv/Ryv)+π/2; where Rxv is a cross-covariance of the pre-forward acceleration component and the vertical acceleration component and Ryv is a cross-covariance of the pre-lateral acceleration component and the vertical acceleration component.