Tracking activity, velocity, and heading using sensors in mobile devices or other systems
First Claim
1. A method for locating and tracking a mobile device, the method comprising:
- reading sensor data from each of a plurality of sensors within the mobile device, the sensors providing the sensor data without relying on a signal from any device external to the mobile device; and
determining motion, velocity, and heading of the mobile device through a machine learning process based on the sensor data without relying on a signal from any device external to the mobile device, wherein the machine learning process comprises;
collecting sensor data and noise from each of the plurality of sensors over a time period,fusing the collected sensor data and noise to produce estimates for each of a plurality of indications related to orientation of the mobile device,normalizing each of the estimates,forming a plurality of vectors representing activity of the mobile device based on the normalized estimates,classifying the activity of the mobile device over the time period as motion or static based on the plurality of vectors, andin response to classifying activity of the mobile device as motion, determining a velocity for the mobile device based on the sensor data and determining a heading for the mobile device based on the sensor data.
1 Assignment
0 Petitions
Accused Products
Abstract
Embodiments of the invention provide systems and methods for tracking a mobile device using sensors within the device and without using external signals to determine location, velocity, or heading. According to one embodiment, locating and tracking a mobile device can comprise reading sensor data from each of a plurality of sensors within the mobile device. The sensors can provide the sensor data without relying on a signal from a device external to the mobile device. For example, the plurality of sensors can comprise a compass, a gyroscope, and an accelerometer. Motion, velocity, and heading of the mobile device can be determined based on the sensor data.
-
Citations
24 Claims
-
1. A method for locating and tracking a mobile device, the method comprising:
-
reading sensor data from each of a plurality of sensors within the mobile device, the sensors providing the sensor data without relying on a signal from any device external to the mobile device; and determining motion, velocity, and heading of the mobile device through a machine learning process based on the sensor data without relying on a signal from any device external to the mobile device, wherein the machine learning process comprises; collecting sensor data and noise from each of the plurality of sensors over a time period, fusing the collected sensor data and noise to produce estimates for each of a plurality of indications related to orientation of the mobile device, normalizing each of the estimates, forming a plurality of vectors representing activity of the mobile device based on the normalized estimates, classifying the activity of the mobile device over the time period as motion or static based on the plurality of vectors, and in response to classifying activity of the mobile device as motion, determining a velocity for the mobile device based on the sensor data and determining a heading for the mobile device based on the sensor data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
-
-
9. A system comprising:
-
a processor; and a memory coupled with and readable by the processor and having stored therein a sequence of instructions which, when executed by the processor, causes the processor to locate and track a mobile device by; reading sensor data from each of a plurality of sensors within the mobile device, the sensors providing the sensor data without relying on a signal from any device external to the mobile device; and determining motion, velocity, and heading of the mobile device through a machine learning process based on the sensor data without relying on a signal from any device external to the mobile device, wherein the machine learning process comprises; collecting sensor data and noise from each of the plurality of sensors over a time period, fusing the collected sensor data and noise to produce estimates for each of a plurality of indications related to orientation of the mobile device, normalizing each of the estimates, forming a plurality of vectors representing activity of the mobile device based on the normalized estimates, classifying the activity of the mobile device over the time period as motion or static based on the plurality of vectors, and in response to classifying activity of the mobile device as motion, determining a velocity for the mobile device based on the sensor data and determining a heading for the mobile device based on the sensor data. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
-
-
17. A computer-readable memory having stored thereon a sequence of instructions which, when executed by a processor, causes the processor to locate and track a mobile device by:
-
reading sensor data from each of a plurality of sensors within the mobile device, the sensors providing the sensor data without relying on a signal from any device external to the mobile device; and determining motion, velocity, and heading of the mobile device through a machine learning process based on the sensor data without relying on a signal from any device external to the mobile device, wherein the machine learning process comprises; collecting sensor data and noise from each of the plurality of sensors over a time period, fusing the collected sensor data and noise to produce estimates for each of a plurality of indications related to orientation of the mobile device, normalizing each of the estimates, forming a plurality of vectors representing activity of the mobile device based on the normalized estimates, classifying the activity of the mobile device over the time period as motion or static based on the plurality of vectors, and in response to classifying activity of the mobile device as motion, determining a velocity for the mobile device based on the sensor data and determining a heading for the mobile device based on the sensor data. - View Dependent Claims (18, 19, 20, 21, 22, 23, 24)
-
Specification