Source: https://patents.justia.com/patent/9398558
Timestamp: 2019-04-24 04:51:33+00:00

Document:
Sep 13, 2013 - SKYHOOK WIRELESS, INC.
This application is a continuation of U.S. application Ser. No. 13/658,322, filed Oct. 23, 2012, entitled Continuous Data Optimization of Moved Access Points in Positioning Systems, now U.S. Pat. No. 8,538,457, which is a continuation of U.S. application Ser. No. 13/572,952, filed Aug. 13, 2012, entitled Continuous Data Optimization of Moved Access Points in Positioning Systems, now U.S. Pat. No. 8,478,297, which is a continuation of U.S. application Ser. No. 11/359,154, filed Feb. 22, 2006, entitled Continuous Data Optimization of Moved Access Points in Positioning Systems, now U.S. Pat. No. 8,244,272, which claimed the benefit under 35 U.S.C. §119(e) to U.S. Provisional Patent Application No. 60/654,811, filed on Feb. 22, 2005, entitled Continuous Data Optimization in Positioning System, and which was also a continuation-in-part of and claimed the benefit under 35 U.S.C. §120 to U.S. patent application Ser. No. 11/261,988, filed on Oct. 28, 2005, entitled Location-Based Services that Choose Location Algorithms Based on Number of Detected Access Points Within Range of User Device, now U.S. Pat. No. 7,305,245, which claimed the benefit under 35 U.S.C. §119(e) to U.S. Provisional Patent Application No. 60/623,108, filed on Oct. 29, 2004, entitled Wireless Data Scanning Network for Building Location Beacon Database, the contents of each of which are incorporated herein.
There have been some more recent alternative models developed to try and address the known issues with GPS, A-GPS and cell tower positioning. One of them, known as TV-GPS, utilizes signals from television broadcast towers. (See, e.g., Muthukrishnan, Maria Lijding, Paul Having a, Towards Smart Surroundings: Enabling Techniques and Technologies for Localization, Lecture Notes in Computer Science, Volume 3479, Jan 2Hazas, M., Scott, J., Krumm, J.: Location-Aware Computing Comes of Age. IEEE Computer, 37(2):95-97, February 2004 005, Pa005, Pages 350-362.) The concept relies on the fact that most metropolitan areas have 3 or more TV broadcast towers. A proprietary hardware chip receives TV signals from these various towers and uses the known positions of these towers as reference points. The challenges facing this model are the cost of the new hardware receiver and the limitations of using such a small set of reference points. For example, if a user is outside the perimeter of towers, the system has a difficult time providing reasonable accuracy. The classic example is a user along the shoreline. Since there are no TV towers out in the ocean, there is no way to provide reference symmetry among the reference points resulting in a calculated positioning well inland of the user.
Microsoft Corporation and Intel Corporation (via a research group known as PlaceLab) have deployed a Wi-Fi Location system using the access point locations acquired from amateur scanners (known as “wardrivers”) who submit their Wi-Fi scan data to public community web sites. (See, e.g., LaMarca, A., et. al., Place Lab: Device Positioning Using Radio Beacons in the Wild.) Examples include WiGLE, Wi-FiMaps.com, Netstumbler.com and NodeDB. Both Microsoft and Intel have developed their own client software that utilizes this public wardriving data as reference locations. Because individuals voluntarily supply the data the systems suffer a number of performance and reliability problems. First, the data across the databases are not contemporaneous; some of the data is new while other portions are 3-4 years old. The age of the access point location is important since over time access points can be moved or taken offline. Second, the data is acquired using a variety of hardware and software configurations. Every 802.11 radio and antenna has different signal reception characteristics haffecting the representation of the strength of the signal. Each scanning software implementation scans for Wi-Fi signals in different ways during different time intervals. Third, the user-supplied data suffers from arterial bias. Because the data is self-reported by individuals who are not following designed scanning routes, the data tends to aggregate around heavily traffic areas. Arterial bias causes a resulting location pull towards main arteries regardless of where the user is currently located causing substantial accuracy errors. Fourth, these databases include the calculated position of scanned access points rather than the raw scanning data obtained by the 802.11 hardware. Each of these databases calculates the access point location differently and each with a rudimentary weighted average formula. The result is that many access points are indicated as being located far from their actual locations including some access points being incorrectly indicated as if they were located in bodies of water.
There have been a number of commercial offerings of Wi-Fi location systems targeted at indoor positioning. (See, e.g., Kavitha Muthukrishnan, Maria Lijding, Paul Having a, Towards Smart Surroundings: Enabling Techniques and Technologies for Localization, Lecture Notes in Computer Science, Volume 3479, Jan 2Hazas, M., Scott, J., Krumm, J.: Location-Aware Computing Comes of Age. IEEE Computer, 37(2):95-97, February 2004 005, Pa005, Pages 350-362.) These systems are designed to address asset and people tracking within a controlled environment like a corporate campus, a hospital facility or a shipping yard. The classic example is having a system that can monitor the exact location of the crash cart within the hospital so that when there is a cardiac arrest the hospital staff doesn't waste time locating the device. The accuracy requirements for these use cases are very demanding typically calling for 1-3 meter accuracy. These systems use a variety of techniques to fine tune their accuracy including conducting detailed site surveys of every square foot of the campus to measure radio signal propagation. They also require a constant network connection so that the access point and the client radio can exchange synchronization information similar to how A-GPS works. While these systems are becoming more reliable for these indoor use cases, they are ineffective in any wide-area deployment. It is impossible to conduct the kind of detailed site survey required across an entire city and there is no way to rely on a constant communication channel with 802.11 access points across an entire metropolitan area to the extent required by these systems. Most importantly outdoor radio propagation is fundamentally different than indoor radio propagation rendering these indoor positioning algorithms almost useless in a wide-area scenario.
Identifying suspect access points for a client device when there is a history of user movement is based on the previous location of the client device. An exemplary implementation of this determination is shown in FIG. 6. In an embodiment where there is location history, the client device location calculation is calculated continuously every period of time, usually once every second. If the distance of any individual observed access point  to that historical reference point (the prior location calculation) is more than a given distance , then it is ruled as a suspect access point, added to the Feedback File and removed from calculation. The intent of this filter is to try and use the access points that are nearest to the user/device  to provide the highest potential accuracy. This filter is called an adaptive filter since the threshold distance to filter suspect access points is changed dynamically. The threshold distance, which is used to identify suspect access points, is changed dynamically based on the number of access points that are considered of good quality to calculate location of the client device. Therefore, the adaptive filter contains two factors, 1) the minimum number of required access points to locate a user device and 2) the minimum threshold of distance to identify suspect access points. The adaptive filter starts with the minimum threshold of distance. If number of access points within that distance is above the minimum number of access points necessary to calculate the client location, then location of the device is calculated. For example, if we find five access points which are within 20 meters of the prior reading, then we filter out all observed access points greater than 20 meters. If the filter criteria is not met then the adaptive filter threshold of the distance is increased until the minimum number of access points is considered or the maximum acceptable distance is reached, and then the access points within the threshold distance are used to locate the user device. If no access point can be located within the maximum threshold of distance  from the previous location, then no location is calculated.
in response to inferring that the identified WiFi access point has moved from its previously-determined location that is recorded in the reference database, at least one of the mobile WiFi-enabled device and the server system in communication with the mobile WiFi-enabled device updating the location information that is associated with the identified WiFi access point recorded in the reference database and associating a quality attribute with the updated location.
2. The method of claim 1, wherein an initial value of the quality attribute for the updated location information indicates a level of confidence in the updated location information that is less than a level of confidence associated with updated location information that is validated by multiple mobile WiFi-enabled devices.
3. The method of claim 1, wherein the inferring includes determining whether the recorded location information associated with the identified WiFi access point is more than a threshold distance from the location estimate of the mobile WiFi-enabled device.
4. The method of claim 3, wherein the determination of the location estimate of the mobile WiFi-enabled device is based on signals received from a number of WiFi access points, and where the threshold distance varies based on the number of WiFi access points used to determine the location estimate for the mobile WiFi-enabled device.
5. The method of claim 1, wherein the at least one location estimate of the mobile WiFi-enabled device is retrieved from the server system.
6. The method of claim 1, wherein the reference database is located remotely relative to the WiFi-enabled device.
7. The method of claim 1, wherein the reference database is located on the mobile WiFi-enabled device.
8. The method of claim 1, wherein the updating the location information that is associated with the identified WiFi access point recorded in the reference database includes estimating a geographic position of the identified WiFi access point.
9. The method of claim 8, wherein the estimating the geographic position of the identified WiFi access point that is inferred to have moved from its previously-determined location is based at least in part on location information associated with WiFi access points other than WiFi access points that are inferred to have moved from their corresponding previously-determined locations.
10. The method of claim 1, further comprising altering the quality attribute associated with the updated location information of the identified WiFi access point in response to receiving validation from one or more other mobile WiFi-enabled devices of the updated location information of the identified WiFi access point.
11. The method of claim 10, wherein the alteration of the quality attribute reflects an increased level of confidence in the updated location information associated with the identified WiFi access point.
a storage device storing Wi-Fi positioning system (WPS) client software that includes: a scanner configured to use the Wi-Fi radio to identify a Wi-Fi access point within range of the mobile WiFi-enabled device, a locator configured to search for and retrieve location information that is associated with the identified WiFi access point from a local copy of a reference database on the mobile WiFi-enabled device or a remote reference database on a server system, the location information specifying a previously-determined location of the identified WiFi access point, a location calculation component configured to determine a location estimate of the mobile WiFi-enabled device, and a quality filter configured to compare the location estimate of the mobile WiFi-enabled device to the recorded location information associated with the identified WiFi access point to infer whether the identified WiFi access point has moved from its previously-determined location, and to generate in response to inferring that the identified WiFi access point has moved from its previously-determined location feedback, wherein the feedback is usable to update the location information that is associated with the identified WiFi access point.
13. The mobile WiFi-enabled device of claim 12, wherein the locator is configured to search for and retrieve location information that is associated with the identified WiFi access point from the local copy of the reference database on the mobile WiFi-enabled device, and the feedback is one or more feedback files that are stored locally and then transmitted to the server system over a network connection.
14. The mobile WiFi-enabled device of claim 12, wherein the locator is configured to search for and retrieve location information that is associated with the identified WiFi access point from the reference database on the server system, and the feedback is sent substantially in real-time to the server system over a network connection.
15. The mobile WiFi-enabled device of claim 12, wherein the inference whether the identified WiFi access point has moved from its previously-determined location is based on whether the location information for the WiFi access point recorded in the reference database indicates a location that is more than a threshold distance away from the location estimate of the mobile WiFi-enabled device.
16. The mobile WiFi-enabled device of claim 12, wherein the feedback is further usable to update a quality attribute associated with the identified WiFi access point in the reference database on the server system, where the quality attribute indicates a level of confidence in the updated location.
17. The mobile WiFi-enabled device of claim 16, wherein the level of confidence indicated by an initial value of the quality attribute is less than a level of confidence associated with an updated location that is validated by multiple mobile WiFi-enabled devices.
in response to the feedback from the mobile WiFi-enabled device, updating the location of the Wi-Fi access point recorded in the reference database based at least in part on locations of one or more other Wi-Fi access points identified to be within range of the WiFi radio of the mobile WiFi-enabled device, and associating the updated location of the Wi-Fi access point with a quality attribute, wherein the quality attribute indicates a level of confidence in the updated location.
19. The non-transitory computer readable medium of claim 18, wherein the level of confidence indicated by an initial value of the quality attribute is less than a level of confidence associated with an updated location that is validated by multiple mobile WiFi-enabled devices.
20. The non-transitory computer readable medium of claim 18, wherein the feedback is one or more feedback files that are received over a network connection.
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Primary Examiner: Willie J Daniel, Jr.

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