WO2019023642A1

WO2019023642A1 - Systems and methods for atmospheric vapor observation

Info

Publication number: WO2019023642A1
Application number: PCT/US2018/044193
Authority: WO
Inventors: Kevin LAYTON
Original assignee: Layton Kevin
Priority date: 2017-07-27
Filing date: 2018-07-27
Publication date: 2019-01-31

Abstract

Embodiments of the present disclosure relate to determining atmospheric vapor conditions. In an embodiment, a method for determining atmospheric vapor conditions comprises forming a sensor network having one or more devices. Each device of the one or more devices is configured to receive raw data from at least one GNSS satellite. The method further comprises receiving, by a remote processing device, raw data from at least one device of the one or more devices. And, determining, by the remote processing device using the received raw data, atmospheric vapor conditions associated with the at least one device.

Description

SYSTEMS AND METHODS FOR ATMOSPHERIC VAPOR OBSERVATION

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to Provisional Application No. 62/537,821 , filed July 27, 2017, which is herein incorporated by reference in its entirety.

TECHNICAL FIELD

[0002] Embodiments of the present disclosure relate to atmospheric vapor observation. Certain embodiments of the present disclosure relate to determining atmospheric vapor conditions using sensor networks. Other embodiments relate to collating data obtained by the sensor networks related to atmospheric vapor conditions and assessing conditions for farmers, ranchers or regional businesses of tested area(s) for implementation of programs or procedures based on the data collected. In certain embodiments, raw data can be further processed and evaluated to predict conditions in favor or adverse to the local population, for example, predicting conditions and taking precautions necessary to reduce catastrophic events.

BACKGROUND

[0003] Determining the amount of vapor in the atmosphere can be beneficial to many industries including, for example, weather forecasting, aviation, agriculture, atmospheric research and environmental research. Using conventional methods, however, can be cost prohibitive for determining atmospheric vapor levels in some locations. As such, there is a need in the art for less expensive systems and methods that determine atmospheric vapor conditions.

SUMMARY

[0004] Embodiments of the present disclosure relate to systems and methods for determining atmospheric vapor conditions and methods of use regarding the same. Examples embodiments include the following.

[0005] In an Example 1 , a method for determining atmospheric vapor conditions comprises forming a sensor network having one or more devices, each device of the one or more devices being configured to receive raw data from at least one GNSS satellite; receiving, by a remote processing device, raw data from at least one device of the one or more devices; and determining, by the remote processing device using the received raw data, atmospheric vapor conditions associated with the at least one device.

[0006] In an Example 2, the method of Example 1 , further including instructing the at least one device to transmit the raw data to the remote processing device.

[0007] In an Example 3, the method of any one of Examples 1 -2, further including releasing each device of the one or more devices from the sensor network.

[0008] In an Example 4, the method of any one of Examples 1 -3, further including: receiving, by the remote processing device, positional information from at least one reference receiver; and wherein determining the atmospheric vapor conditions comprises using the received positional information to correct the atmospheric vapor condition estimate.

[0009] In an Example 5, the method of Example 4, wherein the at least one reference receiver is not included in the sensor network.

[0010] In an Example 6, the method of any one of Examples 1 -5, wherein the one or more devices include at least one of: a mobile device and a stationary device.

[0011] In an Example 7, the method of any one of Examples 1 -6, wherein the one or more devices include a reference receiver.

[0012] In an Example 8, the method of any one of Examples 1 -7, wherein determining the atmospheric vapor conditions includes receiving one or more signals from a source not included in the sensor network and determining the atmospheric vapor conditions based on the raw data and the one or more signals from the source not included in the sensor network.

[0013] In an Example 9, the method of any one of Examples 1 -8, wherein the raw data is used by a device that receives the raw data to determine a position of the device.

[0014] In an Example 10, a non-transitory computer readable medium having a computer program stored thereon for determining which size medical device to implant in a patient, the computer program comprising instructions for causing one or more processors to: provide instructions to one or more devices to form a sensor network, each device of the one or more devices being configured to receive raw data from at least one GNSS satellite; receive raw data from at least one device of the one or more devices; and determine atmospheric vapor conditions associated with the at least one device.

[0015] In an Example 1 1 , the non-transitory computer readable medium of Example 10, the computer program including instructions for causing the one or more processors to instruct the at least one device to transmit the raw data to the remote processing device.

[0016] In an Example 12, the non-transitory computer readable medium of any one of Examples 10-1 1 , the computer program including instructions for causing the one or more processors to release each device of the one or more devices from the sensor network.

[0017] In an Example 13, the non-transitory computer readable medium of any one of Examples 10-12, the computer program including instructions for causing the one or more processors to: receive positional information from at least one reference receiver; and wherein determining the atmospheric vapor conditions includes using the received positional information to correct the atmospheric vapor condition estimate.

[0018] In an Example 14, the non-transitory computer readable medium of Example 13, wherein the at least one reference receiver is not included in the sensor network.

[0019] In an Example 15, the non-transitory computer readable medium of any one of Examples 10-14, wherein the one or more devices include at least one of: a mobile device and a stationary device.

[0020] In an Example 16, the non-transitory computer readable medium of any one of Examples 10-15, wherein the one or more devices include a reference receiver.

[0021] In an Example 17, the non-transitory computer readable medium of any one of Examples 10-16, wherein determining the atmospheric vapor conditions comprises receiving one or more signals from a source not included in the sensor network and determining the atmospheric vapor conditions based on the raw data and the one or more signals from the source not included in the sensor network. [0022] In an Example 18, the non-transitory computer readable medium of any one of Examples 10-17, wherein the raw data is used by a device that receives the raw data to determine a position of the device.

[0023] In an Example 19, a device configured to facilitate determining atmospheric vapor conditions includes: memory; and a processor, the processor configured to execute instructions stored on the memory and in response to executing the instructions, the processor is configured to: receive raw data from at least one GNSS satellite; and transmit the raw data to a remote processing device, wherein the remote processing device is configured to determine atmospheric vapor conditions associated with the device.

[0024] In an Example 20, the device of Example 19, the processor further configured to receive the determined atmospheric moisture conditions. In certain embodiments, raw data can be used to determine atmospheric moisture conditions and further processed and/or assessed for collating data obtained by the sensor networks related to atmospheric vapor conditions and assessing conditions for farmers, ranchers or regional businesses of tested area(s) for implementation of programs or procedures based on the data collected. In certain embodiments, raw data can be further processed and evaluated to predict conditions in favor or adverse to the local population, for example, predicting conditions and taking precautions necessary to reduce catastrophic events. In accordance with these embodiments, conditions for travel can be assessed and more accurate predictions made to increase predictability of adverse weather events.

[0025] In an Example 21 , the device of Example 20, wherein the processor is further configured to process atmospheric moisture conditions data and predict real-time moisture conditions for further assessment by a professional.

[0026] While multiple embodiments are disclosed, still other embodiments of the presently disclosed subject matter will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative embodiments of the disclosed subject matter. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not restrictive. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] FIG. 1 is a schematic diagram of an illustrative system for determining atmospheric vapor conditions using one or more sensor networks.

[0028] FIG. 2 is a schematic diagram depicting an illustrative signal path refracted by atmospheric vapor.

[0029] FIGs. 3A-3B are diagrams depicting illustrative portions of sensor networks used for determining atmospheric vapor conditions.

[0030] FIG. 4 is a block diagram depicting an illustrative device and/or remote processing device.

[0031] FIG. 5 is a flow diagram depicting an illustrative method for determining atmospheric vapor conditions.

[0032] While the disclosed subject matter is amenable to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and are described in detail below. The intention, however, is not to limit the disclosed subject matter to the particular embodiments described. On the contrary, the disclosure is intended to cover all modifications, equivalents, and alternatives falling within the scope of the disclosed subject matter as defined by the appended claims.

DETAILED DESCRIPTION

[0033] The term "GNSS device" as used herein throughout this disclosure may refer to any non-traditional source of raw global navigation satellite system (GNSS) data, whether mobile or stationary. This will include, but not be limited to, co-use GNSS sources (e.g., cell phone towers, GNSS receivers, remote utility meters with GNSS receivers, etc.)

[0034] As the terms are used herein with respect to ranges of measurements (such as those disclosed immediately above), "about" and "approximately" may be used, interchangeably, to refer to a measurement that includes the stated measurement and that also includes any measurements that are reasonably close to the stated measurement, but that may differ by a reasonably small amount such as will be understood, and readily ascertained, by individuals having ordinary skill in the relevant arts to be attributable to measurement error, differences in measurement and/or manufacturing equipment calibration, human error in reading and/or setting measurements, adjustments made to optimize performance and/or structural parameters in view of differences in measurements associated with other components, particular implementation scenarios, imprecise adjustment and/or manipulation of objects by a person or machine, and/or the like.

[0035] Although the term "block" may be used herein to connote different elements illustratively employed, the term should not be interpreted as implying any requirement of, or particular order among or between, various blocks disclosed herein. Similarly, although illustrative methods may be represented by one or more drawings (e.g., flow diagrams, communication flows, etc.), the drawings should not be interpreted as implying any requirement of, or particular order among or between, various steps disclosed herein. Additionally, a "set," "subset," or "group" of items (e.g., inputs, algorithms, data values, etc.) may include one or more items, and, similarly, a subset or subgroup of items may include one or more items. A "plurality" means more than one.

[0036] Fixed, dedicated sensors have been conventionally used to determine vapor conditions throughout the lower atmosphere. Some dedicated sensors used to determine vapor content include ground-based sensors such as radiosondes, microwave water vapor radiometers, light detection and ranging systems (lidars), and towers with meteorological sensors placed at various altitudes. These types of conventional dedicated sensors, however, are generally expensive to deploy. Due to the cost, these conventional dedicated sensors may be somewhat localized to a specific geographic area (e.g., covering an area located within 1 km of an airport). Furthermore, these conventional dedicated sensors may be deployed in fewer areas than one may desire because of the expense to deploy the sensors. In addition, once deployed, these conventional dedicated sensors are not easily redeployed without significant expense. The embodiments disclosed herein may reduce some of these problems associated with conventional dedicated sensors.

[0037] Devices configured to receive raw global navigation satellite system (GNSS) data from satellites have become or are becoming ubiquitous throughout our society. For example, some smartphones are configured to receive raw GNSS data in order to determine the location of the smartphone. Other examples of devices configured to receive raw GNSS data may include, but are not limited to, smartphones, GNSS receivers associated with cell phone towers or utility meters, remote utility meters with GNSS receivers, OnStar devices, devices used in precision agriculture and/or the like. As stated above, devices that receive GNSS data that are not conventional dedicated sensors may be referred to herein as GNSS devices. The GNSS devices may be mobile or stationary. GNSS systems as the term is used herein may include, but are not limited to, the Global Positioning System (GPS), Glonass, Compass and/or Beidou.

[0038] The embodiments disclosed herein take advantage of the ubiquity of GNSS devices to determine atmospheric vapor conditions (e.g., the troposphere and/or between 1 -3 kilometers (km) above the Earth's surface) by forming sensor networks using the GNSS devices. As such, for areas densely populated with GNSS devices, such as urban centers and transportation hubs (e.g., airports), atmospheric vapor conditions may be determined whereas atmospheric vapor conditions may have not been able to be determined in these environments using conventional dedicated sensors due to the lack of conventional dedicated sensors being implemented in said locations. Furthermore, by densely packing GNSS devices into a sensor network, the resolution of atmospheric vapor conditions determined by such a sensor network may be better than the resolution produced by conventional embodiments. Both of which may be able to yield more accurate weather predictions at significant less expenditure than what it may cost to design and implement a conventional sensor network used to determine atmospheric moisture conditions. In embodiments, after a sensor network is used to determine atmospheric vapor conditions, the GNSS devices included in the sensor network may be released.

[0039] In addition, due to the embodiment disclosed herein, i.e., forming sensor networks using GNSS devices instead of requiring conventional dedicated sensors, weather conditions may be determined over a larger area and with more specificity. Weather conditions may be determined with more specificity because vapor content in the lower atmosphere (as opposed to vapor content in the upper atmosphere) may be indicative of the absence or presence of severe weather events, wind shears, microbursts and/or other types of localized weather events. As such, the embodiments disclosed herein may be useful in one or more of the following industries: weather forecasting, aviation, precision agriculture, wildfire management, atmospheric and climate research, etc.

[0040] Aspects of the embodiments included here, including, for example, forming sensor networks, are described in the following paragraphs as well as in the Examples. It should be readily understood by those having ordinary skill in the relevant arts that the descriptions in the Examples are provided as examples only and are not meant to limit the scope of the subject matter disclosed herein to those embodiments described. Similarly, although the description in the Examples makes reference to programming in MATLAB and Python, it should be understood that aspects of embodiments of the subject matter described herein may be programmed using any number of different programming languages, techniques, libraries, and/or the like.

[0041] FIG. 1 is a schematic diagram depicting an illustrative system 100 for determining atmospheric vapor conditions, in accordance with embodiments of the disclosure. In embodiments, the system 100 includes a plurality of satellites 102, one or more sensor networks 104A-104E and a remote processing device 106. In embodiments, the system 100 may also include a reference sensor network 108.

[0042] The satellites 102 may be included in a global navigation satellite system. For example, the satellites 102 may be included in the Global Positioning System (GPS), Galileo, GLONASS, Compass and/or the like. In embodiments, each of the sensor networks 104A-104E include one or more GNSS devices 1 12. And, the satellites 102 are configured to transmit signals 1 10 to one or more of the GNSS device 1 12 included in a mobile sensor network 104A-104E. Each GNSS device 1 12 respectively includes a receiver functionality included therein. The receiver functionality of a GNSS device 1 12 is configured to receive satellites signals 1 10 including raw data from a plurality of satellites 102. Based on the amount of time it takes the signals 1 10 to travel from the satellites 102 to the GNSS devices 1 12, the amount of vapor content that the signals 1 10 travel through may be determined, as explained in more detail below in relation to FIG. 2.

[0043] The GNSS devices 1 12 may be portable and not have a fixed location. Alternatively, the GNSS devices 1 12 may have a fixed location. As stated above, each of the GNSS devices 1 12 may be configured to receive signals 1 10 from satellites 102. Additionally, each GNSS device 1 12 may include a transmitter configured to transmit signals 1 14 to a remote processing device 106. In embodiments, the GNSS devices 1 12 may receive raw data from one or more satellites 102 and transmit the raw data to a remote processing device 106. Receiving signals 1 10 from the satellites 102 and transmitting signals 1 14 to a remote processing device 106 may be one of many functions performed by the GNSS devices 1 12. For example, the GNSS devices 1 12 may be smartphones, GNSS receivers associated with cell phone towers or utility meters, remote utility meters with GNSS receivers, OnStar devices, devices used in precision agriculture and/or the like, which perform functions other than receiving and transmitting satellite signal raw data.

[0044] Because GNSS devices 1 12 (e.g., smartphones) are ubiquitous, they may provide persistent access to satellite 102 signals over populated land masses. That is, the GNSS devices 1 12 may be combined into sensor networks 104A-104E and provide the requested coverage and resolution for the populated area, and then be released when no longer needed. For example, to support weather forecasting around the United States, multiple sensor networks 104A-104E could exist concurrently in geographically separated locations exhibiting potential severe weather conditions. As storms dissipate or conditions change, the sensor networks 104A-104E that are no longer needed may be released. For example, the sensor network 104A may be released and sensor network 104E may be formed as a storm moves from the mountains 1 16 towards the city 1 18 in Fig. 1 .

[0045] In embodiments, a program running on a GNSS device 1 12 or remote processing device 106 may direct the GNSS device 1 12 to join a mobile sensor network 104A-104E. For example, a program running on the GNSS devices 1 12 or remote processing device 106 may determine the timing and the number of GNSS devices 1 12 that will be used to form a mobile sensor network 104A-104E. The number of GNSS devices 1 12 included in a sensor network 104A-104E may depend on the desired density and/or coverage area of the mobile sensor network 104A-104E, as explained below in the relation to FIG. 3. In embodiments, the number of GNSS devices 1 12 included in a sensor network 104A-104E may range from one to more than two hundred (200) GNSS devices. Additionally or alternatively, the GNSS devices 1 12 that form a sensor network 104A-104E may be separated from each other by a distances from 100 meters to 5km or more. To provide accurate atmospheric vapor condition predictions at altitudes less than 1 .5km, however, the GNSS devices 1 12 may be configured to be separated by distances less than or equal to 1 km.

[0046] In embodiments, the formation of one or more sensor networks 104A- 104E may be in response to a request to determine the vapor content in a volume (also referred to herein as voxels) of the lower atmosphere over a specific land mass. The request to determine the vapor content may be sent from the remote processing device 106 to one or more GNSS devices 1 12 located in or near the specific land mass in which the vapor content of the lower atmosphere is to be determined. Additionally or alternatively, the formation of one or more sensor networks 104A-104E may be the result of a periodic schedule. For example, every 10 minutes to 10 hours, a plurality of GNSS devices 1 12 may form into a sensor network 104A-104E in response to a received signal from a remote processing device 106 and/or in response to a program running on the GNSS devices 1 12.

[0047] As stated above, data included in the received signals 1 10 may be transmitted via signals from the GNSS devices 1 12 to a remote processing device 106. After receiving the transmitted signals 1 14 from one or more GNSS devices 1 12, the remote processing device 106 may determine the vapor content in which the received signals 1 10 pass through, as described below in relation to the discussion of FIG. 2. The vapor content may be determined by the remote processing device 106 instead of the GNSS devices 1 12 to reduce the computational demands on the GNSS devices 1 12 and/or to aggregate data of the signals 1 10 received by a plurality of GNSS devices 1 12. For example, since the remote processing device 106 may perform the computations to calculate the vapor content in the atmosphere, the data collection on the GNSS device 1 12 can be developed with a "thin client" approach that minimizes power requirements, data transmission rates and memory usage of the GNSS devices 1 12. In embodiments, the transmitted signals 1 14 may be transmitted from the GNSS devices 1 12 to the remote processing device 106 using one or more communication networks such as, for example, a short messaging service (SMS), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), the Internet, a P2P network, and/or the like.

[0048] In embodiments, the GNSS devices 1 12 may calculate the vapor content in the atmosphere using the data included in the received signals 1 10 in addition or alternative to the remove processing device 106 determining the vapor content of the atmosphere. In embodiments where the GNSS devices 1 12 determine the vapor content of the atmosphere, the determined vapor content may be transmitted from the GNSS device 1 12 to the remote processing device 106. After the GNSS devices 1 12 transmits the data used to calculate the vapor content in the atmosphere to the remote processing device 106 and/or the GNSS devices 1 12 calculate the vapor content in the atmosphere themselves, the GNSS devices 1 12 may be released from the sensor network 104A-104E.

[0049] In embodiments, a reference sensor network 108 may also receive signals 120 from one or more of the satellites 102. The reference sensor network 108 may be comprised of one or more reference receivers 122. Data included in the received signals 120 may be transmitted from the reference sensor network 108 to the remote processing device 106 via one or more signals 124. From the data of the received signals 120, the remote processing device 106 may determine vapor content of the atmosphere that the received signals 120 passed through. In embodiments, the reference sensor network 108 may determine vapor content of the atmosphere in addition or alternative to the remote processing device 106 determining the vapor content of the atmosphere. In embodiments where the reference sensor network 108 determines the vapor content of the atmosphere, the determined vapor content may be transmitted from the reference sensor network 108 to the remote processing device 106. In embodiments, the remote processing device 106 may use the determined vapor content based on the received signals 120 in combination with and/or to verify the determine vapor content calculations using the received signals 1 10. In embodiments, the signals 124 may be transmitted from the reference sensor network 108 to the remote processing device 106 using one or more communication networks such as, for example, a short messaging service (SMS), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), the Internet, a P2P network, and/or the like. [0050] The system 100 shown in FIG. 1 is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the present disclosure. Neither should the illustrative system 100 be interpreted as having any dependency or requirement related to any single component or combination of components illustrated therein. Additionally, various components depicted in FIG. 1 may be, in embodiments, integrated with various ones of the other components depicted therein (and/or components not illustrated), all of which are considered to be within the ambit of the present disclosure.

[0051] FIG. 2 is a schematic diagram of a system 200 depicting an illustrative signal 202 being transmitted by a satellite 204 and received by a GNSS device 206, in accordance with embodiments of the disclosure. In embodiments, the satellite 204 may have the same or similar functionality as the satellite 102 depicted in FIG. 1 . Additionally or alternatively, the GNSS device 206 may have the same or similar functionality as the GNSS device 1 12 depicted in FIG. 1. For example, the GNSS device 206 may receive signals from a plurality of satellites 204 and transmit data of the received signals to a remote processing device (e.g., the remote processing device 106 depicted in FIG. 1 ). Based on data of the received signals from the satellite 204, a remote processing device may estimate three-dimensional distributions of water vapor at 1 -3 km above the Earth's surface.

[0052] That is, for example, the signal 202 transmitted by the satellite 204 and received by the GNSS device 206 passes through the troposphere. If the troposphere includes vapor, however, the actual path 208 of the signal 202 differs from a straight- line path 210 connecting the satellite 204 to the GNSS device 206. That is, the vapor content of the troposphere diffracts the signal 202 being transmitted from the satellite 204 to the GNSS device 206 and introduces a delay in the signal 202. Once the tropospheric delay is determined, the amount of delay due to vapor in the troposphere that the signal 202 travels through can be determined. This delay due to water vapor may also be referred to herein as the slant-water delay. Remote Sensing of Atmospheric Water Vapor with the Global Positioning System by Dr. John Joseph Braun, UNIVERSITY OF COLORADO BOULDER SCHOLAR (May 1 , 2004), discloses how to calculate, from the delay of the signal 202, the vapor content included in the troposphere that the signal 202 passes through, which is herein incorporated by reference in its entirety. Based on the vapor content included in the troposphere, the strength and location of a possible storm can be estimated.

[0053] As stated above in relation to FIG. 1 , the GNSS device 206 may be, for example, a smartphone, a GNSS receiver associated with a cell phone tower or utility meter, a remote utility meter with a GNSS receiver, an OnStar device, a device used in precision agriculture and/or the like. In embodiments, however, the chip sets, the antennas and/or the configuration of one of these types of GNSS devices 206 may yield insufficient accuracy to compute vapor content of the lower atmosphere. For example, positional accuracy within 5-10 centimeters (cm) may be required to accurately calculate vapor content of the lower atmosphere. To obtain this level of accuracy, frequency-based data may need to be extracted from the raw data of the transmitted satellite signal 202. In embodiments, however, GNSS devices may be configured to receive code-based data from a satellite 204 instead of frequency-based data from a satellite 204; and, code-based data may not provide sufficient accuracy to compute vapor content of the lower atmosphere.

[0054] In response to this problem, the GNSS device 206 may be configured to begin receiving frequency-based data from the satellite 204 when the GNSS device 206 is included in a mobile sensor network (e.g., one or more of the mobile sensor networks 104A-104E depicted in FIG. 1 ). For example, the GNSS device 206 may start collecting frequency-based data in response to an instruction from a program installed on the GNSS device 206 that indicates the GNSS device 206 is being included in a mobile sensor network. The GNSS device 206 may then collect frequency-based data from the satellite 204 (and a plurality of other satellites) for a fixed period of time (e.g., 15 min., 30 min., 45 min., 60 min., and/or the like). After the fixed period of time, the GNSS device 206 may be released from the mobile sensor network and may no longer collect frequency-based data from the satellite 204 (and the plurality of other satellites). Once released, the GNSS device 206 will no longer expend the extra power that is required to collect frequency-based data. Additionally or alternatively, the GNSS device 206 may be configured to receive frequency-based data from the satellite 204 continuously for a period of time (e.g., 1 min to 10 hours). [0055] Another potential problem with using a smartphone as the GNSS device 206 is that continuous carrier phase tracking of a GPS signal is a power intensive operation. As such, smartphones may employ a duty cycle implementation to reduce power consumption. In embodiments, however, continuous carrier phase measurements may be needed to accurately calculate atmospheric vapor conditions.

[0056] In response to this problem, the GNSS device 206 may be configured to temporarily disable duty cycling and, therefore, perform continuous carrier phase tracking while the GNSS device 206 is receiving signals used to calculate atmospheric vapor conditions. Additionally or alternatively, in response this problem, a continuous carrier phase signal may be reconstructed from intermittent phase measurement intervals. A Phase-Reconstruction Technique for Low-Power Centimeter-Accurate Mobile Positioning by Kenneth M Pesyna, Zaher M Kassas, Robert W Heath, Todd E Humphreys, IEEE Transactions on Signal Processing (May 15, 2014), discloses how to reconstruct a continuous carrier phase signal from intermittent phase measurement intervals, which is herein incorporated by reference in its entirety. In embodiments, the continuous carrier phase signal may be constructed from carrier phase measurement signals having duty cycles as low as 5% and may improve even more in the future.

[0057] In embodiments, the GNSS device 206 may include a single frequency GPS chip configured to receive frequency-based data from the satellite 204. In other embodiments, the GNSS device 206 may include a dual-frequency GPS chip and/or a higher quality antenna so more accurate positional data can be acquired. While 5-10 cm of accuracy is preferred in the signal 202, signals having less accuracy may also be used to determine the vapor moisture content of the atmosphere.

[0058] In addition or alternative to collecting frequency-based data, information from other systems may be used to increase the positional accuracy determined from the signal 202. For example, the GNSS device 206 and/or a remote processing device may receive information from the National Oceanic and Atmospheric Administration (NOAA). The NOAA provides atmospheric data from the High-Resolution Rapid Refresh (HRRR) system (Reference "High Resolution Rapid Refresh (HRRR)"). This atmospheric data can provide an a priori estimate of atmospheric conditions to improve the accuracy and/or to confirm the accuracy of the vapor content in the atmosphere that is calculated using the signal 202. Additionally or alternatively, if the GNSS device 206 receives satellite signals 202 from GPS satellites, cell tower ranging measurements may be used to strengthen the GPS solution. The GNSS device 206 may also be configured to receive signals from satellites included in other GNSS constellations, such as Galileo, Glonass, Compass and/or Beidou, in order to compliment the signal 202 received from a GPS satellite 204. Based on the signal 202 received from a GPS satellite 204 and a signal received from a satellite included in another GNSS constellation, the vapor content of the atmosphere may be determined. Signals from more than one GNSS constellation may also help reduce time-to-ambiguity resolution (TAR) due to multipath.

[0059] The system 200 shown in FIG. 2 is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the present disclosure. Neither should the illustrative system 200 be interpreted as having any dependency or requirement related to any single component or combination of components illustrated therein. Additionally, various components depicted in FIG. 2 may be, in embodiments, integrated with various ones of the other components depicted therein (and/or components not illustrated), all of which are considered to be within the ambit of the present disclosure.

[0060] FIGs. 3A-3B are diagrams depicting illustrative portions of mobile sensor networks 300A, 300B used for determining atmospheric vapor levels, in accordance with embodiments of the present disclosure. The networks 300A, 300B include a plurality of GNSS devices 302 configured to receive signals 304 transmitted by a plurality of satellites (e.g., the satellites 102 depicted in FIG. 1 and/or the satellite 204 depicted in FIG. 2). In embodiments, the GNSS devices 302 may have some or all of the same functionality as the GNSS devices 1 12 depicted in FIG. 1 and/or the GNSS device 206 depicted in FIG. 2.

[0061] A grid is depicted on each network 300A, 300B that includes atmospheric volume elements, also referred to voxels. For example, the networks 300A, 300B respectively include voxel elements 306A, 306B. The size of each voxel element corresponds to the resolution of each network 300A, 300B and the shading corresponds to the coverage of each network 300A, 300B. To determine a vapor profile of a voxel, a satellite signal that passes through the voxel may be used. With more signals passing through a voxel and received by a GNSS device 302, the accuracy of the vapor profile for the voxel may be improved. Accuracy of a vapor profile may also increase when signals passing through the same voxel are received by different GNSS devices 302. Stated another way, the resolution and coverage of each network 300A, 300B may be determined by the number of GNSS devices 302 included in each network 300A, 300B.

[0062] FIGs. 3A and 3B respectively include networks 300A, 300B having a different number and density of GNSS devices 302 included therein. As shown, network 300A includes a plurality of GNSS devices 302 that are spaced farther apart than the GNSS devices 302 included in network 300B. These different densities of GNSS devices 302 result in different coverages. For example, the lower density network 300A has more empty voxels near the bottom of the grid than the high-density network 300B and the lower density network 300A has fewer voxels crossed by multiple signals 304 than the higher density network 300B. As shown, the poorest coverage occurs at lower altitudes of the atmosphere (e.g., 1 -3 kilometers above the Earth's surface), which may be of the most interest for determining adverse weather events. Accordingly, a network 300A, 300B including more GNSS devices 302 may be advantageous under various circumstances.

[0063] As an example, the network 300A does not include enough GNSS devices 302 to calculate the vapor profile of voxel element 306A. Alternatively, the network 300B does include enough GNSS devices 302 to calculate the vapor profile of voxel element 306B. As such, if the vapor profile of voxel 306A is desired, one or more GNSS devices 302 may be added to the network 300A until a level of coverage is obtained where the vapor profile of voxel element 306A can be calculated. Alternatively, if the vapor profile of voxel 306B is not desired, one or more GNSS devices 302 may be released from network 300B until a desired level of coverage is obtained. Stated another way, GNSS devices 302 may be dynamically added or removed from the respective networks 300A, 300B, depending on a desired amount of coverage.

[0064] As shown from the comparison of networks 300A, 300B, having more GNSS devices 302 in a network provides better coverage and resolution. However, some conventional implementations do not include networks that have a level of coverage and resolution that may be desired because of economic reasons. For example, not only are a large number of GNSS devices required for dense networks, but conventional implementations usually include a closed architecture design. That is, a single sensor network is integrated with a single dedicated processing system. Therefore, each region to be monitored requires a separate network of GNSS receivers and processing systems which must be purchased, operated and maintained. The associated costs have been perceived as prohibitive, precluding much further development or widespread applications. The embodiments disclosed herein provide a solution to this problem by creating sensor networks using GNSS devices instead of using dedicated sensor, as is done in conventional embodiments.

[0065] The embodiments depicted in FIGs. 3A and 3B are not intended to suggest any limitation as to the scope of use or functionality of embodiments of the present disclosure. Neither should the illustrative networks 300A, 300B be interpreted as having any dependency or requirement related to any single component or combination of components illustrated therein. Additionally, various components depicted in FIGs. 3A and 3B may be, in embodiments, integrated with various ones of the other components depicted therein (and/or components not illustrated), all of which are considered to be within the ambit of the present disclosure.

[0066] FIG. 4 is a block diagram depicting an illustrative computing device 400, in accordance with embodiments of the disclosure. In embodiments, the computing device 400 may be, include or be included in the remote processing device 106 and/or the GNSS devices 1 12 depicted in FIG. 1 , the GNSS device 206 device depicted in FIG. 2 and/or the GNSS device 302 depicted in FIGs. 3A and 3B. The computing device 400 may include any type of computing device suitable for implementing aspects of embodiments of the disclosed subject matter. Examples of computing devices include specialized computing devices or general-purpose computing devices such "workstations," "servers," "laptops," "desktops," "tablet computers," "hand-held devices," "general-purpose graphics processing units (GPGPUs)," and the like, all of which are contemplated and within the computing device 400. [0067] In embodiments, the computing device 400 includes a bus 402 that, directly and/or indirectly, couples the following devices: a processor 404, a memory 406, an input/output (I/O) port 408, an I/O component 410, and a power supply 412. Any number of additional components, different components, and/or combinations of components may also be included in the computing device 400. The I/O component 410 may include a presentation component configured to present information to a user such as, for example, a display device 414, a speaker, a printing device, and/or the like, and/or an input device 416 such as, for example, a microphone, a joystick, a satellite dish, a scanner, a printer, a wireless device, a keyboard, a pen, a voice input device, a touch input device, a touch-screen device, an interactive display device, a mouse, and/or the like.

[0068] The bus 402 represents what may be one or more busses (such as, for example, an address bus, data bus, or combination thereof). Similarly, in embodiments, the computing device 400 may include a number of processors 404, a number of memory components 406, a number of I/O ports 408, a number of I/O components 410, and/or a number of power supplies 412. Additionally any number of these components, or combinations thereof, may be distributed and/or duplicated across a number of computing devices.

[0069] In embodiments, the memory 406 includes computer-readable media in the form of volatile and/or nonvolatile memory and may be removable, nonremovable, or a combination thereof. Media examples include Random Access Memory (RAM); Read Only Memory (ROM); Electronically Erasable Programmable Read Only Memory (EEPROM); flash memory; optical or holographic media; magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices; data transmissions; and/or any other medium that can be used to store information and can be accessed by a computing device such as, for example, quantum state memory, and/or the like. In embodiments, the memory 406 stores computer-executable instructions 418 for causing the processor 404 to implement aspects of embodiments of system components discussed herein and/or to perform aspects of embodiments of methods and procedures discussed herein. [0070] As an example, the computer-executable instructions 418 may include instructions for determining whether a GNSS device is included in a mobile sensor network, how many GNSS devices are included in a mobile sensor network, and when a GNSS device is included in a mobile sensor network. The computer-executable instructions may also include instructions for controlling what type of data is extracted from a satellite signal (e.g., code-based data, frequency-based data of a single frequency, frequency-based data of more than one frequency and/or the like). Additionally or alternatively, the computer-executable instructions may include instructions for calculating the amount of delay due to vapor in the atmosphere that a satellite signal passes through. However, these are only examples and not meant to be limiting.

[0071] The computer-executable instructions 418 may include, for example, computer code, machine-useable instructions, and the like such as, for example, program components capable of being executed by one or more processors 404 associated with the computing device 400. Program components may be programmed using any number of different programming environments, including various languages, development kits, frameworks, and/or the like. Some or all of the functionality contemplated herein may also, or alternatively, be implemented in hardware and/or firmware.

[0072] The illustrative computing device 400 shown in FIG. 4 is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the present disclosure. Neither should the illustrative computing device 400 be interpreted as having any dependency or requirement related to any single component or combination of components illustrated therein. Additionally, various components depicted in FIG. 4 may be, in embodiments, integrated with various ones of the other components depicted therein (and/or components not illustrated), all of which are considered to be within the ambit of the present disclosure.

[0073] FIG. 5 is a flow diagram depicting an illustrative method 500 for determining atmospheric vapor content, in accordance with embodiments of the present disclosure. Aspects of embodiments of the illustrative method 500 may be performed by any number of different components discussed above with regard to FIGs. 1 - 4. As shown in FIG. 5, embodiments of the method 500 include forming a sensor network having a plurality of GNSS devices (block 502). In embodiments, the sensor network may be, be similar to, include or be included in the mobile sensor networks 104A-104E depicted in FIG. 1 . In embodiments, the GNSS devices may be, be similar to, include or be included in the GNSS devices 1 12 depicted in FIG. 1 , the GNSS device 206 depicted in FIG. 2 and/or the GNSS devices 302 depicted in FIGs. 3A-3B. For example, the mobile sensor network may be formed in response to a signal received by a GNSS device from a remote processing device to form a mobile sensor network. Additionally or alternatively, the number and density of GNSS devices included in the sensor network may be determined based on a desired level of coverage, e.g., as discussed above in relation to FIGs. 3A-3B.

[0074] The method 500 may further include receiving signals, by the GNSS devices, from at least one satellite, wherein the signals include positional information (block 504). In embodiments, the signals may be, be similar to, include or be included in the signals 1 10 depicted in FIG. 1 , the signal 202 depicted in FIG. 2 and/or the signals 304 depicted in FIGs. 3A-3B. For example, the GNSS devices may be configured to receive specific type of data (e.g., frequency-based data) on a recurring schedule and/or for certain time periods.

[0075] According to embodiments, the method 500 may include transmitting the positional information to a remote processing device (block 506). In embodiments, the positional information may include the raw data received from the satellites. Additionally or alternatively, the positional information may include code-based positional data, frequency-based positional data of a single frequency, frequency-based positional data of multiple frequencies, and/or the like.

[0076] The method 500 may also include determining atmospheric vapor conditions based on the positional information (block 508). In embodiments, the atmospheric vapor conditions may be determined using the embodiments discussed above in relation to FIG. 2 and/or using the calculations discussed in Remote Sensing of Atmospheric Water Vapor with the Global Positioning System by Dr. John Joseph Braun, University of Colorado Boulder Scholar (May 1 , 2004). In embodiments, the remote processing device may determine the atmospheric vapor conditions. Additionally or alternatively, the GNSS devices determining atmospheric vapor conditions based on the positional information. In embodiments where the GNSS devices determine the atmospheric moisture conditions, the GNSS devices may or may not transmit the positional information to a remote processing device.

[0077] In embodiments, the method 500 may include releasing the GNSS devices from the sensor network (block 510). In embodiments, the GNSS devices may be released from the mobile sensor network and/or may no longer collect the type of data required to accurately calculate atmospheric vapor conditions in order to reduce the power requirements of doing so.

[0078] The method 500 shown in FIG. 5 is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the present disclosure. Neither should the illustrative method 500 be interpreted as having any dependency or requirement related to any single block or combination of blocks illustrated therein. Additionally, various blocks depicted in FIG. 5 may be, in embodiments, integrated with various ones of the other blocks depicted therein (and/or blocks not illustrated), all of which are considered to be within the ambit of the present disclosure.

[0079] Various modifications and additions can be made to the exemplary embodiments discussed without departing from the scope of the present disclosure. For example, while the embodiments described above refer to particular features, the scope of this disclosure also includes embodiments having different combinations of features and embodiments that do not include all of the described features. Accordingly, the scope of the present disclosure is intended to embrace all such alternatives, modifications, and variations as fall within the scope of the claims, together with all equivalents thereof.

Claims

CLAIMS What is claimed is:

1 . A method for determining atmospheric vapor conditions, the method comprising: forming a sensor network having one or more devices, each device of the one or more devices being configured to receive raw data from at least one GNSS satellite; receiving, by a remote processing device, raw data from at least one device of the one or more devices; and determining, by the remote processing device using the received raw data,

atmospheric vapor conditions associated with the at least one device.

2. The method of claim 1 , further comprising instructing the at least one device to transmit the raw data to the remote processing device.

3. The method of any one of claims 1 -2, further comprising releasing each device of the one or more devices from the sensor network.

4. The method of any one of claims 1 -3, further comprising: receiving, by the remote processing device, positional information from at least one reference receiver; and wherein determining the atmospheric vapor conditions comprises using the

received positional information to correct the atmospheric vapor condition estimate.

5. The method of claim 4, wherein the at least one reference receiver is not included in the sensor network.

6. The method of any one of claims 1 -5, wherein the one or more devices comprise at least one of: a mobile device and a stationary device.

7. The method of any one of claims 1 -6, wherein the one or more devices comprise a reference receiver.

8. The method of any one of claims 1 -7 wherein determining the atmospheric vapor conditions comprises receiving one or more signals from a source not included in the sensor network and determining the atmospheric vapor conditions based on the raw data and the one or more signals from the source not included in the sensor network.

9. The method of any one of claims 1 -8, wherein the raw data is used by a device that receives the raw data to determine a position of the device.

10. A non-transitory computer readable medium having a computer program stored thereon for determining which size medical device to implant in a patient, the computer program comprising instructions for causing one or more processors to: provide instructions to one or more devices to form a sensor network, each

device of the one or more devices being configured to receive raw data from at least one GNSS satellite; receive raw data from at least one device of the one or more devices; and determine atmospheric vapor conditions associated with the at least one device.

1 1 . The non-transitory computer readable medium of claim 10, the computer program comprising instructions for causing the one or more processors to instruct the at least one device to transmit the raw data to the remote processing device.

12. The non-transitory computer readable medium of any one of claims 10-1 1 , the computer program comprising instructions for causing the one or more processors to release each device of the one or more devices from the sensor network.

13. The non-transitory computer readable medium of any one of claims 10-12, the computer program comprising instructions for causing the one or more processors to: receive positional information from at least one reference receiver; and wherein determining the atmospheric vapor conditions comprises using the received positional information to correct the atmospheric vapor condition estimate.

14. The non-transitory computer readable medium of claim 13, wherein the at least one reference receiver is not included in the sensor network.

15. The non-transitory computer readable medium of any one of claims 10-14, wherein the one or more devices comprise at least one of: a mobile device and a stationary device.

16. The non-transitory computer readable medium of any one of claims 10-15, wherein the one or more devices include a reference receiver.

17. The non-transitory computer readable medium of any one of claims 10-16, wherein determining the atmospheric vapor conditions comprises receiving one or more signals from a source not included in the sensor network and determining the atmospheric vapor conditions based on the raw data and the one or more signals from the source not included in the sensor network.

18. The non-transitory computer readable medium of any one of claims 10-17, wherein the raw data is used by a device that receives the raw data to determine a position of the device.

19. A device configured to facilitate determining atmospheric vapor conditions, the device comprising: memory; and a processor, the processor configured to execute instructions stored on the

memory and in response to executing the instructions, the processor is configured to: receive raw data from at least one GNSS satellite; and transmit the raw data to a remote processing device, wherein the remote processing device is configured to determine atmospheric vapor conditions associated with the device.

20. The device of claim 19, the processor further configured to receive the determined atmospheric moisture conditions.

21 . The device of claim 20, wherein the processor is further configured to process atmospheric moisture conditions data and predict real-time moisture conditions for further assessment by a professional.