Method for locating signal sources in a wireless network

Abstract

A method for estimating the location of a wireless transmitter includes: collecting multiple wireless measurements between the transmitter and a receiver; drawing a buffer circle around each measurement, the buffer circle having a radius defined by a timing advance delay measurement; plotting the multiple buffer circles and identifying only the intersections of adjacent measurements; and estimating the location based on the intersections of delay measurements from the multiple wireless measurements.

Description

[0001] Cross-reference to related applications

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 001,003, filed with the U.S. Patent and Trademark Office on March 27, 2020, and U.S. Provisional Patent Application No. 63 / 199,622, filed with the U.S. Patent and Trademark Office on January 13, 2021, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0003] The invention herein relates to identifying and estimating the geographic location of a transmitter in a wireless network by analyzing multiple measurements from at least one wireless device and using the delay measurement of that wireless device and known coordinates. Background Technology

[0004] Modern wireless networks typically consist of multiple wireless base stations located in various stationary and mobile positions. A wireless network typically consists of multiple wireless base stations communicating with mobile devices. Typically, both the wireless base stations and the mobile devices contain transceivers for bidirectional communication. In modern wireless systems, the timing of transmissions between transmitters and receivers is strictly controlled to reduce interference in the shared radio spectrum.

[0005] The location of wireless network transmission equipment is of great interest to many parties. For example, wireless network operators are interested in where competing networks have already deployed wireless base station equipment. A wireless base station is a device that provides connectivity between mobile phones and wider telephone networks via radio waves transmitted or received through antennas, and typically includes several transceivers and / or transmitters, with one transmitter transmitting in a different direction than another, thereby providing greater coverage from a single point. This information can help improve decision-making regarding the location and prioritization of their own new wireless transceivers. For example, an operator may learn that a competitor has already established a wireless base station in an area not currently covered by their own network and may therefore decide to establish a wireless base station in that area to eliminate the competitor's service advantage. Additionally, companies that supply infrastructure such as cellular towers (which house transceivers) to wireless network operators (such as tower owners and companies that acquire rooftop rights to lease to wireless network operators) are interested in knowing where operators have already deployed base stations so they can identify areas with potential unmet needs for increased coverage or capacity, or identify potentially underutilized assets, such as cellular tower structures used by only one network operator, which could also be beneficial to other operators. Furthermore, building owners and tenants may be interested in knowing where the wireless network infrastructure is located relative to their property, for example, to estimate whether coverage there will be good or bad on any individual network (or all networks).

[0006] Regardless of the medium used, there is an inherent delay between the transmission and reception of wireless signals. In wireless networks, the medium is space, and the factors causing the delay are either the medium itself (free space), obstacles in the path between the transmitter and receiver (causing reflections, refractions, etc.), or the distance between the transmitter and receiver. To achieve communication synchronization, wireless signals are transmitted in advance to account for the resulting reception delay. In cellular networks, this early transmission is measured in increments of predefined time intervals and delay intervals (e.g., timing advance (“TA”)). Wireless networks continuously monitor the time difference between signal transmission and reception. If a wireless signal arrives too early, the transmitter is instructed to transmit the signal later via a lower TA offset, and vice versa. Mobile devices can use installed applications to record these TA values, along with the geographic location (latitude, longitude, altitude) where the TA measurement was collected and the identifier of the wireless signal source (in general, TA data is geographic location and TA value). Therefore, a single device moving from point A to point B may capture and record several different TA data points because each measurement will result in a different geographic location and potentially a different TA value. The collection of measurement results was then used to better identify the location of wireless base stations (transmitters).

[0007] The applicant has created a new and useful method to improve the identification and location of transceiver base stations by using TA data, which includes latency measurement data (such as TA values) and geographic location. Summary of the Invention

[0008] The method presented in this paper proposes a solution for identifying the geographical location of wireless transmitters in a network based on wireless signal measurements collected by wireless devices served by the network. For example, this is useful for estimating the location of one or more wireless transmitters, including wireless network base stations, cellular sites, transceiver stations, wireless towers, etc.

[0009] Compared to current methods that rely solely on the received signal level value and are susceptible to degradation due to signal fading, penetration loss, path obstacles, and low spatial diversity of measurements, this invention will achieve better accuracy and precision in detecting the geographic location of wireless transmitters.

[0010] The embodiments described herein feature an application running on a mobile device capable of collecting the device's geolocation and extensive wireless network information. The methods described herein are used to analyze data collected from one or more devices to determine the location of wireless network base stations, and in some embodiments, to iteratively improve the accuracy of transmitter location identification.

[0011] In a preferred embodiment, a method for estimating the location of a wireless tower includes: (a) collecting multiple wireless measurements between a transmitter and a receiver, such as timing advance (TA) data, and collecting timing advance (TA) between the transmitter and the receiver; (b) drawing a buffer circle around the receiver, wherein the circle has a radius equal to (c) Extract the intersections between adjacent wireless measurement results; (d) Identify the clusters of intersections from step (c); (e) Identify the cluster with the highest number of intersections; (f) Generate a polygon corresponding to the cluster from step (e); (g) Extract the center of the polygon from step (f); (h) Draw the circumcircle of the polygon from step (f); and (i) Determine the initial estimated location of the wireless tower corresponding to the location within the circle.

[0012] In another embodiment, the method further includes: wherein the intersection has an inter-point distance equal to a threshold D (distance) and a minimum of M points (measurement results).

[0013] In another embodiment, the method involves setting thresholds D and M to sufficiently small values ​​to group the intersections of densely located locations.

[0014] In another embodiment, the method, based on experience, finds that the values ​​of D and M are approximately D = 30 meters and M = 5 points in rural areas and approximately D = 10 meters and M = 10 points in suburban and urban areas, respectively.

[0015] In another embodiment, the method further includes the following steps: (j) wherein the position determined from step (i) is set as an initial estimated position, and the minimum distance from that position to all buffer circles is calculated; (k) the initial position is shifted to a new position by distance D and angle A, and the distance to all buffer circles is calculated; (l) the calculated distances between step (j) and step (k) are compared; and (m) the new position is set as the next estimated position, wherein the new position has a shorter distance than the previously estimated position.

[0016] In another embodiment, the method further measures the signal level at the new location in step (m) and modifies the new location based on the measured signal level.

[0017] In another preferred embodiment, a method for estimating the location of a wireless transmitter includes: (a) collecting multiple wireless measurements between the transmitter and a receiver; and (b) estimating the location based on the intersection of delay measurements from the multiple wireless measurements.

[0018] In another embodiment, the method wherein the intersection of the delay measurement results is defined by drawing a buffer circle around the wireless measurement results, wherein the radius of the circle is defined as... And to identify the intersection between two measurements collected adjacent to each other.

[0019] In another embodiment, the method, wherein “adjacent” means adjacent in time or in location.

[0020] In another embodiment, the method involves estimating the estimated position by identifying clusters of intersections and circumscribing a circle around a polygon created from the clusters of intersections, wherein the estimated position lies within the circumscribing circle.

[0021] In another embodiment, the method involves estimating the position by plotting a location point within the circle.

[0022] In another embodiment, the method involves plotting location points to create the shortest distance to each intersection point within the circle.

[0023] In another embodiment, a method for estimating the location of a wireless transmitter, such as a base station that may be on a tower, rooftop, streetlight, or billboard, includes: collecting multiple wireless measurements between the transmitter and a receiver; drawing a buffer circle around each measurement, having a radius defined by a timing advance delay measurement; plotting the multiple buffer circles corresponding to the multiple wireless measurements and identifying at least one intersection point of at least two adjacent measurements; and estimating the location based on the intersection points of delay measurements from the multiple wireless measurements.

[0024] This method uses only adjacent measurements to identify at least one intersection point.

[0025] This method, where the receiver is a mobile device.

[0026] In another preferred embodiment, a method for estimating the location of a wireless transmitter includes the following steps: (a) collecting multiple wireless measurements between the wireless transmitter and a receiver, the wireless measurements including a delay measurement (TA value) between the wireless transmitter and the receiver, and the location of the receiver; (b) drawing a buffer circle around the receiver location, wherein the buffer circle has a radius equal to Where x represents the distance measurement result for each unit of TA value; (c) extract the intersection points between adjacent wireless measurement results; (d) identify at least one cluster from the intersection points of step (c); (e) identify the cluster with the highest number of intersection points; and (f) determine the initial estimated location of the wireless transmitter from the cluster with the highest number of intersection points.

[0027] In another embodiment, the method further includes the following steps: (e1) immediately following step (e), generating a polygon corresponding to the cluster with the highest number of intersections from step (e); (e2) extracting the center of the polygon from step (e1); (e3) drawing the circumcircle of the polygon; and (e4) determining a first initial estimated position of the wireless transmitter corresponding to the position within the polygon whose circumcircle has been drawn.

[0028] In another embodiment, the method further includes: wherein the intersection has an inter-point distance equal to a threshold D and a minimum of M points.

[0029] In another embodiment, the method is used where the values ​​of D and M are D = 30 meters and M = 5 points.

[0030] In another embodiment, the method is used where the values ​​of D and M are D = 10 meters and M = 10 points.

[0031] In another embodiment, the method involves modifying the TA value based on the receiver's hardware or software.

[0032] In another embodiment, the method includes a TA value reported by the receiver that is specific to the device manufacturer, chipset, and software version, wherein a unique profile normalizes the reported TA value.

[0033] In another embodiment, the method further includes the following steps: (e5) calculating the minimum distance from the first initial estimated position to all buffer circles within the cluster; (e6) shifting the initial estimated position to a new position by distance D and angle A, and recalculating the distance to all buffer circles within the cluster; (e7) comparing the calculated distances between step (e5) and step (e6); and (e8) setting a second new position, wherein the second new position is shorter than the distance from the first initial estimated position to all buffer circles.

[0034] In another embodiment, the method includes a plurality of wireless measurements, including signal levels.

[0035] In another embodiment, the method further measures the signal level at the new location in step (e8) and modifies the new location based on the measured signal level.

[0036] In another preferred embodiment, the method involves drawing the polygon by connecting the intersections in the cluster that have the highest number of intersections.

[0037] In another preferred embodiment, a method for estimating the location of a wireless transmitter includes: (a) collecting a plurality of wireless measurements between the transmitter and a receiver, including location and TA value; (b) for each of the plurality of wireless measurements, drawing a buffer circle around the location of the receiver, wherein the buffer circle has a radius equal to (c) Identify the intersection point between at least two buffer circles; and (d) estimate the position of the transmitter based on the position of the intersection point.

[0038] In another embodiment, the method involves the plurality of wireless measurements being adjacent measurements.

[0039] In another embodiment, the method, wherein “adjacent” means adjacent in time or in location.

[0040] In another embodiment, the method wherein the step of estimating the location is performed by identifying a cluster of intersections and circumscribing a circle around a polygon created from the cluster of intersections, wherein the estimated location is within the circumscribing circle.

[0041] In another embodiment, the method involves estimating the position by plotting a location point within the circle.

[0042] In another embodiment, the method involves plotting location points to create the shortest distance to each intersection point within the circle.

[0043] In another preferred embodiment, a method for estimating the location of a wireless transmitter includes: collecting multiple wireless measurements between the transmitter and a receiver; drawing a buffer circle around the location of each wireless measurement, the buffer circle having a radius defined by timing advance / delay measurements collected by the receiver; plotting the multiple buffer circles and identifying only the intersections of adjacent measurements; and estimating the location based on the intersections of the timing advance / delay measurements from the multiple wireless measurements.

[0044] In another preferred embodiment, a method for estimating the location of a wireless transmitter includes the following steps: (a) collecting multiple wireless measurements between the wireless transmitter and a receiver, the wireless measurements including a delay measurement (TA value) between the wireless transmitter and the receiver, and the location of the receiver; (b) drawing a buffer circle around the receiver location, wherein the buffer circle has a radius equal to Where x represents the distance measurement result for each unit of TA value; (c) extract the intersection points between adjacent wireless measurement results; (d) identify at least one cluster from the intersection points of step (c); (e) identify the cluster with the highest number of intersection points; (f) generate a polygon corresponding to the cluster with the highest number of intersection points from step (e); (g) extract the center of the polygon from step (f); (h) draw the circumcircle of the polygon; and (i) determine the initial estimated position of the wireless transmitter corresponding to the position within the polygon with the drawn circumcircle.

[0045] In another embodiment, the method further includes: (j) calculating the minimum distance from the initial estimated position to all buffer circles within the cluster; (k) shifting the initial estimated position to a new position by distance D and angle A, and recalculating the distance to all buffer circles within the cluster; (l) comparing the calculated distances between step (j) and step (k); and (m) setting a new position, wherein the new position is shorter than the distance from the initial estimated position to all buffer circles. Attached Figure Description

[0046] Figure 1 A single measurement result received from the serving cell site is depicted. Depicted are: the receiver, here mobile device 1; the transmitter, here wireless base station 3; and delay measurement result 2, which is defined as the line drawn from the mobile device to the wireless tower, having a TA equal to 5.

[0047] Figure 2 A buffer circle 4 is depicted around the mobile device 1, the radius of which is equal to the received TA expressed as distance, wherein the drawn 1 / 2 TA offset buffer circle (4X and 4Y) takes into account the incremental difference between sequential TAs.

[0048] Figure 3 The image depicts wireless mobile devices at three separate locations (1A, 1B, and 1C) and overlapping buffer circles (4A, 4B, and 4C) that intersect to provide information about the location of the wireless tower.

[0049] Figure 4 A flowchart depicts an embodiment of the cell site identification method for Phase I.

[0050] Figure 5Wireless devices at four different locations (point A 43, point B 44, point C 45, point D 46) are depicted, and the intersections between buffer circles (buffer circle A 7, buffer circle B 8, buffer circle C 42, buffer circle D 41) obtained for time-adjacent measurements are calculated, namely the intersections between buffer circles A and B (intersections 6A and 6B), the intersections between B and C (intersections 9A and 9B), and the intersections between C and D (intersections 47A and 47B), thus obtaining a cluster 50 of intersection points 5, which is a set of measurements collected geographically within a predefined distance from each other.

[0051] Figure 6 Take and Figure 5 The same data was used, but the buffer circle was removed to simply show cluster 52, the intersection point, on the map. The clusters were labeled using a density-based spatial clustering algorithm for noisy applications, where the threshold distance between points was 20 meters and the minimum number of points was set to three. Cluster 52 had the highest number of points among the four clusters, while the remaining clusters (cluster 2 51, cluster 3 53, and cluster 4 54) each had one point.

[0052] Figure 7 An example of Delaunay triangulation plotted towards multiple points is depicted. The circumcircle 56 of the Delaunay triangle is used to generate a Voronoi diagram of the intersection points (55, 57, 58, 59). Folding (merging) the Delaunay triangle created from the cluster of points yields a single geometry (polygon) that can be used to represent the estimated location of the wireless base station and define the boundary of the cluster.

[0053] Figure 8 The sample geometry 61 is depicted using Delaunay triangulation from points in cluster 52, where triangles from three points are used.

[0054] Figure 9 More details of the geometry 61 (triangle) are provided, and the centroid 62 with a "+" sign is shown in the central part of the triangle.

[0055] Figure 10 The circumcircle of geometry 61 is depicted to represent the margin of the location of the wireless base station; geometry 61 is inside circle 63.

[0056] Figure 11 The set of intersections 73 within cluster 72 is depicted, resulting in the drawing of geometry 75, and then the initial estimated wireless base station location 76 is calculated. The shortest distance 74 from the initial estimated wireless base station location to each individual buffer circle 71 is also drawn.

[0057] Figure 12 The initial estimated transmitter (wireless base station) position is depicted to a spatial displacement of a new position 77 at a predefined distance and angle, which may potentially result in a shorter distance 74 from all buffer circles 71 compared to the initial estimated position.

[0058] Figure 13 A flowchart is defined illustrating a Phase II optimization method for increasing the accuracy of wireless base station location, including options for incorporating signal level measurements to increase prediction accuracy, and specifically, where the signal level can also help determine the transmitter's azimuth.

[0059] Figure 14 Examples of the results for Phase I and Phase II are depicted, showing the initial predicted signal source location 83, the final predicted signal source location 81 and the actual signal source location 90, together with the buffer circle 84 and their intersection 85 (clustered based on the distance between points) and the iterative path 82 taken by the iterative method detailed herein.

[0060] Figure 15 An example of an embodiment of the above-described method in a cell analysis web portal GUI is depicted. In this view, the estimated locations of cellular network radio base stations are indicated by points on a map, where the legend shows different base stations and cluster 50. Source measurement results are collected in the background from users of the Speedtest application. This web portal GUI allows for representation using a web-based portal to view the estimated locations of cellular network radio base stations with higher accuracy than existing methods.

[0061] Figure 16 The azimuth angle of the transmitters, typically spread out at approximately 120 degrees, is detailed. It shows three different transmitters (92, 93, and 94) at base station 95 with different orientations, which are surrounded by a buffer circle 91. Detailed Implementation

[0062] Historically, the location of wireless transmitters has been estimated using various signal level values, which are affected by fading, penetration loss, path obstacles, and more. Wireless transmitters can include cellular network base stations, two-way terrestrial mobile communication sites, broadcast transmitters, mobile radios, Internet of Things (IoT) devices, and other similar systems that transmit signals. Therefore, due to these various influences, these estimates result in inaccurate location positioning and imprecise identification of wireless transmitter locations, among other errors. Methods are needed to increase both accuracy and precision in defining wireless transmitter locations. This location data is of significant value to the industry. For example, the ability to identify the presence of a wireless transmitter and, more precisely, the location of that transmitter can be a useful tool, allowing wireless network operators to gain insights into, for example, the locations of competitor wireless base stations containing that transmitter or the locations of transmitters in general. For infrastructure companies (i.e., those that manufacture, install, or manage cellular network towers and rooftop locations), implementations can facilitate the financial valuation of existing towers (which house or hold one or more transmitters), identify potential locations for building new towers, and visualize tower locations to obtain rights to locations based on the highest value.

[0063] Measuring the travel time of a wireless signal (e.g., radio waves) provides an indication of the distance between a receiver and a transmitter. Here, the receiver is a wireless device (telephone, tablet, computer, radio, other communication device, etc.), and its location can be defined by longitude and latitude, while the transmitter has an indeterminate location. Because radio waves travel at a finite speed, transmitters in modern networks “pre-transmit” to arrive at the receiver at the precise correct time, thus avoiding interference with transmissions in adjacent “time slots.” This “timing advance” value corresponds to the distance between the transmitter and receiver, as longer distances require earlier transmissions to arrive at the receiver at the appropriate time. Using the method described herein, by combining the known longitude and latitude of the receiver (wireless device), and aggregating and processing multiple timing advance measurements, the transmitter's location can be accurately estimated.

[0064] Since timing advance (TA) is a delay measurement that indicates the duration of the incremental signal propagation time, it is possible to convert it into a distance measurement by multiplying this value by the speed of light (c = 299,792 m / s) under the general assumptions of free-space propagation and line-of-sight path. In Wideband Code Division Multiple Access (WCDMA) networks, each TA unit equals 3.69 μs, which yields a distance of 1106 meters. In LTE networks, each TA unit equals 0.52 μs, which yields a distance of 156 meters with a round-trip delay. Therefore, specific network types and network hardware relate to the distance within the delay measurement, and thus this variable can be controlled based on the obtained measurements. Some hardware devices misrepresent TA values, and therefore it is important to compensate for these discrepancies for optimal accuracy. In fact, hardware implementations (in the form of chipsets) and the software that controls them produce different conversion formulas from TA units to meters or seconds. Certain hardware and software profiles can be created, and even updated based on software updates, to allow for the normalization of all data in the dataset.

[0065] For any given TA measurement, the one-way distance from transmitter to receiver can be calculated by taking half of the TA value expressed in distance (meters). Figure 1 This paper details an example scenario in an LTE network (TA unit equals 1.56 meters), depicting the actual locations of a wireless base station (transmitter) 3 and a receiver (wireless device) 1, with a TA value of 5 recorded by the receiver. The distance between the two devices is calculated as 1 / 2 × (5 × 156), or 390 meters. Transmitter 3 is a wireless base station, and receiver 1 is wireless device 1, such as a cellular phone, laptop, computer, radio, etc., which are known to those skilled in the art as electronic devices with wireless receiving and / or transmitting capabilities. The TA line 2 is the distance between transmitter 3 and wireless device 1 at the time of measurement, where the precise distance is adjusted based on the hardware and software on the device.

[0066] A single measurement result of the TA received by the wireless device 1 is insufficient to determine the location of the transmitter 3, as it only indicates that the transmitter 3 is "x" meters away from the device 1.

[0067] Figure 2An example of a single mobile device 1 is depicted, which records that an LTE transmitter 3 has requested the wireless device 1 to use TA = 10 (equal to 780 meters) during its communication with the transmitter 3. The transmitter 3 can be located anywhere on the edge of a buffer circle 4 with a radius of 780 meters centered on the location of the wireless device 1. More precisely, the buffer circle 4 is actually the band between the offset buffer circles (4X and 4Y), which corresponds to a single TA value obtained due to the incremental difference between the sequence values ​​(i.e., TA = 10 will be received by a mobile device located anywhere between TA = 10 ± 1 / 2). Therefore, when we do not know where the signal comes from, we can assume that the wireless device 1 is receiving at a certain TA and generate the buffer circle 4 to produce the possible location of the transmitter 3, where the transmitter 3 should be located on the buffer circle 4, but its actual location is in the region associated with 1 / 2 of the TA, within the margin associated with that 1 / 2 TA of the offset buffer circle corresponding to 4X and 4Y.

[0068] Multiple measurements from the same transmitter 3, with TA values ​​recorded by the receiver at different locations, will produce different buffer circles. These buffer circles should then intersect (or form an intersection point 5), which can be used to identify the possible locations of transmitter 3. When these buffer circles overlap, as... Figure 3 As depicted, they can narrow down the range of the location of the source emitter 3, because in this example, it can only be located at the position where three or more buffer circles intersect 5. Figure 3 Specifically, TAs are used to indicate three distinct locations (1A, 1B, and 1C) of the wireless mobile device, each with a different TA: 1A has a TA of four, 1B has a TA of two, and 1C has a TA of five. These three overlapping buffer circles (4A, 4B, and 4C) utilize the radius of the TA expressed in meters. Here, in a simplified example, the three buffer circles intersect at point 5, which will be the only possible source of the transmitted signal and thus identifies the location of transmitter 3. Other unlabeled intersections cannot be the source of this transmission because not all transmissions will precisely define the intersection, even if, for example, some intersections are within another buffer circle. The overlapping buffer circles (4A, 4B, and 4C) depict possible transmitter locations, which are essentially just data points, and the more data points in a particular set, the higher the confidence level of the estimated transmitter location.

[0069] Since a single signal wireless base station source can use multiple transmitters (with antennas at different horizontal azimuth angles, hardware configurations, etc.), location determination is performed in the first stage to provide a first location determination, and then the first location determination is fine-tuned in an optional second stage. These stages include:

[0070] Phase I: Estimate the geographical location of the signal source of the transmitter (e.g., one or all transmitters at the base station location).

[0071] Phase II: Fine-tune the geographic location by estimating the signal source location of each transmitter (e.g., the transmitter at the location of a wireless base station).

[0072] Finally, we can use signal strength to identify the transmitter's azimuth at any given stage.

[0073] Phase I: Geographic Location Estimation of Signal Source

[0074] Figure 4 A flowchart detailing the Phase I steps for estimating the initial geographic location of a single signal source is provided, which are performed as follows: Step 1: Collect all wireless device measurements 15 (the measurements are collected from one or more wireless devices 10, 11, 12, 13 and 14) and their locations, and identify a given signal source by its unique source ID. Figure 1 Mobile device 1, TA 2, and transmitter 3 are identified, wherein wireless device measurement result 15 includes TA data, which includes location and TA value.

[0075] Step 2: Filter measurements with at least N points for the lowest reported TA value. This step excludes TA measurements with low sample counts that may be insufficient to reliably detect the location of transmitter 3, or that may contain too many outliers. In practice, outlier measurements are those with high vertical and / or horizontal inaccuracies in the reported geographic location (latitude / longitude), or incorrect TA values ​​affected by RF conditions or fast-moving mobile devices. Empirical testing has shown that N ≥ 10 is a good starting point for providing reliable data; however, higher N values ​​increase data reliability, for example, where N is greater than 50, although as few as three samples are possible.

[0076] Step 3: Draw a buffer circle 17 centered on the location (latitude / longitude) of each measurement result, with a radius equal to Where x represents the distance measurement result per unit of TA (e.g., approximately 156 meters for LTE). This step is performed by... Figure 1 and Figure 2 As shown, where Figure 1 It is TA, and Figure 2 A depiction of a buffer circle 4 with a buffer between 4X and 4Y is shown, having a radius as described herein. This distance can be adjusted based on hardware and software implications from the mobile device (receiver).

[0077] Step 4: Extract the buffer circle 18 for the intersection of the mobile device and location for each report, where the intersection is performed on time-adjacent measurements sorted in ascending order according to the recorded timestamps of the measurement results. Figure 3 A simple example of this step is provided, which has three measurements and three TAs, where intersection point 5 is the point where the three measurement locations intersect. In practice, a perfect intersection point 5 may not exist, and therefore in some embodiments, a cluster of closely related intersection points is utilized. This step reduces the number of desired intersection points that need to be calculated and ensures that buffer circle intersection is performed only on spatially separated measurements. In fact, as in Figure 3 As in the previous example, it is not necessary to calculate all intersections because only adjacent measured intersections—that is, those based on timestamps recorded in ascending order—are used for calculation. Therefore, the intersection between circles 4A and 4C will not be an adjacent measurement result and will not be used; only the intersections between 4A and 4B, and between 4B and 4C, will be used.

[0078] Figure 5 Another example is provided, illustrating the measurement locations (points A 43, B 44, C 45, and D 46) for a device reporting data along its path from point A 43 to point D 46. The buffer circles 7, 8, 41, and 42 at each location have radii equal to the lowest recorded TA of a single transmitter, expressed in meters. Intersections between reported adjacent measurements are also shown (A&B 6A and 6B for the intersection of buffer A and buffer B, B&C 9A and 9B for the intersection of buffer B and buffer C, etc.).

[0079] Here, with Figure 3 Unlike the simplified version, the four buffer circles (7, 8, 41, and 42) do not perfectly intersect at a single point, and therefore we labeled multiple intersection points (6B, 9B, and 47B) to define cluster 50. However, we did not label intersection points for every possible intersection, as this would result in thousands or millions of intersection points, which is unnecessary and would lead to too much data. Instead, we only utilize temporally adjacent measurements (i.e., sorted according to the recorded timestamps). In the given example, the device is traveling in a clockwise pattern (A→B→C→D), and the intersection points of the buffer circles are only taken at A&B, B&C, and C&D.

[0080] Step 5: Identify intersection clusters with a point-to-point distance equal to threshold D and a minimum of M points (sample locations). Thresholds D and M are set to sufficiently small values ​​to group densely located intersections. Empirically, the values ​​of D and M are approximately D = 30 meters and M = 5 points in rural areas, and approximately D = 10 meters and M = 10 points in suburban and urban areas, respectively.

[0081] Figure 6 An example of clustering intersection points (51, 52, 53, and 54) to depict the intersections of buffer circles is described in detail. When clustered (i.e., grouped), intersection points (51, 53, and 54) produce one point per cluster (cluster 2 51, cluster 3 53, and cluster 4 54), while cluster 1 52 has the highest net number of intersection points. We use a function to allow us to cluster points based on proximity from one point to another and based on the minimum desired number of points.

[0082] Specifically, the cluster with the highest number of intersections uses the density-based spatial clustering (DBSCAN) algorithm with noisy applications, which has D and M thresholds as defined above. Figure 6 In this cluster, cluster 52 is the cluster with the highest number of intersections.

[0083] Step 6: Generate the maximum cluster 20 as polygons based on the Delaunay triangulation of points within the identified clusters. This step allows for the representation of intersections with a single geometric feature. Figure 7 A general example of creating the circumcircle 56 of the Delaunay triangle to produce a Venn diagram of the intersection points is detailed, resulting in a polygon created from points 55, 57, 58, and 59 using Delaunay triangulation. Folding (merging) the created Delaunay triangle from the cluster of points produces a single geometry (polygon) that can be used to represent the estimated location of the wireless tower.

[0084] Figure 8 The sample geometry drawn from cluster 1's 52 points is depicted. Due to the simple sample size, geometry 61 here is a triangle created from three points. Therefore, in Figure 8 In this process, we create polygons based on these points and Delaunay triangulation. For example, with more data points, using Delaunay triangulation, we cluster all the triangles into one shape and finally obtain a polygon with the fewest points.

[0085] Step 7: Extract the centroid 62 of the generated geometry, where centroid 62 represents the estimated position 21 of the signal source. Figure 9 An example of the centroid 62 extracted for cluster 1 52 polygons is detailed (shown as a + sign within geometry 61 (here, a triangle)). The centroid 62 is calculated as the initial estimated location of the source wireless transmitter.

[0086] Step 8: (Summarize the polygon as circle 22) Draw circle 63 around centroid 62 as a confidence indicator of accuracy and / or precision. Figure 10An example of a circle 63 surrounding geometry 61 and centroid 62 of cluster 1 52 is detailed. While the cell site is most likely within geometry 61 itself, an error margin is created to improve accuracy, and therefore the actual transmitter location is within circle 63. Thus, a precise location is possible within geometry 61, but circle 63 represents the confidence level of its actual location. However, the size of circle 63 does not represent a quantitative value of confidence. Generally, the smaller the circle, the higher the confidence level. However, here, it is the opposite: the smaller the circle, the lower the confidence level, and the larger the circle, the greater the confidence that the wireless base station is located within that circle. Simply put, a smaller circle means higher precision, and a larger circle means higher accuracy.

[0087] As in step 9, the circumscribed circle 63 is a simplified representation of the wireless base station location (i.e., presenting an output 23 with the transmitter location and a circle). Based on this circle 63, we can... Figure 4 The flowchart is used to determine location 24. Therefore, the circle can accurately identify the location of the wireless base station within circle 63.

[0088] Phase II: Fine-tuning of the geographical location of the signal source

[0089] The estimated location of a wireless transmitter can be further improved by recalculating it using previously calculated locations and based on more data or an improved fit of the data. For example, the estimated location can be run monthly using measurements from the previous year. The new, improved estimated location can be the average of the old and new estimated site locations, or the old and new locations can be weighted by counting or spatial diversity of the measurement samples. The old location can also be the seed location in the initial step of the location estimation process. This data can be used to train a machine learning system that incorporates data from all estimated locations and continuously updates the location as more data is collected. It is worth noting that at a certain point, the calculated location is not modified; that is, consensus is determined. However, the calculation can still be rerun, and a new location is determined only if the data shows a divergence from the previous consensus location. For example, the transmitter location may have been moved to a new tower, which would be a divergence, even if the move is short.

[0090] While the initial determination of position 24 may be sufficient in many cases, modifications can be made to change the determined position 24 in order to increase the accuracy of the estimated transmitter position, i.e., to fine-tune the geographic location. Figure 13 A flowchart detailing the steps for fine-tuning the geographic location of a signal source based on the signal from each transmitter through an iterative process is provided, and the following is accomplished:

[0091] Step 1: For all TA measurements grouped by the unique ID of each transmitter, calculate the minimum distance D 25 from the initial location 24 (Loc_0) of the signal source found in Phase I to all buffer circles.

[0092] Figure 11 An example of Loc_0, the location of wireless base station 76 marked with a "+" sign, is detailed, along with the geometry (triangles) 75 of buffer circle 71, intersections (e.g., 73), and the intersection cluster created from buffer circle 71 at intersection cluster 72. The estimated initial wireless base station location 76 is then marked using the minimum / shortest distance 74 from each buffer circle 71, as shown below. Figure 11 The dotted line in the middle depicts it.

[0093] We know that the initial estimated transmitter location may not be optimal because all the buffer circles do not intersect at the same point. The goal is to identify the location closest to all the buffer circles from the cluster of intersections.

[0094] Step 2: The estimated wireless base station location 26Loc_0 ( Figure 11 (76) Shift the position by a distance D (user-defined distance) from the initial position at an azimuth angle of 0° to generate a new position. Figure 12 (77 in the text). Therefore, Figure 12 The initial position 76 (from) is depicted Figure 11 The position is modified to a new position 77, and then the process is recalculated again using a distance 74 from all buffer circles 71. If the new position gives a better (shorter) distance than the previous position, we recalculate and repeat the calculation. We then use this as the new estimated position for the transmitter until we find a better result. Finally, we repeat this process until no shorter distance is found. We can use the previously calculated positions in the dataset to eventually train the machine for machine learning of the iterative process of calculating the transmitter position.

[0095] Step 3: Recalculate the shortest distance from the new position 77 to all buffer circles grouped by the unique ID of each transmitter.

[0096] Step 4: Compare distance 28, where if the distance calculated in Step 3 is less than the distance calculated in Step 2, then set position 77 as the new estimated position of the signal source. Otherwise, shift the initial estimated position of the signal source with the azimuth angle of D meters and +A degrees calculated in this recalculation, and return (return the iteration process) 29 to Step 2 to place the new position for calculating the shortest distance.

[0097] Step 5: Iteratively repeat steps 3 and 4 until the calculated distance remains constant. Above this point, the distance to the estimated location is shifted down to a predefined threshold in small increments of D and A. With appropriate computing power, this can be repeated within fractional seconds, allowing for real-time calculations of the transmitter. This is particularly useful where the transmitter may be in a specific location for only a short period of time, but it is necessary to calculate that location at that time. Examples include mobile towers in use, or mobile transmitters / transceivers under vehicles communicating with other mobile devices or transceivers.

[0098] Step 6: If the calculation does not result in a reduction in the calculated distance, the final position is considered to be the fine-tuned signal source position 30. This position can then be set as the confirmed position.

[0099] Step 7: The measured signal level 31 at the location of each transmitter cluster can be used to further improve the accuracy and precision of the estimated signal source location. It can also be used to estimate the azimuth angle of an individual transmitter's antenna relative to the physical location of the cell site. This is in Figure 16 The diagram is described in more detail, showing several transmitter antennas 92, 93, and 94 located on tower 95, and the specific azimuth angle of a particular transmitter antenna being oriented within buffer circle 91. A specific azimuth angle of the transmitter can also be determined using the signal level. For example, a moving receiver can identify different signal levels along a path, and once the location is confirmed, data regarding the signal level can outline the azimuth angle and its directional reach. Typically, the antenna indicated by the arrow (e.g., 92) will have a 120-degree reach, approximately 60 degrees on each side of the arrow. The signal is strongest in the direction of the antenna's main beam (indicated by the azimuth arrow) and decreases at the edges of the 120° reach.

[0100] Therefore, incorporating the signal level measurement result 31 can further improve the accuracy and precision of the estimated source transmitter position 33, because the degraded signal level can indicate the deviation from the transmitter position or the main beam path of the antenna. In particular, this can produce the directional aspect of the transmitter antenna, and this directional information can be included in the information about the transmitter.

[0101] Figure 13 The flowchart outlines these steps, including an iterative process 29 to continuously recalculate the position until a best fit and position are estimated based on the provided data. When the data is modified—that is, when the set is open (introducing new or additional data)—the transmitter position can be continuously modified until a consensus is reached.

[0102] Move to real data examples, Figure 14 The diagram details a cluster of collected measurements 85 and an example of the corresponding buffer circle 84 attached to them. An initial location (point 83) is determined from the cluster of collected measurements 85. An iterative process is then used to fine-tune the initial estimated location 83 of the wireless transmitter, and the estimated path of the wireless tower location is plotted (line 82), finally yielding the final predicted transmitter location (point 81). The final estimated transmitter location is close to the actual location (point 90), but the diagram illustrates improvements in the location obtained by using an iterative process.

[0103] Figure 15 This paper details a web GUI view of multiple wireless base stations calculated using the methods described herein. This allows users to identify the locations of transmitters or towers providing services within their own networks, as well as transmitters or towers from other service providers, and to better identify locations that are needed or valuable for improving wireless service.

[0104] In some applications, whether using Phase I alone or in conjunction with Phase I in conjunction with Phase II, the method can be used to quickly determine and identify the transmitter's location. Based on the TA data, the azimuth angle of the transmitter antenna can also be estimated. In some applications, the transmitter may only be stationary (or even moving) for several seconds or minutes. However, it may be necessary to calculate this point to use as a reference point for other devices communicating with that transmitter.

[0105] Therefore, the methods described herein teach those skilled in the art a novel approach for estimating the location of a wireless transmitter using TA data. Those skilled in the art will recognize that conventional and understood aspects of the invention may have been generalized or omitted, as will be understood by those of ordinary skill in the art, and that these methods can be modified to incorporate known and understood elements without altering the scope and inventive nature of these methods.

Claims

1. A method for estimating the location of a wireless transmitter, comprising the following steps: a. Collect multiple wireless measurement results between the wireless transmitter and the receiver, each of the wireless measurement results including the timing advance (TA) value between the wireless transmitter and the receiver, and the receiver location; b. For each of the wireless measurement results, draw a buffer circle around the receiver location, wherein the buffer circle has a radius equal to ,in x This represents the distance measurement result for each unit of the TA value; c. Extract multiple intersection points between buffer circles corresponding to temporally adjacent wireless measurement results; d. Identify at least one cluster from the intersection of step (c); e. Identify the cluster with the highest number of intersections; as well as f. Determine the initial estimated location of the wireless transmitter from the cluster with the highest number of intersections.

2. The method according to claim 1, further comprising the following steps: e1. Immediately following step (e), generate a polygon corresponding to the cluster with the highest number of intersections from step (e); e2. Extract the center of the polygon from step (e1); e3. Draw the circumcircle of the polygon; as well as e4. Determine the first initial estimated position of the wireless transmitter corresponding to the position within the polygon.

3. The method according to claim 1, further comprising: The intersection point has a threshold value. D The distance between points and the minimum M One point.

4. The method according to claim 3, wherein D and M The value is D =30 meters and M =5 points.

5. The method according to claim 3, wherein D and M The value is D =10 meters and M =10 points.

6. The method of claim 1, wherein the TA value is modified based on the hardware or software of the receiver.

7. The method of claim 1, wherein the TA value reported by the receiver is specific to the device manufacturer, chipset, and software version, wherein a unique profile normalizes the reported TA value.

8. The method of claim 2, further comprising the following steps: e5. Calculate the minimum distance from the first initial estimated position to all buffer circles within the cluster that have the highest number of intersections; e6. By distance D and angle A The first initial estimated position is shifted to the new position, and the distances to all buffer circles within the cluster with the highest number of intersections are recalculated; e7. Compare the calculated distances between steps (e5) and (e6); and e8. Set a second new position, where the distance from the second new position to all buffer circles is shorter than the distance from the first initial estimated position to all buffer circles.

9. The method of claim 1, wherein the plurality of wireless measurement results include signal level.

10. The method of claim 8, wherein in step (e8), the new location further measures the signal level, and the new location is modified based on the measured signal level.

11. The method of claim 2, wherein the polygon is drawn by connecting the intersections of the cluster having the highest number of intersections.

12. A method for estimating the location of a wireless transmitter, comprising: a. Collect multiple wireless measurement results between the transmitter and the receiver, each wireless measurement result including the location of the receiver and the timing advance (TA) value between the transmitter and the receiver; b. For each of the plurality of wireless measurement results, draw a buffer circle around the location of the receiver, wherein the buffer circle has a radius equal to ,in x This represents the distance measurement result for each unit of the TA value; c. Identify multiple intersections between buffer circles corresponding to temporally adjacent wireless measurement results; as well as d. Estimate the position of the transmitter based on the position of the intersection point. e. The step of estimating the location is done by identifying clusters of intersections and drawing a circumcircle around a polygon created from the clusters of intersections, wherein the estimated location is within the circumcircle.

13. The method of claim 12, wherein the estimated position is estimated by plotting a position point within the circumcircle.

14. The method of claim 13, wherein the plotted location points are plotted to create the shortest distance to each intersection point within the circumcircle.

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