EP4133755A2 - Positionnement basé sur un mobile à l'aide de données d'assistance fournies par un micro-bsa embarqué - Google Patents

Positionnement basé sur un mobile à l'aide de données d'assistance fournies par un micro-bsa embarqué

Info

Publication number
EP4133755A2
EP4133755A2 EP21784157.6A EP21784157A EP4133755A2 EP 4133755 A2 EP4133755 A2 EP 4133755A2 EP 21784157 A EP21784157 A EP 21784157A EP 4133755 A2 EP4133755 A2 EP 4133755A2
Authority
EP
European Patent Office
Prior art keywords
cells
mobile device
assistance data
bsa
tdoa
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP21784157.6A
Other languages
German (de)
English (en)
Other versions
EP4133755A4 (fr
Inventor
Steven C. Thompson
Zane RAU
Raphael MALL
Neal Riedel
Steve J. CALIGURI
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Phy Wireless LLC
Original Assignee
Phy Wireless LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Phy Wireless LLC filed Critical Phy Wireless LLC
Priority claimed from PCT/US2021/026725 external-priority patent/WO2021207707A2/fr
Publication of EP4133755A2 publication Critical patent/EP4133755A2/fr
Publication of EP4133755A4 publication Critical patent/EP4133755A4/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0205—Details
    • G01S5/0236—Assistance data, e.g. base station almanac
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0205—Details
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0257—Hybrid positioning
    • G01S5/0268—Hybrid positioning by deriving positions from different combinations of signals or of estimated positions in a single positioning system
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/10—Position of receiver fixed by co-ordinating a plurality of position lines defined by path-difference measurements, e.g. omega or decca systems

Definitions

  • the present disclosure relates to a system and method for determining position of a user terminal or other communication equipment based on time of arrival measurements in a wireless environment.
  • BACKGROUND Measurements of times of arrival (TOA) for signals from a set of wireless base stations can aid in determining a user's position or location.
  • UE-assisted UE-A
  • UE-A The state-of-the-art in downlink cellular positioning of a wireless device (also referred to as a “user equipment” or “UE”) is UE-assisted (UE-A).
  • UE-A UE-assisted
  • a location server provides assistance data to nearby cells (also known as base stations, eNBs for 4G LTE, gNBs for 5G NR), which communicates this to a UE.
  • the UE performs measurements on the current downlink radio conditions (i.e., power, timing measurements) and transmits these measurements in the uplink back to the location server.
  • the location server uses the measurements to estimate the location of the UE.
  • An example positioning method is OTDOA (observed time difference of arrival) where the UE performs TDOA (also known as RSTD in 3GPP) measurements and the location server performs a hyperbolic TDOA position calculation.
  • Another positioning method is and roundtrip timing estimates of the UE serving cell. Release 93GPP E-CID also incorporates angle-of-arrival (AoA) measurements at the eNB.
  • AoA angle-of-arrival
  • the most basic cellular positioning method is basic cell ID where the position is estimated at the centroid of the serving cell, or at the serving cell transmission point.
  • the location server has the additional information needed to estimate the UE location. This additional information includes cell location, cell transmission power, relative cell timing offsets, antenna direction and aperture details, etc.
  • LTE positioning protocol described for example in 3GPP TS 36.355 version 13.3.0, which is referenced in release 13 of the 3GPP LTE specification, provides for UE-A positioning.
  • PRS positioning reference signal
  • OFDM orthogonal frequency division multiplexing
  • the UE may measure the time of arrival (TOA) of PRS subframes from each accessible base station (which the 3GPP LTE specification calls the eNodeB).
  • the user equipment preferably measures at least one reference signal time difference (RSTD) between two different eNodeBs (one called the reference and the other called the neighbor).
  • the reference signal time difference is related to the established measure for observed time difference of arrival (OTDOA) described in the LTE positioning protocol.
  • Determining the position of user equipment proceeds by measuring the TOA of the first reference signal path from each eNodeB of interest followed by determining the reference signal time difference (RSTD) between pairs of designated eNodeB base stations using the respectively measured times of arrival at the user equipment.
  • RSTD reference signal time difference
  • LTE's fundamental modulation scheme to transmit bits over the air uses OFDM. That is, bits are generated by applying quadrature amplitude modulation (QAM) to each active subcarrier that makes up an OFDM symbol.
  • QAM quadrature amplitude modulation
  • an LTE OFDM symbol may have 2048 time samples representing 600 active subcarriers out of a maximum of 1200 subcarriers.
  • Each subcarrier may be assigned a function at the receiver, such as transmitting bits known a priori to the receiver and thus enabling different calculations.
  • FIG. 1 provides a functional block diagram of apparatus for determining position using observed time difference of arrival (OTDOA) based on the reference signal time difference (RSTD) measurement specified in LTE.
  • the illustrated user equipment receiver 110 receives a plurality of OFDM symbols from two base stations 101, 103. Receiver 110 may use one or more antennas to receive the symbols.
  • FIG. 1 illustrates the position determining functionality using as an example signals received from two base stations 101, 103 with the receiver 110 using a single antenna, which is the minimum configuration for an RSTD measurement. This configuration can be extended to a greater number of base stations and a greater number of user equipment antennas.
  • the receiver can process received OFDM symbols to provide best estimates of the transmitted bits.
  • Such a receiver 110 can identify the first path using one or more first path identification (FP-ID) modules 130, 140, which are responsive to subcarriers assigned to calculate positioning information.
  • Each first path identification module 130, 140 is responsive to information 132, 142 provided by the user equipment receiver 110 about the subcarriers to be used for positioning measurements. For example, the information may be stored within tables in non- volatile memory.
  • the first path identification units 130, 140 identify the respective first path for the received OFDM symbols from a known eNodeB.
  • the reference signal time difference (RSTD) measurement typically is based on a predetermined duration of OFDM symbols to achieve the desired accuracy.
  • each first path identification module 130, 140 is the time of arrival (TOA) at the user equipment of a signal from the corresponding base station.
  • TOA time of arrival
  • the RSTD k,j between base stations indexed as k and j is determined as [0014]
  • FIG. 1 shows that module 150 of the receiver 110 provides as its output 152 the equation 1 reference signal time difference computation.
  • This output RSTD0,1152 is the output 134 of first path identification module 130 minus the output 144 of first path identification module 140.
  • the calculation of RSTD k,j is simple given a reliable estimation of the TOAk and TOA j , knowing the structure of the signal received at the first path identification FP-ID module 130, 140.
  • the standard such as the LTE standard, specifies the structure of the symbol, which can be generalized as shown in FIG. 2.
  • specifying channels can be achieved using orthogonal schemes, which include OFDM and code division multiple access (CDMA).
  • CDMA code division multiple access
  • the wireless standards may increase capacity by using quasi-orthogonal channels achieved in myriad spatial and temporal strategies.
  • FIG. 2 simplifies the explanation of the signals involved in an observed time difference of arrival (OTDOA) measurement by showing a method that assumes orthogonal channelization. That is, while orthogonality' is retained, the crosstalk between channels is kept to insignificant levels,
  • FIG. 2 horizontal axis 201 represents time, qualitatively representing the time occupied by received symbols
  • FIG. 2's vertical axis show's a second channel dimension such that FIG. 2 qualitatively shows channels as having no overlap.
  • the vertical axis channel separation can represent segments of frequency, as in the case of OFDM, or the indexing of different codes in CDMA.
  • the segmentation in the frequency axis can represent 15 kHz of bandwidth for a subcarrier, with an OFDM symbol possibly consisting of up to 600 active subcarriers out of 1024 total subcarriers in one symbol. This is only an example and other allocations are known.
  • each 15 kHz (y-axis) by 71.4 ps (x-axis) block in the grid is called a resource element (RE).
  • a seven- symbol (500 ps) by 12-subcarrier (180 kHz) time-frequency allocation is called a resource block (RB) in 3 GPP LTE.
  • FIG. 2 can equally illustrate other transmission systems.
  • FIG. 2 can illustrate other orthogonal schemes such as CDMA transmission as well as transmission strategies such as those being used for next generation wireless (5G).
  • the orthogonal or quasi - orthogonal transmission strategies might be used for subchannels or for signaling related to observed time difference of arrival (OTDOA) measurements, among other transmission strategies.
  • OTDOA observed time difference of arrival
  • certain wireless standards assign subcarriers in the grid to be used for determining position or accomplishing OTDOA functionality.
  • exemplary OTDOA subcarriers are designated as “location pilots” (LP) 212, 214, 216 in FIG. 2.
  • the term pilot is used to denote a subcarrier with a known transmit modulation at the receiver. These pilot subcarriers are in contrast to data subcarriers, which have unknown modulation characteristics because they are encoded with unknown information bits.
  • This pilot scheme allows a compatible terminal to accomplish various measurements. User equipment terminals generally need to estimate the channel impulse response (CIR) and other parameters for successful reception and demodulation of OFDM symbols. Consequently, the grid shown in FIG.2 likely contains other subcarriers designated as pilots.
  • These persistent pilots are denoted as estimation pilots (EP) and are indicated as 221, 223, 225 in FIG. 2.
  • the system 300 includes network infrastructure 310 including a base station almanac (BSA) 320, a position assistance data calculator 324 and a position estimator 328 located in, for example, a location server.
  • BSA base station almanac
  • position assistance data calculator 324 a position estimator 328 located in, for example, a location server.
  • the location server in a conventional UE-assisted positioning system is comprised of the position assistance data calculator 324 and the position estimator 328.
  • the mobile network operator MNO
  • MNO mobile network operator
  • the position estimator 328 is configured to provide position estimates in a known manner based upon measurements 330 received from a UE 350.
  • a power and timing measurements module 354 generates the measurements 330 based upon assistance data 358 received from the position assistance data calculator 324.
  • a location server may provide information from the BSA 320 to nearby cells to facilitate positioning calculations.
  • the assistance data 358 generated from information in the BSA 320 must be downloaded to the UE 350 and measurements 330 uploaded to the position estimator 328 for each position update. This creates network congestion and compromises the battery life of the UE 350.
  • the assistance data 358 is comprised of 10s of cells used for positioning. For example, in 3GPP TS 36.355, “LTE Positioning Protocol (LPP)”, the number of OTDOA neighbor cells for a given frequency layer is 24.
  • LTP LTE Positioning Protocol
  • the location server derives the “best set” of 24 cells using the ECGI of the serving cells, a globally unique identifier of a cell.
  • the location server only has a very rough estimate of the location of the UE 350 when deriving the 24 cells.
  • This rough estimate may be, for example, the centroid or transmission point of the serving cell, i.e., the basic cell ID position.
  • the initial position estimate used to derive the assistance data 358 is defined as the seed estimate 410.
  • a seed estimate 410 based on basic cell ID positioning can result in a poor set of cells selected for the assistance data 358.
  • UE-based (“UE-B”) cellular positioning is similar to UE-assisted where a location server supplies assistance data (AD) to a UE.
  • the assistance data includes the additional information required for the UE to estimate the location locally.
  • UE-B has technical advantages over UE-A. In UE-B, the UE is not required to transmit measurements to the location server.
  • UE-A Uplink transmissions are costly in terms of battery drain, which renders UE- A approaches disadvantageous relative to UE-B approaches in terms of battery life.
  • UE-B also relieves network congestion since measurements are not transmitted in the uplink.
  • 5G massive IoT (internet of things) scenarios with many thousands or even millions connected devices per cell excess uplink transmissions for UE-A positioning purposes may exhaust spectral and time resources.
  • UE-B reduces the uplink traffic, easing the rollout of services at scale.
  • Both UE-A and UE-B cellular positioning offer technical advantages over traditional GNSS positioning in terms of indoor coverage and lower power consumption.
  • SUMMARY [0024] Disclosed herein is a mobile-based positioning system and method which uses assistance data provided by a repository of base station almanac (BSA) information downloaded from a server and stored onboard the mobile device (or user equipment, UE) as a micro-BSA.
  • BSA base station almanac
  • the disclosed system and method improves upon the state-of-the-art in downlink cellular positioning by offering, for example, improvements in network efficiency, positioning accuracy, cost and device battery life. These improvements are achieved at least in part by leveraging the location awareness of the UE arising from the disclosed UE-based positioning method.
  • the micro-BSA stored on the UE is used for improved assistance data generation, which may be refined based upon measurements and position estimates made on the device.
  • This higher quality assistance data leads to more accurate position estimates being generated by the UE relative to the estimates generated using state-of- the-art TDOA techniques.
  • the onboard generation of assistance data enables the UE to roam and generate updated position estimates of high accuracy without the need for interaction with the network. This therefore relieves network congestion and improves the battery life of the device.
  • the disclosure relates to a method performed in a mobile device for estimating position of the mobile device.
  • the method includes receiving, from a network server, observed time difference of arrival (OTDOA) assistance data for a first plurality of cells from a base station almanac (BSA) accessible to the network server.
  • OTDOA assistance data is stored within a memory of the mobile device as a first micro-BSA.
  • An initial position estimate for the mobile device is determined based upon time difference of arrival (TDOA) measurements associated with an initial subset of the first plurality of cells and initial OTDOA assistance data corresponding to the initial subset of the first plurality of cells.
  • the initial OTDOA assistance data is generated by the micro-BSA based upon an initial seed estimate.
  • An improved position estimate for the mobile device is determined based upon TDOA measurements associated with an additional subset of the first plurality of cells and improved OTDOA assistance data corresponding to the additional subset of the first plurality of cells.
  • the improved OTDOA assistance data is generated by the micro-BSA based upon the initial position estimate.
  • the method may further include storing, within the memory of the mobile device, additional OTDOA assistance data for a second plurality of cells from the BSA as a second micro-BSA.
  • the second plurality of cells includes at least one cell not included within the first plurality of cells.
  • the determining the improved position estimate may further include calculating a first position estimate for the mobile device based upon TDOA measurements associated with a first configuration of cells within the first plurality of cells and first improved OTDOA assistance data corresponding to the first configuration of cells wherein the first configuration of cells is characterized by a first geometric dilution of precision (GDOP).
  • a second position estimate for the mobile device is determined based upon TDOA measurements associated with a second configuration of cells within the first plurality of cells and second improved OTDOA assistance data corresponding to the second configuration of cells wherein the second configuration of cells is characterized by a second GDOP.
  • a third position estimate for the mobile device may also be calculated using an alternate positioning method not involving TDOA measurements.
  • the determining the improved position estimate may then further include selecting among the first position estimate, the second position estimate and the third position estimate.
  • the mobile device transitions, after determining the initial position estimate, into a low power sleep mode.
  • the method for estimating position of the mobile device further includes determining, upon the mobile device temporarily transitioning out of the low power sleep mode, an updated position estimate for the mobile device based upon additional time difference of arrival (TDOA) measurements associated with the initial subset of the first plurality of cells and additional OTDOA assistance data corresponding to the initial subset of the first plurality of cells wherein the additional OTDOA assistance data is generated by the micro-BSA based upon the initial position estimate.
  • TDOA time difference of arrival
  • the mobile device is configured to transition, after determining the updated position estimate, into the low power sleep mode.
  • the disclosure is also directed to a mobile device including a processor, a receiver in communication with the processor, and a memory including program code executable by the processor for estimating position of the mobile device.
  • the program code includes code for receiving, from a network server, observed time difference of arrival (OTDOA) assistance data for a first plurality of cells, the OTDOA assistance data included within a base station almanac (BSA) accessible to the network server.
  • the program code further includes code for storing, within the memory, the OTDOA assistance data as a first micro-BSA.
  • the program code also includes code for determining an initial position estimate for the mobile device based upon time difference of arrival (TDOA) measurements associated with an initial subset of the first plurality of cells and initial OTDOA assistance data corresponding to the initial subset of the first plurality of cells.
  • the initial OTDOA assistance data is generated by the micro-BSA based upon an initial seed estimate.
  • the program code further includes code for determining an improved position estimate for the mobile device based upon TDOA measurements associated with an additional subset of the first plurality of cells and improved OTDOA assistance data corresponding to the additional subset of the first plurality of cells.
  • the improved OTDOA assistance data is generated by the micro-BSA based upon the initial position estimate.
  • the disclosure also pertains to a method performed in a mobile device for estimating position of the mobile device.
  • the method includes receiving, from a network server, first observed time difference of arrival (OTDOA) assistance data for a first plurality of cells and second OTDOA assistance data for a second plurality of cells from a base station almanac (BSA) accessible to the network server, the first plurality of cells corresponding to a first geographic area and the second plurality of cells corresponding to a second geographic area different from the first geographic area.
  • the second plurality of cells include at least one cell not included within the first plurality of cells.
  • the method further includes storing, within a memory of the mobile device, the first OTDOA assistance data as a first micro-BSA and the second OTDOA assistance data as a second micro-BSA.
  • a first position estimate for the mobile device is then determined, without receiving additional OTDOA assistance data from the network server, based upon first time difference of arrival (TDOA) measurements associated with a subset of the first plurality of cells and first OTDOA assistance data corresponding to the subset of the first plurality of cells.
  • the first OTDOA assistance is generated by the first micro-BSA based upon a prior position estimate.
  • the method also includes determining, without receiving additional OTDOA assistance data from the network server, a second position estimate for the mobile device based upon second time difference of arrival (TDOA) measurements associated with a subset of the second plurality of cells and second OTDOA assistance data corresponding to the subset of the second plurality of cells.
  • the second OTDOA assistance is generated by the second micro-BSA based upon the first position estimate.
  • the disclosure concerns a method performed in a mobile device for estimating position of the mobile device. The method includes receiving, from a network server, observed time difference of arrival (OTDOA) assistance data for a first plurality of cells from a base station almanac (BSA) accessible to the network server.
  • OTDOA observed time difference of arrival
  • the OTDOA assistance data is stored, within a memory of the mobile device, as a first micro-BSA.
  • the method further includes determining a first position estimate for the mobile device based upon time difference of arrival (TDOA) measurements associated with a subset of the first plurality of cells and initial OTDOA assistance data corresponding to the subset of the first plurality of cells.
  • An artificial intelligence (AI) management module predicts an expected route to be traveled by the mobile device.
  • OTDOA assistance data is received, from the network server, for a second plurality of cells from the BSA.
  • the second plurality of cells are determined by the AI management module to be proximate an expected route to be traveled by the mobile device, the second plurality of cells including at least one cell not included within the first plurality of cells.
  • a second position estimate is then determined based at least in part upon second OTDOA assistance data corresponding to a subset of the second plurality of cells.
  • the disclosure is also directed to a method performed in a mobile device for estimating position of the mobile device. The method includes determining a first position estimate for the mobile device based upon time difference of arrival (TDOA) measurements associated with at least a subset of a first plurality of cells and observed time difference of arrival OTDOA assistance data corresponding to the subset of the first plurality of cells.
  • TDOA time difference of arrival
  • the TDOA measurements are associated with a first TDOA measurement vector and the OTDOA assistance data is stored within a micro-BSA within a memory of the mobile device.
  • the method further includes determining a quality of the first position estimate by at least calculating a first TDOA residual error vector using the first position estimate and detecting, based upon the first TDOA residual error vector, a bad cell included within the subset of the first plurality of cells.
  • a second TDOA measurement vector is then constructed by removing the TDOA measurements associated with the bad cell from the first TDOA measurement vector and a quality of the second position estimate for the mobile device is determined based upon the second TDOA measurement vector.
  • the disclosure relates to a method performed in a mobile device for estimating position of the mobile device.
  • the method includes determining time of arrival (TOA) estimates of signals received from a plurality of cells in a vicinity of the mobile device and determining values of a quality metric associated with the TOA estimates.
  • TOA time of arrival
  • One of the plurality of cells is selected, based upon the values of the quality metric associated with the TOA estimates, as a reference cell.
  • the method further includes determining time difference of arrival (TDOA) measurements between the reference cell and neighbor cells included within the plurality of cells.
  • TDOA time difference of arrival
  • An initial estimate of a position of the mobile device is determined using a plurality of the TDOA measurements and associated observed time difference of arrival (OTDOA) stored within a micro-BSA within a memory of the mobile device.
  • the plurality of TDOA measurements are associated with a subset of the TOA estimates having values of the quality metric relative to value of the quality metric corresponding to TOA estimates associated with other of the TDOA measurements.
  • the method further includes determining sequential additional position estimates by updating the initial estimate of the position of the mobile device using additional ones of the TDOA measurements and associated OTDOA assistance data until a stopping criteria is satisfied.
  • the determining sequential additional position estimates may include assessing a quality of the sequential additional position estimates by at least one of: (i) evaluating contours in a position estimation cost function, and (ii) calculating a time difference of arrival (TDOA) residual error vector.
  • the disclosure also concerns a method which involves sending, by a network server to a mobile device, observed time difference of arrival (OTDOA) assistance data for a first plurality of cells from a base station almanac (BSA) accessible to the network server.
  • the OTDOA assistance data is stored, within a memory of the mobile device, as a first micro-BSA.
  • the mobile device is configured to determine a first position estimate for the mobile device based upon time difference of arrival (TDOA) measurements associated with a subset of the first plurality of cells and initial OTDOA assistance data corresponding to the subset of the first plurality of cells.
  • An expected route to be traveled by the mobile device is predicted by an artificial intelligence (AI) management module.
  • the network server then sends, to the mobile device, observed time difference of arrival (OTDOA) assistance data for a second plurality of cells from the BSA.
  • the second plurality of cells are determined by the AI management module to be proximate an expected route to be traveled by the mobile device and include at least one cell not included within the first plurality of cells.
  • the mobile device is configured to determine a second position estimate based at least in part upon second OTDOA assistance data corresponding to a subset of the second plurality of cells.
  • the disclosure is additionally directed to a method which involves determining time of arrival (TOA) estimates of signals received from a plurality of cells in a vicinity of a mobile device.
  • the method includes determining time difference of arrival (TDOA) measurements between a reference cell and neighbor cells included within the plurality of cells and determining an estimated position of the mobile device using a plurality of the TDOA measurements and associated observed time difference of arrival (OTDOA).
  • TOA time of arrival
  • FIG. 1 provides a functional block diagram of apparatus for determining position using observed time difference of arrival (OTDOA) based on the reference signal time difference (RSTD) measurement specified in the LTE standard.
  • FIG.2 illustrates a generalized view of a symbol structure of the type used in the LTE standard.
  • FIG. 8 illustrates a Bad Cell Detection process implemented by a Bad Cell Detector for improving quality of position estimates produced by a position estimator included in the UE of FIG. 5.
  • FIG.9 illustrates a method of Circular Sector Assistance Data Generation useful in lowering GDOP in accordance with the disclosure.
  • FIG. 10 shows a screenshot capture of a map of a portion of New York City which illustrates an arrangement of cells lacking geometric diversity.
  • FIG. 11 is a screenshot capture of a map which illustrates an arrangement of cells selected in accordance with a circular sector assistance data generation method in order to have improved geometric diversity.
  • FIG. 12 includes a block diagram representation of a particular implementation of a UE configured in accordance with an embodiment.
  • FIGS.13-15 illustrate exemplary potential strategies for forming cell-pairs comprised of reference and neighbor cells.
  • FIG. 16 illustrates an estimated confidence ellipse formed by evaluating multiple observations.
  • FIG. 17 illustrates an exemplary contour of error surface generated to establish a confidence region.
  • FIG.18 illustrates an attenuation profile associated with a commonly used attenuation model.
  • FIGS. 19A-19C depict the results of simulations performed using a power-time hybrid positioning method in accordance with an embodiment.
  • FIG. 5 illustrates a functional view of a positioning system 500 in accordance with an embodiment.
  • the system 500 includes a UE 510 configured to generate positioning assistance data 526 based upon a relatively small subset of a base station almanac (BSA) 512 accessible to a BSA server 511 within a network 514.
  • BSA base station almanac
  • the BSA 512 may reside on a server in communication with the BSA server 511 or may be included on the BSA server 511.
  • information from the BSA 512 is provided to the UE 510 by a micro-BSA cloud assist server 516 within the network 514.
  • the functional elements of the UE 510 include one or more micro-BSA(s) 520 configured to store information corresponding to the subset of the BSA 512 received by the UE 510 from the micro-BSA cloud assist server 516.
  • the micro-BSA(s) 520 may be computed with information of the UE 510 serving cell ECGI to provide a rough estimate of the UE location.
  • a positioning assistance data calculator 524 is configured to receive cell parameters from the micro-BSA(s) 520 for use in generating the assistance data 526.
  • the positioning assistance data calculator 524 may use the ECGI of the serving cell to derive a rough estimate of the UE location from which to calculate the assistance data 526.
  • the assistance data 526 is provided to a power and timing measurements module 530 and a position estimator 540.
  • the calculator 524 is further configured to be responsive to measurement feedback 532 and position estimate feedback 534 in providing cell selection feedback 536 useful in intelligently updating the contents of the micro-BSA(s) 520.
  • the position estimator 540 is configured to provide position estimates and the position estimate feedback 534 based upon the assistance data 526 and upon measurements 542 received from the power and timing measurements module 530.
  • the power and timing measurements module 530 generates the measurements 542 and the measurement feedback 532 based upon the assistance data 526 and feedback 546 received from the position estimator 540.
  • the position estimator 540 performs OTDOA calculations based upon the assistance data 526 and measurements by the UE 510 of the time of arrival (TOA) of positioning reference signals (PRS) received from the base stations (e.g., eNodeBs) of multiple cells associated with the assistance data 526.
  • the position estimator 540 subtracts the TOA of a reference cell (which may be selected by the UE 510 using known techniques) from the measured TOAs corresponding to such multiple cells in order to form reference signal time difference (RSTD) or time difference of arrival (TDOA) measurements.
  • RSTD reference signal time difference
  • TDOA time difference of arrival
  • the BSA 512 may contain information corresponding to hundreds of thousands of cells, the micro-BSA(s) 520 may contain information for hundreds of cells, and the assistance data 526 may pertain to tens of ceils.
  • This BSA 512 is typically managed by a mobile network operator (MNO) and includes a database containing the cell parameters defining the network layout. Each cell in the database of the BSA 512 is typically characterized by a unique cell identifier (ECGI), a latitude and longitude of the cell transmission point, a physical cell index (PCI), antenna aperture and orientation details, transmission power, and various other parameters.
  • MNO mobile network operator
  • ECGI unique cell identifier
  • PCI physical cell index
  • the cloud assist server 516 interacts with the BS A 512 to provide the UE 510 with a small subset of the contents of the BSA 512.
  • the resulting micro-BSA 520 may consist of several hundred cells close to the serving cell of the UE 510.
  • the information comprising this 15kB, 1000-cell micro-BSA may be transferred to the UE 510 in a few seconds over the wireless link while the UE is in LTE connected mode.
  • a smaller micro-BSA can be requested for shorter download times and less storage, and a larger micro-BSA can be requested for greater coverage and less overall interaction with the cloud assist server 516 or otherwise with the network 514.
  • a 1000-cell micro-BSA 520 provides coverage for a 1000 km2 area. With a single micro-BSA 520 many position fixes can be obtained. Therefore, once the information for the micro-BSA 520 is downloaded, the UE 510 requires minimal additional interaction with elements of the network 514.
  • the position estimator 540 will be able to generate many position estimates even when the UE 510 is in motion based solely upon the measurements 542 and the assistance data 526 derived from information within the micro- BSA(s) 520. This advantageously improves battery life of the UE 510 and reduces network congestion. This is because almanac information is not provided by the BSA 512 nor is assistance data otherwise provided to the UE 510 by network in connection with each position estimate generated by the position estimator.
  • positioning accuracy is enhanced relative to the case in which such almanac information and/or assistance data is provided to the UE 510 to facilitate each position measurement since the UE 510 may, in some embodiments, employ filtering and other techniques to average or otherwise smooth the position estimates locally generated by the position estimator 540.
  • the current state of the art UE-assisted method is considered a “single shot” estimate where assistance data is provided to the UE from the location server, the UE then reports measurements, and the location server estimates location with a single set of measurements.
  • the estimation algorithms cannot practically benefit from filtering since continuous measurement reporting is not feasible both from a UE battery drain and network congesti on perspective.
  • the measurements 542 are more efficiently supplied to the position estimator 540 for enhanced estimation processing.
  • the UE 510 may be configured to sense when new BSA information is required. For example, when the position estimator 540 is deriving high-quality position estimates, then no new' BSA information is required from the micro-BSA cloud assist server 516.
  • the position estimator 540 can determine if the estimates are of high quality by studying the contours of the likelihood or a posteriori function surface, or by calculating a TDOA residual error vector.
  • the TDOA residual error vector is denoted by e and is given by: where r is a TDOA measurement vector for one of the additional position estimates and wherein each element of r includes a TDOA measurement associated with one of the set of cells included in the assistance data 526, and where h(x) is a TDOA vector for a position estimate and is given by: where x m is the location of the with cell and xi is the location of a TDOA reference cell which is included in the assistance data 526 and selected by the UE 510. If the elements in e are relatively small, then the position estimator 540 has greater confidence that the position estimates are of high quality.
  • the position estimator 540 develops position estimates using a TDOA hyperbolic location signal model.
  • TDOA hyperbolic location signal model [0064] In embodiments in which the UE 510 implements downlink time-difference of arrival (TDOA) hyperbolic location estimation, the UE 510 performs time of arrival (TOA) estimates on surrounding cells.
  • TOA time of arrival
  • cells are used interchangeably with “base stations” or “transmission points”.
  • RSTD reference signal time difference” as defined in the 3GPP standards
  • the surrounding cells are assumed to be time-synchronized and transmitting positioning reference signals (“pilots”) at some time near seconds.
  • the kth cell transmits at time where is a relatively small transmit synchronization term.
  • the TOA of the kth cell is where is the speed of light in a vacuum, is the Cartesian coordinates of the unknown UE location, is the known Cartesian coordinates of the kth cell, and is a non-line-of-sight (NLOS) bias.
  • NLOS non-line-of-sight
  • the UE 510 estimates the TOA of the serving cell to be where is a synchronization error term. The UE 510 then uses this time estimate to form a relative local time. Adjusting for the serving cell synchronization, the relative TOA of the Kth cell becomes
  • the term is the TDOA between the 4th cell and the serving cell.
  • TDOA TDOA measure corrupted by cell transmit synchronization error
  • the UE 510 performs estimates of the relative TO As: . where is due to estimation error.
  • the unknown location of the UE x may now be estimated from the relative TO A estimates and the known cell locations .
  • a more robust approach may be to first form the TOO A estimates: m ----- 1, 2, . . . , M.
  • the subtraction of the relative TO A estimates removes the synchronization error term ⁇
  • Removal of the synchronization error term may be beneficial in the event that the UE 510 is not well synchronized to the network. This is the method employed by the 3 GPP specification.
  • the i(m)th cell is considered the RSTD neighbor cell of the mth measurement
  • the J(m)th cell is considered the RSTD reference cell of the mth measurement.
  • a common RSTD reference cell is used: j(m) ------ A1 ⁇ 4f for some .,K ⁇ .
  • the remaining cells are candidate RSTD neighbor cells.
  • FIGS. 13-15 illustrate exemplary potential strategies for forming cell-pairs comprised of reference and neighbor cells.
  • FIG. 13 depicts a single cell reference cell pairing strategy
  • FIG. 14 illustrates a neighbor index cell pairing strategy
  • FIG. 15 illustrates an N-choose-2 cell pairing strategy.
  • TDOA position estimators [0071] The TDOA position estimator 540 functions to determine a good estimate for x given the measurements in r.
  • the position estimator 540 may utilize various different methods in making this determination including, for example, least squares, weighted least squares, and Gaussian maximum likelihood. The methods described below do not preclude the use of other possibilities.
  • the position estimator 540 has estimated the location of the UE 510 to be at . At this location the ground-truth TDOA between the i(m)th and j(m)th cell is while the estimated TDOA is rm. Their difference is called the residual error. This term is useful since it is computationally realizable while the statistical error nm is unknowable at the receiver of the UE 510. The sum of the squared residual error components is: . To the extent the position estimator 540 is configured to find to minimize , the position estimator 540 may be characterized as a least-squares (LS) estimator: . [0073] Suppose some of the measurements in r to be of higher quality than others.
  • LS least-squares
  • the weighted least squares (WLS) estimator does this: , where is a diagonal weighting matrix. WLS is equivalent to LS when the weights are all the same.
  • a special case of the ML estimator is the Gaussian maximum likelihood (GML) estimator where the likelihood function is expressed as: where is the M by M covariance matrix of the TDOA noise, E() is the expectation operator,
  • the GML estimator simplifies to . This shows the GML estimator is a type of WLS: GML is WLS where the weighting matrix is the noise covariance inverse.
  • a generalized weighted least squares estimator is expressed as , where W is a weighting matrix. For the three estimators identified above: with I being the identify matrix.
  • the implementation of the position estimator 540 as the above GML estimator assumes the TOA to be drawn from a Gaussian distribution.
  • a known generalization of this framework is to assume the TOA is drawn from a Gaussian Mixture Model (GMM). See, e.g., F. Perez-Cruz, C. Lin and H. Huang, "BLADE: A Universal, Blind Learning Algorithm for ToA Localization in NLOS Channels," 2016 IEEE Globecom Workshops (GC Wkshps), Washington, DC, USA, 2016.
  • GMM Gaussian Mixture Model
  • the ML estimator implemented by the position estimator 540 can be generalized into the maximum a posteriori (MAP) estimator.
  • MAP maximum a posteriori
  • the weighted least squares estimator derived above minimizes the quadratic cost function .
  • the minimization can be performed with numerical sampling of a rectangular or hexagonal grid, or by statistical sampling methods like Markov Chain Monte Carlo (MCMC) where the Metropolis Hastings algorithm is one example. Alternatively, it can be solved analytically through Taylor series expansion See, e.g., Torrieri, D. J. “Statistical Theory of Passive Location Systems,” IEEE Trans. on Aerospace and Electronic Systems AES-20, 2 (Mar. 1984).
  • the BSA coherence time may be defined to be the time duration in which the BSA information remains relatively static and useful for positioning.
  • the BSA coherence time is large relative to the position measurement update rate.
  • the BSA coherence time can be on the order of days or months while the position measurements might be updated once an hour. This allows for the same BSA information within the micro-BSA(s) 520 to be used across multiple position measurement events.
  • the UE is not location aware. Therefore this network relieving feature is not possible.
  • the current state of the art is not efficient in that assistance data must be downloaded to the UE and measurements uploaded to a location server for each position update. This causes the problem of network congestion and compromises the battery life of the device.
  • the UE 510 is “location aware” so as to better, and more efficiently, enable applications like geofencing. This location awareness also allows for a faster positioning fix, reducing latency and improving time-to-first fix (TTFF).
  • TTFF time-to-first fix
  • FIG.6 is a flow chart of a sequence of operations 600 performed by the UE 510 which highlight one way in which intelligent and adaptive generation of assistance data 526 within the UE 510 may be utilized to address this shortcoming in the current state of the art.
  • the micro-BSA(s) 520 include information pertaining to more cells than the cells represented in the assistance data 526. This larger collection of cells in the micro-BSA(s) 520 provides improved flexibility in generating a good set of cells for the assistance data 526.
  • an initial set of assistance data cells is generated (stage 608) using a basic cell ID 604 for the seed estimate.
  • an initial position estimate is derived with a position estimate that is better than the basic cell ID estimate (stage 612). This new estimate is used to re-derive the assistance data and thereby generate improved assistance data (stage 616).
  • a micro-BSA 520 could include parameters for 1,000 cells that make up a metropolitan area including an urban downtown and surrounding areas.
  • the micro-BSA 520 has parameters needed to perform the OTDOA algorithms.
  • the OTDOA algorithms consist of, for example: (i) generating assistance data (AD) from the micro-BSA 520, (ii) using assistance data (AD) to perform TOA/TDOA measurements, and using TOA/TDOA measurements plus assistance data (e.g., cell latitude/longitude) to estimate the UE’s location.
  • the AD is a subset of the micro-BSA 520. For example, it may consist of the parameters of 50 cells.
  • the UE 510 may have one or more micro-BSAs 520.
  • the UE 510 may have more than one micro-BSA 520 to provide service for a few different areas around a town that the UE 510 frequents.
  • the UE 510 communicates with a BSA server 511 via the micro-BSA cloud assist server 516. It informs the BSA server 511 of the ECGI of the serving cell, and possibly the ECGI’s or PCI’s of neighbor cells.
  • the UE 510 might inform the BSA server 511 via the micro-BSA cloud assist server 516 that the serving cell ECGI is “xyz”, and that the UE 510 would like a micro-BSA 520 of 200 cells.
  • These 3 kBs of BSA information are retrieved from the BSA 512 by the BSA server 511 and then communicated by the micro-BSA cloud assist server 516 in the downlink channel to the UE 510 and stored.
  • the UE 510 With the micro-BSA 520 instantiated on the UE 510, the UE 510 effectively has access to a “mini-map” of those 200 cells. Since the UE 510 is aware of its location, the UE 510 knows if it remains in the service area of these 200 cells. If the UE 510 roams outside of these cells it might want to request a new micro-BSA 520. If the UE 510 is stationary and not detecting as many cells as expected given the contents of the current micro-BSA 520, it might be the case that the cell topology has changed and it might be a good time to get the micro- BSA 520 refreshed.
  • the ECGI of the serving cell can provide a seed estimate of the UE 510 from which to derive a set of micro-BSA cells. That seed estimate can simply be the transmission point of the serving cell. With additional neighbor information, the server can derive a better seed estimate, like the centroid of the serving and surrounding cells. [0089] To determine a good set of AD cells, the seed estimate can be something similar as the seed estimate used to get the micro-BSA 520 from the BSA server 511; that is, something akin to a cell ID. Or, suppose the UE 510 is roaming. The serving cell may change from one ECGI to another with hand over.
  • the reference timing on the UE 510 will likely change as the UE 510 synchronizes to some new serving cell. This time change can be logged to adjust the current set of timing measurements for the current set of cells being monitored.
  • the positioning assistance data calculator 524 on the UE 510 will likely want to obtain a new set of AD cells from the micro-BSA 520.
  • the seed estimate for new AD can be the most- recent UE location estimate (using OTDOA, for example). If the new serving cell is in the micro-BSA 520, then no further action is needed. If not, the UE 510 will need to retrieve a new micro-BSA 520 from the BSA server 511, which hosts or has access to the BSA 512.
  • the positioning assistance data calculator 524 and/or position estimator 540 may cause the UE 510 to retrieve, from the BSA server 511, BSA information corresponding to a new micro-BSA 520.
  • the BSA server 511 may keep track of the cells for which information is stored in the micro-BSA 520 of the UE 510. That way the BSA server 511 can give the UE 510 information for new cells that are not duplicates of the current micro-BSA. It may be advantageous to send just differences from the prior micro-BSA when populating a new micro-BSA.
  • the improved assistance data 616 is made possible by the improved seed estimate in 612.
  • the feedback 532 also allows for improved assistance data. For example, consider the case when the serving cell is significantly farther from the UE 510 than other surrounding cells. This can happen, for instance, when the serving cell is transmitting at high power on top of a hill that is in a line of sight with the receiver of the UE 510.
  • the receiver of the UE 510 may sense this hilltop cell to be of the highest signal-to-noise- plus-interference ratio (SINR) of all its surrounding cells and use it for its serving cell.
  • SINR signal-to-noise- plus-interference ratio
  • the timing measurements in 530 may detect closer-by cells (with a delay negative relative to the serving cell timing). For example, a high quality (PAPR, or SINR, low variance, etc.) negative TOA of 1000 meters can be present in the list of detected cells. This implies that the negative TOA cell is 1000 meters closer to the UE than the serving cell. Re-seeding the assistance data calculation by incorporating this information can be beneficial. For example, the closer-by cell latitude/longitude coordinates can be used as the new assistance data seed estimate 612. [0094] Similarly, the timing advance (TA) in the receiver can be used to detect a far-away serving cell.
  • TA timing advance
  • the TA is used to signal a far-away receiver to transmit early so the far-away and closer-by device uplink transmissions arrive at the base station receiver around the same time.
  • the assistance data calculator can therefore use TA information available in the host modem 1224 in FIG.12 to improve the assistance data generation strategy. For example, if the TA is high, implying a far-away serving cell, the number of cells in the assistance data for TOA measurements may be expanded to farther distances from the serving cell. Then, as closer-by TOAs are detected, the assistance data can be recalculated as described above.
  • the present system advantageously allows a device such as the UE 510 to specialize in location services for a broader range of use cases.
  • the current state of the art only supplies 10s of cells in the assistance data, intended primarily for the single use case of emergency services (e911). This current state of the art is not well suited for roaming use cases, for example.
  • the present system solves this problem with the use of micro-BSA(s) 520 stored on the UE 510. For a UE implemented as a specialized location device, more memory may be allocated on the UE 510 to storing the micro-BSA(s) 520.
  • the UE 510 may store 1000 cells using 15 kilobytes of memory. Assuming a cell density of 1 cell per square kilometer, the micro-BSA(s) 520 may have a location service area of 1000 square kilometers, thus allowing the UE 510 to roam.
  • An example use case here is the tracking of rental scooters where the devices roam around a city.
  • FIG. 7 an illustration is provided of the geographic footprints associated with multiple micro-BSAs 520 stored on the UE 510. For some use cases it may be desired that the UE 510 store multiple micro-BSAs to span a greater geographical region. As shown, information concerning cells located in a first geographic footprint 710 corresponding to a region of high cell density is stored within a first micro-BSA 520 1 .
  • information concerning cells located in a second geographic footprint 720 corresponding to a region of medium cell density is stored within a second micro-BSA 5202 and information concerning cells located in a third geographic footprint 730 corresponding to a region of low cell density is stored within a third micro-BSA 520 3 .
  • the UE 510 is known to commonly travel across the first geographic footprint 710, the second geographic footprint 720 and the third geographic footprint 730.
  • the UE 510 By storing a micro-BSA 520 1 , 520 2 , 520 3 for each geographic footprint 710, 720, 730, the UE 510 has all the needed cell information to perform location functions without interacting with the micro-BSA cloud assist server 516 or other elements of the network 514. And this can be done using less storage than storing the superset of the geographic footprints 710, 720, 730, which is represented by circle 750. As the UE 510 roams throughout the geographic footprints 710, 720, 730, the UE 510 intelligently derives its assistance data by making cell selections across the multiple locally-stored micro-BSAs 520 1 , 5202, 5203. [0098] A specific use case exemplified by FIG.
  • a company may have a high-valued generator or reciprocating saw that travels from job site to job site. Assume the company has three job sites (respectively located within geographic footprints 710, 720, 730) and a manager has misplaced a tool. In this case the manager may use a small tracker (an implementation of the UE 510) attached to the tool to determine its location.
  • a small tracker an implementation of the UE 510
  • AI Artificial Intelligence
  • the UE 510 can sense high mobility (with Doppler estimation, for example) and the cell parameter information downloaded to the device micro-BSA 520 can be accordingly adapted.
  • the UE 510 can sense high mobility (with Doppler estimation, for example) and the cell parameter information downloaded to the device micro-BSA 520 can be accordingly adapted.
  • a micro-BSA “artificial intelligence” (AI) management module 550 can assist in the management of the information included in the micro-BSA(s) 520.
  • the micro-BSA AI management module 550 can implement pattern recognition algorithms capable of identifying with high likelihood that when the UE 510 is located on an interstate highway and traveling at a certain velocity it will best benefit from a certain set of micro-BSA cells. Similarly, when the UE 510 is determined to be stationary in a city center the UE 510 will likely benefit from a different strategy. In this latter case, the UE 510 may be attached to a smart meter, traffic sign, or Automatic Teller Machine (ATM) cash machine that is not intended to travel for the life of the UE 510. For these application the micro-BSA download management effected by the AI management module 550 will be different than for the high-velocity interstate traveling use case.
  • ATM Automatic Teller Machine
  • This management of micro-BSA information using AI can benefit subterranean use cases. For example, if the device serving cell is underground in a metropolitan subway system, then the AI management module 550 may consider only providing underground cells in the micro-BSA 520. Considering another use case, the AI management module 550 can learn from patterns in commuting. For example, a commuter line will have a finite number of transfer routes. The download of information to a micro-BSA 520 of a UE 510 being transported by the line may benefit by including cells in the most common transfer routes, and this can depend on the time of day/week.
  • the AI management module 550 may be able to “learn” that devices traveling at speed on a particular highway during a particular time (e.g., on Interstate 8 at 9am on a Tuesday 20 miles east of El Centro) have a 90% likelihood of ending up in Glendale, AZ. This knowledge may then be utilized to download information to the micro-BSA 520 pertaining to cells more likely to be utilized by the UE 510 when transiting such a highway at the particular time.
  • the AI management module 550 within the BSA server 511 may be complemented by an optional AI management module 552 disposed within the UE 510.
  • the an optional AI management module may be configured to perform at least some of the processing otherwise performed by the AI management module 550.
  • FIG. 8 illustrates a Bad Cell Detection process 800 implemented by a Bad Cell Detector 544 (FIG. 5) for improving quality of position estimates produced by the position estimator 540.
  • the TDOA residual error vector provides insights into the quality of the position estimate and these insights may be leveraged in the process 800.
  • a position estimate is computed using the TDOA measurements in a first TDOA measurement vector r 1 (stage 810).
  • the resulting position estimate is used to construct the TDOA residual error vector e1 (stage 820).
  • Cell 5 is detected to be, and labeled, a “Bad Cell” because of its relatively high error value (stage 830).
  • This Bad Cell Detection process 800 can be coupled with other criteria to determine the quality the position estimate. For example, a minimum number of cells may be required. For 2D hyperbolic TDOA positioning, measurements from at least three distinct cell sites are required. It may be advantageous to require more than three distinct cells sites for extra redundancy and added robustness in the position calculation. Moreover, the GDOP between the estimated UE location and the cells used for positioning can be calculated. If the GDOP raises above a certain threshold it may be determined that the environment is not well suited for hyperbolic TDOA.
  • the position estimate may return no result, and error result, or may fall back to another positioning method like E-CID.
  • the threshold setting for the residual error, the minimum number of cells, and the minimum GDOP can be dynamically determined, or multiple static configurations can operate independently in parallel.
  • config A may be a strict configuration
  • config B may be a less strict configuration
  • config C may be E-CID.
  • the thresholds can be set dynamically. For dense cellular environments where many cells are measured, the minimum threshold can be raised, for example.
  • the UE 510 may be configured to leverage feedback from the Bad Cell Detector 544 to improve the assistance data 526. In the specific case of FIG.8, the fact that Cell 5 is deemed poor by the Bad Cell Detector 544 is useful information that may be included in the feedback 534 to the positioning assistance data calculator 524.
  • Cell 5 may be deemed of low quality due to challenging multipath channel conditions where the TOA estimation is compromised.
  • Cell 5 may be relatively out of synchronization relative to other cells in the group.
  • the Bad Cell Detector 544 provides an algorithmic means of dealing with networks that are not well synchronized. Specifically, by excluding a few cells in the list that are relatively out-of-sync with the other cells, improved performance is achieved.
  • the level of asynchronization can be a relatively static quantity to be estimated and compensated for in the position estimator 540.
  • Another feature of the present system is to exclude cells in the assistance data 526 that are rarely or never detected. Attempting to detect cells that are not detectable wastes computing resources of the UE 510. Therefore there are efficiency gains to be had by ignoring cells that are difficult to detect.
  • the estimation algorithms executed by the position estimator 540 can monitor which cells are being detected and which cells are not being detected. This information can be included in the feedback 534 provided to the positioning assistance data calculator 524 in order to enable incremental efficiency improvements.
  • Good Cell Selector [0107] In alternative embodiments a position estimation method may be performed by a Good Cell Selector (GCS) 560 of the position estimator 540 in lieu of the method performed by the Bad Cell Detector 544.
  • GCS Good Cell Selector
  • the Good Cell Selector 560 ranks the estimated TOA of the surrounding cells in terms of quality.
  • the quality metric might be based on signal-to-noise- plus-interference ratio (SINR) or peak-to-average-power ratio (PAPR) of the correlator output.
  • SINR signal-to-noise- plus-interference ratio
  • PAPR peak-to-average-power ratio
  • the estimated TOA of the surrounding cells may be ranked by peak-to-average power ratio of the pseudospectrum in the multiple signal classification (MUSIC) super resolution algorithm. See, e.g., X. Li and K.
  • the updated position estimate is studied to determine if including the new cell is beneficial.
  • the quantity is a measure of the minimum residual error in meters. If this measure exceeds an established threshold with the inclusion of the new cell, the new call can be excluded from the list of used cells.
  • Another method is to study the contour of Q(x). If a clear global minimum is identified with a small minimum region, the new cell may be deemed good. On the other hand, if a secondary local minimum is present (thus making the overall minimum less distinct), the new cell may be identified as not good.
  • the Good Cell Selector 560 trials new candidate cells (i.e., TDOA measurements), it is advantageous to use the cells in their ranked order of quality. It may also be beneficial to select the next trial cell that improves the estimate geometry (i.e., reduces the geometric dilution of precision (GDOP)).
  • GDOP geometric dilution of precision
  • surrounding cells can be categorized in terms of circular sectors. Cells in underrepresented sectors can be prioritized for improved geometry. This circular sector method is similar to the selection of assistance data cells using the Circular Sector Assistance Data (CSAD) described with reference to FIGS. 9-11.
  • the estimated UE location is placed at the origin of the circular sectors, and, for example, six sectors are established around this origin.
  • the Good Cell Selector 560 continues execution until a desired number of cells are included in the position calculation.
  • This stopping criteria may be established with a threshold on P, or once a desired GDOP level is attained, or once an uncertainty region from the contours in is confined to a desirable level, or by some other means.
  • the stopping rule is not limited to these criteria, of course, and a mix of different criteria may be effective.
  • Micro-BSA refinements responsive to measurements can be refined and pruned with feedback 532 from the radio condition measurements and position estimates performed by the module 530. For example, if a particular cell is not detectable it may be beneficial to remove the cell from the micro-BSA 520 to free memory. Likewise, if a cell is consistently deemed “bad” in the Bad Cell Detector 544, it may also be removed. BSA request optimizations [0113] Additional BSA information is not required if the position estimator 540 is generating reliable estimates, as stated above. Other methods for determining if new BSA information is required include: Cell statistics tracking (power received, timing measurements, etc.).
  • the UE 510 can assume that the network configurations have not changed, and no BSA update is needed.
  • BSA updates responsive to UE mobility. When the UE 510 is detected as being highly mobile, with a Doppler estimator, for example, or with a high rate of serving cell changes, the efficiency gains may be had by pausing BSA updates until lower speeds are achieved. This depends on the number of store cells in the micro-BSA 520.
  • Such requests conventionally trigger the full process described in the Background section; that is, assistance data delivery from the network to the UE, measurements on the UE, transfer of measurements from the UE to the network, and, finally, location estimation and delivery to the application.
  • the latency involved in such a conventional approach may be 10s of seconds.
  • the present system reduces the time latency between the application position request and position delivery. Since the UE 510 is location aware, the delivery can be “instantaneous”: on the order of 10s of milliseconds if the application is hosted by the micro-BSA cloud assist server 516 or is otherwise cloud-based in the network 514.
  • the time required to transmit the UE location latitude and longitude coordinates in the uplink may be on the order of 10s of nanoseconds.
  • the location awareness of the UE 510 is possible with the present system due to the UE-based positioning method.
  • the intelligent handling of the micro-BSA 520 allows for the UE 510 to require no interaction with the micro- BSA cloud assist server 516 or other elements of the network 514 in the event of a position request.
  • the UE 510 can thus periodically update its position estimate with no interaction with the micro-BSA cloud assist server 516 or other elements of the network 514 in between position requests. This type of location awareness on the part of a mobile device between positioning requests to a network is not made possible by existing approaches.
  • the position of a conventional UE traveling at high velocity could change substantially between the times of position requests made to a network.
  • the “instantaneous” position estimate is immediately available by the present system and delivered to the application.
  • a time stamp may also be supplied, signaling to the application when this last position update was performed.
  • the position estimate may be updated in the background once per hour.
  • the positioning assistance data calculator utilizes intelligence in the selection of micro-BSA information and assistance data 526.
  • Cellular position estimation is sensitive to the geometric dilution of precision (GDOP).
  • the assistance data 526 is generated using a seed estimate of the location of the UE 510 as described above.
  • simply selecting the N nearest cells to the UE seed estimate may be a suboptimal strategy in terms of geometry. It can be advantageous to select cells that improve GDOP and the overall stability of the position calculation.
  • FIG.9 illustrates a method 900 of Circular Sector Assistance Data Generation useful in lowering GDOP in accordance with the disclosure.
  • the assistance data 526 is built starting in sector 0 and (9040) rotating counter-clockwise, successively adding 1 cell from each sector. In the example of FIG.
  • the first cell 910 1 included in the assistance data is in Sector 0 (904 0 ).
  • the second cell 910 2 is in Sector 2 (904 2 ) even though there is a closer cell (9103) in Sector 0.
  • the cell 9102 in Sector 2 is prioritized since it provides a more geometrically diverse set.
  • the third cell 910 3 is in Sector 0. [0119]
  • FIG.10 there is illustrated a screenshot capture of a map 1000 of a portion of New York City.
  • the map 1000 shows a location 1002 of a UE, which is a challenging location from the perspective of positioning since there are no immediate cell sites on the west and south side of the UE.
  • FIG. 11 illustrates screenshot capture of a map 1100 which corresponds to the same screenshot capture illustrated in FIG. 10. However, in this case the circular sector assistance data generation method of the present disclosure is used to select the cells for which assistance data will be used in determining the location 1102 of the UE. The number of cells in the assistance data is the same as in the example of FIG.
  • Another example is the 3GPP ECGI described in the LPP consisting of MCC (mobile country code), MNC (mobile network code) and a 28-bit cell identity. The MCC and MNC require 24 bits, but those can be common to a network operator. So instead of using the full 52 bits for the 3GPP ECGI, 28 bits for the cell identity can be used for a particular deployment, a 46% reduction.
  • FIG. 12 includes a block diagram representation of a particular implementation of the UE 1200, in this case a mobile or cellular phone, configured in accordance with the disclosure. It will be apparent that certain details and features of the UE 1200 have been omitted for clarity, however, in various implementations, various additional features of a mobile device as are known will be included.
  • the UE 1200 need not be implemented as a personal communications device, such as a mobile or cellular phone, and in other implementations may comprise a tracking device or the like lacking certain features and characteristics of the implementation of FIG.12.
  • the UE 1200 includes a processor 1220 operatively coupled to a touch-sensitive display 1204 configured to present a user interface 1208.
  • the user interface 1208 may include a physical keypad or keyboard, audio input device and/or any other device capable of receiving user input or instructions.
  • the UE 1200 includes a memory 1240 comprised of one or more of, for example, random access memory (RAM), read-only memory (ROM), flash memory and/or any other media enabling the processor 1220 to store and retrieve data.
  • the memory 1240 stores the micro-BSA(s) 520 and programs or including instructions executable by the processor 1220. These modules include the positioning assistance data calculator 524, the power and timing measurements module 530, the position estimator 540 and the micro-BSA AI management module 552.
  • the UE 1200 includes a wireless transceiver and modem 1224 for communication with a network, such as the network 514, which may include, for example, the Internet, and/or a wireless network such as a cellular network and/or other wired or wireless networks.
  • the UE 1200 may also include a camera 228 and other ancillary modules.
  • Uncertainty calculation for UE-based positioning [0127] A common approach to estimate the quality of a location estimate is to study many position estimates and form a confidence ellipse. See, e.g., Chew, V. “Confidence, Prediction, and Tolerance Regions for the Multivariate Normal Distribution,” Journal of the American Statistical Association. 61, 315 (Sept.
  • FIG. 16 shows an example in which multiple (i.e., 50) position estimates (observations 1610) are used to form a confidence ellipse 1602.
  • FIG. 17 there is illustrated an exemplary contour of error surface 1700 generated to establish a confidence region in accordance with an embodiment. In this method the contours of are evaluated as shown in FIG. 17.
  • the darkest blue region 1704 tightly surrounds the true location 1710 of the UE and the estimated location 1720 of the UE.
  • the uncertainty in the estimated location of the UE can therefore be determined by finding the area of the darkest blue region.
  • the onboard micro-BSA positioning method described herein allows for the confidence region (location estimation uncertainty) to be computed on the UE 510, and this has technical advantages over the state of the art UE-assisted location estimation. For example, performing multiple estimates to form a confidence ellipse can be done using the presently disclosed method without interacting with the network 514, with the UE 510 in a receive-only mode.
  • This method can be performed while the UE 510 is technically in RRC idle, eDRX, or PSM from a data communication aspect.
  • the contour of error surface method can be performed on the UE 510 with a single set of TDOA measurements, which in contrast to the method of forming a confidence ellipse advantageously does not require multiple observations.
  • Hybrid power-timing-based positioning [0130] In the TDOA positioning method discussed above, timing measurements are used to estimate the location of the UE. As has been explained herein, TDOA is a hyperbolic location method, with each cell pair providing a hyperbola on a two dimensional map. The intersection between multiple hyperbolas is the estimated location of the device.
  • a hybrid power-time positioning method is described in this section. It may be appreciated that a power-measurement-to-distance relationship is typically less clear than a time-measurement-to-distance relationship. As a consequence, a power-only positioning method will typically underperform a timing-only measurement positioning method ⁇ so long as there are a sufficient number of detected cells.
  • a power-only method can outperform a time-only method, and a hybrid power-time method can perform best.
  • the received signal power measurement for the mth cell can be modeled as follows: dBm, where is the mth transmitter power in dBm, is the mth transmitter attenuation factor in dB, is the average path loss in dB experienced by the mth transmitted signal traveling meters, x is the unknown UE location, xm is the known location of the mth transmitter, and is a zero-mean Gaussian random variable with standard deviation
  • a common path loss model is the so-called log-distance model, where See, e.g., Rappaport, T.
  • FIG.18 illustrates an attenuation profile associated with a commonly used attenuation model. See 3GPP TR 36.814: Table A.2.1.1-2 "Further advancements for E-UTRA physical layer aspects".
  • This model may be represented as: where is the angle between the direction of interest (azimuth angle) and the boresight of the antenna, is the 3 dB beamwidth of the antenna aperture, and Amax is the maximum attenuation in decibels.
  • FIG. 18 sets degrees and [0134] Now, defining , where is the average received power, and is the measured power error term due to shadow fading. This formulation allows for the vector form where the mth element is . This vector form allows for the direct application of the least-squares estimator described above in the timing-based context.
  • a power-based least- squares estimator is therefore: [0135]
  • the subscript “t” is introduced where the mth TDOA measurement is where is the ground-truth TDOA component and is the TDOA noise component.
  • the vector form is [0136] Since the power measurements and the TDOA measurements have different units (dBm for the power measurements and seconds for the timing measurements), exactly how to combine the measurements is unclear and not suggested by prior positioning approaches. In order to circumvent this issue a Gaussian maximum likelihood method is pursued.
  • FIGS. 19A-19C depict the results of simulations demonstrating that positioning using the power-time hybrid approach disclosed herein may be advantageous in environments with a limited number of cells.
  • FIG. 19A-19C depict the results of simulations demonstrating that positioning using the power-time hybrid approach disclosed herein may be advantageous in environments with a limited number of cells.
  • FIG. 19A illustrates results of using power only to estimate the position of a UE within an environment in which the UE receives signals from two cells.
  • FIG. 19B illustrates results of using time only to estimate the position of the UE within the same two-cell environment as FIG. 19A.
  • FIG. 19C illustrates results of using the power-time hybrid approach to estimate the position of the UE within the same two-cell environment.
  • the power-time hybrid approach demonstrates a benefit relative to the power-only and time-only methods.
  • Such software may be written using any of a number of suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.
  • Examples of computer code include, but are not limited to, micro-code or micro- instructions, machine instructions, such as produced by a compiler, code used to produce a web service, and files containing higher-level instructions that are executed by a computer using an interpreter.
  • embodiments may be implemented using imperative programming languages (e.g., C, Fortran, etc.), functional programming languages (Haskell, Erlang, etc.), logical programming languages (e.g., Prolog), object-oriented programming languages (e.g., Java, C++, etc.) or other suitable programming languages and/or development tools.
  • imperative programming languages e.g., C, Fortran, etc.
  • functional programming languages Haskell, Erlang, etc.
  • logical programming languages e.g., Prolog
  • object-oriented programming languages e.g., Java, C++, etc.
  • Additional examples of computer code include, but are not limited to, control signals, encrypted code, and compressed code.
  • program or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the present invention need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention.
  • Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types.
  • data structures may be stored in computer-readable media in any suitable form.
  • data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields.
  • any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.
  • various inventive concepts may be embodied as one or more methods, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way.
  • embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.
  • a reference to “A and/or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.
  • “or” should be understood to have the same meaning as “and/or” as defined above.
  • At least one of A and B can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

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Abstract

L'invention concerne un procédé d'estimation de la position d'un dispositif mobile, consistant à recevoir, à partir d'un serveur de réseau, des données d'assistance de différence de temps d'arrivée observée (OTDOA) pour une première pluralité de cellules, à partir d'un almanach de stations de base (BSA) accessible au serveur de réseau. Les données d'assistance OTDOA sont stockées dans une mémoire du dispositif mobile, en tant que premier micro-BSA. Une estimation de position du dispositif mobile est déterminée sur la base de mesures d'une différence de temps d'arrivée (TDOA) associées à un sous-ensemble initial de la première pluralité de cellules et de données d'assistance OTDOA initiales correspondant au sous-ensemble initial de la première pluralité de cellules. Les données d'assistance OTDOA initiales peuvent être générées par le micro-BSA sur la base d'une estimation de valeur initiale.
EP21784157.6A 2020-04-09 2021-04-09 Positionnement basé sur un mobile à l'aide de données d'assistance fournies par un micro-bsa embarqué Pending EP4133755A4 (fr)

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US9949067B2 (en) * 2016-07-29 2018-04-17 Qualcomm Incorporated Enhancing PRS searches for shorter LPP-type positioning sessions
US10015767B2 (en) * 2016-09-14 2018-07-03 Qualcomm Incorporated Enhancing OTDOA BSA accuracy using unsolicited RSTD measurements
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