Method for positioning and device therefor

Abstract

The present disclosure proposes a method for positioning and a device therefor. Specifically, a method performed by a terminal may comprise the steps of: generating a first terminal location estimate based on first positioning; generating a second terminal location estimate based on second positioning different from the first positioning; and generating a hybrid location estimate by means of an extended Kalman filter on the basis of one or more pieces of motion sensor data, the first terminal location estimate, and the second terminal location estimate.

Description

Method and apparatus for positioning

[0001] The present disclosure relates to a method for positioning and an apparatus for the same.

[0002] Location tracking and navigation systems have established themselves as essential elements in modern society and are utilized as core technologies for various Location-Based Services (LBS), such as in-vehicle navigation, asset tracking, unmanned aerial vehicle (UAV) operations, and emergency response services. Traditionally, Global Navigation Satellite Systems (GNSS), including the U.S. Global Positioning System (GPS) and China's Beidou system, have been the primary means of providing accurate location information in outdoor environments.

[0003] GNSS positioning is based on pseudorange, that is, the measurement of the apparent distance between a User Equipment (UE) and multiple satellites within its line of sight. Each GNSS satellite continuously transmits a signal containing its precise location information and transmission time, and the UE receives this signal to measure the time difference between the time of transmission and the time of reception. The pseudorange to each satellite is calculated by multiplying this time difference by the speed of light. Subsequently, based on the pseudoranges received from at least four satellites, the user's position is calculated by solving the equation between the known satellite positions and the unknown UE positions using triangulation. This enables three-dimensional positioning, including elevation.

[0004] Meanwhile, 5th generation mobile communication (5G New Radio, NR) technology provides the Downlink Time Difference of Arrival (DL-TDOA) method, a new positioning method utilizing ground-based fixed infrastructure. DL-TDOA is based on Positioning Reference Signals (PRS) transmitted from multiple 5G base stations (gNBs; gNodeBs) that are precisely time-synchronized. The UE receives these PRS signals and measures the Time of Arrival (TOA) of each signal. Since the location and time synchronization information between each gNB is already known, the UE can calculate the TDOA between different pairs of base stations. Each TDOA represents the distance difference between two base stations and the UE, forming a hyperbolic positional constraint. By combining multiple TDOAs for various pairs of gNBs, the UE's location can be estimated using a multilateration technique.

[0005] However, GNSS-based positioning suffers from a problem where position accuracy deteriorates rapidly in environments with limited satellite visibility, such as urban canyons, densely populated urban areas, or indoors, due to the effects of multipath propagation, non-line-of-sight (NLOS) reception, and satellite signal attenuation caused by high-rise buildings and structures. 5G-based DL-TDOA positioning also has limitations in position accuracy and stability due to factors such as multipath interference, limitations on base station visibility, and network deployment structures.

[0006] Hybrid positioning systems combining GNSS and 5G DL-TDOA technology are attracting attention as a solution to overcome these limitations. Hybrid positioning can leverage the complementary advantages of GNSS and 5G signals by fusing them, ensuring the continuity and accuracy of location estimation even in situations where signals from a specific technology are unavailable or unreliable. For example, GNSS generally has a low update cycle of once per second (1 Hz); while it offers high precision, it is vulnerable to indoor environments. In contrast, 5G PRS-based measurements enable high-speed updates of up to 250 Hz, but their precision may vary depending on the base station placement. These differences in measurement cycles and noise characteristics present technical challenges in effectively fusing the two technologies.

[0007] Existing hybrid positioning systems have primarily utilized methods that estimate position by fusing GNSS and inertial sensors (IMUs) based on Kalman filters. However, navigation systems are inherently nonlinear dynamic systems, making the accurate estimation of various state variables within the system a critical challenge. Against this backdrop, the Extended Kalman Filter (EKF) is widely adopted as a method capable of fusing various sensor measurements in real time by applying first-order Taylor series expansions to linearize nonlinear systems and measurement models, and by assuming state estimation errors follow a Gaussian distribution. However, hybrid position estimation techniques based on the Extended Kalman Filter that integrate GNSS and 5G DL-TDOA data, particularly in complex urban environments, remain an area that has not yet been sufficiently studied.

[0008] The present disclosure aims to provide a method for positioning and an apparatus for the same.

[0009] The problems to be solved by the present disclosure are not limited to those mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art to which the present disclosure belongs from the description below.

[0010] The present disclosure relates to a method for positioning performed by a terminal, wherein the method may include: generating a first terminal position estimate based on a first positioning; generating a second terminal position estimate based on a second positioning different from the first positioning; and generating a hybrid position estimate through an extended Kalman filter based on one or more motion sensor data, the first terminal position estimate, and the second terminal position estimate.

[0011] And / or, the present disclosure may be configured to perform a positioning terminal, wherein the terminal comprises: a transceiver; and a controller connected to the transceiver, the controller comprising: a step of generating a first terminal position estimate based on a first positioning; a step of generating a second terminal position estimate based on a second positioning different from the first positioning; and a step of generating a hybrid position estimate through an extended Kalman filter based on one or more motion sensor data, the first terminal position estimate, and the second terminal position estimate.

[0012] And / or, the present disclosure may comprise, in one or more non-transitory computer-readable media storing one or more instructions, said one or more instructions executable by one or more processors, the step of generating a first terminal position estimate based on a first positioning; the step of generating a second terminal position estimate based on a second positioning different from the first positioning; and the step of generating a hybrid position estimate through an extended Kalman filter based on one or more motion sensor data, said first terminal position estimate, and said second terminal position estimate.

[0013] And / or, a method for positioning performed by a terminal according to another embodiment of the present disclosure may include the step of receiving at least one signal for a first positioning, the step of determining whether the reception strength of the at least one signal for the first positioning is greater than or equal to a preset threshold, and the step of performing the positioning based on the result of the determination, based on the at least one signal for the first positioning and / or at least one signal for a second positioning.

[0014] And / or, in the method of the present disclosure, when the reception strength of at least one signal for the first positioning is all greater than or equal to a preset threshold, the step of performing the positioning may include: determining whether the sum of the number of at least one signal for the first positioning and the number of at least one signal for the second positioning is greater than or equal to a specific value; and when the sum of the number of at least one signal for the first positioning and the number of at least one signal for the second positioning is greater than or equal to a specific value, transmitting the calculated position of the terminal to a network using the at least one signal for the first positioning and the at least one signal for the second positioning.

[0015] And / or, in the method of the present disclosure, when the reception strength of at least a portion of at least one signal for the first positioning is greater than or equal to a preset threshold, the step of performing the positioning may include: determining whether the sum of the number of signals excluding at least a portion of signals for the first positioning in which the reception strength is less than a preset threshold and the number of at least one signal for the second positioning is greater than or equal to a specific value; and when the sum of the number of signals excluding at least a portion of signals for the first positioning in which the reception strength is less than a preset threshold and the number of at least one signal for the second positioning is greater than or equal to a specific value, transmitting the calculated position of the terminal to a network using the signal excluding at least a portion of signals for the first positioning in which the reception strength is less than a preset threshold and the at least one signal for the second positioning.

[0016] And / or, in the method of the present disclosure, if the positioning is three-dimensional positioning, the specific value may be 4.

[0017] And / or, in the method of the present disclosure, if the positioning is two-dimensional positioning, the specific value may be 3.

[0018] And / or, a terminal performing positioning according to another embodiment of the present disclosure may include a transceiver and a controller connected to the transceiver, wherein the controller may be configured to perform the steps of receiving at least one signal for a first positioning, determining whether the reception strength of at least one signal for the first positioning is greater than or equal to a preset threshold, and performing the positioning based on at least one signal for the first positioning and / or at least one signal for a second positioning based on the result of the determination.

[0019] And / or, in the terminal of the present disclosure, when the reception strength of at least one signal for the first positioning is all greater than or equal to a preset threshold, the step of performing the positioning may include: determining whether the sum of the number of at least one signal for the first positioning and the number of at least one signal for the second positioning is greater than or equal to a specific value; and when the sum of the number of at least one signal for the first positioning and the number of at least one signal for the second positioning is greater than or equal to a specific value, transmitting the calculated position of the terminal to a network using the at least one signal for the first positioning and the at least one signal for the second positioning.

[0020] And / or, in the terminal of the present disclosure, when the reception strength of at least a portion of at least one signal for the first positioning is greater than or equal to a preset threshold, the step of performing the positioning may include: determining whether the sum of the number of signals excluding at least a portion of signals for the first positioning in which the reception strength is less than a preset threshold and the number of at least one signal for the second positioning is greater than or equal to a specific value; and when the sum of the number of signals excluding at least a portion of signals for the first positioning in which the reception strength is less than a preset threshold and the number of at least one signal for the second positioning is greater than or equal to a specific value, transmitting the calculated position of the terminal to a network using the signal excluding at least a portion of signals for the first positioning in which the reception strength is less than a preset threshold and the at least one signal for the second positioning.

[0021] And / or, in the terminal of the present disclosure, if the positioning is three-dimensional positioning, the specific value may be 4.

[0022] And / or, in the terminal of the present disclosure, if the positioning is two-dimensional positioning, the specific value may be 3.

[0023] And / or, in one or more non-transitory computer-readable media storing one or more instructions according to another embodiment of the present disclosure, said one or more instructions executable by one or more processors may include the step of receiving at least one signal for a first positioning; the step of determining whether the received strength of said at least one signal for the first positioning is greater than or equal to a preset threshold; and the step of performing said positioning based on the result of said determination, based on said at least one signal for the first positioning and / or said at least one signal for a second positioning.

[0024] And / or, in the computer-readable medium of the present disclosure, when the reception strength of at least one signal for the first positioning is all greater than or equal to a preset threshold, the step of performing the positioning may include: determining whether the sum of the number of at least one signal for the first positioning and the number of at least one signal for the second positioning is greater than or equal to a specific value; and when the sum of the number of at least one signal for the first positioning and the number of at least one signal for the second positioning is greater than or equal to a specific value, transmitting the calculated position of the terminal to a network using the at least one signal for the first positioning and the at least one signal for the second positioning.

[0025] And / or, in the computer-readable medium of the present disclosure, when the reception strength of at least a portion of at least one signal for the first positioning is greater than or equal to a preset threshold, the step of performing the positioning may include: determining whether the sum of the number of signals excluding at least a portion of signals for the first positioning in which the reception strength is less than a preset threshold and the number of at least one signal for the second positioning is greater than or equal to a specific value; and when the sum of the number of signals excluding at least a portion of signals for the first positioning in which the reception strength is less than a preset threshold and the number of at least one signal for the second positioning is greater than or equal to a specific value, transmitting to a network the calculated position of the terminal using the signal excluding at least a portion of signals for the first positioning in which the reception strength is less than a preset threshold and the at least one signal for the second positioning.

[0026] And / or, in the computer-readable medium of the present disclosure, if the positioning is three-dimensional positioning, the specific value may be 4.

[0027] And / or, in the computer-readable medium of the present disclosure, if the positioning is two-dimensional positioning, the specific value may be 3.

[0028] The present disclosure has the effect of providing a method for positioning and an apparatus for the same.

[0029] The effects obtainable from the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure belongs from the description below.

[0030] FIG. 1 illustrates a block diagram of an extended Kalman filter-based hybrid positioning system according to one embodiment of the present disclosure.

[0031] FIG. 2 illustrates a block diagram of a hybrid positioning system that estimates the position of a receiver by fusing position data received from a GNSS receiver and a 5G positioning receiver according to one embodiment of the present disclosure.

[0032] FIG. 3 is a flowchart illustrating a series of processes performed by an extended Kalman filter to estimate the position of a receiver using hybrid GNSS and 5G-based position data according to one embodiment of the present disclosure.

[0033] FIG. 4 is a block diagram of a hybrid GNSS and 5G positioning system according to another embodiment of the present disclosure.

[0034] FIG. 5 is a flowchart of a GNSS and 5G positioning method according to another embodiment of the present disclosure.

[0035] FIG. 6 is a flowchart of a 5G DL-TDOA positioning procedure at the receiver side according to another embodiment of the present disclosure.

[0036] FIG. 7 is a flowchart of a hybrid GNSS and QZSS positioning method according to another embodiment of the present disclosure.

[0037] FIG. 8 is a flowchart of a proposed hybrid GNSS and QZSS positioning method using signal threshold filtering according to another embodiment of the present disclosure.

[0038] FIG. 9 is a drawing illustrating the structure of a terminal according to one embodiment of the present disclosure.

[0039] FIG. 10 is a drawing illustrating the structure of a base station in one embodiment of the present disclosure.

[0040] FIG. 11 is a flowchart illustrating an example in which a positioning method according to one embodiment of the present disclosure is performed by a terminal.

[0041] FIG. 12 is a flowchart illustrating an example in which a positioning method according to another embodiment of the present disclosure is performed by a terminal.

[0042] Embodiments of the present disclosure will be described in detail below with reference to the drawings. However, detailed descriptions of known functions or configurations that may obscure the gist of the present disclosure in the following description and the attached drawings are omitted. Additionally, throughout the present disclosure, the term "comprising" any component means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0043] Additionally, terms such as first, second, etc. may be used to describe various components, but said components should not be limited by said terms. Such terms may be used for the purpose of distinguishing one component from another. For example, without departing from the scope of the rights of the present disclosure, the first component may be named the second component, and similarly, the second component may be named the first component.

[0044] The terms used in this disclosure are used merely to describe specific embodiments and are not intended to limit this disclosure. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as “comprising” or “comprising” are intended to specify the existence of the described features, numbers, steps, actions, components, parts, or combinations thereof, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0045] Unless specifically defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which this disclosure pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this disclosure.

[0046] Hereinafter, Embodiment 1 or Embodiment 2 of the present disclosure may be implemented in whole or in part, and all or part of Embodiment 1 and all or part of Embodiment 2 may be combined and applied / implemented.

[0047] (Example 1) - Hybrid location tracking system to solve the problem of degraded location estimation performance

[0048] The present disclosure relates to a hybrid position tracking system for solving the problem of degraded position estimation performance that occurs in environments where GNSS (Global Navigation Satellite System) signals are weakened or blocked due to urban infrastructure, such as urban canyons densely populated with high-rise buildings, indoor-outdoor transition areas, or environments with many complex structures. More specifically, the disclosure relates to a technology capable of effectively preventing the degradation of position estimation accuracy and reliability caused by GNSS-based pseudorange measurement and 5G-based DL-TDOA (Downlink Time Difference of Arrival) measurement becoming inaccurate or fluctuating due to the influence of signal interference, multipath propagation, non-line-of-sight (NLOS) conditions, and time synchronization errors between base stations and satellites.

[0049] The present disclosure can improve position accuracy and enhance position estimation continuity by fusing GNSS and 5G DL-TDOA data using an Extended Kalman Filter (EKF). The Extended Kalman Filter can adaptively respond to various dynamic changes occurring in the real environment by linearly approximating the nonlinearity of the system state and measurement model through a first-order Taylor series expansion, and can ensure the stability of real-time position estimation and prediction reliability by assuming the state error as a Gaussian probability distribution.

[0050] The hybrid positioning system according to the present disclosure performs highly reliable position estimation based on absolute position information when GNSS measurement is possible, and performs complementary position estimation by utilizing 5G DL-TDOA measurement even when GNSS measurement reliability is degraded or reception is impossible, thereby preventing the problem of intermittent position estimation that may occur when relying on a single position measurement technology. In particular, since GNSS generally has a low measurement frequency (e.g., 1 Hz) and DL-TDOA using 5G-based PRS (Positioning Reference Signal) has a high measurement frequency (e.g., up to 250 Hz), the present invention enables effective integration between two heterogeneous technologies by appropriately correcting these differences in measurement frequency and temporal discrepancies through dynamic state modeling based on an extended Kalman filter.

[0051] As such, the present disclosure provides a structure capable of responding to various real-world problems that may occur in a hybrid positioning system that fuses GNSS and 5G DL-TDOA technologies, thereby providing robust and continuous position estimation performance even in complex urban environments, mixed indoor and outdoor areas, or situations where either the GNSS or 5G signal is incomplete or temporarily unavailable. Furthermore, the structure of the present invention can ensure system scalability by fusing auxiliary sensors, such as additional inertial sensors (IMUs), and can be usefully applied to various application fields such as autonomous driving, drone operation, industrial automation, and smart cities.

[0052] Below, the hybrid positioning method proposed in the present disclosure will be described in detail.

[0053] FIG. 1 illustrates a block diagram of an extended Kalman filter-based hybrid positioning system according to one embodiment of the present disclosure.

[0054] The present system may be configured in a terminal, and for convenience of explanation, the present system is described below on the premise that it is configured in a terminal, but is not limited thereto. Referring to FIG. 1, the terminal is configured to receive location data individually from a GPS receiver (110) that provides GNSS (Global Navigation Satellite System)-based location measurement information and a 5G positioning device (130) that provides DL-TDOA (Downlink Time Difference of Arrival)-based measurement information received from a 5G base station (gNB).

[0055] Additionally, the sensor (120) included in the terminal receives speed and motion-related data provided by an Inertial Measurement Unit (IMU), a pedometer, or other motion detection sensors capable of detecting user movement in real time. Such user-side sensor data is used as auxiliary information for position estimation.

[0056] GNSS position information is input into the first prediction block (140), and the first prediction block (140) predicts the user's future position based on two main inputs, namely (i) speed and motion information received from a user sensor and (ii) a hybrid position estimate from the previous time point. Here, the hybrid position estimate from the previous time point refers to the optimal user position value calculated by fusing GNSS information and 5G position information.

[0057] Similarly, location information received from the 5G positioning device is input into the second prediction block (150), and the second prediction block (150) also refers to the same speed and motion information used in the first prediction block (140) and performs a user location prediction process based on previous hybrid location estimates. The first prediction block (140) and the second prediction block (150) operate in parallel and each independently continuously performs user location prediction based on real-time motion data and previous location information.

[0058] Such a dual prediction mechanism contributes to improving positioning continuity by enabling reliable position prediction even when one or more of the GNSS or 5G positioning signals are intermittently attenuated or temporarily unavailable. In particular, since signal blocking or multipath phenomena occur frequently in complex environments such as urban canyons or indoor-outdoor transition sections, the prediction structure according to the present disclosure can significantly improve the accuracy and robustness of position estimation.

[0059] Next, the predicted GNSS position calculated in the first prediction block (140) is compared with the newly received GNSS position information, and a GNSS prediction residual is calculated through the difference between the two positions. Similarly, the predicted 5G position calculated in the second prediction block (150) is compared with the newly received 5G position information to calculate a 5G prediction residual. Each of these residuals represents the deviation between the predicted position and the actual received position and reflects both random noise and systematic error attributable to the positioning source.

[0060] The calculated GNSS and 5G prediction residuals are input into an Extended Kalman Filter along with time series information of past residuals and error estimates. The Extended Kalman Filter fuses these inputs to estimate the measurement error state associated with each positioning source and dynamically adjusts filter parameters in proportion to the reliability and accuracy of the GNSS and 5G systems that change over time.

[0061] Through such dynamically adjusted filtering, the extended Kalman filter comprehensively considers GNSS data, 5G DL-TDOA data, and user sensor-based prediction data to calculate an optimal estimate of the user's current location. This optimal estimate is output as hybrid fused position information, which can effectively compensate for position errors and continuity degradation issues that may occur when relying on a single positioning source.

[0062] Finally, the fused hybrid position estimate is fed back to the first prediction block and the second prediction block to replace the existing hybrid position information. This feedback mechanism ensures that all future predictions reflect the latest position estimate, thereby enabling the system to adapt in real time to changing location environments and sensor conditions.

[0063] As a result, the hybrid positioning system according to the present disclosure enables high-precision, high-reliability position estimation that is difficult to achieve with GNSS or 5G standalone systems, and can provide improved positional accuracy and continuity even under conditions where the positioning environment is poor, such as urban canyons, tunnels, and indoor-outdoor transition sections.

[0064] FIG. 2 illustrates a block diagram of a hybrid positioning system that estimates the position of a receiver by fusing position data received from a GNSS receiver and a 5G positioning receiver according to one embodiment of the present disclosure.

[0065] The hybrid positioning system according to the present embodiment can be configured and implemented in a terminal, and is configured to generate a high-precision position estimate of a user or terminal by combining GNSS-based position information and 5G-based position information, and to this end, an Extended Kalman Filter (EKF) is implemented and operated within the positioning processor.

[0066] The above GNSS receiver (210) is configured to receive position measurement signals from one or more GNSS (Global Navigation Satellite System) satellites and to calculate GNSS-based position estimates based thereon. The GNSS receiver (210) has strengths in absolute position determination using satellite signals and exhibits high accuracy in an open outdoor environment.

[0067] A 5G receiver (220) receives a Downlink Positioning Reference Signal (DL-PRS) from one or more 5G base stations, calculates the Time of Arrival (TOA) based on the arrival time information of the signal, and calculates the Downlink Time Difference of Arrival (DL-TDOA) through the difference in TOA between multiple base stations. Subsequently, the calculated TDOA information is transmitted to a 5G core network, and the core network calculates a 5G location estimate based on this and provides it to the receiver (220).

[0068] The input processing unit (230) is configured to receive position estimates from a GNSS receiver and a 5G positioning receiver, and to receive additional sensor data from one or more motion sensors. The motion sensors may include sensors capable of providing various environment and motion-related data, such as, for example, an accelerometer, a gyroscope, and a real air velocity sensor. The input processing unit (230) combines the received position and sensor data into a standardized data format that can be processed by a positioning processor and converts them into input values ​​for executing EKF.

[0069] An extended Kalman filter implemented within the positioning processor (250) fuses the GNSS-based position estimate, the 5G-based position estimate, and motion sensor data provided from the input processing device (240) to generate an optimal hybrid position estimate. This fused position estimate may be output to a navigation display device or provided to various location-based services (LBS), such as navigation, tracking, and mapping applications, through a separate external positioning application interface.

[0070] In one embodiment, the EKF generates correction data or state estimation update information to improve position estimation accuracy, which is fed back to an input processing unit and used to improve the quality of future data collection and preprocessing steps.

[0071] The EKF operates according to a standard prediction and update cycle. In the prediction phase, it estimates the future state based on the current state (position, velocity, optionally sensor bias, etc.), and in the update phase, it updates the state estimate using the difference from actual measurements received from GNSS and 5G receivers. The covariance matrix of the EKF is updated to reflect the noise characteristics of the received measurements and the uncertainty of the state variables, thereby enabling optimal state estimation and position fusion.

[0072] Additionally, the input processing device (240) is configured to monitor the availability and quality of GNSS and 5G positioning data in real time. When both positioning sources are available, the EKF fuses the two data sources simultaneously to derive a more precise position estimate. However, if the GNSS signal is temporarily blocked in an environment such as an urban canyon, for example, the EKF continues the estimation process without interruption using available 5G data and sensor data. Subsequently, when the GNSS signal is restored, the EKF immediately integrates the data to restore fusion performance.

[0073] During operation, the EKF repeatedly performs time updates and measurement updates to continuously update the system state estimates and covariance matrix. In particular, during the measurement update phase, real-time GNSS and 5G location data are integrated, and the relative reliability of each data source is dynamically reflected in the measurement noise covariance matrix. By analyzing the residual between predicted measurements and actual received measurements, the system reflects positioning error factors that commonly occur in urban environments, such as multipath, signal blocking, or building reflections, and is configured so that the EKF's state and error estimates can be continuously adjusted accordingly.

[0074] Furthermore, the Kalman gain is dynamically calculated at each measurement update step and is determined based on an error covariance matrix that reflects the uncertainty of the predicted system state and the reliability of the received measurements. Through this dynamically adjusted Kalman gain, the system can automatically reduce its reliance on data when the quality of a specific measurement source degrades, and assign weights to other more reliable sources, thereby simultaneously maintaining position estimation accuracy and system robustness.

[0075] The basic formula for EKF is as shown in Table 1 below.

[0076]

[0077] Referring to Table 1 above, according to one embodiment of the present invention, the state of a receiver is represented by a state vector x that includes position, velocity, clock bias, and other related state variables. The state vector changes over time, and this temporal evolution is controlled by a state transition matrix F that mathematically models the system dynamics. Uncertainty regarding the state of the receiver is tracked through a covariance matrix P, which numerically represents the reliability of the state estimate at each time step. Additionally, dynamic disturbances arising from uncertainty in receiver operation or external environmental factors are considered as process noise, and such noise is modeled by a process noise covariance matrix Q. GNSS and 5G-based position measurement may include measurement noise due to environmental influences such as multipath interference and signal blocking, and such measurement noise is mathematically represented through a measurement noise covariance matrix R. The relationship between the system state and the measurement value is defined through a measurement sensitivity matrix H, which serves to transform the state vector into the measurement space.

[0078] In the measurement update step, a Kalman gain K is calculated by considering the balance between the uncertainty of the predicted state estimate and the reliability of the GNSS and 5G measurements. The Kalman gain is a coefficient that adjusts the degree to which new measurements are reflected in the system state and is a key factor for optimal state estimation.

[0079] The extended Kalman filter operates iteratively over time, and at each time step, the following two steps are performed sequentially. First, in the time update step (or prediction step), the state is predicted for the future based on the system model. Second, in the measurement update step (or correction step), the predicted state is corrected based on real-time measurement data received from GNSS and 5G.

[0080] The system according to the present disclosure can continuously improve position estimation performance even in environments where GNSS or 5G signal quality may degrade by dynamically updating the covariance matrix P, Kalman gain K, and state vector x at each time step. Each equation of the extended Kalman filter is applied sequentially according to the time index k, and no additional iteration is performed within each time step. This design enables the realization of a hybrid positioning system that maintains high accuracy and robustness in complex environments, such as urban canyons as well as open-sky environments, by ensuring computational efficiency while allowing real-time fusion of GNSS and 5G positioning data.

[0081] FIG. 3 is a flowchart illustrating a series of processes performed by an extended Kalman filter to estimate the location of a receiver using hybrid GNSS and 5G-based location data according to one embodiment of the present disclosure. The operation performed by the Kalman filter may be performed by a processor / controller included in a terminal (receiver). For convenience of explanation, the following description assumes that the operation is performed by a terminal (processor / controller of the terminal), but is not limited thereto.

[0082] 310: The terminal initializes a state vector to define the initial state of the receiver. The state vector includes position, velocity, clock bias, etc., and together with this, a covariance matrix representing the initial uncertainty for the state estimate is also initialized.

[0083] 320: Subsequently, the terminal receives GNSS positioning data received from one or more satellites. The GNSS positioning data includes information such as pseudorange, Doppler shift measurements, the position and velocity of the satellites. At the same time, the terminal also receives 5G positioning data including Time Difference of Arrival (TDOA) measurements derived from downlink Positioning Reference Signals (PRS) transmitted from nearby 5G base stations.

[0084] 330: Afterwards, the terminal performs the time update (or prediction step) of the extended Kalman filter. In the time update step, the state vector and covariance matrix are predicted to a future time point based on the system's dynamic model, and changes due to system operation and process noise are reflected in the prediction process.

[0085] 340: Afterward, after the terminal performs the prediction step, the terminal determines whether the receiver is moving by analyzing the rate of change of the latest GNSS and 5G positioning data.

[0086] 351: When it is determined that the terminal is moving, the terminal performs a measurement update (or correction step) using an extended Kalman filter. In this step, the uncertainty of the entire system is reduced by updating the state estimate using the most recent GNSS and 5G measurements and updating the covariance matrix accordingly.

[0087] 353: Meanwhile, when the terminal's position is determined to be fixed, the terminal adjusts the process noise covariance matrix Q to reflect a situation with minimal dynamic change. Through this adjustment, the extended Kalman filter can converge more quickly to a state estimate in a static environment with low uncertainty. This series of prediction and correction processes is performed iteratively as new GNSS and 5G positioning data are periodically received, thereby enabling real-time tracking of the receiver's position and providing a stable fused position estimate.

[0088] 360: Finally, the terminal can output the estimated location.

[0089] effect

[0090] According to the present disclosure, by adopting a hybrid positioning technology that fuses GNSS (Global Navigation Satellite System) positioning data and 5G-based DL-TDOA (Downlink Time Difference of Arrival) data using an Extended Kalman Filter, the accuracy and reliability of a satellite-based positioning system can be significantly improved.

[0091] In particular, by dynamically fusing GNSS measurements and 5G DL-TDOA measurements in a temporally synchronized manner, continuous and precise position estimation is possible even in environments where urban canyons, indoors, tunnels, signal blockage, and multipath phenomena frequently occur. This effectively overcomes the problems of poor signal reception and reduced accuracy inherent in existing single GNSS-based positioning systems.

[0092] In addition, the present disclosure reflects different measurement noise characteristics and reliability changes of GNSS and 5G measurements in real time through an extended Kalman filter, and accordingly, optimally estimates the position of a receiver by adaptively updating the state estimate and covariance matrix. Furthermore, by integrating GNSS and 5G data, multipath interference can be effectively mitigated, and the uncertainty of position estimation can be significantly reduced, thereby providing stable and reliable positioning performance even under demanding conditions such as high-rise building areas, dense urban areas, or complex indoor environments.

[0093] Accordingly, the present disclosure can be effectively utilized in various high-precision location-based services, such as autonomous driving, smart cities, indoor navigation, and disaster rescue, by ensuring excellent performance in terms of location accuracy, environmental adaptability, real-time capability, and robustness.

[0094] (Example 2) - Hybrid positioning system integrating multiple positioning technologies to improve the accuracy and reliability of position determination

[0095] The present disclosure relates to a hybrid positioning system and method that integrates multiple positioning technologies to improve the accuracy and reliability of position determination in the field of positioning and location-based services. More specifically, the present disclosure uses a method that utilizes all of the following for positioning: a terrestrial communication network such as 5G, a Global Navigation Satellite System (GNSS), and a satellite-based navigation system including a Quasi-Zenith Satellite System (QZSS). A positioning method according to another embodiment of the present disclosure is applicable to various industries requiring precise location identification and navigation, such as autonomous vehicles, mobile devices, smart cities, and Internet of Things (IoT) applications.

[0096] In particular, the present disclosure may be intended to prevent the deterioration of positional accuracy and reliability of a hybrid positioning system due to variations and inaccuracies in pseudo-range measurements caused by signal interference, multipath effects, and synchronization errors in environments where GNSS signals are weak or disrupted. The present disclosure aims to improve positional accuracy and system robustness in urban canyons, indoor environments, and other challenging environments by effectively integrating 5G, GNSS, and QZSS technologies.

[0097] Hereinafter, a positioning method according to another embodiment of the present disclosure will be described in more detail.

[0098] The positioning method proposed in the present disclosure may integrate 5G downlink time difference of arrival (DL-TDOA) positioning with GNSS. This hybrid approach may be particularly effective in scenarios where the number of GNSS satellite signals is less than the number of features (e.g., 4), because when the number of GNSS satellite signals is less than a certain number of features, GNSS pseudo-range measurements alone are unsuitable for accurate position determination.

[0099] A positioning integrating 5G downlink time difference of arrival (DL-TDOA) positioning and GNSS according to another embodiment of the present disclosure can dynamically select GNSS and 5G DL-TDOA measurements based on signal availability and quality.

[0100] FIG. 4 is a block diagram of a hybrid GNSS and 5G positioning system according to another embodiment of the present disclosure.

[0101] Referring to FIG. 4, both GPS and 5G receivers (410, 420) provide location data to a central controller (430). The controller (430) may include a current location data output device (431) and a measurement mode detector (433). The measurement mode detector (433) can monitor the availability of GNSS signals and determine the operating mode. If fewer than a certain number (e.g., 4) of reliable GNSS satellite signals are detected, the measurement mode detector (433) can switch the location measurement method to use 5G DL-TDOA measurement for location measurement.

[0102] Subsequently, the controller (430) determines the current location using map data (440) along with the selected location measurement and displays it through the display (150). Positioning integrating 5G downlink time difference of arrival (DL-TDOA) positioning and GNSS according to one embodiment of the present disclosure can ensure accurate location measurement by dynamically using GNSS or 5G-based methods depending on signal conditions.

[0103] FIG. 5 is a flowchart of a GNSS and 5G positioning method according to another embodiment of the present disclosure.

[0104] More specifically, FIG. 5 illustrates an example in which 5G downlink time difference of arrival (DL-TDOA) positioning and GNSS integrated positioning are performed. The 5G downlink time difference of arrival (DL-TDOA) positioning and GNSS integrated positioning illustrated in FIG. 5 may be an operation performed by a terminal. For convenience of explanation, the description is based on the premise that the operation is performed by a terminal, but is not limited thereto.

[0105] First, the terminal determines whether three-dimensional positioning is performed (501). If it is determined that three-dimensional positioning is performed, the terminal determines whether the number of satellite signals related to GNSS is four or more. If it is determined that the number of satellite signals is four or more, the terminal can calculate positioning data using the GNSS satellite signals (507) and output positioning data expressed in three dimensions (509).

[0106] Conversely, if it is determined that the number of satellite signals is less than 4, the terminal determines whether the number of 5G signals is 4 or more (505). If it is determined that the number of 5G signals is 4 or more, the terminal calculates positioning data (DL-TDOA) using the 5G signals (507) and can output positioning data expressed in three dimensions (509). Alternatively, if it is determined that the number of 5G signals is less than 4, the terminal may not output any data (521).

[0107] In operation 501, if it is determined that 3D positioning is not performed, the terminal determines whether 2D positioning is performed (511). At this time, if it is determined that 2D positioning is performed, the terminal determines whether the number of satellite signals related to GNSS is 3 or more (513). If it is determined that the number of satellite signals is 3 or more, the terminal calculates positioning data using the GNSS satellite signals (517) and can output positioning data expressed in 3D (519).

[0108] Conversely, if it is determined that the number of satellite signals is less than 3, the terminal determines whether the number of 5G signals is 3 or more (515). If it is determined that the number of 5G signals is 3 or more, the terminal calculates positioning data (DL-TDOA) using the 5G signals (517) and can output positioning data expressed in two dimensions (519). Alternatively, if it is determined that the number of 5G signals is less than 3, the terminal may not output any data (521).

[0109] In addition, if it is determined that 2D positioning is performed during the 211 process, the terminal may not output any data (521).

[0110] The DL-TDOA positioning method uses multiple 5G base stations to calculate the location of a receiver based on the TDOA of different base station signals.

[0111] FIG. 6 is a flowchart of a 5G DL-TDOA positioning procedure at the receiver side according to another embodiment of the present disclosure.

[0112] First, the 5G receiver can send a positioning request to the 5G network (601). Based on the positioning request, the DL-TDOA measurement process can be started.

[0113] Afterwards, the 5G receiver receives positioning reference signal (PRS) configurations from several surrounding 5G base stations (603), and through this, parameters necessary to measure the signal timing of these base stations can be set.

[0114] Subsequently, the 5G receiver can measure the time of arrival (TOA) of PRS signals from multiple base stations (605). Subsequently, the 5G receiver can use these TOA measurements to calculate the TDOA between a pair of base stations (607). Next, the 5G receiver can report the TDOA measurements back to the core network (609).

[0115] Afterward, the network uses this value together with the known location of the base station to calculate the location of the receiver, and the 5G receiver can receive the calculated location from the network (611).

[0116] In addition, the positioning method proposed in the present disclosure may be a method of integrating GNSS with QZSS signals. A method of integrating GNSS with QZSS signals may be particularly effective in scenarios where there are fewer than four available GNSS satellite signals and accurate position determination is insufficient using only GNSS pseudorange measurements.

[0117] A positioning method integrating GNSS and QZSS may operate by dynamically combining signals from GNSS and QZSS satellites to calculate the receiver's position more accurately. This method may involve continuously monitoring the availability and quality of GNSS satellite signals. If fewer than four reliable GNSS satellite signals are detected, the integrated GNSS and QZSS positioning method may begin collecting positioning signals from QZSS satellites. QZSS satellite constellations designed to improve positioning accuracy in Japan and the Asia-Oceania region provide additional satellite signals to GNSS receivers. The integrated GNSS and QZSS positioning method can improve accuracy by integrating pseudorange measurements from both available GNSS and QZSS satellites. This integrated dataset can be used to calculate the receiver's position more accurately. By integrating QZSS signals, the system compensates for the reduced number of GNSS signals, enabling it to maintain high positional accuracy even in challenging environments such as urban canyons or densely forested areas.

[0118] FIG. 7 is a flowchart of a hybrid GNSS and QZSS positioning method according to another embodiment of the present disclosure. More specifically, FIG. 7 illustrates an example in which a positioning method integrating GNSS and QZSS is performed. The operation illustrated in FIG. 7 may be an operation performed by a terminal. For convenience of explanation, the description is based on the premise that the operation is performed by a terminal, but is not limited thereto.

[0119] First, the terminal determines whether three-dimensional positioning is performed (701). If it is determined that three-dimensional positioning is performed, the terminal determines whether the number of satellite signals related to GNSS is four or more (703). If it is determined that the number of satellite signals is four or more, the terminal can calculate positioning data using the GNSS satellite signals (707) and output positioning data expressed in three dimensions (709).

[0120] Conversely, if it is determined that the number of satellite signals related to GNSS is less than 4, the terminal determines whether the sum of the satellite signals related to GNSS and the satellite signals related to QZSS is 4 or more (705). If it is determined that the sum of the satellite signals related to GNSS and the satellite signals related to QZSS is 4 or more, the terminal calculates positioning data using all the satellite signals related to GNSS and the satellite signals related to QZSS (707) and can output positioning data expressed in three dimensions (709). Alternatively, if it is determined that the sum of the satellite signals related to GNSS and the satellite signals related to QZSS is less than 4, the terminal may not output any data (721).

[0121] In operation 701, if it is determined that 3D positioning is not performed, the terminal determines whether 2D positioning is performed (711). At this time, if it is determined that 2D positioning is performed, the terminal determines whether the number of satellite signals related to GNSS is 3 or more (713). If it is determined that the number of satellite signals is 3 or more, the terminal can calculate positioning data using the GNSS satellite signals (417) and output positioning data expressed in 3D (719).

[0122] Conversely, if it is determined that the number of satellite signals related to GNSS is less than 3, the terminal determines whether the sum of the satellite signals related to GNSS and the satellite signals related to QZSS is 3 or more (715). If it is determined that the sum of the satellite signals related to GNSS and the satellite signals related to QZSS is 3 or more, the terminal calculates positioning data using the satellite signals related to GNSS and the satellite signals related to QZSS (717) and outputs positioning data expressed in two dimensions (719). Alternatively, if it is determined that the sum of the satellite signals related to GNSS and the satellite signals related to QZSS is less than 3, the terminal may not output any data (721).

[0123] In addition, if it is determined that 2D positioning is performed during the 711 process, the terminal may not output any data (721).

[0124] In addition, the positioning method proposed in the present disclosure may use a method of filtering out unreliable GNSS signals by utilizing a signal power threshold while simultaneously integrating GNSS with QZSS signals. The method of filtering out unreliable GNSS signals by utilizing a signal power threshold while simultaneously integrating GNSS with QZSS signals can resolve scenarios where accurate position determination is insufficient with only GNSS pseudo-range measurements because the received GNSS satellite signal is too weak.

[0125] The method of filtering out unreliable GNSS signals using signal power thresholds while integrating GNSS with QZSS signals operates by continuously monitoring the received signal power of each GNSS satellite. A signal threshold (e.g., -200 dBW) can be set to determine the reliability of each GNSS signal. If the received signal power of a GNSS satellite falls below this threshold, the satellite signal is excluded from position calculations to prevent errors caused by weak signals. If the number of reliable GNSS satellites—defined as those with a signal output of -200 dBW or higher—is insufficient for accurate position determination (i.e., fewer than four satellites), QZSS signals can be dynamically integrated. QZSS constellations designed to improve positional accuracy in the Asia-Oceania region provide additional satellite signals to the GNSS receiver. These QZSS signals are combined with filtered GNSS data to enable the use of sufficient and reliable satellite information for accurate position estimation.

[0126] FIG. 8 is a flowchart of a proposed hybrid GNSS and QZSS positioning method using signal threshold filtering according to another embodiment of the present disclosure. More specifically, FIG. 8 illustrates an example in which a method is performed to filter out unreliable GNSS signals by utilizing a signal power threshold while simultaneously integrating GNSS with QZSS signals. The operation illustrated in FIG. 8 may be an operation performed by a terminal. For convenience of explanation, the description is based on the premise that the operation is performed by a terminal, but is not limited thereto.

[0127] First, the terminal determines whether three-dimensional positioning is being performed (801). If it is determined that three-dimensional positioning is being performed, the terminal can determine whether the reception strength of satellite signals related to GNSS is -200dBW or higher (803).

[0128] If none of the satellite signals related to GNSS have a reception strength of less than -200dBW, it is determined whether the sum of the number of received satellite signals related to GNSS and the number of satellite signals related to QZSS is 4 or more (805). If the sum of the number of received satellite signals related to GNSS and the number of satellite signals related to QZSS is 4 or more, the terminal can calculate positioning data using the satellite signals related to GNSS and the satellite signals related to QZSS (807) and output positioning data expressed in three dimensions (809). Conversely, if the sum of the number of received satellite signals related to GNSS and the number of satellite signals related to QZSS is less than 4, the terminal may not output any data (821).

[0129] Conversely, if there are satellite signals related to GNSS with a reception strength of less than -200dBW, the terminal can delete data related to satellite signals with a reception strength of less than -200dBW (804). Afterwards, the terminal determines whether the sum of the number of satellite signals related to QZSS and the number of satellite signals related to QZSS, excluding signals with a reception strength of less than -200dBW from the received satellite signals related to GNSS, is 4 or more (805). If the sum of the number of satellite signals related to QZSS, excluding signals with a reception strength of less than -200dBW from the received satellite signals related to GNSS, is 4 or more, the terminal can calculate positioning data using the satellite signals related to GNSS and the satellite signals related to QZSS (807) and output positioning data expressed in three dimensions (509). Conversely, if the number of satellite signals excluding signals with a reception strength of less than -200dBW from the received GNSS-related satellite signals and the number of satellite signals related to QZSS is less than 4, the terminal may not output any data (821).

[0130] In operation 501, if it is determined that 3D positioning is not being performed, the terminal determines whether 2D positioning is being performed (811). At this time, if it is determined that 2D positioning is being performed, the terminal can determine whether the reception strength of satellite signals related to GNSS is -200dBW or higher (813).

[0131] If none of the satellite signals related to GNSS have a reception strength of less than -200dBW, it is determined whether the sum of the number of received satellite signals related to GNSS and the number of satellite signals related to QZSS is 3 or more (815). If the sum of the number of received satellite signals related to GNSS and the number of satellite signals related to QZSS is 4 or more, the terminal can calculate positioning data using the satellite signals related to GNSS and the satellite signals related to QZSS (817) and output positioning data expressed in two dimensions (819). Conversely, if the sum of the number of received satellite signals related to GNSS and the number of satellite signals related to QZSS is less than 4, the terminal may not output any data (821).

[0132] Conversely, if there are satellite signals related to GNSS with a reception strength of less than -200dBW, the terminal can delete data related to satellite signals with a reception strength of less than -200dBW (814). Afterwards, the terminal determines whether the sum of the number of satellite signals related to QZSS and the number of satellite signals related to QZSS, excluding signals with a reception strength of less than -200dBW from the received satellite signals related to GNSS, is 3 or more (815). If the sum of the number of satellite signals related to QZSS, excluding signals with a reception strength of less than -200dBW from the received satellite signals related to GNSS, is 3 or more, the terminal can calculate positioning data using the satellite signals related to GNSS and the satellite signals related to QZSS (817) and output positioning data expressed in three dimensions (819). Conversely, if the number of satellite signals excluding signals with a reception strength of less than -200dBW from the received GNSS-related satellite signals and the number of satellite signals related to QZSS are less than 3, the terminal may not output any data (821).

[0133] effect

[0134] According to the present disclosure, a hybrid positioning technology can be used that integrates 5G DL-TDOA with GNSS and QZSS signals to significantly improve the accuracy and reliability of a satellite-based positioning system. The present disclosure can ensure continuous and accurate positioning even in urban areas or environments with obstacles by utilizing 5G DL-TDOA positioning when the number of received GNSS satellite signals is less than four. Additionally, positioning accuracy can be improved by integrating QZSS signals with GNSS even in areas where positioning accuracy is insufficient with GNSS signals alone, such as dense urban areas or areas with a high concentration of high-rise buildings.

[0135] FIG. 9 is a drawing illustrating the structure of a terminal according to one embodiment of the present disclosure.

[0136] Referring to FIG. 9, the terminal may include a transceiver (referring to a terminal receiver (900) and a terminal transmitter (910)), a memory (not shown), and a terminal processing unit (905, or a terminal control unit or processor). Depending on the communication method of the terminal described above, the transceiver (900, 910), memory, and terminal processing unit (905) of the terminal may operate. However, the components of the terminal are not limited to the examples described above. For example, the terminal may include more components or fewer components than the components described above. Furthermore, the transceiver, memory, and processor may be implemented in the form of a single chip.

[0137] The transceiver can transmit and receive signals with a server. Here, the signal may include control information and data. To this end, the transceiver may be composed of an RF transmitter that up-converts and amplifies the frequency of a transmitted signal, and an RF receiver that low-noise amplifies a received signal and down-converts its frequency. However, this is merely one embodiment of the transceiver, and the components of the transceiver are not limited to an RF transmitter and an RF receiver.

[0138] In addition, the transceiver can receive a signal through a wireless channel and output it to a processor, and transmit the signal output from the processor through a wireless channel.

[0139] Memory can store programs and data necessary for the operation of the terminal. Additionally, memory can store control information or data included in signals transmitted and received by the terminal. Memory may be composed of storage media or combinations of storage media such as ROM, RAM, hard disk, CD-ROM, and DVD. Additionally, there may be multiple memories.

[0140] In addition, the processor can control a series of processes to enable the terminal to operate according to the aforementioned embodiment. For example, the processor can receive a DCI composed of two layers and control the components of the terminal to receive multiple PDSCHs simultaneously. There may be multiple processors, and the processors can perform the operation of controlling the components of the terminal by executing a program stored in memory.

[0141] FIG. 10 is a drawing illustrating the structure of a server in one embodiment of the present disclosure.

[0142] Referring to FIG. 10, the server may include a transceiver unit referring to a server receiver (1030) and a server transmitter (1010), a memory (not shown), and a server processing unit (1005, or a server control unit or processor). Depending on the communication method of the server described above, the transceiver unit (1000, 1010), memory, and server processing unit (1005) of the server may operate. However, the components of the server are not limited to the examples described above. For example, the server may include more components or fewer components than the components described above. Furthermore, the transceiver unit, memory, and processor may be implemented in the form of a single chip.

[0143] The transceiver can transmit and receive signals with a terminal. Here, the signal may include control information and data. To this end, the transceiver may be composed of an RF transmitter that up-converts and amplifies the frequency of a transmitted signal, and an RF receiver that low-noise amplifies a received signal and down-converts its frequency. However, this is merely one embodiment of the transceiver, and the components of the transceiver are not limited to an RF transmitter and an RF receiver.

[0144] In addition, the transceiver receives a signal through a wireless channel and outputs it to a processor, and can transmit the signal output from the processor through a wireless channel.

[0145] Memory can store programs and data necessary for the operation of the server. Additionally, memory can store control information or data included in signals transmitted and received by the server. Memory can be composed of storage media or combinations of storage media such as ROM, RAM, hard disk, CD-ROM, and DVD. Additionally, there may be multiple memory units.

[0146] The processor can control a series of processes to enable the server to operate in accordance with the embodiments of the present disclosure described above. For example, the processor can control each component of the server to configure two layers of DCIs containing allocation information for a plurality of PDSCHs and to transmit them. There may be multiple processors, and the processors can perform control operations on the components of the server by executing a program stored in memory.

[0147] All / part of embodiments 1 and 2 and combinations thereof described with reference to FIGS. 1 to 8 may be implemented by at least one of the devices of FIGS. 9 to 10.

[0148] FIG. 11 is a flowchart illustrating an example in which a positioning method according to one embodiment of the present disclosure is performed by a terminal.

[0149] First, the terminal can generate a first terminal location estimate based on the first positioning (1110).

[0150] Next, the terminal can generate a second terminal position estimate based on a second positioning different from the first positioning (1120).

[0151] Finally, the terminal can generate a hybrid position estimate through an extended Kalman filter based on one or more motion sensor data, the first terminal position estimate, and the second terminal position estimate (1130).

[0152] FIG. 12 is a flowchart illustrating an example in which a positioning method according to another embodiment of the present disclosure is performed by a terminal.

[0153] First, the terminal can receive at least one signal for first positioning (1210).

[0154] Next, the terminal can determine whether the reception strength of at least one signal for the first positioning is greater than or equal to a preset threshold (1220).

[0155] Finally, the terminal can perform the positioning based on at least one signal for the first positioning and / or at least one signal for the second positioning, based on the result of the above judgment (1230).

[0156] The methods according to the claims of the present disclosure or the embodiments described in the disclosure may be implemented in the form of hardware, software, or a combination of hardware and software.

[0157] When implemented in software, a computer-readable storage medium may be provided for storing one or more programs (software modules). One or more programs stored in the computer-readable storage medium are configured for execution by one or more processors within an electronic device. One or more programs include instructions that cause the electronic device to execute methods according to the embodiments described in the claims or description of the present disclosure.

[0158] Such programs (software modules, software) may be stored in random access memory, non-volatile memory including flash memory, read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic disc storage devices, compact disc-ROM (CD-ROM), digital versatile discs (DVDs), or other forms of optical storage devices, magnetic cassettes. Alternatively, they may be stored in memory composed of some or all of these. Additionally, each constituent memory may include multiple units.

[0159] Additionally, the program may be stored on an attachable storage device that can be accessed via a communication network such as the Internet, Intranet, LAN (local area network), WAN (wide area network), or SAN (storage area network), or a combination thereof. Such a storage device may be connected to a device performing an embodiment of the present disclosure through an external port. Additionally, a separate storage device on a communication network may be connected to a device performing an embodiment of the present disclosure.

[0160] In the specific embodiments of the present disclosure described above, the components included in the disclosure are expressed in a singular or plural form according to the specific embodiments presented. However, the singular or plural expression is selected to suit the situation presented for convenience of explanation, and the present disclosure is not limited to singular or plural components; even if a component is expressed in the plural form, it may be composed of a singular form, and even if a component is expressed in the singular form, it may be composed of a plural form.

[0161] Meanwhile, although specific embodiments have been described in the detailed description of the present disclosure, it is understood that various modifications are possible within the scope of the present disclosure. Therefore, the scope of the present disclosure should not be limited to the described embodiments, but should be defined by the claims set forth below as well as equivalents thereof.

Claims

1. A method for positioning performed by a terminal, wherein the method comprises: A step of generating a first terminal location estimate based on a first positioning; A step of generating a second terminal location estimate based on a second positioning different from the first positioning; and A method comprising the step of generating a hybrid position estimate through an extended Kalman filter based on one or more motion sensor data, the first terminal position estimate, and the second terminal position estimate.

2. In Paragraph 1, The first positioning above is GNSS (Global Navigation Satellite System), and the second positioning above is 5G (5th generation) positioning, and A method in which the first terminal position estimate is generated based on pseudorange and Doppler shift measurements.

3. In paragraph 1, the step of generating a hybrid position estimate through the extended Kalman filter is: A step of predicting the current state and covariance matrix of the above terminal; and A method comprising the step of updating the position state of the terminal through a Kalman gain calculated based on the reliability of the first terminal position estimate and the second terminal position estimate.

4. In Paragraph 3, A method in which the above-described extended Kalman filter is configured to correct a position estimation error by analyzing the residual between the predicted position and the received actual position measurement.

5. In Paragraph 3, A method in which the Kalman gain is calculated by adjusting the measurement noise covariance matrix in real time according to the reliability of the first terminal position estimate and the second terminal position estimate.

6. In a terminal performing positioning, the terminal, transceiver; and Includes a controller connected to the above transceiver, The above controller is, A step of generating a first terminal location estimate based on a first positioning; A step of generating a second terminal location estimate based on a second positioning different from the first positioning; and A terminal configured to perform the step of generating a hybrid position estimate through an extended Kalman filter based on one or more motion sensor data, the first terminal position estimate, and the second terminal position estimate.

7. In Paragraph 6, The first positioning above is GNSS (Global Navigation Satellite System), and the second positioning above is 5G (5th generation) positioning, and The above first terminal position estimate is a terminal generated based on pseudorange and Doppler shift measurements.

8. In paragraph 6, the step of generating a hybrid position estimate through the extended Kalman filter is: A step of predicting the current state and covariance matrix of the above terminal; and A terminal comprising the step of updating the position state of the terminal through a Kalman gain calculated based on the reliability of the first terminal position estimate and the second terminal position estimate.

9. In Paragraph 8, A terminal configured such that the above extended Kalman filter corrects position estimation errors by analyzing the residual between the predicted position and the received actual position measurement.

10. In Paragraph 8, The above Kalman gain is calculated by adjusting the measurement noise covariance matrix in real time according to the reliability of the above first terminal position estimate and the above second terminal position estimate.

11. In one or more non-transitory computer-readable media storing one or more instructions, said one or more instructions executable by one or more processors, said one or more instructions, A step of generating a first terminal location estimate based on a first positioning; A step of generating a second terminal location estimate based on a second positioning different from the first positioning; and A computer-readable medium comprising the step of generating a hybrid position estimate through an extended Kalman filter based on one or more motion sensor data, the first terminal position estimate, and the second terminal position estimate.

12. In Paragraph 11, The first positioning above is GNSS (Global Navigation Satellite System), and the second positioning above is 5G (5th generation) positioning, and A computer-readable medium in which the first terminal position estimate is generated based on pseudorange and Doppler shift measurements.

13. In Paragraph 11, The step of generating a hybrid position estimate through the above-mentioned extended Kalman filter is, A step of predicting the current state and covariance matrix of the above terminal; and A computer-readable medium comprising the step of updating the position state of the terminal through a Kalman gain calculated based on the reliability of the first terminal position estimate and the second terminal position estimate.

14. In Paragraph 13, A computer-readable medium configured such that the above-mentioned extended Kalman filter corrects position estimation errors by analyzing the residual between the predicted position and the received actual position measurement.

15. In Paragraph 13, A computer-readable medium in which the Kalman gain is calculated by adjusting the measurement noise covariance matrix in real time according to the reliability of the first terminal position estimate and the second terminal position estimate.

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