CN116338570A - Positioning method, positioning device, computer apparatus, storage medium, and program product - Google Patents
Positioning method, positioning device, computer apparatus, storage medium, and program product Download PDFInfo
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- 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
- G01S5/0258—Hybrid positioning by combining or switching between measurements derived from different systems
- G01S5/02585—Hybrid positioning by combining or switching between measurements derived from different systems at least one of the measurements being a non-radio measurement
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
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- 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
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- 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
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- 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
- G01S5/0247—Determining attitude
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The present application relates to a positioning method, apparatus, computer device, storage medium and program product for a target terminal, the method comprising: receiving uplink signal measurement data respectively sent by a plurality of base stations, and acquiring inertial measurement data of a target terminal, wherein the uplink signal measurement data comprises AOA data; inputting the uplink signal measurement data, the inertia measurement data and the positioning data of the target terminal at the moment into a positioning state space model, and solving the positioning state space model based on a preset algorithm to obtain the positioning data of the target terminal at the current moment; the positioning state space model comprises a positioning observation model and a positioning state model, the positioning observation model comprises an incidence angle observation model, and the incidence angle observation model is used for representing the relation between AOA data in each uplink signal measurement data and first antenna data of a target terminal and second antenna data of each base station. The method can improve the positioning accuracy.
Description
Technical Field
The present application relates to the field of wireless communications technologies, and in particular, to a positioning method, an apparatus, a computer device, a storage medium, and a program product.
Background
The wireless base station can measure an AOA (Angle of Arrival) of the terminal, and can estimate the position of the terminal based on the position information and attitude information of the plurality of wireless base stations, the measured AOA of the terminal, and measurement information of an IMU (Inertial Measurement Unit ) of the terminal.
In the conventional technology, an AOA of a terminal measured by a wireless base station is used as an azimuth angle, an AAOM (Azimuth Angle Observation Model ) is established, and then an IMU loose coupling positioning algorithm of the wireless base station and the terminal based on the AAOM is adopted to position the terminal.
However, AAOM is an effective approximation of a real world observation model under a far field positioning and tracking condition, in an actual wireless base station deployment environment, a terminal is usually very close to a wireless base station antenna, and belongs to a near field positioning condition, at this time, a certain height difference exists between an installation position of the wireless base station antenna and a terminal position, so that the AAOM cannot effectively approximate the real world observation model, and a loose coupling positioning algorithm is easy to implement but has lower precision, so that the problem of lower positioning precision exists in the conventional technology.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a positioning method, apparatus, computer device, computer-readable storage medium, and computer program product that can improve positioning accuracy.
In a first aspect, the present application provides a positioning method. The method is used in the target terminal, and comprises the following steps: receiving uplink signal measurement data respectively sent by a plurality of base stations, and acquiring inertial measurement data of a target terminal, wherein the uplink signal measurement data comprises arrival angle AOA data; inputting the uplink signal measurement data, the inertia measurement data and the positioning data of the target terminal at the moment into a positioning state space model, and solving the positioning state space model based on a preset algorithm to obtain the positioning data of the target terminal at the current moment; the positioning state space model comprises a positioning observation model and a positioning state model, the positioning observation model comprises an incidence angle observation model, the incidence angle observation model is used for representing the relation between AOA data in uplink signal measurement data and first antenna data of a target terminal and second antenna data of base stations, and the positioning state model is used for representing the relation between positioning data of the current moment of the target terminal and positioning data and inertial measurement data of the last moment of the target terminal.
In one embodiment, the uplink signal measurement data further includes time difference of arrival TDOA data; the positioning observation model further includes a TDOA observation model for characterizing a relationship between TDOA data in each uplink signal measurement data and the first antenna data and each second antenna data.
In one embodiment, the first antenna data includes antenna position data of the target terminal, and each of the second antenna data includes antenna position data and antenna attitude data of each of the base stations.
In one embodiment, the antenna position data of the target terminal includes a position coordinate of a phase center of an antenna of the target terminal in a local rectangular coordinate system; the antenna position data of each base station comprises the position coordinates of the phase center of the antenna of each base station in a local rectangular coordinate system; the antenna attitude data of each base station includes an attitude angle of an antenna of each base station in a local rectangular coordinate system.
In one embodiment, the method further comprises: constructing a target state equation between first-order differentiation of positioning data of the target terminal at the current moment and positioning data and inertial measurement data of the target terminal at the current moment; discretizing the target state equation to obtain a positioning state model.
In one embodiment, the positioning data includes terminal attitude data, terminal velocity data, and terminal position data, and the target state equations include a first state equation, a second state equation, and a third state equation; the first state equation is a state equation between first-order differentiation of terminal attitude data at the current moment of the target terminal, the terminal attitude data at the current moment of the target terminal and inertial measurement data; the second state equation is a state equation between the first-order differential of the terminal speed data of the target terminal at the current moment, the terminal speed data of the target terminal at the current moment and the inertial measurement data; the third state equation is a state equation between the first-order differential of the terminal position data at the current time of the target terminal and the terminal position data and the inertial measurement data at the current time of the target terminal.
In one embodiment, the inertial measurement data includes: white noise of a gyroscope carried on a target terminal on a carrier coordinate system, a rotation matrix between the carrier coordinate system and a navigation coordinate system, white noise of an accelerometer carried on the target terminal on the carrier coordinate system, a projection component of an angular velocity of the carrier coordinate system relative to the navigation coordinate system on the carrier coordinate system, random constant drift of the gyroscope on the carrier coordinate system, specific force output by the accelerometer on the carrier coordinate system, and random constant drift of the accelerometer on the carrier coordinate system.
In one embodiment, solving the positioning state space model based on a preset algorithm to obtain positioning data of the target terminal at the current moment includes: acquiring constraint conditions between the first antenna data and the second antenna data; and solving the positioning state space model based on a preset algorithm and constraint conditions to obtain positioning data of the target terminal at the current moment.
In one embodiment, the predetermined algorithm is a particle filter algorithm.
In a second aspect, the present application also provides a positioning device. The device is used in the target terminal, and comprises: the receiving module is used for receiving uplink signal measurement data respectively sent by the plurality of base stations and acquiring inertial measurement data of the target terminal, wherein the uplink signal measurement data comprises AOA data; the calculation module is used for inputting the uplink signal measurement data, the inertia measurement data and the positioning data of the target terminal at the moment into the positioning state space model, and solving the positioning state space model based on a preset algorithm to obtain the positioning data of the target terminal at the current moment; the positioning state space model comprises a positioning observation model and a positioning state model, the positioning observation model comprises an incidence angle observation model, the incidence angle observation model is used for representing the relation between AOA data in uplink signal measurement data and first antenna data of a target terminal and second antenna data of base stations, and the positioning state model is used for representing the relation between positioning data of the current moment of the target terminal and positioning data and inertial measurement data of the last moment of the target terminal.
In one embodiment, the uplink signal measurement data further includes TDOA data; the positioning observation model further includes a TDOA observation model for characterizing a relationship between TDOA data in each uplink signal measurement data and the first antenna data and each second antenna data.
In one embodiment, the first antenna data includes antenna position data of the target terminal, and each of the second antenna data includes antenna position data and antenna attitude data of each of the base stations.
In one embodiment, the antenna position data of the target terminal includes a position coordinate of a phase center of an antenna of the target terminal in a local rectangular coordinate system; the antenna position data of each base station comprises the position coordinates of the phase center of the antenna of each base station in a local rectangular coordinate system; the antenna attitude data of each base station includes an attitude angle of an antenna of each base station in a local rectangular coordinate system.
In one embodiment, the apparatus further comprises: constructing a target state equation between first-order differentiation of positioning data of the target terminal at the current moment and positioning data and inertial measurement data of the target terminal at the current moment; discretizing the target state equation to obtain a positioning state model.
In one embodiment, the positioning data includes terminal attitude data, terminal velocity data, and terminal position data, and the target state equations include a first state equation, a second state equation, and a third state equation; the first state equation is a state equation between first-order differentiation of terminal attitude data at the current moment of the target terminal, the terminal attitude data at the current moment of the target terminal and inertial measurement data; the second state equation is a state equation between the first-order differential of the terminal speed data of the target terminal at the current moment, the terminal speed data of the target terminal at the current moment and the inertial measurement data; the third state equation is a state equation between the first-order differential of the terminal position data at the current time of the target terminal and the terminal position data and the inertial measurement data at the current time of the target terminal.
In one embodiment, the inertial measurement data includes: white noise of a gyroscope carried on a target terminal on a carrier coordinate system, a rotation matrix between the carrier coordinate system and a navigation coordinate system, white noise of an accelerometer carried on the target terminal on the carrier coordinate system, a projection component of an angular velocity of the carrier coordinate system relative to the navigation coordinate system on the carrier coordinate system, random constant drift of the gyroscope on the carrier coordinate system, specific force output by the accelerometer on the carrier coordinate system, and random constant drift of the accelerometer on the carrier coordinate system.
In one embodiment, the computing module is specifically configured to obtain a constraint condition between the first antenna data and the second antenna data; and solving the positioning state space model based on a preset algorithm and constraint conditions to obtain positioning data of the target terminal at the current moment.
In one embodiment, the predetermined algorithm is a particle filter algorithm.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method according to any of the first aspects above when the processor executes the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of any of the first aspects above.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprising a computer program which, when executed by a processor, implements the steps of the method according to any of the first aspects above.
The positioning method, the device, the computer equipment, the storage medium and the program product are used in the target terminal, and the uplink signal measurement data comprising the AOA data are obtained by receiving the uplink signal measurement data respectively sent by a plurality of base stations, then the uplink signal measurement data, the inertia measurement data and the positioning data of the target terminal at the last moment are input into a positioning state space model, and the positioning state space model is solved based on a preset algorithm to obtain the positioning data of the target terminal at the current moment; the positioning state space model comprises a positioning observation model and a positioning state model, the positioning observation model comprises an incidence angle observation model, the incidence angle observation model is used for representing the relation between AOA data in uplink signal measurement data and first antenna data of a target terminal and second antenna data of base stations, and the positioning state model is used for representing the relation between positioning data of the current moment of the target terminal and positioning data and inertial measurement data of the last moment of the target terminal. According to the method and the device, the height between the target terminal and the base station can be considered through the incident angle observation model, and the positioning state space model realizes tight coupling of the uplink signal measurement data and the inertia measurement data, so that the positioning accuracy can be improved.
Drawings
Fig. 1 is a wireless base station antenna coordinate system in one embodiment;
FIG. 2 is a schematic diagram of an AOA data modeling as azimuth in one embodiment;
FIG. 3 is a schematic diagram of AOA data modeled as incident angles in one embodiment;
FIG. 4 is a flow chart of a positioning method according to an embodiment;
FIG. 5 is an application environment diagram of a positioning method in one embodiment;
FIG. 6 is a schematic flow chart of solving a positioning state space model based on a particle filter algorithm in one embodiment;
FIG. 7 is a block diagram of a positioning device according to one embodiment;
FIG. 8 is an internal block diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The wireless base station can measure the AOA data of the uplink signal of the terminal, thereby obtaining the AOA data of the terminal. The position of the terminal can be estimated from the position and posture information of the plurality of wireless base stations themselves and the measured terminal AOA data. In wireless network deployment, the number of wireless base stations simultaneously reachable by the terminal signal is limited, so that part of the area does not meet the convergence condition of the conventional positioning algorithm. In addition, there are many obstacles in the indoor environment, and the non-line-of-sight propagation phenomenon of signals between the wireless base station and the terminal is more common, which may cause an increase in positioning error. And the update period of the wireless base station positioning signal is usually more than hundred milliseconds, which is difficult to meet the positioning requirement of the medium-high speed mobile terminal. The precision, reliability and continuity of positioning can be improved by fusing the IMU of the terminal with the positioning result of the wireless base station, and the requirement of the middle-high speed mobile terminal on the positioning update rate is met. The combined positioning of the wireless base station and IMU based on AOA data can be classified into a loose coupling mode and a tight coupling mode, and few researches on this aspect are currently reported and the loose coupling mode is general. Compared with a tight coupling mode, the loose coupling mode is simpler to realize, but has lower precision and poorer stability. The observed quantity of the wireless base station used in the traditional technology of combining and positioning the wireless base station and the IMU based on the AOA data is the azimuth angle, the pitch angle and the distance of the terminal and adopts a loose coupling mode. However, most wireless base stations have a relatively small number of antennas for cost reasons and can only perform The one-dimensional AOA data is accurately measured, so that the combined positioning algorithm in the traditional technology cannot be widely applied. Furthermore, the one-dimensional AOA data is typically modeled as azimuth in a conventional positioning algorithm
And AAOM is adopted for terminal positioning, as shown in figure 1, a wireless base station antenna coordinate system is provided, and under the coordinate system, AOA data is modeled as a schematic diagram of azimuth angle, as shown in figure 2. The AAOM is an effective approximation of the real world observation model under the far field positioning and tracking condition, so that on one hand, the parameter quantity of the observation model can be reduced, meanwhile, calculation is simplified, and on the other hand, the positioning and tracking performance loss caused by the approximation of the observation model under the far field positioning condition is negligible. However, in an actual wireless base station deployment environment, a terminal is usually close to a wireless base station antenna, and belongs to a near field positioning situation, at this time, since a certain height difference exists between an installation position of the wireless base station antenna and a terminal position, AAOM cannot effectively approximate to a real world observation model, and the performance of an existing positioning algorithm based on AAOM is rapidly deteriorated, so that it is necessary to propose a positioning method capable of accurately positioning in both far field and near field. In the present application, AOA data measured by a base station is modeled as an incident angle η and an IAOM ((Incidence Angle Observation Model, incident angle observation model) is used for terminal positioning, and a schematic diagram of the AOA data modeled as an incident angle is shown in fig. 3, and the following embodiments are described.
In an embodiment, as shown in fig. 4, a flowchart of a positioning method is provided, and the embodiment is applied to a terminal, where the terminal may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices and portable wearable devices, and the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. In the embodiment of the application, the method is used in the target terminal and comprises the following steps:
Optionally, as shown in fig. 5, an application environment diagram of a positioning method is provided, where the base station is a wireless base station, after the target terminal sends uplink signals to each base station, each base station measures received uplink signals to obtain AOA data, and sends the AOA data and second antenna data of the base station to the target terminal through an antenna, where the target terminal includes an antenna, a CPU (central processing unit, a central processing unit) and an IMU, the antenna is configured to receive the multiple data sent by each base station, the IMU includes a gyroscope and an accelerometer, and is configured to measure inertial measurement data of the target terminal, and the CPU is configured to calculate positioning data of the target terminal at a current time according to the AOA data, the inertial measurement data and positioning data of the target terminal at a previous time.
Alternatively, the target state equation is
x(t)＝[ψ θ γ v E v N v U L λ h ε bx ε by ε bz ▽ bx ▽ by ▽ bz ] T (3)
Wherein,,
representing a first order derivative of x (t); psi, theta and gamma are azimuth, pitch and roll angles, respectively, of the carrier coordinate system relative to an east-north-day navigation coordinate system; v E 、v N And v U The east speed, the north speed and the sky speed of the target terminal in an east-north-sky navigation coordinate system are respectively; l, lambda and h are the latitude, longitude and altitude of the target terminal in the geocentric earth fixed coordinate system respectively; epsilon bx 、ε by 、ε bz 、▽ bx 、▽ by 、▽ bz 、
And->
Is inertial measurement data, wherein ε bx 、ε by And epsilon bz Random constant drift of gyroscopes on target terminals in the directions of x axis, y axis and z axis in a carrier coordinate system bx 、▽ by And bz random constant drift of accelerometers on target terminals in x-, y-and z-axis directions in carrier coordinate system, respectively, +.>
For the rotation matrix between the carrier coordinate system and the navigation coordinate system, 0 1×9 Pure zero line vector representing dimension 9, < >>
And->
White noise of the gyroscope in the x-axis, y-axis and z-axis directions in the carrier coordinate system, respectively,/->
And->
White noise of the accelerometer in the x-axis, y-axis and z-axis directions in the carrier coordinate system, respectively; w (t) represents the total measured white noise of the IMU of the target terminal at time t.
The specific meaning of f [ DEG ] is shown in the formulae (6) to (11)
Wherein,,
and f bx 、f by 、f bz For inertial measurement data, +.>
And->
The projected components of the angular velocity of the carrier coordinate system in the directions of the x-axis, y-axis and z-axis in the carrier coordinate system relative to the "east-north-sky" navigation coordinate system, respectively, f bx 、f by And f bz Specific forces output by the accelerometer in the directions of the x axis, the y axis and the z axis in the carrier coordinate system are respectively g is gravity acceleration and omega ie For the rotation angular velocity of the earth, R M For the principal radius of curvature of the earth's meridian, R N The main curvature radius of the earth's mortise unitary circle.
Discretizing the target state equation by using an Euler discretizing method to obtain a positioning state model, wherein the positioning state model is as follows:
x k ＝F k-1 x k-1 +w k-1 (12)
x k ＝[ψ k θ k γ k v E,k v N,k v U,k L k λ k h k ε bx,k ε by,k ε bz,k ▽ bx,k ▽ by,k ▽ bz,k ] T (13)
wherein x is k State information, x, indicating the current time (time k) of the target terminal k-1 Is the state information of the last time (k-1 time) of the target terminal, F k-1 Is a function f [. Cndot.]W is in discrete form of k-1 Is a discrete form of w (t), ψ k θ k γ k v E,k v N,k v U,k L k λ k h k ε bx,k ε by,k ε bz,k ▽ bx,k ▽ by,k ▽ bz,k The positioning data of the current moment (k moment) of the target terminal at the k moment.
Optionally, the positioning observation model is (Incidence Angle Observation Model, IAOM) is
α i ＝cosψ i cosγ i -sinψ i sinθ i sinγ i (20)
β i ＝sinψ i cosγ i +cosψ i sinθ i sinγ i (21)
υ k ＝[v 1,k v 2,k … v N,k σ k ] T (24)
Wherein equation (14) is also the angle of incidence observation model (Incidence Angle Observation Model, IAOM), ζ k An AOA observation vector representing measurements of a plurality of base stations at time k;
representing the incidence angle of the antenna phase center of the target terminal at the k moment in the antenna coordinate system of the ith base station, namely AOA data measured by the ith base station at the k moment; psi (χ) k ) An initial AOA observation vector representing the target terminal measurement at time k; h is a i (χ k ) The ith initial AOA data measured for the target terminal at the k moment; psi phi type i 、θ i 、γ i And->
Second antenna data of the i-th base station, wherein ψ is i 、θ i And gamma i The antenna attitude data of the ith base station, specifically, the antenna attitude data of the antenna of the ith base station in a local rectangular coordinate system are azimuth angle, pitch angle and roll angle respectively, < > >
And->
The antenna position data of the ith base station is specifically the antenna position data of the antenna phase center of the ith base station in a local rectangular coordinate system.
And->
The antenna position data is first antenna data of the target terminal, and is specifically antenna position data of the antenna phase center of the target terminal at the moment k in a local rectangular coordinate system; arccosi (·) is an inverse cosine function; upsilon (v) k The method comprises the steps that a total observation noise vector of a plurality of base stations at k moment is obtained; v i,k The AOA observation noise of the ith base station at the k moment; the subscript N represents the number of base stations;
The antenna phase center of the target terminal at the moment k is the height coordinate of the antenna phase center in a local rectangular coordinate system;
Known error-containing quantity sigma measured for multiple base stations k Height coordinate, sigma, of the antenna phase center of the target terminal at time k in a local rectangular coordinate system k Representing the corresponding observed noise; f is usually 1/298.257223563; l (L) k 、λ k And h k Latitude, longitude and altitude of the antenna phase center of the target terminal at time k in the geocentric geodetic system, respectively,/->
The coordinate conversion function from the geocentric fixed coordinate system to the local rectangular coordinate system can be directly obtained from the origin coordinates of the local rectangular coordinate system, namely
Wherein,, 
And->
L is the coordinate of the origin of the local rectangular coordinate system in the geocentric and geodetic fixed coordinate system 0 And lambda (lambda) 0 Latitude and longitude, x, respectively, of origin of the local rectangular coordinate system in the geocentric and geodetic fixed coordinate system e 、y e And z e Is an argument, which is the coordinates of the target terminal in the x-, y-and z-directions in the geocentric fixed coordinate system.
Thus, the positioning state space model is
AOA data and angular velocity measured by each base station
Specific force f bx 、f by 、f bz And the positioning data of the target terminal at the moment is input into a formula (28), and then the positioning state space model is solved based on a Bayesian filtering algorithm, so that the positioning data of the target terminal at the current moment can be obtained.
In summary, the target terminal receives uplink signal measurement data sent by a plurality of base stations respectively, and obtains inertial measurement data of the target terminal, wherein the uplink signal measurement data comprises AOA data, then each uplink signal measurement data, the inertial measurement data and positioning data of the target terminal at the previous moment are input into a positioning state space model, and the positioning state space model is solved based on a preset algorithm to obtain positioning data of the target terminal at the current moment; the positioning state space model comprises a positioning observation model and a positioning state model, the positioning observation model comprises an incidence angle observation model, the incidence angle observation model is used for representing the relation between AOA data in uplink signal measurement data and first antenna data of a target terminal and second antenna data of base stations, and the positioning state model is used for representing the relation between positioning data of the current moment of the target terminal and positioning data and inertial measurement data of the last moment of the target terminal. According to the method and the device, the height between the target terminal and the base station can be considered through the incident angle observation model, and the positioning state space model realizes tight coupling of the uplink signal measurement data and the inertia measurement data, so that the positioning accuracy can be improved.
In one embodiment, the uplink signal measurement data further includes TDOA data; the positioning observation model further includes a TDOA observation model for characterizing a relationship between TDOA data in each uplink signal measurement data and the first antenna data and each second antenna data.
Optionally, after the positioning observation model includes the TDOA (Time Difference of Arrival ) observation model, the positioning observation model is also expressed by formula (14), but the amount of change is as follows:
υ k ＝[v 1,k v 2,k … v N,k τ 1,k τ 2,k … τ N-1,k σ k ] T (32)
equations (33) and (34) are the incident angle observation model and the TDOA observation model, respectively; wherein, xi k An AOA and TDOA observation vector representing measurements of a plurality of base stations at time k; psi (χ) k ) Representing initial AOA and initial TDOA observation vectors measured by a target terminal at the moment k;
the TDOA data between the antenna phase center of the target terminal at the k moment and the antenna of the (i+1) th base station and the antenna of the 1 st base station, namely the TDOA data measured by the ith base station at the k moment; g i (χ k ) Initial TDOA data measured for the target terminal at time k, here omitting the speed of light c; τ i,k The TDOA observation noise of the ith base station at the k moment; the other amounts have the same meaning as the corresponding amounts in formulas (15) to (26).
In one embodiment, the first antenna data includes antenna position data of the target terminal, and each of the second antenna data includes antenna position data and antenna attitude data of each of the base stations.
In one embodiment, the antenna position data of the target terminal includes a position coordinate of a phase center of an antenna of the target terminal in a local rectangular coordinate system; the antenna position data of each base station comprises the position coordinates of the phase center of the antenna of each base station in a local rectangular coordinate system; the antenna attitude data of each base station includes an attitude angle of an antenna of each base station in a local rectangular coordinate system.
Wherein, in the positioning observation model
And->
The position coordinate of the antenna phase center of the target terminal at the moment k in a local rectangular coordinate system. Locating +.>
And->
Is the position coordinate of the antenna phase center of the ith base station in the local rectangular coordinate system. Psi in positioning observation model i 、θ i And gamma i Is the attitude angle of the antenna of the ith base station in a local rectangular coordinate system.
In one embodiment, the method further comprises: constructing a target state equation between first-order differentiation of positioning data of the target terminal at the current moment and positioning data and inertial measurement data of the target terminal at the current moment; discretizing the target state equation to obtain a positioning state model.
Wherein, the first-order differentiation of the positioning data of the target terminal at the current moment is as follows 
The positioning data of the current moment of the target terminal is phi theta gamma v E v N v U L λ h ε bx ε by ε bz ▽ bx ▽ by ▽ bz Inertial measurement data of +.>
And f bx 、f by 、f bz The objective state equations constructed are formulas (1) to (11). And then discretizing the target state equation by using an Euler discretization method to obtain a positioning state model shown in formulas (12) and (13).
In one embodiment, the positioning data includes terminal attitude data, terminal velocity data, and terminal position data, and the target state equations include a first state equation, a second state equation, and a third state equation; the first state equation is a state equation between first-order differentiation of terminal attitude data at the current moment of the target terminal, the terminal attitude data at the current moment of the target terminal and inertial measurement data; the second state equation is a state equation between the first-order differential of the terminal speed data of the target terminal at the current moment, the terminal speed data of the target terminal at the current moment and the inertial measurement data; the third state equation is a state equation between the first-order differential of the terminal position data at the current time of the target terminal and the terminal position data and the inertial measurement data at the current time of the target terminal.
In one embodiment, the inertial measurement data includes: white noise of a gyroscope carried on a target terminal on a carrier coordinate system, a rotation matrix between the carrier coordinate system and a navigation coordinate system, white noise of an accelerometer carried on the target terminal on the carrier coordinate system, a projection component of an angular velocity of the carrier coordinate system relative to the navigation coordinate system on the carrier coordinate system, random constant drift of the gyroscope on the carrier coordinate system, specific force output by the accelerometer on the carrier coordinate system, and random constant drift of the accelerometer on the carrier coordinate system.
Wherein the terminal attitude data are ψ, θ and γ in formulas (2) to (6), and represent azimuth, pitch and roll angles of the carrier coordinate system with respect to the "east-north-day" navigation coordinate system, respectively. The terminal speed data is v in the formula (2), the formula (3) and the formulas (7) to (9) E 、v N And v U The east speed, north speed and sky speed of the target terminal in the east-north-day navigation coordinate system are respectively represented. The terminal position data are L, λ, and h in the formula (2), the formula (3), the formula (8), and the formula (9), respectively, representing latitude, longitude, and altitude of the target terminal in the geocentric fixed coordinate system. The inertial measurement data includes those in formulas (1) to (11)
f bx 、f by 、f bz 、ε bx 、ε by 、ε bz 、▽ bx 、▽ by 、▽ bz And->
Wherein->
And->
White noise of a gyroscope carried on a target terminal on a carrier coordinate system is represented;
And->
Representing white noise of an accelerometer carried on a target terminal on a carrier coordinate system;
And->
Representing a projected component of the angular velocity of the carrier coordinate system relative to the navigational coordinate system on the carrier coordinate system; f (f) bx 、f by And f bz Representing the specific force output by the accelerometer on the carrier coordinate system; epsilon bx 、ε by And epsilon bz Representing random constant drift of the gyroscope on a carrier coordinate system; (V) bx 、▽ by And bz representing random constant drift of the accelerometer on the carrier coordinate system; / >
Representing a rotation matrix between the carrier coordinate system and the navigation coordinate system. The reference k positioning data and inertial measurement data in equation (13) have the same meaning as the corresponding amounts described above, and are the k positioning data and inertial measurement data, e.g., ψ k 、θ k And gamma k The azimuth, pitch and roll angles of the carrier coordinate system at time k are shown relative to the "east-north-day" navigational coordinate system, respectively.
In addition, the first state equation is equation (6); the second state equation is equation (7); the third state equation is (9). For ease of reading, equations (6), (7) and (9) are set forth again below.
In one embodiment, solving the positioning state space model based on a preset algorithm to obtain positioning data of the target terminal at the current moment includes: acquiring constraint conditions between the first antenna data and the second antenna data; and solving the positioning state space model based on a preset algorithm and constraint conditions to obtain positioning data of the target terminal at the current moment.
In one embodiment, the predetermined algorithm is a particle filter algorithm.
Alternatively, the constraint is that
Wherein,,
and->
For the first antenna data, ψi, θ i 、
And->
Is the second antenna data.
As shown in fig. 6, a schematic flow chart for solving a positioning state space model based on a particle filtering algorithm is provided, and the process for solving the positioning state space model based on the particle filtering algorithm and the constraint condition is as follows:
it may be that the prior probability density function p (x) is derived from only the AOA measurements of the total base station 0 ) Alternatively, the prior probability density function p (x) may be obtained from the AOA and TDOA measurements of the total base station 0 ) Then the particle filter initialization is performed, i.e. from the prior probability density function p (x 0 ) Extracting lambda particles
The initial particle weight is
Subsequently importance sampling is performed and Λ particles are obtained using formula (12)>
I.e.
Then the particle weight is updated, namely
Wherein,,
is particle->
Likelihood function, ζ k And->
The meaning of (c) may be in accordance with the meanings in the formulas (14) to (26) or in accordance with the meanings in the formulas (29) to (34); r is R k Observed noise vector v for single radio base station k Covariance matrix of>
For the normalized weight, resampling is then carried out to obtain a new particle set
The corresponding particle weight is +.>
Resampling refers to the step of copying particles from large to small according to the weight of the updated particles after the particles are subjected to weight update, namely formula (40), until the number of the particles reaches Λ. For example, there are 5 particles, and the updated weights are 0.6,0.4, 0, and 0, respectively, and 3 particles out of the 5 particles are particles corresponding to 0.6 and 2 particles are particles corresponding to 0.4 after resampling.
Finally, the positioning data of the current moment of the target terminal can be expressed as
In addition, during the above calculation, due to the AOA and TDOA measurements ζ of the total base station k The calculation is needed at the base station side and then the calculation is sent to the target terminal, so that the AOA and TDOA measured value xi of the total base station received by the target terminal k There is a certain time delay, and because the time delay can estimate its length range in advance, the problem can be solved by storing in the target terminal the AOA and TDOA measurements ζ (earlier than the total base station) from the current time to a time before k Moment of occurrence) as AOA and TDOA measurements η of the total base station k When the terminal is reached, AOA and TDOA measurement value xi of the total base station can be carried out according to stored inertial measurement data k The estimate of the positioning data of the target terminal at the moment of occurrence is then used to infer the positioning data of the target terminal at the current moment of time using stored inertial measurement data.
In summary, in the application for a target terminal, uplink signal measurement data sent by a plurality of base stations respectively is received first, and inertial measurement data of the target terminal is obtained, where the uplink signal measurement data includes AOA data and TDOA data, and the inertial measurement data includes: white noise of a gyroscope carried on a target terminal on a carrier coordinate system, a rotation matrix between the carrier coordinate system and a navigation coordinate system, white noise of an accelerometer carried on the target terminal on the carrier coordinate system, a projection component of an angular velocity of the carrier coordinate system relative to the navigation coordinate system on the carrier coordinate system, random constant drift of the gyroscope on the carrier coordinate system, specific force output by the accelerometer on the carrier coordinate system, and random constant drift of the accelerometer on the carrier coordinate system. And then, inputting the uplink signal measurement data, the inertia measurement data and the positioning data of the target terminal at the moment into a positioning state space model. Finally, constraint conditions between the first antenna data and the second antenna data are obtained; and solving the positioning state space model based on the particle filtering algorithm and the constraint condition to obtain positioning data of the target terminal at the current moment. The positioning state space model comprises a positioning observation model and a positioning state model, the positioning observation model comprises an incidence angle observation model and a TDOA observation model, the incidence angle observation model is used for representing the relation between AOA data in each uplink signal measurement data and first antenna data of a target terminal and second antenna data of each base station, the TDOA observation model is used for representing the relation between TDOA data in each uplink signal measurement data and the first antenna data and the second antenna data, and the positioning state model is used for representing the relation between positioning data of the target terminal at the current moment and positioning data and inertial measurement data of the target terminal at the last moment; the first antenna data includes a position coordinate of a phase center of an antenna of the target terminal in a local rectangular coordinate system, the second antenna data includes a position coordinate of a phase center of an antenna of each base station in the local rectangular coordinate system, and the antenna attitude data of each base station includes an attitude angle of an antenna of each base station in the local rectangular coordinate system. The construction process of the positioning state model comprises the following steps: constructing a target state equation between first-order differentiation of positioning data of the target terminal at the current moment and positioning data and inertial measurement data of the target terminal at the current moment; discretizing the target state equation to obtain a positioning state model. The positioning data comprise terminal attitude data, terminal speed data and terminal position data, and the target state equation comprises a first state equation, a second state equation and a third state equation; the first state equation is a state equation between first-order differentiation of terminal attitude data at the current moment of the target terminal, the terminal attitude data at the current moment of the target terminal and inertial measurement data; the second state equation is a state equation between the first-order differential of the terminal speed data of the target terminal at the current moment, the terminal speed data of the target terminal at the current moment and the inertial measurement data; the third state equation is a state equation between the first-order differential of the terminal position data at the current time of the target terminal and the terminal position data and the inertial measurement data at the current time of the target terminal.
In addition, the positioning state model, i.e., equation (12), includes the measurement value of the IMU on the target terminal, and the positioning observation model, i.e., equation (14), includes AOA data and TDOA data measured by a plurality of base stations, wherein equation (33) is IAOM, and equation (34) is a TDOA observation model. The positioning state model and the positioning observation model form a positioning state space model, namely formula (28), so that the tight coupling positioning algorithm of the AOA and TDOA of the plurality of base stations and the IMU of the target terminal is realized. While simultaneously using AOA data and TDOA data of a plurality of base stationsThe IMU measured value of the target terminal can realize high-precision continuous positioning of the target terminal. Ensuring that the positioning state space model can realize continuous high-precision positioning in far and near fields has two aspects, namely, the known height coordinate of the antenna phase center of the k-moment target terminal containing error amount in a local rectangular coordinate system is
Another aspect is the constraint, equation (35).
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a positioning device for realizing the positioning method. The implementation of the solution provided by the device is similar to that described in the above method, so the specific limitations in one or more embodiments of the positioning device provided below may be referred to above for limitations of the positioning method, which are not repeated here.
In one embodiment, as shown in fig. 7, there is provided a block diagram of a positioning apparatus 700 for use in a target terminal, including: a receiving module 701 and a calculating module 702, wherein:
the receiving module 701 is configured to receive uplink signal measurement data sent by each of the plurality of base stations, and obtain inertial measurement data of the target terminal, where the uplink signal measurement data includes AOA data.
The calculation module 702 is configured to input each uplink signal measurement data, each inertial measurement data, and the positioning data of the target terminal at a previous time into a positioning state space model, and solve the positioning state space model based on a preset algorithm to obtain the positioning data of the target terminal at the current time; the positioning state space model comprises a positioning observation model and a positioning state model, the positioning observation model comprises an incidence angle observation model, the incidence angle observation model is used for representing the relation between AOA data in uplink signal measurement data and first antenna data of a target terminal and second antenna data of base stations, and the positioning state model is used for representing the relation between positioning data of the current moment of the target terminal and positioning data and inertial measurement data of the last moment of the target terminal.
In one embodiment, the uplink signal measurement data further includes TDOA data; the positioning observation model further includes a TDOA observation model for characterizing a relationship between TDOA data in each uplink signal measurement data and the first antenna data and each second antenna data.
In one embodiment, the first antenna data includes antenna position data of the target terminal, and each of the second antenna data includes antenna position data and antenna attitude data of each of the base stations.
In one embodiment, the antenna position data of the target terminal includes a position coordinate of a phase center of an antenna of the target terminal in a local rectangular coordinate system; the antenna position data of each base station comprises the position coordinates of the phase center of the antenna of each base station in a local rectangular coordinate system; the antenna attitude data of each base station includes an attitude angle of an antenna of each base station in a local rectangular coordinate system.
In one embodiment, the apparatus further comprises: constructing a target state equation between first-order differentiation of positioning data of the target terminal at the current moment and positioning data and inertial measurement data of the target terminal at the current moment; discretizing the target state equation to obtain a positioning state model.
In one embodiment, the positioning data includes terminal attitude data, terminal velocity data, and terminal position data, and the target state equations include a first state equation, a second state equation, and a third state equation; the first state equation is a state equation between first-order differentiation of terminal attitude data at the current moment of the target terminal, the terminal attitude data at the current moment of the target terminal and inertial measurement data; the second state equation is a state equation between the first-order differential of the terminal speed data of the target terminal at the current moment, the terminal speed data of the target terminal at the current moment and the inertial measurement data; the third state equation is a state equation between the first-order differential of the terminal position data at the current time of the target terminal and the terminal position data and the inertial measurement data at the current time of the target terminal.
In one embodiment, the inertial measurement data includes: white noise of a gyroscope carried on a target terminal on a carrier coordinate system, a rotation matrix between the carrier coordinate system and a navigation coordinate system, white noise of an accelerometer carried on the target terminal on the carrier coordinate system, a projection component of an angular velocity of the carrier coordinate system relative to the navigation coordinate system on the carrier coordinate system, random constant drift of the gyroscope on the carrier coordinate system, specific force output by the accelerometer on the carrier coordinate system, and random constant drift of the accelerometer on the carrier coordinate system.
In one embodiment, the calculating module 702 is specifically configured to obtain a constraint condition between the first antenna data and the second antenna data; and solving the positioning state space model based on a preset algorithm and constraint conditions to obtain positioning data of the target terminal at the current moment.
In one embodiment, the predetermined algorithm is a particle filter algorithm.
The various modules in the positioning device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 8. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a positioning method. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 8 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to comply with the related laws and regulations and standards of the related countries and regions.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (13)
1. A positioning method, which is used in a target terminal, the positioning method comprising:
receiving uplink signal measurement data respectively sent by a plurality of base stations, and acquiring inertial measurement data of the target terminal, wherein the uplink signal measurement data comprises arrival angle AOA data;
inputting the uplink signal measurement data, the inertia measurement data and the positioning data of the target terminal at the moment into a positioning state space model, and solving the positioning state space model based on a preset algorithm to obtain the positioning data of the target terminal at the current moment;
The positioning state space model comprises a positioning observation model and a positioning state model, the positioning observation model comprises an incidence angle observation model, the incidence angle observation model is used for representing the relation between the AOA data in the uplink signal measurement data and the first antenna data of the target terminal and the second antenna data of the base station, and the positioning state model is used for representing the relation between the positioning data of the target terminal at the current moment and the positioning data and the inertia measurement data of the target terminal at the last moment.
2. The method of claim 1, wherein the upstream signal measurement data further comprises time difference of arrival TDOA data;
the positioning observation model further includes a TDOA observation model for characterizing a relationship between the TDOA data and the first antenna data and the second antenna data in each of the uplink signal measurement data.
3. The method of claim 1, wherein the first antenna data comprises antenna position data for the target terminal, and wherein each of the second antenna data comprises antenna position data and antenna pose data for each of the base stations.
4. A method according to claim 3, wherein the antenna position data of the target terminal comprises position coordinates of the phase center of the antenna of the target terminal in a local rectangular coordinate system;
the antenna position data of each base station comprises the position coordinates of the phase center of the antenna of each base station in a local rectangular coordinate system;
the antenna attitude data of each base station includes an attitude angle of an antenna of each base station in a local rectangular coordinate system.
5. The method according to any one of claims 1 to 4, further comprising: constructing a target state equation between first-order differential of the positioning data of the target terminal at the current moment and the inertial measurement data;
discretizing the target state equation to obtain the positioning state model.
6. The method of claim 5, wherein the positioning data comprises terminal attitude data, terminal velocity data, and terminal position data, and the target state equations comprise a first state equation, a second state equation, and a third state equation;
the first state equation is a state equation between first-order differential of terminal posture data of the target terminal at the current moment, the terminal posture data of the target terminal at the current moment and the inertial measurement data;
The second state equation is a state equation between first-order differential of terminal speed data of the target terminal at the current moment, the terminal speed data of the target terminal at the current moment and the inertial measurement data;
the third state equation is a state equation between the first-order differential of the terminal position data of the target terminal at the current moment and the terminal position data and the inertial measurement data of the target terminal at the current moment.
7. The method of claim 5, wherein the inertial measurement data comprises: white noise of a gyroscope carried on the target terminal on a carrier coordinate system, a rotation matrix between the carrier coordinate system and a navigation coordinate system, white noise of an accelerometer carried on the target terminal on the carrier coordinate system, a projection component of an angular velocity of the carrier coordinate system relative to the navigation coordinate system on the carrier coordinate system, random constant drift of the gyroscope on the carrier coordinate system, specific force output by the accelerometer on the carrier coordinate system and random constant drift of the accelerometer on the carrier coordinate system.
8. The method according to any one of claims 1 to 4, wherein the solving the positioning state space model based on a preset algorithm to obtain the positioning data of the current time of the target terminal includes:
Acquiring constraint conditions between the first antenna data and the second antenna data;
and solving the positioning state space model based on the preset algorithm and the constraint condition to obtain positioning data of the target terminal at the current moment.
9. The method of claim 8, wherein the predetermined algorithm is a particle filter algorithm.
10. A positioning device for use in a target terminal, the positioning device comprising:
the receiving module is used for receiving uplink signal measurement data respectively sent by a plurality of base stations and acquiring inertial measurement data of the target terminal, wherein the uplink signal measurement data comprises AOA data;
the calculation module is used for inputting the uplink signal measurement data, the inertia measurement data and the positioning data of the target terminal at the previous moment into a positioning state space model, and solving the positioning state space model based on a preset algorithm to obtain the positioning data of the target terminal at the current moment;
the positioning state space model comprises a positioning observation model and a positioning state model, the positioning observation model comprises an incidence angle observation model, the incidence angle observation model is used for representing the relation between AOA data in uplink signal measurement data and first antenna data of the target terminal and second antenna data of the base stations, and the positioning state model is used for representing the relation between positioning data of the target terminal at the current moment and positioning data of the target terminal at the last moment and the inertia measurement data.
11. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 9 when the computer program is executed.
12. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 9.
13. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any one of claims 1 to 9.
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| WO2024140646A1 (en) * | 2022-12-31 | 2024-07-04 | 网络通信与安全紫金山实验室 | Positioning method and apparatus, and computer device, storage medium and program product |
| WO2024140654A1 (en) * | 2022-12-31 | 2024-07-04 | 网络通信与安全紫金山实验室 | Positioning method and apparatus, computer device, storage medium, and program product |
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| CN101413800B (en) * | 2008-01-18 | 2010-09-29 | 南京航空航天大学 | Navigating and steady aiming method of navigation / steady aiming integrated system |
| WO2019239365A1 (en) * | 2018-06-13 | 2019-12-19 | Purohit Ankit | System and method for position and orientation tracking of multiple mobile devices |
| CN113938825B (en) * | 2021-10-15 | 2024-06-14 | 太原理工大学 | Bluetooth AOA-based fully mechanized coal mining face coal mining machine positioning method and system |
| CN114509069B (en) * | 2022-01-25 | 2023-11-28 | 南昌大学 | Indoor navigation positioning system based on Bluetooth AOA and IMU fusion |
| CN115480212A (en) * | 2022-09-15 | 2022-12-16 | 网络通信与安全紫金山实验室 | Positioning method, positioning device, base station, storage medium and computer program product |
| CN115932723A (en) * | 2022-12-31 | 2023-04-07 | 网络通信与安全紫金山实验室 | Positioning method, positioning device, computer equipment, storage medium and program product |
| CN116125376A (en) * | 2022-12-31 | 2023-05-16 | 网络通信与安全紫金山实验室 | Positioning method, apparatus, device, storage medium and computer program product |
| CN116338570A (en) * | 2022-12-31 | 2023-06-27 | 网络通信与安全紫金山实验室 | Positioning method, positioning device, computer apparatus, storage medium, and program product |
| CN116017690A (en) * | 2022-12-31 | 2023-04-25 | 网络通信与安全紫金山实验室 | Terminal position determining method, device, computer equipment and storage medium |
-
2022
- 2022-12-31 CN CN202211736382.5A patent/CN116338570A/en active Pending
-
2023
- 2023-12-26 WO PCT/CN2023/141816 patent/WO2024140646A1/en unknown
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024140646A1 (en) * | 2022-12-31 | 2024-07-04 | 网络通信与安全紫金山实验室 | Positioning method and apparatus, and computer device, storage medium and program product |
| WO2024140654A1 (en) * | 2022-12-31 | 2024-07-04 | 网络通信与安全紫金山实验室 | Positioning method and apparatus, computer device, storage medium, and program product |
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| Publication number | Publication date |
|---|---|
| WO2024140646A1 (en) | 2024-07-04 |
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