WO2022087998A1 - 通信终端的定位跟踪方法、系统、设备和可读存储介质 - Google Patents

通信终端的定位跟踪方法、系统、设备和可读存储介质 Download PDF

Info

Publication number
WO2022087998A1
WO2022087998A1 PCT/CN2020/125057 CN2020125057W WO2022087998A1 WO 2022087998 A1 WO2022087998 A1 WO 2022087998A1 CN 2020125057 W CN2020125057 W CN 2020125057W WO 2022087998 A1 WO2022087998 A1 WO 2022087998A1
Authority
WO
WIPO (PCT)
Prior art keywords
communication terminal
motion model
motion
probability
tracking
Prior art date
Application number
PCT/CN2020/125057
Other languages
English (en)
French (fr)
Inventor
李晓东
齐望东
Original Assignee
网络通信与安全紫金山实验室
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 网络通信与安全紫金山实验室 filed Critical 网络通信与安全紫金山实验室
Priority to PCT/CN2020/125057 priority Critical patent/WO2022087998A1/zh
Publication of WO2022087998A1 publication Critical patent/WO2022087998A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Definitions

  • the present application relates to the field of communication technologies, and in particular, to a method, system, device and readable storage medium for positioning and tracking a communication terminal.
  • the communication base station can estimate the position of the communication terminal by measuring the uplink reference signal of the communication terminal. At present, this method is suitable for locating communication terminals. Therefore, when communication base stations are widely deployed, the communication base stations can be used to locate communication terminals within their coverage.
  • the communication terminal generally moves continuously, and the motion modeling in the related positioning and tracking method usually adopts a simple uniform motion model.
  • the motion mode of the communication terminal cannot be a simple uniform motion.
  • the positioning and tracking accuracy will be greatly reduced, resulting in a model mismatch problem.
  • a method for positioning and tracking a communication terminal includes the following steps:
  • the final state estimation value of the communication terminal is obtained, wherein the different motion models correspond to different motion modes of the communication terminal.
  • the step of obtaining the final state estimate value of the communication terminal according to the state estimate value and motion model probability of the observation vector under different motion models includes:
  • the input value of the Kalman subfilter for the current moment of different motion models is obtained.
  • the corresponding input values are processed respectively, and the output values of the Kalman sub-filters at the current moment for different motion models are obtained as the state for different motion models at the current moment Estimates and covariance matrix estimates.
  • the step of obtaining the model mixing probability at the last moment according to the motion model probability at the last moment and the preset transition probability includes:
  • the first motion model and the second motion model are any two motion models among the respective motion models.
  • the method further includes:
  • the motion model probability of the motion model at the current moment is updated.
  • the step of obtaining the likelihood function of each motion model at the current moment includes:
  • the innovation sequence and innovation covariance matrix of the motion model according to the motion model obtain the innovation sequence and innovation covariance matrix of the motion model according to the motion model, the observation vector set and the preset Kalman filter algorithm, and obtain the innovation sequence and innovation covariance matrix of the motion model according to the innovation sequence and the new
  • the information covariance matrix is used to obtain the likelihood function of the motion model at the current moment.
  • the method further includes:
  • the state prediction error covariance matrix of the motion model at the current moment is obtained, according to the estimated value of the overall observation noise covariance matrix, the state prediction error covariance matrix, the information sequence of the motion model and For the observation vector at the current moment, obtain the estimated value of the observation noise covariance matrix of the motion model at the next moment;
  • the estimated value of the overall observation noise covariance matrix at the next moment is obtained according to the estimated value of the observed noise covariance matrix of each motion model at the next moment and the probability of each motion model at the current moment.
  • the method further includes:
  • the tracking data rate for positioning tracking is set according to the final state estimate of the communication terminal.
  • the different motion models include a uniform motion model, a uniform acceleration motion model, and a coordinated turning motion model.
  • the method further includes:
  • a positioning and tracking system for a communication terminal comprising:
  • an information acquisition module used for acquiring the arrival angle information and arrival time information of the uplink signal of the communication terminal
  • a vector acquisition module configured to acquire an observation vector for the communication terminal according to the arrival angle information and the arrival time information
  • a state estimation module configured to obtain the final state estimation value of the communication terminal according to the state estimation value and motion model probability of the observation vector under different motion models, wherein the different motion models correspond to different motion models of the communication terminal sport mode.
  • an apparatus for positioning and tracking a communication terminal including a communication access device and a data computing device;
  • the communication access device is configured to receive data sent by a communication terminal, and obtain angle of arrival information and time-of-arrival information of the communication terminal according to the data;
  • the data computing device is configured to use the positioning and tracking method of the communication terminal to perform positioning and tracking of the communication terminal.
  • a computer device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the computer program and the steps of implementing the method for positioning and tracking the communication terminal.
  • a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements the steps of the method for positioning and tracking the communication terminal.
  • the method, system, device, and readable storage medium for positioning and tracking a communication terminal of the present application use the angle of arrival information and time-of-arrival information of the uplink signal of the communication terminal to obtain an observation vector for the communication terminal, On the basis of the observation vector, the state of the communication terminal under different motion models is analyzed. Due to the uncertainty of the motion state of the communication terminal itself, combined with the motion model probabilities of different motion models, the final state estimation value of the communication terminal can be determined, regardless of the communication terminal. In any movement state, the positioning and tracking of the communication terminal can be realized, and the accuracy of the positioning and tracking can be ensured at the same time.
  • FIG. 1 is an application scenario diagram of a method for positioning and tracking a communication terminal according to an embodiment of the present application.
  • FIG. 2 is a schematic flowchart of a method for positioning and tracking a communication terminal according to an embodiment of the present application.
  • FIG. 3 is a schematic flowchart of step S200 in the method for positioning and tracking a communication terminal according to an embodiment of the present application.
  • FIG. 4 is a schematic diagram of an overall process of an MDR-AIMM algorithm according to an embodiment of the present application.
  • FIG. 5 is a schematic structural diagram of a positioning and tracking system for a communication terminal according to an embodiment of the present application.
  • FIG. 6 is a schematic structural diagram of a positioning and tracking system for a communication terminal according to another embodiment of the present application.
  • FIG. 7 is a schematic structural diagram of a positioning and tracking system for a communication terminal according to still another embodiment of the present application.
  • FIG. 8 is a schematic structural diagram of a positioning and tracking system for a communication terminal according to another embodiment of the present application.
  • FIG. 9 is a schematic structural diagram of an apparatus for positioning and tracking a communication terminal according to an embodiment of the present application.
  • FIG. 10 is a schematic structural diagram of an apparatus for positioning and tracking a 5G terminal according to an embodiment of the present application.
  • FIG. 11 is a schematic diagram of a computer device according to an embodiment of the present application.
  • Words like "connected,” “connected,” “coupled,” and the like referred to in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.
  • the "plurality”, “each” and “different” used in this application refer to two or more.
  • “And/or” describes the association relationship between associated objects, indicating that there can be three kinds of relationships. For example, “A and/or B” can mean that A exists alone, A and B exist at the same time, and B exists alone.
  • the character “/” generally indicates that the associated objects are an “or” relationship.
  • the terms “first”, “second”, “third”, etc. involved in this application are only to distinguish similar objects, and do not represent a specific order for the objects.
  • FIG. 1 is an application scenario diagram of a method for positioning and tracking a communication terminal in an embodiment of the present application.
  • a base station can communicate with a communication terminal through a signal network, and the communication terminal can be an electronic device with an Internet access function, such as a smart Mobile phone, tablet, MP3 player (Moving Picture Experts Group Audio Layer III, Moving Picture Experts Group Audio Layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Group Audio Layer 4) player or portable Personal computers, desktop computers, notebook computers, etc.
  • the communication terminal can support 4G or 5G communication
  • the base station can be a 4G or 5G base station, etc.
  • the base station can locate and track the communication terminal by receiving the information sent by the communication terminal.
  • FIG. 2 is a schematic flowchart of a method for positioning and tracking a communication terminal in an embodiment of the present application, and the method is applied to the base station in FIG. 1 as an example for description.
  • the method for positioning and tracking a communication terminal includes the following steps:
  • Step S100 Acquire the angle of arrival information and the time of arrival information of the uplink signal of the communication terminal, and obtain the observation vector for the communication terminal according to the information of the angle of arrival and the time of arrival information.
  • the communication terminal can send an uplink signal to the base station, and the base station can obtain the angle of arrival (Angle of Arrival, AOA) and time of arrival (Time of Arrival, TOA) information of the uplink signal by analyzing the uplink signal.
  • the azimuth angle, elevation angle and radial distance of the communication terminal relative to the base station can be obtained from the arrival angle information and the arrival time information, and then an observation vector for the communication terminal can be constructed.
  • observation vector for the communication terminal can be constructed by the following formula:
  • observation vector of the base station to the communication terminal in the local earth coordinate system of the base station at time k is the TOA observation value of the base station to the communication terminal in the local earth coordinate system of the base station at time k, is the azimuth observation value of the base station to the communication terminal in the local earth coordinate system of the base station at time k, is the observation value of the pitch angle of the base station to the communication terminal in the local earth coordinate system of the base station at time k
  • w(k) is the measurement noise
  • c is the speed of light
  • T is the matrix transposition.
  • Step S200 Obtain the final state estimate value of the communication terminal according to the state estimate value and motion model probability of the observation vector under different motion models, wherein the different motion models correspond to different motion modes of the communication terminal.
  • the different motion models correspond to different motion modes of the communication terminal, such as uniform motion, uniform acceleration motion, coordinated turning motion, etc.
  • the uniform motion model can handle Approximate uniform motion
  • uniform acceleration motion model can handle approximate uniform acceleration motion
  • cooperative turning motion model can handle approximate cooperative turning motion, etc.
  • the motion model probability represents the probability that the motion state of the communication terminal is similar to the motion model. Therefore, the motion model probability is used to comprehensively estimate the state estimates under different motion models, and the final state estimate value of the communication terminal is obtained to improve the The accuracy of the positioning and tracking of the communication terminal.
  • the final state estimation value for the communication terminal can be obtained by the following formula:
  • n can represent the number of motion models.
  • the estimated state value under any motion model can be obtained by calculating and processing the observation vector through a corresponding model algorithm, and the estimated state value under any motion model can include information such as the position, speed, and acceleration of the communication terminal. .
  • the angle of arrival information and time of arrival information of the uplink signal of the communication terminal are used to obtain the observation vector for the communication terminal, and the state of the communication terminal under different motion models is analyzed on the basis of the observation vector.
  • the uncertainty of its own motion state, combined with the motion model probabilities of different motion models, can determine the final state estimation value of the communication terminal. No matter what motion state the communication terminal is in, the positioning and tracking of the communication terminal can be realized, and the positioning and tracking can be guaranteed at the same time. accuracy.
  • the step of obtaining the final state estimate value of the communication terminal according to the state estimate value and motion model probability of the observation vector under different motion models includes:
  • Step S210 obtaining the estimated state value and the estimated value of the covariance matrix under different motion models at the previous moment;
  • Step S220 obtaining the model mixing probability at the last moment according to the motion model probability at the last moment and the preset transition probability;
  • Step S230 According to the state estimation value and covariance matrix estimation value under different motion models at the last moment and the model mixing probability at the last moment, obtain the current moment of the Kalman subfilter for different motion models.
  • Input value according to different motion models and preset Kalman filtering algorithm, the corresponding input values are processed respectively, and the output value of the Kalman sub-filter at the current moment for different motion models is obtained as the current value for different motion models.
  • the state estimation value and covariance matrix estimation value of the communication terminal can be obtained under different motion models, and the covariance matrix estimation value is used to evaluate the accuracy of the state estimation value, which can be Obtain the state estimate and covariance matrix estimate of the communication terminal, and the state estimate and covariance matrix estimate at the current moment are the state estimate and covariance matrix estimate under different motion models at the previous moment
  • the estimated value of the state and the estimated value of the covariance matrix under each motion model at the current moment are the same as the above.
  • the estimated value of the state under each motion model at a time is related to the estimated value of the covariance matrix, which reflects the continuity of positioning and tracking, which can reduce the error of positioning and tracking, and ensure the accuracy of positioning and tracking.
  • the Kalman subfilter corresponds to the motion model
  • the model mixing probability refers to the probability that the two motion models influence each other, and the two motion models are different.
  • the input value of the Kalman subfilter at the current moment for different motion models can be obtained by the following formula, and the input value can include the state estimation input value and the covariance matrix estimation input value:
  • n is the number of motion models
  • j ) is the model mixing probability between the ith and jth motion models at time k-1, and are the input of the jth Kalman subfilter, respectively;
  • the observation vector can be obtained according to the initial moment, the initial position of the communication terminal can be determined, and the position, speed, acceleration, etc. of the communication terminal at the previous moment are set to default values (such as 0 or the default minimum value). value, etc.), obtain the distance difference and angle difference and the corresponding error according to the initial position and default value, and further calculate the state estimation value and covariance matrix estimation value of the communication terminal at the initial moment according to this.
  • This method is suitable for the initial moment.
  • test a position first take the time of the second position continuous with this position as the initial time, and obtain the distance difference and angle difference and the corresponding The error of , obtains the position, velocity, acceleration, etc., and then determines the state estimation value and covariance matrix estimation value of the communication terminal at the initial moment.
  • the preset Kalman filter algorithm may include Unscented Kalman Filter (UKF) algorithm, Volume Kalman Filter (Cubature Kalman Filter, CKF) algorithm or Central Difference Kalman Filter (Central Difference Kalman Filter, CDKF) )Wait.
  • ULF Unscented Kalman Filter
  • CKF Volume Kalman Filter
  • CDKF Central Difference Kalman Filter
  • the state estimation error covariance matrix of the communication terminal can also be obtained according to the motion model probability and the final state estimation value of the communication terminal, which can be obtained by the following formula: accomplish:
  • n is the number of motion models
  • u j (k) represents the motion model probability of the motion model j at time k.
  • the step of obtaining the model mixing probability at the last moment according to the motion model probability at the last moment and the preset transition probability includes:
  • the first motion model and the second motion model are any two motion models among the respective motion models.
  • the transition probability refers to the probability of being between any two different motion models. Combining the transition probability with the motion model probability can better cover the motion state of the communication terminal between the motion models, so that the models are mixed. Probability is more accurate.
  • the model mixing probability between the first motion model and the second motion model at the previous moment can be obtained by the following formula:
  • ⁇ ij is the transition probability between motion model i and motion model j
  • u i (k-1) is the motion model probability of motion model i at time k-1
  • motion model i can represent the first motion model
  • motion model j may represent the second motion model
  • n is the number of motion models.
  • the method further includes:
  • the motion model probability of the motion model at the current moment is updated.
  • the motion model probability since the estimated state value and the estimated value of the covariance matrix at the current moment for different motion models are related to the estimated value of the state and the estimated value of the covariance matrix at the previous moment, the motion model probability also needs to be appropriately updated,
  • the motion model probability may be updated by using the likelihood function, transition probability and motion model probability of each motion model at the current moment, so as to adapt to the change of the motion state of the communication terminal.
  • the motion model probability can be updated by the following formula:
  • u j (k) represents the motion model probability of motion model j at time k
  • ⁇ j (k) represents the likelihood function of motion model j at the current time
  • ⁇ ij is the difference between motion model i and motion model j
  • the transition probability of u i (k-1) is the motion model probability of motion model i at time k-1
  • n represents the number of motion models.
  • the step of obtaining the likelihood function of each motion model at the current moment includes:
  • the innovation sequence and innovation covariance matrix of the motion model according to the motion model obtain the innovation sequence and innovation covariance matrix of the motion model according to the motion model, the observation vector set and the preset Kalman filter algorithm, and obtain the innovation sequence and innovation covariance matrix of the motion model according to the innovation sequence and the new
  • the information covariance matrix is used to obtain the likelihood function of the motion model at the current moment.
  • the likelihood function of any motion model at the current moment is related to the motion state of the communication terminal in the previous period.
  • the obtained observation The vector set is used to represent the motion state of the communication terminal in the previous period, and the observation vector set is calculated by using the corresponding motion model and the preset Kalman filter algorithm to obtain the information sequence and new information of the corresponding motion model.
  • the likelihood function of the corresponding motion model at the current moment can be obtained, and the likelihood function can help to reflect the probability of the motion model.
  • the likelihood function of the motion model at the current moment can be determined by the following formula:
  • v j (k) represents the innovation sequence
  • S j (k) represents the innovation covariance matrix
  • observation vector in the observation vector set may be selected as required, such as from time 1 to time k-1 and so on.
  • the method further includes:
  • the state prediction error covariance matrix of the motion model at the current moment is obtained, according to the estimated value of the overall observation noise covariance matrix, the state prediction error covariance matrix, the information sequence of the motion model and For the observation vector at the current moment, obtain the estimated value of the observation noise covariance matrix of the motion model at the next moment;
  • the estimated value of the overall observation noise covariance matrix at the next moment is obtained according to the estimated value of the observed noise covariance matrix of each motion model at the next moment and the probability of each motion model at the current moment.
  • the observation noise changes drastically. Therefore, it is necessary to fully consider the uncertainty of observation noise, that is, the problem of observation noise mismatch, so as to realize the dynamic change of observation noise. Therefore, by obtaining the estimated value of the overall observation noise covariance matrix at the current moment, combined with the motion model, the estimated value of the overall observation noise covariance matrix at the next moment is obtained, so as to refer to the overall observation noise in time during positioning and tracking. changes to improve the accuracy of location tracking.
  • the estimated value of the overall observation noise covariance matrix at the next moment can be obtained by the following formula:
  • b is the fading factor
  • d(k) represents the intermediate variable, usually 0.9 or other approximate values
  • d(k) represents the intermediate variable, usually 0.9 or other approximate values
  • d(k) represents the intermediate variable, usually 0.9 or other approximate values
  • d(k) represents the intermediate variable, usually 0.9 or other approximate values
  • d(k) represents the intermediate variable, usually 0.9 or other approximate values
  • v j (k) represents the innovation sequence
  • H is the Jacobian matrix of the observation vector.
  • the method further includes:
  • the tracking data rate for positioning tracking is set according to the final state estimate of the communication terminal.
  • the tracking data rate of the communication terminal in the related art is fixed. Since different motion states have different requirements for the tracking data rate, different tracking data rates can be allocated according to the motion state of the communication terminal, thereby To achieve the purpose of increasing the system capacity or reducing the consumption of computing and energy resources.
  • the different motion models include a uniform motion model, a uniform acceleration motion model, and a coordinated turning motion model.
  • the specific motion models may be added or deleted according to the actual application, and motion models of other motion modes may also be included.
  • the method further includes:
  • the acceleration information of the communication terminal can be obtained under a uniform acceleration motion model, so as to determine whether the communication terminal is in a maneuvering state.
  • the movement of the communication terminal The status changes rapidly.
  • the tracking data rate of the positioning tracking can be set to the preset maximum value to achieve fast and accurate positioning and tracking.
  • the acceleration can be smoothed, which can be implemented according to the following formula:
  • a s (k) ⁇ a s (k-1)+(1- ⁇ )a(k)
  • a s (k) is the smoothed acceleration of the communication terminal at time k
  • a(k) is the acceleration of the communication terminal estimated by the uniform acceleration motion model at time k
  • ⁇ is a smoothing factor, usually 0.9 or its approximate value.
  • the sampling interval for positioning and tracking can be the minimum sampling interval, that is, the tracking data rate is maximized.
  • the positioning and tracking method of the communication terminal can be applied to 5G base stations and 5G terminals, and the 5G base station can obtain the azimuth angle and pitch of the 5G terminal by measuring the arrival angle and arrival time of the uplink reference signal of the 5G terminal. Angular and radial distances.
  • the 5G base station can estimate the position of the 5G communication terminal based on its own position and the measured azimuth angle, elevation angle and radial distance of the 5G terminal. At present, the measurement accuracy of the angle of arrival can be within 3 degrees, and the measurement accuracy of the radial distance can be within 1 meter. Therefore, a single 5G base station can be used for high-precision positioning and tracking of 5G terminals within its coverage.
  • the Extended Kalman Filter is mainly used for the positioning and tracking of the 5G terminal.
  • the present application adopts the unscented Kalman filter, the volume Kalman filter, and the volume Kalman filter. Mann filtering or central difference Kalman filtering is used for positioning and tracking of 5G terminals.
  • different tracking data rates can be allocated according to the motion states of 5G terminals, so as to improve system capacity or reduce Computational and energy resource consumption purposes.
  • observation vector of the 5G base station to the 5G terminal in the local earth coordinate system of the 5G base station at time k is the TOA observation value of the 5G base station on the 5G terminal in the local earth coordinate system of the 5G base station at time k
  • w(k) is the measurement noise
  • c is the speed of light
  • tan -1 () is the arc tangent function
  • T represents the matrix transpose.
  • NCV Near Constant Velocity
  • NCA Near Constant Acceleration
  • NCT Near Coordinated Turn
  • IMM Interacting Multiple Model
  • the IMM algorithm in this application includes the above-mentioned three motion models, which are respectively recorded as model 1 , Model 2 and Model 3, namely three state equations, the transition between motion modes is controlled by the transition probability ⁇ ij , ⁇ ij is selected at the beginning of the calculation, and can be determined according to the empirical occupancy time of each motion mode in practice, since the 5G base station is used
  • the observation equation is nonlinear when positioning and tracking 5G terminals, so UKF can be used in this application to estimate the state of 5G terminals.
  • the IMM algorithm for 5G terminal motion mode mismatch problem is as follows:
  • u i (k-1) is the model probability at time k-1
  • ⁇ ij is transition probability
  • j) is the mixing probability
  • ⁇ j (k) is the likelihood function of model j at time k
  • v j (k) is the innovation sequence output by UKF algorithm and model j
  • S j (k) is the output by UKF algorithm and model j Innovation covariance matrix.
  • b is the fading factor usually taken as 0.9
  • b is the estimated value of the observed noise covariance matrix of model j at time k+1
  • k is the estimated value of the observation noise covariance matrix at time k
  • H is the Jacobian matrix of the observation equation.
  • different tracking data rates can be set according to the motion state of the 5G terminal, which can meet its positioning accuracy requirements while minimizing the consumption of computing resources and energy resources.
  • the algorithm improves the positioning and tracking accuracy when the 5G terminal movement mode changes. This step uses the method shown in the following formula to perform online real-time estimation of the motion mode of the 5G terminal, and adjust the tracking data rate of the 5G terminal accordingly:
  • a s (k) ⁇ a s (k-1)+(1- ⁇ )a(k)
  • a s (k) is the smoothed 5G terminal acceleration at time k
  • a(k) is the 5G terminal acceleration estimated by the NCA model at time k
  • ⁇ is a smoothing factor, usually 0.9.
  • the multiple data rate adaptive IMM Multiple Data Rate Adaptive IMM, MDR-AIMM
  • the overall process of the MDR-AIMM algorithm is shown in Figure 4 shown.
  • steps in the flowcharts in FIGS. 2-3 are shown in sequence according to the arrows, these steps are not necessarily executed in the sequence shown by the arrows. Unless explicitly stated herein, the execution of these steps is not strictly limited to the order, and these steps may be performed in other orders. Moreover, at least a part of the steps in FIGS. 2-3 may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed and completed at the same time, but may be executed at different times. These sub-steps or stages are not necessarily completed at the same time. The order of execution of the steps is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of sub-steps or stages of other steps.
  • a positioning and tracking system for a communication terminal includes:
  • the information acquisition module 310 is used to acquire the arrival angle information and the arrival time information of the uplink signal of the communication terminal;
  • a vector obtaining module 320 configured to obtain an observation vector for the communication terminal according to the angle of arrival information and the time of arrival information
  • the state estimation module 330 is configured to obtain the final state estimation value of the communication terminal according to the state estimation value and motion model probability of the observation vector under different motion models, wherein the different motion models correspond to the state estimation value of the communication terminal. Different sport modes.
  • the state estimation module 330 is further configured to obtain the state estimation value and covariance matrix estimation value under different motion models at the last moment; obtain the last moment according to the motion model probability and the preset transition probability at the last moment The model mixing probability at the moment; according to the state estimation value and the covariance matrix estimation value under different motion models at the last moment and the model mixing probability at the last moment, the Kalman son for the current moment of the different motion models is obtained.
  • the input value of the filter, according to the different motion models and the preset Kalman filter algorithm, the corresponding input values are processed respectively, and the output value of the Kalman sub-filter at the current moment for the different motion models is obtained as the output value for the different motion models.
  • the state estimation module 330 is further configured to obtain the first product of the transition probability between the first motion model and the second motion model and the probability of the first motion model at the previous moment; The sum of the transition probability between the second motion models and the corresponding second products of the probabilities of the respective motion models at the previous moment; the Model mixing probability between a motion model and a second motion model; wherein the first motion model and the second motion model are any two motion models among the respective motion models.
  • the positioning and tracking system of the communication terminal further includes a probability updating module 340 for obtaining the likelihood function of each motion model at the current moment; for any motion model, obtain the motion model The sum of the transition probability between the model and each motion model and the corresponding third product of the probabilities of each motion model at the previous moment; according to the likelihood function of the motion model at the current moment, the sum of the third products and the respective The sum of the likelihood functions of the motion model at the current moment is used to update the motion model probability of the motion model at the current moment.
  • a probability updating module 340 for obtaining the likelihood function of each motion model at the current moment; for any motion model, obtain the motion model The sum of the transition probability between the model and each motion model and the corresponding third product of the probabilities of each motion model at the previous moment; according to the likelihood function of the motion model at the current moment, the sum of the third products and the respective The sum of the likelihood functions of the motion model at the current moment is used to update the motion model probability of the motion model at the current moment.
  • the probability update module 340 is further configured to acquire the observation vector at several moments before the current moment to obtain a set of observation vectors; for any motion model, according to the motion model, the set of observation vectors and the set of observation vectors
  • the predetermined Kalman filtering algorithm is used to obtain the innovation sequence and the innovation covariance matrix of the motion model, and the likelihood function of the motion model at the current moment is obtained according to the innovation sequence and the innovation covariance matrix.
  • the positioning and tracking system of the communication terminal further includes a noise estimation module 350 for obtaining the estimated value of the overall observation noise covariance matrix at the current moment; for any motion model, obtain the the state prediction error covariance matrix of the motion model at the current moment, according to the estimated value of the overall observation noise covariance matrix, the state prediction error covariance matrix, the information sequence of the motion model and the observation vector at the current moment, Obtain the estimated value of the observed noise covariance matrix of the motion model at the next moment; obtain the overall observed noise covariance matrix of the next moment according to the estimated value of the observed noise covariance matrix of each motion model at the next moment and the probability of each motion model at the current moment estimated value.
  • a noise estimation module 350 for obtaining the estimated value of the overall observation noise covariance matrix at the current moment; for any motion model, obtain the the state prediction error covariance matrix of the motion model at the current moment, according to the estimated value of the overall observation noise covariance matrix, the state prediction error covariance matrix, the information sequence of the motion model
  • the positioning tracking system for the communication terminal further includes a tracking data rate setting module 360 for setting the tracking data rate for positioning tracking according to the final state estimation value of the communication terminal.
  • the different motion models include a uniform motion model, a uniform acceleration motion model, and a coordinated turning motion model.
  • the tracking data rate setting module 360 is further configured to obtain the acceleration of the communication terminal according to the state estimation value under the uniform acceleration motion model, and determine whether the communication terminal is in a motorized state according to the acceleration Status, if yes, set the tracking data rate of location tracking to the preset maximum value.
  • Each module in the above-mentioned positioning and tracking system of the communication terminal may be implemented in whole or in part by software, hardware and combinations thereof.
  • the above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
  • the positioning and tracking system of the above-mentioned communication terminal uses the angle of arrival information and the time of arrival information of the uplink signal of the communication terminal to obtain the observation vector for the communication terminal, and analyzes the state of the communication terminal under different motion models on the basis of the observation vector.
  • the uncertainty of the motion state of the communication terminal itself, combined with the motion model probabilities of different motion models, can determine the final state estimate value of the communication terminal. No matter what motion state the communication terminal is in, the positioning and tracking of the communication terminal can be realized, while ensuring Accuracy of location tracking.
  • a positioning and tracking device for a communication terminal includes a communication access device 410 and a data computing device 420;
  • the communication access device 410 is configured to receive data sent by a communication terminal, and obtain angle of arrival information and time of arrival information of the communication terminal according to the data;
  • the data computing device 420 is configured to use the positioning and tracking method of the communication terminal to perform positioning and tracking of the communication terminal.
  • the location tracking device of the communication terminal can be applied to 5G communication equipment
  • the communication access device 410 can be used as a 5G access node
  • the data computing device 420 can be used as an edge cloud data center.
  • 5G access The node consists of two modules, namely the 5G access node antenna module and the 5G access node signal processing module.
  • the 5G access node signal processing module needs to process the signal received by the 5G antenna to obtain the AOA and TOA information of the 5G terminal, and then They are uploaded to the edge cloud data center.
  • the edge cloud data center uses the MDR-AIMM algorithm to locate and track the 5G terminal. Therefore, the widely deployed 5G base station can be used for coverage.
  • the 5G terminal in the device performs continuous high-precision positioning and tracking, and then the situation of the 5G terminal can be displayed.
  • FIG. 11 is a schematic diagram of a computer device in an embodiment of the present application.
  • the computer device may be a terminal, and its internal structure diagram may be as shown in FIG. 11 .
  • the computer equipment includes a processor, memory, a network interface, a display screen, and an input device connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities.
  • the memory of the computer device includes a non-volatile storage medium, an internal memory.
  • the nonvolatile storage medium stores an operating system and a computer program.
  • the internal memory provides an environment for the execution of the operating system and computer programs in the non-volatile storage medium.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection.
  • the display screen of the computer equipment may be a liquid crystal display screen or an electronic ink display screen
  • the input device of the computer equipment may be a touch layer covered on the display screen, or a button, a trackball or a touchpad set on the shell of the computer equipment , or an external keyboard, trackpad, or mouse.
  • FIG. 11 is only a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer equipment to which the solution of the present application is applied. Include more or fewer components than shown in the figures, or combine certain components, or have a different arrangement of components.
  • Above-mentioned computer equipment utilizes the arrival angle information and the arrival time information of the uplink signal of the communication terminal to obtain the observation vector for the communication terminal, and analyzes the state of the communication terminal under different motion models on the basis of the observation vector.
  • the uncertainty of the state combined with the motion model probabilities of different motion models, can determine the final state estimation value of the communication terminal. No matter what motion state the communication terminal is in, the positioning and tracking of the communication terminal can be realized, and the accuracy of the positioning and tracking can be ensured at the same time. sex.
  • a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned method for positioning and tracking a communication terminal is implemented.
  • the above-mentioned computer-readable storage medium obtains the observation vector for the communication terminal by using the angle of arrival information and the arrival time information of the uplink signal of the communication terminal, and analyzes the state of the communication terminal under different motion models on the basis of the observation vector.
  • the uncertainty of the motion state of the terminal itself, combined with the motion model probabilities of different motion models, can determine the final state estimate value of the communication terminal. No matter what motion state the communication terminal is in, the positioning and tracking of the communication terminal can be realized, and the positioning can be guaranteed at the same time. Tracking accuracy.
  • Nonvolatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in various forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Road (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
  • SRAM static RAM
  • DRAM dynamic RAM
  • SDRAM synchronous DRAM
  • DDRSDRAM double data rate SDRAM
  • ESDRAM enhanced SDRAM
  • SLDRAM synchronous chain Road (Synchlink) DRAM
  • SLDRAM synchronous chain Road (Synchlink) DRAM
  • Rambus direct RAM
  • DRAM direct memory bus dynamic RAM
  • RDRAM memory bus dynamic RAM

Abstract

本申请涉及一种通信终端的定位跟踪方法、系统、设备和可读存储介质。所述通信终端的定位跟踪方法、系统、设备和可读存储介质，利用通信终端的上行信号的到达角信息和到达时间信息获取针对所述通信终端的观测矢量，在观测矢量的基础上分析在不同运动模型下的通信终端状态，由于通信终端本身运动状态的不确定性，结合不同运动模型的运动模型概率，可确定通信终端的最终状态估计值，无论通信终端处于何种运动状态，均可实现对通信终端的定位跟踪，同时保证定位跟踪的准确性。

Description

通信终端的定位跟踪方法、系统、设备和可读存储介质 技术领域
本申请涉及通信技术领域，特别是涉及一种通信终端的定位跟踪方法、系统、设备和可读存储介质。
背景技术
通信基站可以通过测量通信终端的上行参考信号来估计通信终端的位置。目前此种方式适用于定位通信终端，因而在通信基站广泛部署的情况下，可使用通信基站对其覆盖范围内的通信终端进行定位。
通信终端一般会不断运动，而相关的定位跟踪方式中运动建模通常采用简单的匀速运动模型。在实际应用中，通信终端运动模式不可能是简单的匀速运动，当通信终端实际运动模式与假设模型不符时定位跟踪精度会大大降低，产生模型失配问题。
针对相关技术中，因模型失配而导致通信终端的定位跟踪精度低的问题，目前尚未提出有效的解决方案。
发明内容
根据本申请的各种实施例，提供一种通信终端的定位跟踪方法，所述方法包括以下步骤：
获取通信终端的上行信号的到达角信息和到达时间信息，根据所述到达角信息和所述到达时间信息获取针对所述通信终端的观测矢量；
根据所述观测矢量在不同运动模型下的状态估计值和运动模型概率，获取所述通信终端的最终状态估计值，其中，所述不同运动模型对应所述通信终端的不同运动模式。
在其中一个实施例中，所述根据所述观测矢量在不同运动模型下的状态估计值和运动模型概率，获取所述通信终端的最终状态估计值的步骤包括：
获取上一时刻在不同运动模型下的状态估计值和协方差矩阵估计值；
根据上一时刻的运动模型概率和预设的过渡概率获取上一时刻的模型混合概率；
根据所述上一时刻在不同运动模型下的状态估计值和协方差矩阵估计值以及所述上一时刻的模型混合概率，获取针对不同运动模型的当前时刻的卡尔曼子滤波器的输入值，根据不同运动模型和预设卡尔曼滤波算法分别对对应的所述输入值进行处理，获取针对不同运动模型的当前时刻的卡尔曼子滤波器的输出值，作为针对不同运动模型的当前时刻的状态估计值 和协方差矩阵估计值。
在其中一个实施例中，所述根据上一时刻的运动模型概率和预设的过渡概率获取上一时刻的模型混合概率的步骤包括：
获取第一运动模型与第二运动模型之间的过渡概率和上一时刻的第一运动模型概率的第一乘积；
获取各个运动模型与所述第二运动模型之间的过渡概率和上一时刻的各个运动模型概率的对应第二乘积之和；
根据所述第一乘积与所述第二乘积之和的比值获取上一时刻的所述第一运动模型与第二运动模型之间的模型混合概率；
其中，所述第一运动模型和所述第二运动模型是各个运动模型中的任意两个运动模型。
在其中一个实施例中，所述方法还包括：
获取各个运动模型在当前时刻的似然函数；
针对任一运动模型，获取该运动模型与各个运动模型之间的过渡概率和上一时刻的各个运动模型概率的对应第三乘积之和；
根据该运动模型在当前时刻的似然函数、所述第三乘积之和以及所述各个运动模型在当前时刻的似然函数之和，对该运动模型在当前时刻的运动模型概率进行更新。
在其中一个实施例中，所述获取各个运动模型在当前时刻的似然函数的步骤包括：
获取在当前时刻之前的若干时刻的所述观测矢量，得到观测矢量集合；
针对任一运动模型，根据该运动模型、所述观测矢量集合和所述预设卡尔曼滤波算法获取该运动模型的新息序列和新息协方差矩阵，根据所述新息序列和所述新息协方差矩阵获取该运动模型在当前时刻的似然函数。
在其中一个实施例中，所述方法还包括：
获取当前时刻的整体观测噪声协方差矩阵估计值；
针对任一运动模型，获取该运动模型在当前时刻的状态预测误差协方差矩阵，根据所述整体观测噪声协方差矩阵估计值、所述状态预测误差协方差矩阵、该运动模型的新息序列和当前时刻的所述观测矢量，获取该运动模型在下一时刻的观测噪声协方差矩阵估计值；
根据各个运动模型在下一时刻的观测噪声协方差矩阵估计值和当前时刻的各个运动模型概率获取下一时刻的整体观测噪声协方差矩阵估计值。
在其中一个实施例中，所述方法还包括：
根据所述通信终端的最终状态估计值设置定位跟踪的跟踪数据率。
在其中一个实施例中，所述不同运动模型包括匀速运动模型、匀加速运动模型和协同 转弯运动模型。
在其中一个实施例中，所述方法还包括：
根据在所述匀加速运动模型下的状态估计值获取所述通信终端的加速度，根据所述加速度判断所述通信终端是否处于机动状态，若是，将定位跟踪的跟踪数据率设置为预设最大值。
根据本申请的各种实施例，还提供一种通信终端的定位跟踪系统，所述系统包括：
信息获取模块，用于获取通信终端的上行信号的到达角信息和到达时间信息；
矢量获取模块，用于根据所述到达角信息和所述到达时间信息获取针对所述通信终端的观测矢量；
状态估计模块，用于根据所述观测矢量在不同运动模型下的状态估计值和运动模型概率，获取所述通信终端的最终状态估计值，其中，所述不同运动模型对应所述通信终端的不同运动模式。
根据本申请的各种实施例，还提供一种通信终端的定位跟踪装置，所述装置包括通信接入设备和数据计算设备；
所述通信接入设备用于接收通信终端发送的数据，根据所述数据获取所述通信终端的到达角信息和到达时间信息；
所述数据计算设备用于采用所述通信终端的定位跟踪方法，对所述通信终端进行定位跟踪。
根据本申请的各种实施例，还提供一种计算机设备，所述计算机设备包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现所述通信终端的定位跟踪方法的步骤。
根据本申请的各种实施例，还提供一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现所述通信终端的定位跟踪方法的步骤。
本申请的有益效果如下：本申请的通信终端的定位跟踪方法、系统、设备和可读存储介质，利用通信终端的上行信号的到达角信息和到达时间信息获取针对所述通信终端的观测矢量，在观测矢量的基础上分析在不同运动模型下的通信终端状态，由于通信终端本身运动状态的不确定性，结合不同运动模型的运动模型概率，可确定通信终端的最终状态估计值，无论通信终端处于何种运动状态，均可实现对通信终端的定位跟踪，同时保证定位跟踪的准确性。
附图说明
为了更好地描述和说明这里公开的那些发明的实施例和/或示例，可以参考一幅或多 幅附图。用于描述附图的附加细节或示例不应当被认为是对所公开的发明、目前描述的实施例和/或示例以及目前理解的这些发明的最佳模式中的任何一者的范围的限制。
图1是本申请实施例的通信终端的定位跟踪方法的应用场景图。
图2是本申请实施例的通信终端的定位跟踪方法的流程示意图。
图3是本申请实施例的通信终端的定位跟踪方法中步骤S200的流程示意图。
图4是本申请实施例的MDR-AIMM算法的整体过程示意图。
图5是本申请实施例的通信终端的定位跟踪系统的结构示意图。
图6是本申请另一实施例的通信终端的定位跟踪系统的结构示意图。
图7是本申请再一实施例的通信终端的定位跟踪系统的结构示意图。
图8是本申请又一实施例的通信终端的定位跟踪系统的结构示意图。
图9是本申请实施例的通信终端的定位跟踪装置的结构示意图。
图10是本申请实施例的5G终端的定位跟踪装置的结构示意图。
图11是本申请实施例的计算机设备的示意图。
具体实施方式
为了便于理解本申请，为使本申请的上述目的、特征和优点能够更加明显易懂，下面结合附图对本申请的具体实施方式做详细的说明。在下面的描述中阐述了很多具体细节以便于充分理解本申请，附图中给出了本申请的较佳实施方式。但是，本申请可以以许多不同的形式来实现，并不限于本文所描述的实施方式。相反地，提供这些实施方式的目的是使对本申请的公开内容理解的更加透彻全面。本申请能够以很多不同于在此描述的其它方式来实施，本领域技术人员可以在不违背本申请内涵的情况下做类似改进，因此本申请不受下面公开的具体实施例的限制。
显而易见地，下面描述中的附图仅仅是本申请的一些示例或实施例，对于本领域的普通技术人员而言，在不付出创造性劳动的前提下，还可以根据这些附图将本申请应用于其他类似情景。此外，还可以理解的是，虽然这种开发过程中所作出的努力可能是复杂并且冗长的，然而对于与本申请公开的内容相关的本领域的普通技术人员而言，在本申请揭露的技术内容的基础上进行的一些设计，制造或者生产等变更只是常规的技术手段，不应当理解为本申请公开的内容不充分。
在本申请中提及“实施例”意味着，结合实施例描述的特定特征、结构或特性可以包含在本申请的至少一个实施例中。在说明书中的各个位置出现该短语并不一定均是指相同的实施例，也不是与其它实施例互斥的独立的或备选的实施例。本领域普通技术人员显式地和 隐式地理解的是，本申请所描述的实施例在不冲突的情况下，可以与其它实施例相结合。
除非另作定义，本申请所涉及的技术术语或者科学术语应当为本申请所属技术领域内具有一般技能的人士所理解的通常意义。本申请所涉及的“一”、“一个”、“一种”、“该”等类似词语并不表示数量限制，可表示单数或复数。本申请所涉及的术语“包括”、“包含”、“具有”以及它们任何变形，意图在于覆盖不排他的包含；例如包含了一系列步骤或模块(单元)的过程、方法、系统、产品或设备没有限定于已列出的步骤或单元，而是可以还包括没有列出的步骤或单元，或可以还包括对于这些过程、方法、产品或设备固有的其它步骤或单元。本申请所涉及的“连接”、“相连”、“耦接”等类似的词语并非限定于物理的或者机械的连接，而是可以包括电气的连接，不管是直接的还是间接的。本申请所涉及的“多个”、“各个”、“不同”是指两个或两个以上。“和/或”描述关联对象的关联关系，表示可以存在三种关系，例如，“A和/或B”可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。字符“/”一般表示前后关联对象是一种“或”的关系。本申请所涉及的术语“第一”、“第二”、“第三”等仅仅是区别类似的对象，不代表针对对象的特定排序。
图1是本申请实施例中通信终端的定位跟踪方法的应用场景图，如图1所示，基站可通过信号网络与通信终端进行通信，通信终端可以是具有互联网访问功能的电子设备，如智能手机、平板电脑、MP3播放器(Moving Picture Experts Group Audio Layer III，动态影像专家压缩标准音频层面3)、MP4(Moving Picture Experts Group Audio Layer IV，动态影像专家压缩标准音频层面4)播放器或便携式个人计算机、台式计算机、笔记本电脑等，通信终端可支持4G或5G通信，基站可以是4G或5G基站等，基站通过接收通信终端发送的信息，可对通信终端进行定位跟踪。
在一个实施例中，图2是本申请实施例中的通信终端的定位跟踪方法的流程示意图，以该方法应用于图1中的基站为例进行说明。通信终端的定位跟踪方法包括以下步骤：
步骤S100：获取通信终端的上行信号的到达角信息和到达时间信息，根据所述到达角信息和所述到达时间信息获取针对所述通信终端的观测矢量。
在本步骤中，通信终端可发送上行信号至基站，基站通过分析该上行信号，可得到该上行信号的到达角信息(Angle of Arrival，AOA)和到达时间信息(Time of Arrival，TOA)，根据所述到达角信息和所述到达时间信息可得到通信终端相对于基站的方位角、俯仰角和径向距离，进而构建针对所述通信终端的观测矢量。
具体的，可通过以下公式构建针对所述通信终端的观测矢量：
Figure PCTCN2020125057-appb-000001
Figure PCTCN2020125057-appb-000002
其中
Figure PCTCN2020125057-appb-000003
为k时刻在基站本地球坐标系下基站对通信终端的观测矢量，
Figure PCTCN2020125057-appb-000004
为k时刻在基站本地球坐标系下基站对通信终端的TOA观测值，
Figure PCTCN2020125057-appb-000005
为k时刻在基站本地球坐标系下基站对通信终端的方位角观测值，
Figure PCTCN2020125057-appb-000006
为k时刻在基站本地球坐标系下基站对通信终端的俯仰角观测值，w(k)为测量噪声，
Figure PCTCN2020125057-appb-000007
为通信终端在基站本地笛卡尔坐标系下的真实坐标值，c为光速，T表示矩阵转置。
步骤S200：根据所述观测矢量在不同运动模型下的状态估计值和运动模型概率，获取所述通信终端的最终状态估计值，其中，所述不同运动模型对应所述通信终端的不同运动模式。
在本步骤中，所述不同运动模型对应所述通信终端的不同运动模式，如匀速运动、匀加速运动，协同转弯运动等等，由于通信终端的实际运动状态多变，因此匀速运动模型可处理近似匀速运动，匀加速运动模型可处理近似匀加速运动，协同转弯运动模型可处理近似协同转弯运动等，由于通信终端的运动状态的不确定性，任何时刻通信终端的运动状态可能是多种运动的混合，运动模型概率表示通信终端的运动状态近似于运动模型的概率，因此采用运动模型概率对不同运动模型下的状态估计值进行综合估计，获取所述通信终端的最终状态估计值，提高所述通信终端的定位跟踪的准确性。
具体的，可通过以下公式获取针对所述通信终端的最终状态估计值：
Figure PCTCN2020125057-appb-000008
上式中，
Figure PCTCN2020125057-appb-000009
可表示k时刻所述通信终端的最终状态估计值，
Figure PCTCN2020125057-appb-000010
可表示在运动模型j下的状态估计值，u j(k)可表示对应运动模型j的运动模型概率，n表示运动模型的数量。
进一步的，在任一运动模型下的状态估计值可通过相应的模型算法对观测矢量进行计算处理后得到，在任一运动模型下的状态估计值可包括所述通信终端的位置、速度、加速度等信息。
在本实施例中，利用通信终端的上行信号的到达角信息和到达时间信息获取针对所述通信终端的观测矢量，在观测矢量的基础上分析在不同运动模型下的通信终端状态，由于通信终端本身运动状态的不确定性，结合不同运动模型的运动模型概率，可确定通信终端的最终状态估计值，无论通信终端处于何种运动状态，均可实现对通信终端的定位跟踪，同时保 证定位跟踪的准确性。
在一个实施例中，如图3所示，所述根据所述观测矢量在不同运动模型下的状态估计值和运动模型概率，获取所述通信终端的最终状态估计值的步骤包括：
步骤S210：获取上一时刻在不同运动模型下的状态估计值和协方差矩阵估计值；
步骤S220：根据上一时刻的运动模型概率和预设的过渡概率获取上一时刻的模型混合概率；
步骤S230：根据所述上一时刻在不同运动模型下的状态估计值和协方差矩阵估计值以及所述上一时刻的模型混合概率，获取针对不同运动模型的当前时刻的卡尔曼子滤波器的输入值，根据不同运动模型和预设卡尔曼滤波算法分别对对应的所述输入值进行处理，获取针对不同运动模型的当前时刻的卡尔曼子滤波器的输出值，作为针对不同运动模型的当前时刻的状态估计值和协方差矩阵估计值。
在本实施例中，在不同运动模型下可获取所述通信终端的状态估计值和协方差矩阵估计值，协方差矩阵估计值是用于评估状态估计值的准确度，在每个时刻均可得到所述通信终端的状态估计值和协方差矩阵估计值，而且当前时刻的状态估计值和协方差矩阵估计值是在所述上一时刻在不同运动模型下的状态估计值和协方差矩阵估计值以及所述上一时刻的模型混合概率的基础上，利用运动模型和预设卡尔曼滤波算法计算处理得到的，当前时刻每个运动模型下的状态估计值和协方差矩阵估计值均与上一时刻各个运动模型下的状态估计值和协方差矩阵估计值有关，体现定位跟踪的连续性，可减小定位跟踪的误差，保证定位跟踪的准确性。
需要说明的是，卡尔曼子滤波器与运动模型相对应，模型混合概率是指两个运动模型相互影响的概率，两个运动模型是不同的。
具体的，可通过以下公式获取针对不同运动模型的当前时刻的卡尔曼子滤波器的输入值，输入值可包括状态估计输入值和协方差矩阵估计输入值：
Figure PCTCN2020125057-appb-000011
Figure PCTCN2020125057-appb-000012
上式中，
Figure PCTCN2020125057-appb-000013
和
Figure PCTCN2020125057-appb-000014
分别为时刻k-1第i个卡尔曼子滤波器输出 的在基站本地笛卡尔坐标系下的状态估计值和协方差矩阵估计值，n为运动模型的数量，w k-1(i|j)为时刻k-1的第i个和第j个运动模型之间的模型混合概率，
Figure PCTCN2020125057-appb-000015
和
Figure PCTCN2020125057-appb-000016
分别为第j个卡尔曼子滤波器的输入；
将
Figure PCTCN2020125057-appb-000017
作为k时刻第j个卡尔曼子滤波器的输入，并依据运动模型j和预设卡尔曼滤波算法得到k时刻第j个卡尔曼子滤波器的输出，即
Figure PCTCN2020125057-appb-000018
和
Figure PCTCN2020125057-appb-000019
进一步的，在初始时刻，可根据初始时刻得到观测矢量，确定所述通信终端的初始位置，将上一时刻所述通信终端的的位置、速度、加速度等设置为默认值(如0或默认最小值等)，根据初始位置和默认值获取距离差和角度差以及相应的误差，据此进一步计算得到初始时刻所述通信终端的状态估计值和协方差矩阵估计值，此种方式适用于初始时刻速度较低的情形；或者，在确定初始时刻之前，先试测一个位置，将与该位置连续的第二位置的时刻作为初始时刻，根据两个位置的观测矢量获取距离差和角度差以及相应的误差，得到位置、速度、加速度等，进而确定初始时刻所述通信终端的状态估计值和协方差矩阵估计值，此种方式适用于初始时刻速度较高的情形。
进一步的，预设卡尔曼滤波算法可包括无迹卡尔曼滤波(Unscented Kalman Filter，UKF)算法、容积卡尔曼滤波(Cubature Kalman Filter，CKF)算法或中心差分卡尔曼滤波(Central Difference Kalman Filter，CDKF)等。
进一步的，在获取针对所述通信终端的最终状态估计值后，还可根据运动模型概率和所述通信终端的最终状态估计值获取所述通信终端的状态估计误差协方差矩阵，可通过以下公式实现：
Figure PCTCN2020125057-appb-000020
上式中，
Figure PCTCN2020125057-appb-000021
表示所述通信终端的状态估计误差协方差矩阵，n为运动模型的数量，u j(k)表示运动模型j在时刻k的运动模型概率。
在一个实施例中，所述根据上一时刻的运动模型概率和预设的过渡概率获取上一时刻的模型混合概率的步骤包括：
获取第一运动模型与第二运动模型之间的过渡概率和上一时刻的第一运动模型概率的第一乘积；
获取各个运动模型与所述第二运动模型之间的过渡概率和上一时刻的各个运动模型概率 的对应第二乘积之和；
根据所述第一乘积与所述第二乘积之和的比值获取上一时刻的所述第一运动模型与第二运动模型之间的模型混合概率；
其中，所述第一运动模型和所述第二运动模型是各个运动模型中的任意两个运动模型。
在本实施例中，过渡概率是指处于任意两个不同运动模型之间的概率，将过渡概率与运动模型概率相结合，更好地涵盖通信终端处于运动模型之间的运动状态，使得模型混合概率更加准确。
具体的，可通过以下公式获取上一时刻的所述第一运动模型与第二运动模型之间的模型混合概率：
Figure PCTCN2020125057-appb-000022
上式中，ρ ij为运动模型i和运动模型j之间的过渡概率，u i(k-1)为时刻k-1的运动模型i的运动模型概率，运动模型i可代表第一运动模型，运动模型j可代表第二运动模型，n为运动模型的数量。
在一个实施例中，所述方法还包括：
获取各个运动模型在当前时刻的似然函数；
针对任一运动模型，获取该运动模型与各个运动模型之间的过渡概率和上一时刻的各个运动模型概率的对应第三乘积之和；
根据该运动模型在当前时刻的似然函数、所述第三乘积之和以及所述各个运动模型在当前时刻的似然函数之和，对该运动模型在当前时刻的运动模型概率进行更新。
在本实施例中，由于针对不同运动模型的当前时刻的状态估计值和协方差矩阵估计值与上一时刻的状态估计值和协方差矩阵估计值有关，因而运动模型概率也需要适当进行更新，可利用各个运动模型在当前时刻的似然函数、过渡概率和运动模型概率对运动模型概率进行更新，以适应所述通信终端的运动状态变化。
具体的，可通过以下公式实现运动模型概率进行更新：
Figure PCTCN2020125057-appb-000023
上式中，u j(k)表示运动模型j在时刻k的运动模型概率，η j(k)表示运动模型j在当前时刻的似然函数，ρ ij为运动模型i和运动模型j之间的过渡概率，u i(k-1)为时刻k-1的运动模型i的运动模型概率，n表示运动模型的数量。
在一个实施例中，所述获取各个运动模型在当前时刻的似然函数的步骤包括：
获取在当前时刻之前的若干时刻的所述观测矢量，得到观测矢量集合；
针对任一运动模型，根据该运动模型、所述观测矢量集合和所述预设卡尔曼滤波算法获取该运动模型的新息序列和新息协方差矩阵，根据所述新息序列和所述新息协方差矩阵获取该运动模型在当前时刻的似然函数。
在本实施例中，任一运动模型在当前时刻的似然函数与所述通信终端在之前一段时间的运动状态有关，通过获取在当前时刻之前的若干时刻的所述观测矢量，用得到的观测矢量集合来表征所述通信终端在之前一段时间的运动状态，利用相应的运动模型和所述预设卡尔曼滤波算法对所述观测矢量集合进行计算，获取相应的运动模型的新息序列和新息协方差矩阵，以此进一步得到相应的运动模型在当前时刻的似然函数，该似然函数可辅助反映运动模型概率。
具体的，可通过以下公式确定运动模型在当前时刻的似然函数：
Figure PCTCN2020125057-appb-000024
上式中，v j(k)表示新息序列，S j(k)表示新息协方差矩阵。
进一步的，所述观测矢量集合中的所述观测矢量所述的若干时刻可根据需要进行选取，如从时刻1至时刻k-1等。
在一个实施例中，所述方法还包括：
获取当前时刻的整体观测噪声协方差矩阵估计值；
针对任一运动模型，获取该运动模型在当前时刻的状态预测误差协方差矩阵，根据所述整体观测噪声协方差矩阵估计值、所述状态预测误差协方差矩阵、该运动模型的新息序列和当前时刻的所述观测矢量，获取该运动模型在下一时刻的观测噪声协方差矩阵估计值；
根据各个运动模型在下一时刻的观测噪声协方差矩阵估计值和当前时刻的各个运动模型概率获取下一时刻的整体观测噪声协方差矩阵估计值。
在本实施例中，由于实际环境中电磁环境复杂多变，从而导致观测噪声出现剧烈变化，因此需要充分考虑观测噪声不确定问题，即观测噪声失配问题，进而实现在观测噪声动态变化情况下的高精度定位跟踪，因此，通过获取当前时刻的整体观测噪声协方差矩阵估计值，结合运动模型，获取下一时刻的整体观测噪声协方差矩阵估计值，从而在定位跟踪时及时参考整体观测噪声的变化，提高定位跟踪的准确性。
具体的，可通过以下公式获取下一时刻的整体观测噪声协方差矩阵估计值：
Figure PCTCN2020125057-appb-000025
Figure PCTCN2020125057-appb-000026
上式中，b为渐消因子，d(k)表示中间变量，通常取0.9或其他近似值，
Figure PCTCN2020125057-appb-000027
为运动模型j在k+1时刻的观测噪声协方差矩阵估计值，
Figure PCTCN2020125057-appb-000028
为k时刻的整体观测噪声协方差矩阵估计值，v j(k)表示新息序列，
Figure PCTCN2020125057-appb-000029
为k时刻运动模型j的状态预测误差协方差矩阵，H为所述观测矢量的雅可比矩阵。需要说明的是，在初始时刻的观测噪声协方差矩阵估计值可设置为默认值，或通过测试设置。
在一个实施例中，所述方法还包括：
根据所述通信终端的最终状态估计值设置定位跟踪的跟踪数据率。
在本实施例中，相关技术中通信终端的跟踪数据率固定不变，由于不同运动状态对跟踪数据率的需求不同，因此，可依据所述通信终端的运动状态分配不同的跟踪数据率，从而达到提高系统容量或者降低计算和能量资源消耗的目的。
在一个实施例中，所述不同运动模型包括匀速运动模型、匀加速运动模型和协同转弯运动模型。具体的运动模型可根据实际应用情况进行增加或删减，也可以包括其他运动模式的运动模型。
在一个实施例中，所述方法还包括：
根据在所述匀加速运动模型下的状态估计值获取所述通信终端的加速度，根据所述加速度判断所述通信终端是否处于机动状态，若是，将定位跟踪的跟踪数据率设置为预设最大值。
在本实施例中，可在匀加速运动模型下得到所述通信终端的加速度信息，以此判断所述通信终端是否处于机动状态，在所述通信终端处于机动状态时，所述通信终端的运动状态变化较为迅速，此时可将定位跟踪的跟踪数据率设置为预设最大值，以实现快速准确的定位跟踪。
进一步的，在判断所述通信终端是否处于机动状态之前，可对所述加速度进行平滑处理，具体可按照以下公式实现：
a s(k)＝αa s(k-1)+(1-α)a(k)
上式中，a s(k)为k时刻平滑后的所述通信终端的加速度，a(k)为k时刻利用匀加速运动模型估计的通信终端的加速度，α为平滑因子，通常取0.9或其近似值。
进一步的，在判断所述通信终端是否处于机动状态时，可根据连续N个a s(k)中是否有M个点递增，确定所述通信终端是否发生了机动，当存在M个点递增时，所述通信终端 处于机动状态，此时，可令定位跟踪的采样间隔为最小的采样间隔，即将跟踪数据率最大化。
在一个具体的实施例中，通信终端的定位跟踪方法可应用于5G基站和5G终端，5G基站可以通过测量5G终端的上行参考信号的到达角和到达时间，从而得到5G终端的方位角、俯仰角和径向距离。5G基站可依据自身的位置以及测量的5G终端方位角、俯仰角和径向距离估计5G通信终端的位置。目前到达角的测量精度可达3度以内，径向距离的测量精度可达1米以内，因而使用单个5G基站即可对其覆盖范围内的5G终端进行高精度定位跟踪。
相关技术中主要采用扩展卡尔曼滤波器(Extended Kalman Filter,EKF)进行5G终端的定位跟踪，考虑到EKF算法容易出现不稳定问题且精度较低，因此本申请采用无迹卡尔曼滤波、容积卡尔曼滤波或中心差分卡尔曼滤波进行5G终端的定位跟踪，此外，由于不同运动状态对跟踪数据率的需求不同，可依据5G终端的运动状态分配不同的跟踪数据率，从而达到提高系统容量或者降低计算和能量资源消耗的目的。
5G终端的定位跟踪方法的具体过程如下：
(1)依据5G基站观测信息建立观测方程
Figure PCTCN2020125057-appb-000030
其中
Figure PCTCN2020125057-appb-000031
为k时刻在5G基站本地球坐标系下5G基站对5G终端的观测矢量，
Figure PCTCN2020125057-appb-000032
为k时刻在5G基站本地球坐标系下5G基站对5G终端的TOA观测值，
Figure PCTCN2020125057-appb-000033
为k时刻在5G基站本地球坐标系下5G基站对5G终端的方位角观测值，
Figure PCTCN2020125057-appb-000034
为k时刻在5G基站本地球坐标系下5G基站对5G终端的俯仰角观测值，w(k)为测量噪声，
Figure PCTCN2020125057-appb-000035
为5G终端在5G基站本地笛卡尔坐标系下的真实坐标值，c为光速，tan -1()为反正切函数，T表示矩阵转置。
(2)对于5G终端而言，其运动模式大体可以分为三类即近匀速(Near Constant velocity，NCV)运动、近匀加速(Near Constant Acceleration，NCA)运动以及近协同转弯(Near Coordinated Turn，NCT)运动，根据5G终端的运动模式，构造适用于5G终端定位跟踪的交互多模型(Interacting Multiple Model，IMM)算法，本申请中IMM算法共包括上述的三个运动模型，分别记为模型1、模型2和模型3，即三个状态方程，运动模式间的转换由过渡概率ρ ij控制，ρ ij在计算开始时选取，可依据实际中各个运动模式的经验占用时间确定，由于使用5G基站对5G终端进行定位跟踪时观测方程是非线性的，因此本申请可采用UKF进行5G终 端状态估计，用于5G终端运动模式失配问题的IMM算法如下所示：
1)输入交互
Figure PCTCN2020125057-appb-000036
Figure PCTCN2020125057-appb-000037
其中，
Figure PCTCN2020125057-appb-000038
和
Figure PCTCN2020125057-appb-000039
分别为时刻k-1第i个子滤波器在5G基站本地笛卡尔坐标系下的状态估计值和协方差矩阵估计值，u i(k-1)为时刻k-1的模型概率，ρ ij为过渡概率，
Figure PCTCN2020125057-appb-000040
和
Figure PCTCN2020125057-appb-000041
分别为子滤波器j的输入，w k-1(i|j)为混合概率。
2)UKF滤波
将
Figure PCTCN2020125057-appb-000042
作为k时刻第j个子滤波器的输入，并依据模型j和UKF算法得到k时刻第j个子滤波器的输出，即
Figure PCTCN2020125057-appb-000043
和
Figure PCTCN2020125057-appb-000044
3)模型概率更新
Figure PCTCN2020125057-appb-000045
Figure PCTCN2020125057-appb-000046
其中，η j(k)为模型j在时刻k的似然函数，v j(k)为利用UKF算法和模型j输出的新息序列，S j(k)为利用UKF算法和模型j输出的新息协方差矩阵。
4)联合估计
Figure PCTCN2020125057-appb-000047
Figure PCTCN2020125057-appb-000048
其中
Figure PCTCN2020125057-appb-000049
为5G终端的最终状态估计值，
Figure PCTCN2020125057-appb-000050
为相应的状态估计误差协方差矩阵。
(3)根据5G终端定位跟踪的交互多模型框架，构造适用于所述移动终端定位跟踪 的观测噪声在线实时估计模型，从而得到下一时刻的观测噪声估计值：
Figure PCTCN2020125057-appb-000051
Figure PCTCN2020125057-appb-000052
Figure PCTCN2020125057-appb-000053
其中b为渐消因子通常取0.9，
Figure PCTCN2020125057-appb-000054
为模型j在k+1时刻的观测噪声协方差矩阵估计值，
Figure PCTCN2020125057-appb-000055
为k时刻的观测噪声协方差矩阵估计值，
Figure PCTCN2020125057-appb-000056
为k时刻运动模型j的状态预测误差协方差矩阵，H为观测方程的雅可比矩阵。
(4)根据5G终端的运动状态，构造适用于所述移动终端定位跟踪的多数据率定位跟踪算法：
在实际应用中，可依据5G终端的运动状态设置不同的跟踪数据率，可在满足其定位精度需求的同时最大限度降低计算资源和能量资源的消耗，同时也可以进一步采用多数据率定位跟踪方法算法提高5G终端运动模式改变时的定位跟踪精度。本步骤使用下式所示的方法对5G终端的运动模式进行在线实时估计，并据此调整对5G终端的跟踪数据率：
a s(k)＝αa s(k-1)+(1-α)a(k)
其中a s(k)为k时刻平滑后的5G终端加速度，a(k)为k时刻利用NCA模型估计的5G终端加速度，α为平滑因子，通常取0.9，借M/N准则，即可以根据判断连续N个a s(k)中是否有M个点递增，来确定5G终端是否发生了机动，当存在M个点递增时目标处于机动状态，此时，令定位跟踪的采样间隔为最小的采样间隔。
利用上述(1)至(4)的步骤过程可得到用于5G终端定位跟踪的多数据率自适应IMM(Multiple Data Rate Adaptive IMM,MDR-AIMM)算法，MDR-AIMM算法的整体过程如图4所示。
应该理解的是，虽然图2-3中的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，图2-3中的至少一部分步骤可以包括多个子步骤或者多个阶段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，这些子步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。
根据本申请的另一个方面，提供了一种通信终端的定位跟踪系统，如图5所示，通信终端的定位跟踪系统包括：
信息获取模块310，用于获取通信终端的上行信号的到达角信息和到达时间信息；
矢量获取模块320，用于根据所述到达角信息和所述到达时间信息获取针对所述通信终端的观测矢量；
状态估计模块330，用于根据所述观测矢量在不同运动模型下的状态估计值和运动模型概率，获取所述通信终端的最终状态估计值，其中，所述不同运动模型对应所述通信终端的不同运动模式。
在一个实施例中，状态估计模块330还用于获取上一时刻在不同运动模型下的状态估计值和协方差矩阵估计值；根据上一时刻的运动模型概率和预设的过渡概率获取上一时刻的模型混合概率；根据所述上一时刻在不同运动模型下的状态估计值和协方差矩阵估计值以及所述上一时刻的模型混合概率，获取针对不同运动模型的当前时刻的卡尔曼子滤波器的输入值，根据不同运动模型和预设卡尔曼滤波算法分别对对应的所述输入值进行处理，获取针对不同运动模型的当前时刻的卡尔曼子滤波器的输出值，作为针对不同运动模型的当前时刻的状态估计值和协方差矩阵估计值。
在一个实施例中，状态估计模块330还用于获取第一运动模型与第二运动模型之间的过渡概率和上一时刻的第一运动模型概率的第一乘积；获取各个运动模型与所述第二运动模型之间的过渡概率和上一时刻的各个运动模型概率的对应第二乘积之和；根据所述第一乘积与所述第二乘积之和的比值获取上一时刻的所述第一运动模型与第二运动模型之间的模型混合概率；其中，所述第一运动模型和所述第二运动模型是各个运动模型中的任意两个运动模型。
在一个实施例中，如图6所示，所述通信终端的定位跟踪系统还包括概率更新模块340，用于获取各个运动模型在当前时刻的似然函数；针对任一运动模型，获取该运动模型与各个运动模型之间的过渡概率和上一时刻的各个运动模型概率的对应第三乘积之和；根据该运动模型在当前时刻的似然函数、所述第三乘积之和以及所述各个运动模型在当前时刻的似然函数之和，对该运动模型在当前时刻的运动模型概率进行更新。
在一个实施例中，概率更新模块340还用于获取在当前时刻之前的若干时刻的所述观测矢量，得到观测矢量集合；针对任一运动模型，根据该运动模型、所述观测矢量集合和所述预设卡尔曼滤波算法获取该运动模型的新息序列和新息协方差矩阵，根据所述新息序列和所述新息协方差矩阵获取该运动模型在当前时刻的似然函数。
在一个实施例中，如图7所示，所述通信终端的定位跟踪系统还包括噪声估计模块350，用于获取当前时刻的整体观测噪声协方差矩阵估计值；针对任一运动模型，获取该运动模型在当前时刻的状态预测误差协方差矩阵，根据所述整体观测噪声协方差矩阵估计值、所 述状态预测误差协方差矩阵、该运动模型的新息序列和当前时刻的所述观测矢量，获取该运动模型在下一时刻的观测噪声协方差矩阵估计值；根据各个运动模型在下一时刻的观测噪声协方差矩阵估计值和当前时刻的各个运动模型概率获取下一时刻的整体观测噪声协方差矩阵估计值。
在一个实施例中，如图8所示，所述通信终端的定位跟踪系统还包括跟踪数据率设置模块360，用于根据所述通信终端的最终状态估计值设置定位跟踪的跟踪数据率。
在一个实施例中，所述不同运动模型包括匀速运动模型、匀加速运动模型和协同转弯运动模型。
在一个实施例中，所述跟踪数据率设置模块360还用于根据在所述匀加速运动模型下的状态估计值获取所述通信终端的加速度，根据所述加速度判断所述通信终端是否处于机动状态，若是，将定位跟踪的跟踪数据率设置为预设最大值。
关于通信终端的定位跟踪系统的具体限定可以参见上文中对于通信终端的定位跟踪方法的限定，在此不再赘述。上述通信终端的定位跟踪系统中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于计算机设备中的处理器中，也可以以软件形式存储于计算机设备中的存储器中，以便于处理器调用执行以上各个模块对应的操作。
上述通信终端的定位跟踪系统，利用通信终端的上行信号的到达角信息和到达时间信息获取针对所述通信终端的观测矢量，在观测矢量的基础上分析在不同运动模型下的通信终端状态，由于通信终端本身运动状态的不确定性，结合不同运动模型的运动模型概率，可确定通信终端的最终状态估计值，无论通信终端处于何种运动状态，均可实现对通信终端的定位跟踪，同时保证定位跟踪的准确性。
根据本申请的另一个方面，提供了一种通信终端的定位跟踪装置，如图9所示，通信终端的定位跟踪装置包括通信接入设备410和数据计算设备420；
通信接入设备410用于接收通信终端发送的数据，根据所述数据获取所述通信终端的到达角信息和到达时间信息；
数据计算设备420用于采用所述通信终端的定位跟踪方法，对所述通信终端进行定位跟踪。
在实际应用中，通信终端的定位跟踪装置可应用于5G通信设备，通信接入设备410可作为5G接入节点，数据计算设备420可作为边缘云数据中心，如图10所示，5G接入节点由两个模块组成即5G接入节点天线模块和5G接入节点信号处理模块，5G接入节点信号处理模块需要将5G天线接收到的信号进行处理得到5G终端的AOA和TOA信息，然后将它 们上传至边缘云数据中心，边缘云数据中心在接收到某个5G终端的AOA和TOA信息后就使用MDR-AIMM算法对5G终端进行定位跟踪，因此，利用广泛部署的5G基站可对覆盖范围内的5G终端进行连续的高精度定位跟踪，随后可以对5G终端的态势进行显示。
在一个实施例中，图11是本申请一个实施例中计算机设备的示意图，该计算机设备可以是终端，其内部结构图可以如图11所示。该计算机设备包括通过系统总线连接的处理器、存储器、网络接口、显示屏和输入装置。其中，该计算机设备的处理器用于提供计算和控制能力。该计算机设备的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作系统和计算机程序。该内存储器为非易失性存储介质中的操作系统和计算机程序的运行提供环境。该计算机设备的网络接口用于与外部的终端通过网络连接通信。该计算机程序被处理器执行时以实现一种通信终端的定位跟踪方法。该计算机设备的显示屏可以是液晶显示屏或者电子墨水显示屏，该计算机设备的输入装置可以是显示屏上覆盖的触摸层，也可以是计算机设备外壳上设置的按键、轨迹球或触控板，还可以是外接的键盘、触控板或鼠标等。
本领域技术人员可以理解，图11中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
上述计算机设备，利用通信终端的上行信号的到达角信息和到达时间信息获取针对所述通信终端的观测矢量，在观测矢量的基础上分析在不同运动模型下的通信终端状态，由于通信终端本身运动状态的不确定性，结合不同运动模型的运动模型概率，可确定通信终端的最终状态估计值，无论通信终端处于何种运动状态，均可实现对通信终端的定位跟踪，同时保证定位跟踪的准确性。
根据本申请的另一个方面，提供了一种计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现上述通信终端的定位跟踪方法。
上述计算机可读存储介质，利用通信终端的上行信号的到达角信息和到达时间信息获取针对所述通信终端的观测矢量，在观测矢量的基础上分析在不同运动模型下的通信终端状态，由于通信终端本身运动状态的不确定性，结合不同运动模型的运动模型概率，可确定通信终端的最终状态估计值，无论通信终端处于何种运动状态，均可实现对通信终端的定位跟踪，同时保证定位跟踪的准确性。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一非易失性计算机可读取存储介质中，该计算机程序在执行时，可包括如上述各方法的实施例的流程。其中，本申请所提供的各实施例中所使用的对存储器、存储、数据库或其它介质的任何引用，均可包括非 易失性和/或易失性存储器。非易失性存储器可包括只读存储器(ROM)、可编程ROM(PROM)、电可编程ROM(EPROM)、电可擦除可编程ROM(EEPROM)或闪存。易失性存储器可包括随机存取存储器(RAM)或者外部高速缓冲存储器。作为说明而非局限，RAM以多种形式可得，诸如静态RAM(SRAM)、动态RAM(DRAM)、同步DRAM(SDRAM)、双数据率SDRAM(DDRSDRAM)、增强型SDRAM(ESDRAM)、同步链路(Synchlink)DRAM(SLDRAM)、存储器总线(Rambus)直接RAM(RDRAM)、直接存储器总线动态RAM(DRDRAM)、以及存储器总线动态RAM(RDRAM)等。
以上所述实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上所述实施例仅表达了本发明的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进，这些都属于本发明的保护范围。因此，本发明专利的保护范围应以所附权利要求为准。

Claims (13)

  1. 一种通信终端的定位跟踪方法，其特征在于，所述方法包括：
    获取通信终端的上行信号的到达角信息和到达时间信息，根据所述到达角信息和所述到达时间信息获取针对所述通信终端的观测矢量；
    根据所述观测矢量在不同运动模型下的状态估计值和运动模型概率，获取所述通信终端的最终状态估计值，其中，所述不同运动模型对应所述通信终端的不同运动模式。
  2. 根据权利要求1所述的方法，其特征在于，所述根据所述观测矢量在不同运动模型下的状态估计值和运动模型概率，获取所述通信终端的最终状态估计值的步骤包括：
    获取上一时刻在不同运动模型下的状态估计值和协方差矩阵估计值；
    根据上一时刻的运动模型概率和预设的过渡概率获取上一时刻的模型混合概率；
    根据所述上一时刻在不同运动模型下的状态估计值和协方差矩阵估计值以及所述上一时刻的模型混合概率，获取针对不同运动模型的当前时刻的卡尔曼子滤波器的输入值，根据不同运动模型和预设卡尔曼滤波算法分别对对应的所述输入值进行处理，获取针对不同运动模型的当前时刻的卡尔曼子滤波器的输出值，作为针对不同运动模型的当前时刻的状态估计值和协方差矩阵估计值。
  3. 根据权利要求2所述的方法，其特征在于，所述根据上一时刻的运动模型概率和预设的过渡概率获取上一时刻的模型混合概率的步骤包括：
    获取第一运动模型与第二运动模型之间的过渡概率和上一时刻的第一运动模型概率的第一乘积；
    获取各个运动模型与所述第二运动模型之间的过渡概率和上一时刻的各个运动模型概率的对应第二乘积之和；
    根据所述第一乘积与所述第二乘积之和的比值获取上一时刻的所述第一运动模型与第二运动模型之间的模型混合概率；
    其中，所述第一运动模型和所述第二运动模型是各个运动模型中的任意两个运动模型。
  4. 根据权利要求2所述的方法，其特征在于，所述方法还包括：
    获取各个运动模型在当前时刻的似然函数；
    针对任一运动模型，获取该运动模型与各个运动模型之间的过渡概率和上一时刻的各个运动模型概率的对应第三乘积之和；
    根据该运动模型在当前时刻的似然函数、所述第三乘积之和以及所述各个运动模型在当前时刻的似然函数之和，对该运动模型在当前时刻的运动模型概率进行更新。
  5. 根据权利要求4所述的方法，其特征在于，所述获取各个运动模型在当前时刻的似然函数的步骤包括：
    获取在当前时刻之前的若干时刻的所述观测矢量，得到观测矢量集合；
    针对任一运动模型，根据该运动模型、所述观测矢量集合和所述预设卡尔曼滤波算法获取该运动模型的新息序列和新息协方差矩阵，根据所述新息序列和所述新息协方差矩阵获取该运动模型在当前时刻的似然函数。
  6. 根据权利要求5所述的方法，其特征在于，所述方法还包括：
    获取当前时刻的整体观测噪声协方差矩阵估计值；
    针对任一运动模型，获取该运动模型在当前时刻的状态预测误差协方差矩阵，根据所述整体观测噪声协方差矩阵估计值、所述状态预测误差协方差矩阵、该运动模型的新息序列和当前时刻的所述观测矢量，获取该运动模型在下一时刻的观测噪声协方差矩阵估计值；
    根据各个运动模型在下一时刻的观测噪声协方差矩阵估计值和当前时刻的各个运动模型概率获取下一时刻的整体观测噪声协方差矩阵估计值。
  7. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    根据所述通信终端的最终状态估计值设置定位跟踪的跟踪数据率。
  8. 根据权利要求1至7中任一项所述的方法，其特征在于，所述不同运动模型包括匀速运动模型、匀加速运动模型和协同转弯运动模型。
  9. 根据权利要求8所述的方法，其特征在于，所述方法还包括：
    根据在所述匀加速运动模型下的状态估计值获取所述通信终端的加速度，根据所述加速度判断所述通信终端是否处于机动状态，若是，将定位跟踪的跟踪数据率设置为预设最大值。
  10. 一种通信终端的定位跟踪系统，其特征在于，所述系统包括：
    信息获取模块，用于获取通信终端的上行信号的到达角信息和到达时间信息；
    矢量获取模块，用于根据所述到达角信息和所述到达时间信息获取针对所述通信终端的观测矢量；
    状态估计模块，用于根据所述观测矢量在不同运动模型下的状态估计值和运动模型概率，获取所述通信终端的最终状态估计值，其中，所述不同运动模型对应所述通信终端的不同运动模式。
  11. 一种通信终端的定位跟踪装置，其特征在于，所述装置包括通信接入设备和数据计算设备；
    所述通信接入设备用于接收通信终端发送的数据，根据所述数据获取所述通信终端的到达角信息和到达时间信息；
    所述数据计算设备用于采用如权利要求1至9中任一项所述的通信终端的定位跟踪方法，对所述通信终端进行定位跟踪。
  12. 一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现权利要求1至9中任一项所述方法的步骤。
  13. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现权利要求1至9中任一项所述的方法的步骤。
PCT/CN2020/125057 2020-10-30 2020-10-30 通信终端的定位跟踪方法、系统、设备和可读存储介质 WO2022087998A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2020/125057 WO2022087998A1 (zh) 2020-10-30 2020-10-30 通信终端的定位跟踪方法、系统、设备和可读存储介质

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2020/125057 WO2022087998A1 (zh) 2020-10-30 2020-10-30 通信终端的定位跟踪方法、系统、设备和可读存储介质

Publications (1)

Publication Number Publication Date
WO2022087998A1 true WO2022087998A1 (zh) 2022-05-05

Family

ID=81381766

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/125057 WO2022087998A1 (zh) 2020-10-30 2020-10-30 通信终端的定位跟踪方法、系统、设备和可读存储介质

Country Status (1)

Country Link
WO (1) WO2022087998A1 (zh)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115499289A (zh) * 2022-08-17 2022-12-20 华电电力科学研究院有限公司 一种设备状态评估预警方法和系统
CN115902980A (zh) * 2023-02-15 2023-04-04 东方空间(西安)宇航技术有限公司 可在线更新的飞行目标天基监视与预测方法及装置
CN116299183A (zh) * 2023-05-24 2023-06-23 至控科技(湖州)创新研究院有限公司 一种使用扩展卡尔曼滤波估计声速的超声定位方法及系统

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1694567A (zh) * 2005-04-29 2005-11-09 重庆邮电学院 一种单基站定位移动终端的方法
WO2009084859A2 (en) * 2007-12-28 2009-07-09 Samsung Electronics Co., Ltd. Time-difference estimation method for location based services in a cellular network
CN103853908A (zh) * 2012-12-04 2014-06-11 中国科学院沈阳自动化研究所 一种自适应交互式多模型的机动目标跟踪方法
CN105493585A (zh) * 2013-12-25 2016-04-13 华为技术有限公司 上行参考信号配置的方法、基站、定位服务器及系统
CN106546976A (zh) * 2016-10-31 2017-03-29 中国电子科技集团公司第二十九研究所 一种基于长周期非均匀采样目标跟踪处理方法及装置
CN107993257A (zh) * 2017-12-28 2018-05-04 中国科学院西安光学精密机械研究所 一种智能imm卡尔曼滤波前馈补偿目标追踪方法及系统
CN108293172A (zh) * 2015-10-08 2018-07-17 波尔特公司 用于追踪对象的到达角度定位系统
CN109031187A (zh) * 2018-05-24 2018-12-18 国网四川省电力公司宜宾供电公司 一种基于uwb技术的无线定位监控系统及方法

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1694567A (zh) * 2005-04-29 2005-11-09 重庆邮电学院 一种单基站定位移动终端的方法
WO2009084859A2 (en) * 2007-12-28 2009-07-09 Samsung Electronics Co., Ltd. Time-difference estimation method for location based services in a cellular network
CN103853908A (zh) * 2012-12-04 2014-06-11 中国科学院沈阳自动化研究所 一种自适应交互式多模型的机动目标跟踪方法
CN105493585A (zh) * 2013-12-25 2016-04-13 华为技术有限公司 上行参考信号配置的方法、基站、定位服务器及系统
CN108293172A (zh) * 2015-10-08 2018-07-17 波尔特公司 用于追踪对象的到达角度定位系统
CN106546976A (zh) * 2016-10-31 2017-03-29 中国电子科技集团公司第二十九研究所 一种基于长周期非均匀采样目标跟踪处理方法及装置
CN107993257A (zh) * 2017-12-28 2018-05-04 中国科学院西安光学精密机械研究所 一种智能imm卡尔曼滤波前馈补偿目标追踪方法及系统
CN109031187A (zh) * 2018-05-24 2018-12-18 国网四川省电力公司宜宾供电公司 一种基于uwb技术的无线定位监控系统及方法

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115499289A (zh) * 2022-08-17 2022-12-20 华电电力科学研究院有限公司 一种设备状态评估预警方法和系统
CN115499289B (zh) * 2022-08-17 2023-08-25 华电电力科学研究院有限公司 一种设备状态评估预警方法和系统
CN115902980A (zh) * 2023-02-15 2023-04-04 东方空间(西安)宇航技术有限公司 可在线更新的飞行目标天基监视与预测方法及装置
CN116299183A (zh) * 2023-05-24 2023-06-23 至控科技(湖州)创新研究院有限公司 一种使用扩展卡尔曼滤波估计声速的超声定位方法及系统
CN116299183B (zh) * 2023-05-24 2023-08-15 至控科技(湖州)创新研究院有限公司 一种使用扩展卡尔曼滤波估计声速的超声定位方法及系统

Similar Documents

Publication Publication Date Title
WO2022087998A1 (zh) 通信终端的定位跟踪方法、系统、设备和可读存储介质
Bisio et al. Smart probabilistic fingerprinting for WiFi-based indoor positioning with mobile devices
Zou et al. An online sequential extreme learning machine approach to WiFi based indoor positioning
CN107255795B (zh) 基于ekf/efir混合滤波的室内移动机器人定位方法和装置
Bae et al. Large-scale indoor positioning using geomagnetic field with deep neural networks
CN109379711B (zh) 一种定位方法
WO2010000465A1 (en) System and method for position estimation
CN108732559B (zh) 一种定位方法、装置、电子设备及可读存储介质
CN113342905B (zh) 一种停留点确定方法及装置
CN109195110B (zh) 基于层次聚类技术和在线极限学习机的室内定位方法
CN110865343B (zh) 基于lmb的粒子滤波检测前跟踪方法及系统
CN111678513A (zh) 一种超宽带/惯导紧耦合的室内定位装置与系统
Chen et al. A wifi indoor localization method based on dilated cnn and support vector regression
CN109239704B (zh) 一种基于序贯滤波交互式多模型的自适应采样方法
CN108680897B (zh) 一种室内定位方法、装置、电子设备及存储介质
Farahmand et al. Tracking target signal strengths on a grid using sparsity
CN110824496B (zh) 运动估计方法、装置、计算机设备和存储介质
CN114415113B (zh) 室内定位方法、装置、计算机设备及存储介质
CN113640791B (zh) 一种基于距离和瞬时速度的空间目标三维姿态重构方法
Zhou et al. Joint positioning and radio map generation based on stochastic variational Bayesian inference for FWIPS
Ghozali et al. Indoor positioning system using regression-based fingerprint method
CN110057355A (zh) 一种室内定位方法、装置、系统及计算设备
CN115494450A (zh) 一种高精度的超宽带室内定位跟踪与控制方法及装置
CN115407299A (zh) 基于伯努利滤波器算法的弱小目标检测方法和装置
CN114501615A (zh) 终端定位方法、装置、计算机设备、存储介质和程序产品

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20959160

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20959160

Country of ref document: EP

Kind code of ref document: A1