WO2020211090A1 - 通信设备的定位方法、装置、计算机设备和存储介质 - Google Patents

通信设备的定位方法、装置、计算机设备和存储介质 Download PDF

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Publication number
WO2020211090A1
WO2020211090A1 PCT/CN2019/083522 CN2019083522W WO2020211090A1 WO 2020211090 A1 WO2020211090 A1 WO 2020211090A1 CN 2019083522 W CN2019083522 W CN 2019083522W WO 2020211090 A1 WO2020211090 A1 WO 2020211090A1
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WIPO (PCT)
Prior art keywords
node
information
parameter
variable
measurement information
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PCT/CN2019/083522
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English (en)
French (fr)
Inventor
刘袁鹏
卢前溪
沈渊
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Oppo广东移动通信有限公司
清华大学
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Application filed by Oppo广东移动通信有限公司, 清华大学 filed Critical Oppo广东移动通信有限公司
Priority to CN201980094680.9A priority Critical patent/CN113631959A/zh
Priority to PCT/CN2019/083522 priority patent/WO2020211090A1/zh
Publication of WO2020211090A1 publication Critical patent/WO2020211090A1/zh
Priority to US17/499,425 priority patent/US12035273B2/en

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    • 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
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0284—Relative positioning
    • G01S5/0289—Relative positioning of multiple transceivers, e.g. in ad hoc networks
    • 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
    • G01S2205/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S2205/01—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations specially adapted for specific applications

Definitions

  • This application relates to the field of vehicle networks, and in particular to a positioning method, device, computer equipment and storage medium for communication equipment.
  • the Internet of Vehicles (Internet of Vehicles) is a huge interactive network composed of information such as vehicle location, speed, and route. In Internet of Vehicles applications, effective vehicle positioning is often required.
  • the 3rd Generation Partnership Project stipulates three vehicle positioning methods, which are cell-ID-based (CID) positioning and Observed Time Difference of Arrival (Observed Time Difference of Arrival).
  • CID cell-ID-based
  • OTDOA Observed Time Difference of Arrival
  • A-GNSS Assisted Global Navigation Satellite System
  • OTDOA and A-GNSS have high accuracy, and they are also the current daily positioning methods.
  • the principle of OTDOA positioning is similar to that of A-GNSS.
  • OTDOA relies on base station positioning, and A-GNSS relies on satellites. Let’s take A-GNSS as an example to briefly explain the process.
  • an embodiment of the present invention provides a method for positioning a communication device, the method including:
  • the parameter variable formula is used to characterize the possibility that the positioning information is the value of the preset variable, and the positioning information includes The position information, clock deviation information and orientation angle information of the node to be located.
  • the parameter formula is:
  • the clock deviation parameter vector between the node i to be located and the reference node is
  • the orientation angle parameter vector between the node i to be located and the reference node is
  • ⁇ ik is the noise variance
  • the reciprocal of, the pseudorange between the node i to be located and the reference node k d ik
  • , p k
  • the substituting the measurement information into a preset parameter variable formula to obtain the positioning information of the vehicle includes:
  • the measurement information is substituted into the cost function corresponding to the parameter variable formula to obtain the positioning information of the node to be located, and the cost function is used to characterize the possibility that the positioning information is the value of the preset variable.
  • the cost function is:
  • the clock deviation parameter vector between the node i to be located and the reference node is
  • the orientation angle parameter vector between the node i to be located and the reference node is
  • the reciprocal of, the pseudorange between the node i to be located and the reference node k d ik
  • , p k is the distance
  • the pitch angle between the node i to be positioned relative to the reference node k The azimuth angle between the node i to be located relative to the reference node k Is the measured value of the pitch angle of the node i to be located relative to the reference node k in the coordinate system of the node i to be located, Is the measured value of the azimuth angle between the node i to be located relative to the reference node k, Is the inverse matrix of the covariance matrix of Gaussian noise.
  • the cost function is:
  • the position parameter p i of the node i to be located [x i ,y i ,z i ] T
  • the clock deviation parameter between the node i to be located and the reference node is b i
  • the direction angle parameter of the node i to be located is ⁇ i
  • an embodiment of the present invention provides a positioning apparatus for communication equipment, including:
  • the receiving module is configured to receive measurement information between the node to be located and at least one set of reference nodes; the measurement information includes initial pseudorange and initial orientation angle measurement information between the node to be located and the reference node; the reference node The set includes at least one reference node;
  • the processing module is used to substitute the measurement information into a preset parameter variable formula to obtain the positioning information of the node to be located; the parameter variable formula is used to characterize the possibility that the positioning information is the value of the preset variable,
  • the positioning information includes position information, clock deviation information, and orientation angle information of the node to be located.
  • a computer device in a third aspect, includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the method described in the first aspect when the computer program is executed.
  • a computer-readable storage medium has a computer program stored thereon, and the computer program implements the steps of the method described in the first aspect when the computer program is executed by a processor.
  • the positioning method, device, computer equipment, and storage medium for communication equipment provided by the embodiments of the application receive measurement information between the node to be located and at least one set of reference nodes, and substitute the measurement information into the preset parameter variable formula to obtain the pending The location information of the bit node; because the measurement information is substituted into the parameter variable formula, the possibility of determining the location information as the value of the preset variable is determined, and the value of the preset variable with the greatest possibility is determined as the location information, which improves the positioning accuracy.
  • the measurement information includes not only the initial pseudorange between the node to be located and the reference node, but also the measurement information of the initial orientation angle between the node to be located and the reference node, and the position information and clock deviation information of the node to be located are obtained.
  • the positioning information combined with the orientation angle information can not only locate the position of the node, but also determine the clock deviation and orientation angle of the node and other status information, which satisfies the needs of the Internet of Vehicles.
  • FIG. 1 is a schematic diagram of an application scenario of a communication device positioning method provided by an embodiment of the application
  • FIG. 2 is a flowchart of a method for positioning a communication device provided by an embodiment of the application
  • Figure 3 is a schematic diagram of a rectangular array angle measurement provided by an embodiment
  • Figure 4 is a schematic diagram of a vehicle orientation angle provided by an embodiment
  • Fig. 5 and Fig. 6 are schematic diagrams of the node angle measurement relationship provided by an embodiment
  • FIG. 7 is a flowchart of a method for positioning a communication device according to another embodiment of this application.
  • FIG. 8 is a flowchart of a method for positioning a communication device according to another embodiment of this application.
  • FIG. 9 is a block diagram of a positioning apparatus of a communication device according to an embodiment of the application.
  • Fig. 10 is a block diagram of a computer device provided by an embodiment of the application.
  • Figure 1 is a schematic diagram of an application scenario of a communication device positioning method provided by an embodiment of this application.
  • the scenario may include a vehicle 1, a base station 2, and a satellite 3.
  • the vehicle 1 is a communication device with a certain communication function. Vehicles. Vehicle 1 can receive signals from satellites 3 and base station 3, and can also send signals to base station 2, and vehicles can also communicate with each other.
  • Figure 1 is only a possible scenario diagram provided by this embodiment, and is not a limitation on the application scenario.
  • the positioning method of the communication device can also be applied to mobile communication devices such as ships, drones, and airplanes. In the positioning scene.
  • the scene may also include vehicles and satellites without base stations, or the scene may include vehicles and base stations without satellites, and this application is not limited thereto.
  • the execution subject of the communication device positioning method provided by the present invention can be a computer device or a vehicle, wherein the execution subject can also be a positioning device of the communication device, and the device can be implemented through software , Hardware or a combination of software and hardware is realized as part or all of the positioning of the communication device.
  • FIG. 2 is a flowchart of a method for positioning a communication device according to an embodiment of the application. This embodiment relates to a specific implementation process of node positioning based on measurement information between a node to be located and a reference node combined with a parameter variable formula. As shown in Figure 2, the method includes the following steps:
  • S201 Receive measurement information between a node to be located and at least one reference node set; the measurement information includes initial pseudorange and initial orientation angle measurement information between the node to be located and the reference node; the reference node set includes at least one reference node.
  • the node to be located may be a mobile communication device such as a vehicle, a ship, a drone, etc.
  • the reference node may be a base station, a satellite, a vehicle, etc.
  • a reference node set may include a reference node, for example, a reference node set includes a base station
  • the reference node set may include multiple reference nodes, for example, the reference node set includes 6 satellites, 2 base stations, and 10 vehicles.
  • the measurement information is used to indicate the distance, orientation angle, and other information between the node to be located and the reference node.
  • the measurement information includes initial pseudorange and initial orientation angle measurement information.
  • the initial orientation angle measurement information includes The pitch and/or azimuth angle of the node to be located relative to the reference node.
  • the measurement information may also include the initial position of the vehicle, the initial clock deviation, and the initial orientation angle.
  • the measurement information can be calculated based on the position information, signals, etc. of each node.
  • the coordinate information of each node is determined in the world coordinate system, and the pseudorange and orientation between the two nodes are calculated based on the coordinate information. Angle and other information.
  • the measurement information may be the measurement information of multiple nodes obtained by the computer device, or it may be that each node obtains its own measurement information.
  • N c ⁇ 1,2,...,N c ⁇
  • N b ⁇ N c +1,N c +2,...,N c +N b ⁇
  • N s ⁇ N c +N b +1,N c +N b +2,...,N c +N b +N s ⁇ .
  • node vehicles, base stations and satellites can all be regarded as nodes
  • the parameter vector containing all vehicle positions is We assume that both the satellite and the base station are synchronized. Due to the limitation of the hardware equipment of the vehicle i, there is a clock synchronization deviation ⁇ i between the vehicle i and the satellite and the base station.
  • the distance d ij and the elevation angle of the node i to the node j And azimuth can be calculated using the following formula:
  • ⁇ i is the orientation angle of node i.
  • the signal from node j received by vehicle i can be expressed by formula (7),
  • s i (t) is a known signal, which is the Fourier transform S i (f), ⁇ ij, respectively, and ⁇ ij is the signal amplitude and delay of the transmission link i to j, ni j (t) is Gaussian white noise with a power spectral density of N 0 /2.
  • the parameter ⁇ ij is introduced here to represent the reciprocal of the noise variance, and its expression is as follows:
  • the parameter variable formula is a preset possibility that the positioning information of the node to be located is the value of a preset variable
  • the parameter variable formula may be a preset likelihood function, cost function, probability function, etc.
  • the preset variables can include variables such as position, orientation angle, and clock deviation.
  • the parameter variable formula can be established in advance, and then given the value of some preset variables, the value of the preset variable is substituted into the parameter variable formula, and the positioning information of the node to be located is judged by the value of the parameter variable formula. Determine the possibility of the value of the preset variable, and determine the value of the preset variable with the greatest possibility as the positioning information of the node to be located. For example, if the parameter variable formula is a likelihood function, the value of the preset variable is substituted into the likelihood function. The larger the value of the likelihood function, the closer the value of the preset variable is to the positioning information.
  • the positioning method for a communication device receives measurement information between a node to be located and at least one set of reference nodes, and substitutes the measurement information into a preset parameter formula to obtain the positioning information of the node to be located;
  • the measurement information is substituted into the parameter variable formula to determine the possibility that the positioning information is the value of the preset variable, and the value of the most likely preset variable is determined as the positioning information, which improves the positioning accuracy, and the measurement information not only includes the node to be located
  • the initial pseudorange with the reference node also includes the initial orientation angle measurement information between the node to be positioned and the reference node, and the positioning information including the position information of the node to be positioned, the clock deviation information and the orientation angle information is obtained.
  • the position of the node can be located, and the state information such as the clock deviation and orientation angle of the node can also be determined, which satisfies the needs of the Internet of Vehicles.
  • the reference node set includes at least four first-type nodes and/or at least one second-type node; the first-type nodes are used to send signals and do not receive signals; the second-type nodes are used to send signals, receive signals, and Signal array processing.
  • the first type of node may be a satellite, and the second type of node may be a base station.
  • the reference node set also includes vehicles.
  • the reference node set may include both satellites and base stations.
  • the method of this embodiment can improve the robustness of the system by fusing the measurements of the satellite and the base station.
  • the method of this embodiment can also achieve positioning when there is only one base station, so the system robustness can be greatly improved;
  • This embodiment combines satellite pseudoranges, mutual pseudoranges and heading angle measurements between vehicles and base stations, and mutual pseudoranges and heading angle measurements between vehicles. Compared with a single positioning solution in 3GPP, the positioning can be greatly improved. Accuracy.
  • the parameter variable formula when constructing the parameter variable formula, the relationship between different variables can be set to construct the parameter variable formula.
  • the positioning information includes the position information of the node to be located, the clock deviation information, and The three kinds of information of the orientation angle information, therefore, the parameter variable formula provided in the embodiment of the present application is a relational expression including the position variable of the node, the clock deviation variable and the orientation angle variable.
  • the set of reference nodes may include multiple reference nodes
  • the above variables may be in the form of vectors.
  • the location variables are vectors containing the location information of multiple nodes
  • the clock deviation variables may include multiple nodes.
  • the vector of clock deviation information, and the direction angle variable is a vector containing the direction angle information of multiple nodes.
  • a parameter formula including the relational expressions of the position variable of the node, the clock deviation variable and the orientation angle variable can be constructed according to the calculation method of the pseudorange and the calculation method of the orientation angle, optionally ,
  • the parameter formula is as follows:
  • ⁇ ik is the reciprocal of the noise variance
  • the pseudorange between the node i to be located and the reference node k d ik
  • , p k is the position vector of the reference node
  • p k [x k ,y k ,z k ] T
  • ⁇ i is the clock synchronization deviation between the node i to be located and the reference anchor point
  • ⁇ ik is the Gaussian error introduced by signal noise.
  • the node i to be located is relative to the reference Pitch angle between node k
  • the azimuth angle between the node i to be located relative to the reference node k Is the measured value of the pitch angle of the node i to be located relative to the reference node k in the coordinate system of the node i to be located, Is the measured value of the azimuth angle between the node i to be located relative to the reference node k, Is the inverse matrix of the covariance matrix of Gaussian noise.
  • the parameter formula provided in this embodiment is a likelihood function constructed based on the relationship between the position variable of the node, the clock deviation variable, and the orientation angle variable.
  • the maximum value of the likelihood function needs to be found, and the purpose of this method is to find a suitable p, b, and ⁇ maximize the above-mentioned likelihood function value.
  • the pseudorange and the orientation angle can be modeled first, and then the pseudorange and the orientation angle Modeling and constructing parametric formulas.
  • the pseudorange modeling is shown in formula (8), and the process of modeling the orientation angle will be described below.
  • a vehicle and a base station are taken as examples to introduce heading angle modeling.
  • the base station and the vehicle we can consider the nature of the goniometer, the node j ⁇ N c ⁇ N b measured node k ⁇ N c ⁇ N b pitch angle And azimuth Then the heading angle is modeled as follows:
  • ⁇ jk is the equivalent zero-mean Gaussian noise on the two-dimensional orientation angle introduced by signal noise. Its covariance matrix is C jk , and C jk is inversely proportional to the signal-to-noise ratio, but its specific form and the spatial structure of the array Relevant (such as rectangular array, circular array, etc.). Take the rectangular array in Figure 3 as an example.
  • ⁇ is the interval of the array elements in the figure
  • ⁇ is the signal wavelength
  • its covariance matrix is the inverse of C jk :
  • the overall likelihood function is the product of the likelihood functions of each variable measurement, because the likelihood function of each variable measurement is in exponential form , So the product is the sum of exponential terms as the following formula (1):
  • the location information of the node to be located can also be determined by the cost function.
  • S202 “substitute the measurement information into the preset parameter variable formula to obtain the positioning information of the vehicle” includes: substituting the measurement information into the parameter In the cost function corresponding to the variable formula, the positioning information of the node to be located is obtained, and the cost function is used to characterize the possibility that the positioning information is the value of the preset variable.
  • the cost function can be constructed by the relational expressions of the position variable of the node, the clock deviation variable and the orientation angle variable, and the positioning information of the node can be determined by the cost function, or, based on the above formula (1), directly construct
  • the cost function corresponding to formula (1) is used to determine the possibility that the positioning information is the value of the preset variable.
  • the step of "substituting the measurement information into the cost function corresponding to the parameter variable formula to obtain the positioning information of the vehicle” includes: using a gradient descent algorithm to substitute the measurement information into the cost function corresponding to the parameter variable formula Iterative operation is performed in the process until the cost function meets the preset convergence condition, and the iteration parameter corresponding to the cost function that meets the convergence condition is determined as the positioning information of the vehicle; where the convergence condition is that the value of the cost function is the smallest or the maximum number of iterations is reached; The parameter is the value of the preset variable obtained according to the iterative operation.
  • the gradient descent algorithm is used to substitute the measurement information into the cost function corresponding to the parameter variable formula for iterative operation.
  • Each iteration operation will obtain a set of values of p, b and ⁇ , and the values of p, b and ⁇ Substituted into the cost function, the values of p, b, and ⁇ that minimize the value of the cost function are determined as the positioning information of the node to be located, or the values of p, b, and ⁇ obtained when the maximum number of iterations are reached are determined as Location information of the node to be located.
  • the following introduces the realization method of "substituting the measurement information into the cost function corresponding to the parameter variable formula to obtain the positioning information of the node to be positioned" through the centralized positioning method and the distributed positioning method respectively.
  • the centralized positioning method refers to a process in which when there are multiple nodes to be located, the positioning information of each node to be located can be centrally settled through computer equipment.
  • the cost function is:
  • the clock deviation parameter vector between the node i to be located and the reference node is
  • the orientation angle parameter vector between the node i to be positioned and the reference node is
  • ⁇ ik is the reciprocal of the noise variance
  • the pseudorange between the node i to be located and the reference node k d ik
  • , p k is the position vector of the reference node
  • C is the speed of light
  • ⁇ i is the clock synchronization deviation between the node i to be located and the reference anchor point
  • ⁇ ik is the Gaussian error introduced by signal noise.
  • the node i to be located is relative to the reference node pitch angle between k
  • the azimuth angle between the node i to be located and the reference node k Is the measured value of the pitch angle of the node i to be located relative to the reference node k in the coordinate system of the node i to be located, Is the measured value of the azimuth angle between the node i to be located relative to the reference node k, Is the inverse matrix of the covariance matrix of Gaussian noise.
  • formula (2) is the cost function of formula (1), and the aforementioned likelihood function (1) is in the form of an exponential function, so the exponential term needs to be maximized, that is, the following cost function H(p,b, ⁇ ) has the smallest value.
  • H(p,b, ⁇ ) is the form of weighted sum of squares, which can be divided into two parts, one part can be the pseudorange and heading angle information of the anchor point (satellite and base station), and the other part can be the pseudorange of other neighboring vehicles And orientation angle information.
  • the purpose of this method is to minimize the calculation, so that the deviation between the pseudorange and direction angle values obtained by the settlement and the measured pseudorange and direction angle is the smallest.
  • the aforementioned cost function (2) since it contains the measurement information of all nodes, that is, all measurement information is collected together for calculation, it is in the form of a centralized algorithm.
  • a commonly used gradient descent algorithm can be used to find the minimum value of the cost function.
  • the typical term in the centralized cost function (2) can be given to the gradient of p, b, and ⁇ (that is, the first derivative).
  • node i indicates a parameter p i, p k, b i , b k, ⁇ i relevant.
  • pseudorange measurement take node i and node k as examples to introduce the gradient expression:
  • the gradient of the heading angle measurement item to the heading angle is:
  • the step of "using a gradient descent algorithm and substituting the measurement information into the cost function corresponding to the parameter variable formula to perform an iterative operation" may include the following steps:
  • the iteration parameters can be the values of the position vector, the clock deviation vector, and the orientation angle vector.
  • a set of position vectors The value of the clock deviation vector and the orientation angle vector, the value of the position vector, the clock deviation vector, and the orientation angle vector are substituted into the cost function. If the cost function reaches the maximum value, the set of position vector, clock deviation vector and orientation angle The value of the vector is determined as the positioning information. If the cost function does not reach the maximum value, the values of the position vector, clock deviation vector, and orientation angle vector are substituted into the gradient expression, and the iteration continues until the value of the cost function is the largest or reaches the maximum iteration frequency.
  • the measurement information is substituted into the gradient expression corresponding to the cost function to perform an iterative operation to obtain the m-th iteration parameter of the node to be located.
  • the m-th iteration parameter and the cost function use Gradient expression, calculate the iterative parameters of the m+1th time of the node to be located, because the above algorithm is iterated through gradient descent, and we give a closed-form expression for the gradient part, which is easy to handle for hardware calculations and has low complexity.
  • each to be located Node i only calculates its own p i , b i , ⁇ i , and can extract the terms related to the node i to be located from the formula (2) of the overall cost function to construct a local cost function H i (p i ,b i , ⁇ i ).
  • the cost function is:
  • the position parameter p i of the node i to be located [x i ,y i ,z i ] T
  • the node to be located The clock deviation parameter between i and the reference node is b i
  • the orientation angle parameter of the node i to be located is ⁇ i
  • the step of "using a gradient descent algorithm and substituting the measurement information into the cost function corresponding to the parameter variable formula for iterative operation" may include the following steps:
  • the node to be located can receive the m-th iteration parameter broadcast by other neighboring nodes, and use the gradient expression according to the m-th iteration parameter and cost function broadcast by the neighboring vehicle.
  • the measurement information is substituted into the gradient expression corresponding to the cost function to perform an iterative operation, to obtain the m-th iteration parameter of the node to be located, and to receive the m-th iteration parameter broadcast by the neighbor node, Broadcast the m-th iteration parameters of the node to be located.
  • the m-th iteration parameters and cost function broadcast by neighboring vehicles use gradient expressions to calculate the m+1th iteration parameters of the node to be located, which can ensure the positioning of the communication device Real-time, to meet the needs of the vehicle network.
  • a positioning apparatus for communication equipment including:
  • the receiving module 11 is configured to receive measurement information between the node to be located and at least one set of reference nodes; the measurement information includes initial pseudorange and initial orientation angle measurement information between the node to be located and the reference node; the reference node set includes at least A reference node;
  • the processing module 12 is used to substitute the measurement information into the preset parameter variable formula to obtain the positioning information of the node to be located; the parameter variable formula is used to characterize the possibility that the positioning information is the value of the preset variable, and the positioning information includes the value of the node to be located Position information, clock deviation information and heading angle information.
  • the parameter formula is a relational formula including the position variable of the node, the clock deviation variable and the orientation angle variable.
  • the position variable is a vector containing position information of multiple nodes
  • the clock deviation variable is a vector containing clock deviation information of multiple nodes
  • the orientation angle variable is a vector containing orientation angle information of multiple nodes.
  • the parameter formula is:
  • the clock deviation parameter vector between the node i to be located and the reference node is
  • the orientation angle parameter vector between the node i to be positioned and the reference node is
  • ⁇ ik is the reciprocal of the noise variance
  • the pseudorange between the node i to be located and the reference node k d ik
  • , p k is the position vector of the reference node
  • p k [x k ,y k ,z k ] T
  • ⁇ i is the clock synchronization deviation between the node i to be located and the reference anchor point
  • ⁇ ik is the Gaussian error introduced by signal noise.
  • the node i to be located is relative to the reference Pitch angle between node k
  • the azimuth angle between the node i to be located and the reference node k Is the measured value of the pitch angle of the node i to be located relative to the reference node k in the coordinate system of the node i to be located, Is the measured value of the azimuth angle between the node i to be located relative to the reference node k, Is the inverse matrix of the covariance matrix of Gaussian noise.
  • substituting the measurement information into the preset parameter variable formula to obtain the positioning information of the vehicle includes:
  • the measurement information is substituted into the cost function corresponding to the parameter variable formula to obtain the positioning information of the node to be located.
  • the cost function is used to characterize the possibility that the positioning information is the value of the preset variable.
  • the cost function is:
  • the clock deviation parameter vector between the node i to be located and the reference node is
  • the orientation angle parameter vector between the node i to be positioned and the reference node is
  • ⁇ ik is the reciprocal of the noise variance
  • the pseudorange between the node i to be located and the reference node k d ik
  • , p k is the position vector of the reference node
  • C is the speed of light
  • ⁇ i is the clock synchronization deviation between the node i to be located and the reference anchor point
  • ⁇ ik is the Gaussian error introduced by signal noise.
  • the node i to be located is relative to the reference node pitch angle between k
  • the azimuth angle between the node i to be located and the reference node k Is the measured value of the pitch angle of the node i to be located relative to the reference node k in the coordinate system of the node i to be located, Is the measured value of the azimuth angle between the node i to be located relative to the reference node k, Is the inverse matrix of the covariance matrix of Gaussian noise.
  • the cost function is:
  • the position parameter p i of the node i to be located [x i ,y i ,z i ] T
  • the node to be located The clock deviation parameter between i and the reference node is b i
  • the orientation angle parameter of the node i to be located is ⁇ i
  • the measurement information is substituted into the cost function corresponding to the parameter variable formula to obtain the positioning information of the vehicle, including:
  • the measurement information is substituted into the cost function corresponding to the parameter variable formula to perform iterative operations until the cost function meets the preset convergence condition, and the iteration parameter corresponding to the cost function that meets the convergence condition is determined as the positioning information of the vehicle;
  • the convergence condition is the minimum value of the cost function or the maximum number of iterations;
  • the iteration parameter is the value of the preset variable obtained according to the iteration operation.
  • the gradient descent algorithm is used to substitute the measurement information into the cost function corresponding to the parameter variable formula for iterative operation, including:
  • the gradient expression is used to calculate the m+1-th iteration parameter of the node to be located.
  • the receiving module 11 is further configured to receive the m-th iteration parameter broadcast by the neighbor node;
  • the processing module 12 is specifically configured to calculate the m+1th iteration parameter of the node to be located by using a gradient expression according to the mth iteration parameter and the cost function broadcast by the neighboring vehicle.
  • the device further includes:
  • the sending module is used to broadcast the m-th iteration parameter of the node to be located.
  • the reference node set includes at least four first-type nodes and/or at least one second-type node; the first-type nodes are used to send signals and do not receive signals; the second-type nodes are used to send signals, Receive signal and signal array processing.
  • the first type of node is a satellite
  • the second type of node is a base station
  • the reference node set also includes vehicles.
  • the node to be located is the vehicle to be located.
  • the initial orientation angle measurement information includes the pitch angle and/or azimuth angle of the node to be located relative to the reference node.
  • Each module in the positioning device of the above-mentioned communication device may be implemented in whole or in part by software, hardware, and a combination thereof.
  • the foregoing modules may be embedded in the form of hardware or independent of the processor in the computer device, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the foregoing modules.
  • a computer device is provided.
  • the computer device may be a server, and its internal structure diagram may be as shown in FIG. 10.
  • the computer equipment includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide calculation 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, a computer program, and a database.
  • the internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium.
  • the database of the computer equipment is used to store resource query processing data.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection.
  • the computer program is executed by the processor to realize a resource query processing method.
  • FIG. 10 is only a block diagram of part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.
  • the specific computer device may Including more or fewer parts than shown in the figure, or combining some parts, or having a different arrangement of parts.
  • a computer device including a memory and a processor, and a computer program is stored in the memory, and the processor implements the following steps when executing the computer program:
  • the parameter variable formula is used to characterize the possibility that the positioning information is the value of the preset variable, and the positioning information includes The position information, clock deviation information and orientation angle information of the node to be located.
  • a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:
  • the parameter variable formula is used to characterize the possibility that the positioning information is the value of the preset variable, and the positioning information includes The position information, clock deviation information and orientation angle information of the node to be located.
  • Non-volatile 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 many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

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Abstract

本申请涉及一种通信设备的定位方法、装置、计算机设备和存储介质，接收待定位节点和至少一种参考节点集合之间的测量信息，将测量信息代入预设的参变量公式，得到待定位节点的定位信息；由于将该测量信息代入参变量公式中，判断定位信息为预设变量的值的可能性，将可能性最大的预设变量的值确定为定位信息，提高了定位精度，并且，测量信息不仅包括待定位节点与参考节点之间的初始伪距，还包括待定位节点与参考节点之间的初始朝向角测量信息，从而得到包括待定位节点的位置信息、时钟偏差信息和朝向角信息的定位信息，不仅可以定位节点的位置，还能确定节点的时钟偏差和朝向角等状态信息，较高的满足了车联网的需求。

Description

通信设备的定位方法、装置、计算机设备和存储介质 技术领域
本申请涉及车辆网领域，特别是涉及一种通信设备的定位方法、装置、计算机设备和存储介质。
背景技术
车联网(Internet of Vehicles)是由车辆位置、速度和路线等信息构成的巨大交互网络，车联网应用中经常需要对车辆进行有效的定位。
目前，第三代合作伙伴计划(3rd Generation Partnership Project，3GPP)里规定了3种车辆定位方法，分别是基于小区ID(cell-ID-based，CID)定位、基于观测到达时间差(Observed Time Difference of Arrival，OTDOA)定位、基于辅助全球导航卫星系统(Assisted Global Navigation Satellite System，A-GNSS)。其中，OTDOA和A-GNSS精度较高，也是目前日常用的定位方式，OTDOA定位原理和A-GNSS类似，OTDOA是依靠基站定位，A-GNSS是依靠卫星。下面以A-GNSS为例简单说明其过程，以某一个卫星作为参考卫星，用车辆和其他卫星的伪距减去车辆和参考卫星的伪距，得到伪距的差值，通过多个伪距的差值，利用最小二乘算法，依靠泰勒展开迭代算法获得车辆位置参数。
然而，上述方法存在定位精度低、车辆状态不明确的问题，导致难以满足车联网的需求。
发明内容
基于此，有必要针对上述方法存在定位精度低、车辆状态不明确的问题，导致难以满足车联网的需求的技术问题，提供一种通信设备的定位方法、装置、计算机设备和存储介质。
第一方面，本发明的实施例提供一种通信设备的定位方法，所述方法包括：
接收待定位节点和至少一种参考节点集合之间的测量信息；所述测量信息包括待定位节点与参考节点之间的初始伪距和初始朝向角测量信息；所述参考节点集合包括至少一个参考节点；
将所述测量信息代入预设的参变量公式，得到所述待定位节点的定位信息；所述参变量公式用于表征所述定位信息为预设变量的值的可能性，所述定位信息包括所述待定位节点的位置信息、时钟偏差信息和朝向角信息。
在其中一个实施例中，所述参变量公式为：
Figure PCTCN2019083522-appb-000001
其中，所有待定位节点i的位置参数向量为
Figure PCTCN2019083522-appb-000002
p i＝[x i,y i,z i] T，所述待定位节点i与参考节点之间的时钟偏差参数向量为
Figure PCTCN2019083522-appb-000003
所述待定位节点i与参考节点之间的朝向角参数向量为
Figure PCTCN2019083522-appb-000004
所述待定位节点集合为 N c＝{1,2,...,N c}；N b和N s为不同种类的参考节点集合，N b＝{N c+1,N c+2,...,N c+N b}，N s＝{N c+N b+1,N c+N b+2,...,N c+N b+N s}，λ ik为噪声方差的倒数，所述待定位节点i与所述参考节点k之间的伪距
Figure PCTCN2019083522-appb-000005
d ik＝||p i-p k||，p k为所述参考节点的位置向量，p k＝[x k,y k,z k] T，时钟偏差所引入的距离偏差b i＝c*δ i，c是光速，δ i是所述待定位节点i与参考锚点之间的时钟同步偏差，ω ik是信号噪声引入的高斯误差，在所述待定位节点i的坐标系中所述待定位节点i相对于所述参考节点k之间的俯仰角
Figure PCTCN2019083522-appb-000006
所述待定位节点i相对于所述参考节点k之间的方位角
Figure PCTCN2019083522-appb-000007
为在所述待定位节点i的坐标系中所述待定位节点i相对于所述参考节点k之间的俯仰角的测量值，
Figure PCTCN2019083522-appb-000008
为所述待定位节点i相对于所述参考节点k之间的方位角的测量值，
Figure PCTCN2019083522-appb-000009
为高斯噪声的协方差矩阵的逆矩阵。
在其中一个实施例中，所述将所述测量信息代入预设的参变量公式，得到所述车辆的定位信息，包括：
将所述测量信息代入所述参变量公式对应的代价函数中，得到所述待定位节点的定位信息，所述代价函数用于表征所述定位信息为预设变量的值的可能性。
在其中一个实施例中，若所述待定位节点包括至少两个节点，所述代价函数为：
Figure PCTCN2019083522-appb-000010
其中，所有待定位节点i的位置参数向量为
Figure PCTCN2019083522-appb-000011
p i＝[x i,y i,z i] T，所述待定位节点i与参考节点之间的时钟偏差参数向量为
Figure PCTCN2019083522-appb-000012
所述待定位节点i与参考节点之间的朝向角参数向量为
Figure PCTCN2019083522-appb-000013
所述待定位节点集合为N c＝{1,2,...,N c}；N b和N s为不同种类的参考节点集合，N b＝{N c+1,N c+2,...,N c+N b}，N s＝{N c+N b+1,N c+N b+2,...,N c+N b+N s}，λ ik为噪声方差的倒数，所述待定位节点i与所述参考节点k之间的伪距
Figure PCTCN2019083522-appb-000014
d ik＝||p i-p k||，p k为所述参考节点的位置向量，p k＝[x k,y k,z k] T时钟偏差所引入的距离偏差b i＝c*δ i，c是光速，δ i是所述待定位节点i与参考锚点之间的时钟同步偏差，ω ik是信号噪声引入的高斯误差，在所述待定位节点i的坐标系中所述待定位节点i相对于所述参考节点k之间的俯仰角
Figure PCTCN2019083522-appb-000015
所述待定位节点i相对于所述参考节点k之间的方位角
Figure PCTCN2019083522-appb-000016
为在所述待定位节点i的坐标系中所述待定位 节点i相对于所述参考节点k之间的俯仰角的测量值，
Figure PCTCN2019083522-appb-000017
为所述待定位节点i相对于所述参考节点k之间的方位角的测量值，
Figure PCTCN2019083522-appb-000018
为高斯噪声的协方差矩阵的逆矩阵。
在其中一个实施例中，若所述待定位节点包括一个节点，所述代价函数为：
Figure PCTCN2019083522-appb-000019
其中，所述待定位节点i的位置参数p i＝[x i,y i,z i] T，所述参考节点k的位置参数向量为p k＝[x k,y k,z k] T，所述待定位节点i与所述参考节点之间的时钟偏差参数为b i，所述待定位节点i朝向角参数为φ i，所述待定位节点i的集合为N c＝{1,2,...,N c}；N b和N s为不同种类的参考节点集合，N b＝{N c+1,N c+2,...,N c+N b}，N s＝{N c+N b+1,N c+N b+2,...,N c+N b+N s}，λ ik为噪声方差的倒数，所述待定位节点i与所述参考节点k之间的伪距
Figure PCTCN2019083522-appb-000020
d ik＝||p i-p k||，时钟偏差所引入的距离偏差b i＝c*δ i，c是光速，δ i是所述待定位节点i与参考锚点之间的时钟同步偏差，ω ik是信号噪声引入的高斯误差，在所述待定位节点i的坐标系中所述待定位节点i相对于所述参考节点k之间的俯仰角
Figure PCTCN2019083522-appb-000021
所述待定位节点i相对于所述参考节点k之间的方位角
Figure PCTCN2019083522-appb-000022
为在所述待定位节点i的坐标系中所述待定位节点i相对于所述参考节点k之间的俯仰角的测量值，
Figure PCTCN2019083522-appb-000023
为所述待定位节点i相对于所述参考节点k之间的方位角的测量值，
Figure PCTCN2019083522-appb-000024
为高斯噪声的协方差矩阵的逆矩阵。
第二方面，本发明的实施例提供一种通信设备的定位装置，包括：
接收模块，用于接收待定位节点和至少一种参考节点集合之间的测量信息；所述测量信息包括待定位节点与参考节点之间的初始伪距和初始朝向角测量信息；所述参考节点集合包括至少一个参考节点；
处理模块，用于将所述测量信息代入预设的参变量公式，得到所述待定位节点的定位信息；所述参变量公式用于表征所述定位信息为预设变量的值的可能性，所述定位信息包括所述待定位节点的位置信息、时钟偏差信息和朝向角信息。
第三方面，一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现第一方面所述方法的步骤。
第四方面，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第一方面所述的方法的步骤。
本申请实施例提供的通信设备的定位方法、装置、计算机设备和存储介质，接收待定位节点和至少一种参考节点集合之间的测量信息，将测量信息代入预设的参变量公式，得到待定位节点的定位信息；由于将该测量信息代入参变量公式中，判断定位信息为预设变量的值的可能性，将可能性最大的预设变量的值确定为定位信息，提高了定位精度，并且，测量信息不仅包括待定位节点与参考节点之间的初始 伪距，还包括待定位节点与参考节点之间的初始朝向角测量信息，从而且得到包括待定位节点的位置信息、时钟偏差信息和朝向角信息的定位信息，不仅可以定位节点的位置，还能确定节点的时钟偏差和朝向角等状态信息，较高的满足了车联网的需求。
附图说明
图1为本申请实施例提供的一种通信设备的定位方法的应用场景示意图；
图2为本申请一实施例提供的通信设备的定位方法的流程图；
图3为一个实施例提供的矩形阵列测角示意图；
图4为一个实施例提供的车辆朝向角示意图；
图5和图6为一个实施例提供的节点测角关系示意图；
图7为本申请另一实施例提供的通信设备的定位方法的流程图；
图8为本申请又一实施例提供的通信设备的定位方法的流程图；
图9为本申请一实施例提供的通信设备的定位装置的框图；
图10为本申请一实施例提供的计算机设备的框图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
图1为本申请实施例提供的一种通信设备的定位方法的应用场景示意图，如图1所示，该场景可以包括车辆1、基站2和卫星3，其中，车辆1为具有一定通信功能的车辆，车辆1可以接收卫星3和基站3的信号，也可以向基站2发送信号，车辆之间也可以相互通信。需要说明的是，图1仅为本实施例提供的一种可能的场景示意，并不是对应用场景的限定，该通信设备的定位方法也可以应用在船只、无人机、飞机等移动通信设备的定位场景中。可选地，该场景中也可以包括车辆和卫星，没有基站，或者该场景中包括车辆和基站，没有卫星，并本申请并不以此为限。
下面将通过实施例并结合附图具体地对本申请的技术方案以及本申请的技术方案如何解决上述技术问题进行详细说明。下面这几个具体的实施例可以相互结合，对于相同或相似的概念或过程可能在某些实施例中不再赘述。需要说明的是，本发明提供的一种通信设备的定位方法，其执行主体可以为计算机设备，也可以为车辆，其中，该执行主体还可以是通信设备的定位装置，其中该装置可以通过软件、硬件或者软硬件结合的方式实现成为通信设备的定位的部分或者全部。
图2为本申请一实施例提供的通信设备的定位方法的流程图，该实施例涉及的是根据待定位节点和参考节点之间的测量信息结合参变量公式，对节点定位的具体实现过程。如图2所示，该方法包括以下步骤：
S201、接收待定位节点和至少一种参考节点集合之间的测量信息；测量信息包括待定位节点与参考节点之间的初始伪距和初始朝向角测量信息；参考节点集合包括至少一个参考节点。
其中，待定位节点可以为车辆、船只、无人机等移动通信设备，参考节点可以为基站、卫星、车辆等等，参考节点集合中可以包括一个参考节点，比如，参考节点集合中包括一个基站，参考节点集合中可以包括多个参考节点，比如，参考节点集合中包括6个卫星、2个基站和10个车辆。测量信息用于 表示待定位节点与参考节点之间的距离、朝向角等信息，在本实施例中，测量信息包括初始伪距和初始朝向角测量信息，可选地，初始朝向角测量信息包括待定位节点相对于参考节点的俯仰角和/或方位角。可选地，测量信息还可以是包括车辆的初始位置、初始时钟偏差和初始朝向角等。
在本实施例中，测量信息可以是根据各节点的位置信息、信号等计算获得，例如，在世界坐标系中确定各个节点的坐标信息，根据坐标信息计算两个节点之间的伪距、朝向角等信息。该测量信息可以是由计算机设备获取多个节点的测量信息，也可以是由每个节点获取自身的测量信息。
下面，以图1中的应用场景为例，假设网络中有N c个车辆、N b基站以及N s卫星，基站和卫星的位置已知。车辆、基站和卫星的集合定义如下：N c＝{1,2,...,N c}，N b＝{N c+1,N c+2,...,N c+N b}和N s＝{N c+N b+1,N c+N b+2,...,N c+N b+N s}。其中，节点车、基站和卫星均可以看作节点，节点l的位置为p i＝[x i,y i,z i] T，包含所有车辆位置的参数向量为
Figure PCTCN2019083522-appb-000025
我们假设卫星和基站都是同步的，车辆i由于硬件设备限制，车辆i和卫星及基站间存在时钟同步偏差δ i，节点i观察节点j的距离d ij、俯仰角
Figure PCTCN2019083522-appb-000026
和方位角
Figure PCTCN2019083522-appb-000027
分别可以采用下面的公式计算获得：
d ij＝||p i-p j||     公式(4)，
Figure PCTCN2019083522-appb-000028
Figure PCTCN2019083522-appb-000029
其中，φ i为节点i的朝向角。
在本实施例中，对于车辆i接收的来自节点j的信号可以采用公式(7)来表示，
ri j(t)＝αi js i(t-τi j)+ni j(t)   公式(7)，
其中，s i(t)是已知信号，其傅里叶变换为S i(f)，α ij和τ ij分别是i到j传输链路的信号幅度和时延，ni j(t)是功率谱密度为N 0/2的高斯白噪声。
在本实施例中，对于车辆i接收的来自节点j的信号，我们采用以下的伪距模型公式(8)计算伪距，
Figure PCTCN2019083522-appb-000030
其中，时钟偏差所引入的距离偏差b i＝c*δ i，c是光速，δ i是节点i与参考锚点(卫星或基站)之间的时钟同步偏差，ω ij是由于信号噪声n ij(t)引入的一个等效零均值高斯误差，对于基站或卫星j 因为假设其同步则相应的b j＝0。其中，ω ij的方差
Figure PCTCN2019083522-appb-000031
满足：
Figure PCTCN2019083522-appb-000032
此处引入参数λ ij表示噪声方差的倒数，其表达式如下：
Figure PCTCN2019083522-appb-000033
从该表达式可以看出该方差和信噪比、等效带宽成反比。
S202、将测量信息代入预设的参变量公式，得到待定位节点的定位信息；参变量公式用于表征定位信息为预设变量的值的可能性，定位信息包括待定位节点的位置信息、时钟偏差信息和朝向角信息。
其中，参变量公式是预先设定的用于表征待定位节点的定位信息为预设变量的值的可能性，参变量公式可以为预设的似然函数、代价函数、概率函数等。预设变量可以包括位置、朝向角、时钟偏差等变量。
在本实施例中，可以预先建立参变量公式，再给定一些预设变量的值，将预设变量的值代入参变量公式中，通过参变量公式的值判断待定位节点的定位信息为给定的预设变量的值的可能性，将可能性最大的预设变量的值确定为待定位节点的定位信息。例如，若参变量公式为似然函数，将预设变量的值代入似然函数中，似然函数的值越大，表示该预设变量的值越接近定位信息。
本实施例提供的通信设备的定位方法，接收待定位节点和至少一种参考节点集合之间的测量信息，将测量信息代入预设的参变量公式，得到待定位节点的定位信息；由于将该测量信息代入参变量公式中，判断定位信息为预设变量的值的可能性，将可能性最大的预设变量的值确定为定位信息，提高了定位精度，并且，测量信息不仅包括待定位节点与参考节点之间的初始伪距，还包括待定位节点与参考节点之间的初始朝向角测量信息，从而且得到包括待定位节点的位置信息、时钟偏差信息和朝向角信息的定位信息，不仅可以定位节点的位置，还能确定节点的时钟偏差和朝向角等状态信息，较高的满足了车联网的需求。
在一些场景中，对于OTDOA和A-GNSS，其至少需要4个基站或者卫星才能实现定位，这在实际中由于高楼遮挡等环境的影响往往不能够满足，此外基站间存在同频干扰，实际中能够使用的基站往往不超过4个，故系统鲁棒性不够。针对该问题，可以将卫星和基站融合来提高系统鲁棒性。可选地，参考节点集合包括至少四个第一类节点和/或至少一个第二类节点；第一类节点用于发送信号且不接收信号；第二类节点用于发送信号、接收信号和信号阵列处理。其中，第一类节点可以为卫星，第二类节点可以为基站。可选地，参考节点集合还包括车辆。
本实施例中，参考节点集合中可以同时包括卫星和基站，一方面，通过将卫星和基站的测量融合，本实施例的方法能够提高系统鲁棒性。另一方面，由于对于车辆和基站之间采用了相互测伪距和测角，故本实施例的方法在只有一个基站的时候也可实现定位，故能够大大提高系统鲁棒性；再一方面，本实施例将卫星伪距、车辆和基站间相互的伪距和朝向角侧测量、车辆之间相互的伪距和朝向角测量融合在一起，相比3GPP中单一的定位方案能够大大提高定位精度。
在上述实施例的基础上，构建参变量公式时，可以设置不同的变量之间的关系来构建参变量公式，由于在本申请中，定位信息中包括待定位节点的位置信息、时钟偏差信息和朝向角信息这三种信息，因此，本申请实施例提供的参变量公式为包括节点的位置变量、时钟偏差变量和朝向角变量的关系式。进一步地，由于参考节点集合中可以包括多个参考节点，因此，上述变量可以是向量的形式，可选地，位置变量为包含多个节点的位置信息的向量，时钟偏差变量为包含多个节点的时钟偏差信息的向量，朝向角变量为包含多个节点的朝向角信息的向量。
进一步地，结合图2所示实施例，可以根据伪距的计算方法、朝向角的计算方法来构建包括节点的位置变量、时钟偏差变量和朝向角变量的关系式的参变量公式，可选地，该参变量公式如下：
Figure PCTCN2019083522-appb-000034
其中，所有待定位节点i的位置参数向量为
Figure PCTCN2019083522-appb-000035
待定位节点i与参考节点之间的时钟偏差参数向量为
Figure PCTCN2019083522-appb-000036
待定位节点i与参考节点之间的朝向角参数向量为
Figure PCTCN2019083522-appb-000037
待定位节点集合为N c＝{1,2,...,N c}；N b和N s为不同种类的参考节点集合，N b＝{N c+1,N c+2,...,N c+N b}，N s＝{N c+N b+1,N c+N b+2,...,N c+N b+N s}，λ ik为噪声方差的倒数，待定位节点i与参考节点k之间的伪距
Figure PCTCN2019083522-appb-000038
d ik＝||p i-p k||，p k为参考节点的位置向量，p k＝[x k,y k,z k] T，时钟偏差所引入的距离偏差b i＝c*δ i，c是光速，δ i是待定位节点i与参考锚点之间的时钟同步偏差，ω ik是信号噪声引入的高斯误差，在待定位节点i的坐标系中待定位节点i相对于参考节点k之间的俯仰角
Figure PCTCN2019083522-appb-000039
待定位节点i相对于参考节点k之间的方位角
Figure PCTCN2019083522-appb-000040
为在待定位节点i的坐标系中待定位节点i相对于参考节点k之间的俯仰角的测量值，
Figure PCTCN2019083522-appb-000041
为待定位节点i相对于参考节点k之间的方位角的测量值，
Figure PCTCN2019083522-appb-000042
为高斯噪声的协方差矩阵的逆矩阵。
本实施例提供的参变量公式为根据节点的位置变量、时钟偏差变量和朝向角变量的关系式构建的似然函数，需要找到该似然函数的最大值，则本方法的目的是找到合适的p、b和φ使得上述的似然函数值最大。
本实施例中，由于参变量公式中涉及到待定位节点与参考节点之间的伪距和朝向角，因此，可以先针对伪距和朝向角进行建模，再根据伪距建模和朝向角建模构建参变量公式。在图2所示实施例中，伪距建模如公式(8)所示，下面介绍朝向角建模的过程。
在本实施例中，以车辆和基站为例来介绍朝向角建模。对于基站和车辆，我们考虑了其可以测角的性质，节点j∈N c∪N b测得的节点k∈N c∪N b的俯仰角
Figure PCTCN2019083522-appb-000043
和方位角
Figure PCTCN2019083522-appb-000044
则朝向角建模如下：
Figure PCTCN2019083522-appb-000045
其中，μ jk是由于信号噪声引入的二维朝向角上的等效零均值高斯噪声，其协方差矩阵为C jk，C jk和信号信噪比成反比，但是其具体形式和阵列的空间结构有关(如矩形阵列、圆形阵列等)。以图3中的矩形阵列为例，此图中的阵列测角时，令
Figure PCTCN2019083522-appb-000046
Figure PCTCN2019083522-appb-000047
Δ为图中阵元间隔，λ为信号波长，其协方差矩阵为C jk的逆矩阵为：
Figure PCTCN2019083522-appb-000048
其中，
Figure PCTCN2019083522-appb-000049
Figure PCTCN2019083522-appb-000050
Figure PCTCN2019083522-appb-000051
其中，对于基站k，其朝向角默认为φ k＝0，车辆i的方位角定义为φ i，在车辆i本地参考系中节点j到节点i的方位角定义为
Figure PCTCN2019083522-appb-000052
在世界参考系中从节点j到节点i的方位角定义为φ ij，如图4所示。车辆节点之间朝向角的关系满足公式(11)和公式(12)，进一步地，根据图5和6可以得到φ ij和φ ji之间的关系满足公式(13)：
Figure PCTCN2019083522-appb-000053
Figure PCTCN2019083522-appb-000054
Figure PCTCN2019083522-appb-000055
下面，重点介绍根据伪距建模(8)和朝向角建模(9)构建参变量公式(1)的具体过程。对于公式(8)中的伪距测量，其似然函数为：
Figure PCTCN2019083522-appb-000056
对于公式(9)中的朝向角测量，其似然函数为：
Figure PCTCN2019083522-appb-000057
由于不同的变量测量之间是相互独立的，所以把所有变量测量整合到一起后，其总体的似然函数为各个变量测量的似然函数的乘积，由于各个变量测量的似然函数为指数形式，故乘积为指数项相加的形式如下公式(1)：
Figure PCTCN2019083522-appb-000058
需要说明的是，上述公式(1)仅是参变量公式的一种可能实现形式，对于包括上述公式(1)以及关于上述公式(1)的变型公式均属于本申请的保护范围。
在一些场景中，还可以通过代价函数来确定待定位节点的定位信息，可选地，S202“将测量信息代入预设的参变量公式，得到车辆的定位信息”，包括：将测量信息代入参变量公式对应的代价函数中，得到待定位节点的定位信息，代价函数用于表征定位信息为预设变量的值的可能性。
在本实施例中，可以节点的位置变量、时钟偏差变量和朝向角变量的关系式构建代价函数，通过代价函数来确定节点的定位信息，或者，在上述公式(1)的基础上，直接构造公式(1)对应的代价函数来判断定位信息为预设变量的值的可能性。
可选地，基于上述梯度表达式，步骤“将测量信息代入参变量公式对应的代价函数中，得到车辆的定位信息”，包括：采用梯度下降算法，将测量信息代入参变量公式对应的代价函数中进行迭代运算，直至代价函数满足预设的收敛条件，将满足收敛条件的代价函数对应的迭代参数确定为车辆的定位信息；其中，收敛条件为代价函数的值最小或者达到最大迭代次数；迭代参数为根据迭代运算得到预设变量的值。
本实施例中，采用梯度下降算法，将测量信息代入参变量公式对应的代价函数中进行迭代运算，每一次迭代运算会得到一组p、b和φ的值，将p、b和φ的值代入代价函数中，将使得代价函数的值最小的p、b和φ的值确定为待定位节点的定位信息，或者，将达到最大迭代次数时解算得到的p、b和φ的值确定为待定位节点的定位信息。
下面分别通过集中式定位方法和分布式定位方法来介绍“将测量信息代入参变量公式对应的代价函数中，得到待定位节点的定位信息”的实现方式。
集中式定位方法是指有多个待定位节点时，可以通过计算机设备集中的结算各个待定位节点的定位信息的过程。可选地，若待定位节点包括至少两个节点，代价函数为：
Figure PCTCN2019083522-appb-000059
其中，所有待定位节点i的位置参数向量为
Figure PCTCN2019083522-appb-000060
p i＝[x i,y i,z i] T，待定位节点i与参考节点之间的时钟偏差参数向量为
Figure PCTCN2019083522-appb-000061
待定位节点i与参考节点之间的朝向角参数向量为
Figure PCTCN2019083522-appb-000062
待定位节点集合为N c＝{1,2,...,N c}；N b和N s为 不同种类的参考节点集合，N b＝{N c+1,N c+2,...,N c+N b}，N s＝{N c+N b+1,N c+N b+2,...,N c+N b+N s}，λ ik为噪声方差的倒数，待定位节点i与参考节点k之间的伪距
Figure PCTCN2019083522-appb-000063
d ik＝||p i-p k||，p k为参考节点的位置向量，p k＝[x k,y k,z k] T时钟偏差所引入的距离偏差b i＝c*δ i，c是光速，δ i是待定位节点i与参考锚点之间的时钟同步偏差，ω ik是信号噪声引入的高斯误差，在待定位节点i的坐标系中待定位节点i相对于参考节点k之间的俯仰角
Figure PCTCN2019083522-appb-000064
待定位节点i相对于参考节点k之间的方位角
Figure PCTCN2019083522-appb-000065
为在待定位节点i的坐标系中待定位节点i相对于参考节点k之间的俯仰角的测量值，
Figure PCTCN2019083522-appb-000066
为待定位节点i相对于参考节点k之间的方位角的测量值，
Figure PCTCN2019083522-appb-000067
为高斯噪声的协方差矩阵的逆矩阵。
在本实施例中，公式(2)为公式(1)的代价函数，上述似然函数(1)为指数函数形式，因此需要使指数项最大，即让下述代价函数H(p,b,φ)的值最小。H(p,b,φ)为加权平方和的形式，主要可以分为两部分，一部分可以是锚点(卫星和基站)的伪距及朝向角信息，另一部分可以是其他邻居车辆的伪距和朝向角信息。该方法的目的是进行最小化解算，是的结算得到的伪距、朝向角值和测量得到的伪距及朝向角之间的偏差最小。对于上述的代价函数(2)，由于其包含所有节点的测量信息，即所有测量信息收集在一起进行计算，所以为集中式算法的形式。
具体的，可以采用常用的梯度下降算法来求该代价函数的最小值。对于梯度下降算法，可以给定集中式的代价函数(2)中的典型项对于p,b,φ的梯度(即一阶导数)。对于H(p,b,φ)中的每个测量项，表示和待定位节点i的参数p i,p k,b i,b k,φ i有关。其中对于伪距测量，这里以节点i和节点k为例来介绍梯度表达式：
Figure PCTCN2019083522-appb-000068
Figure PCTCN2019083522-appb-000069
Figure PCTCN2019083522-appb-000070
Figure PCTCN2019083522-appb-000071
对于节点i和节点k朝向角测量，首先定义：
Figure PCTCN2019083522-appb-000072
则朝向角测量项对位置的梯度为：
Figure PCTCN2019083522-appb-000073
Figure PCTCN2019083522-appb-000074
朝向角测量项对于朝向角的梯度为：
Figure PCTCN2019083522-appb-000075
基于上述梯度函数表达式，如图7所示，步骤“采用梯度下降算法，将所述测量信息代入所述参变量公式对应的代价函数中进行迭代运算”可以包括以下步骤：
S301、将测量信息代入代价函数对应的梯度表达式中进行迭代运算，获取待定位节点第m次的迭代参数；m为大于等于0的整数。
S302、根据第m次的迭代参数和代价函数，采用梯度表达式，计算待定位节点第m+1次的迭代参数。
在本实施例中，迭代参数可为位置向量、时钟偏差向量和朝向角向量的值，每次将测量信息代入代价函数对应的梯度表达式中进行迭代运算，可以计算的到一组位置向量、时钟偏差向量和朝向角向量的值，将该组位置向量、时钟偏差向量和朝向角向量的值代入代价函数中，若代价函数达到最大值，则将该组位置向量、时钟偏差向量和朝向角向量的值确定为定位信息，若代价函数未达到最大值，则将该组位置向量、时钟偏差向量和朝向角向量的值代入梯度表达式，继续迭代，直至代价函数的值最大或者到达最大迭代次数。
本实施例提供的通信设备的定位方法，将测量信息代入代价函数对应的梯度表达式中进行迭代运算，获取待定位节点第m次的迭代参数，根据第m次的迭代参数和代价函数，采用梯度表达式，计算 待定位节点第m+1次的迭代参数，因为上述算法通过梯度下降进行迭代，而梯度部分我们给出了闭式表达式，对于硬件计算来说易于处理，复杂度低。
在移动网络中，为了保持实时性，很多时候无法将所有数据收集到中心点计算，且大部分节点的计算能力无法实时解算出所有节点的参数，因此需要设计分布式方法，即每个待定位节点i只计算自己的p i,b i,φ i，可以从整体代价函数的公式(2)中把和待定位节点i有关的项提取出来，构造局部代价函数H i(p i,b i,φ i)。可选地，若待定位节点包括一个节点，代价函数为：
Figure PCTCN2019083522-appb-000076
其中，待定位节点i的位置参数p i＝[x i,y i,z i] T，参考节点k的位置参数向量为p k＝[x k,y k,z k] T，待定位节点i与参考节点之间的时钟偏差参数为b i，待定位节点i朝向角参数为φ i，待定位节点i的集合为N c＝{1,2,...,N c}；N b和N s为不同种类的参考节点集合，N b＝{N c+1,N c+2,...,N c+N b}，N s＝{N c+N b+1,N c+N b+2,...,N c+N b+N s}，λ ik为噪声方差的倒数，待定位节点i与参考节点k之间的伪距
Figure PCTCN2019083522-appb-000077
d ik＝||p i-p k||，时钟偏差所引入的距离偏差b i＝c*δ i，c是光速，δ i是待定位节点i与参考锚点之间的时钟同步偏差，ω ik是信号噪声引入的高斯误差，在待定位节点i的坐标系中待定位节点i相对于参考节点k之间的俯仰角
Figure PCTCN2019083522-appb-000078
待定位节点i相对于参考节点k之间的方位角
Figure PCTCN2019083522-appb-000079
Figure PCTCN2019083522-appb-000080
为在待定位节点i的坐标系中待定位节点i相对于参考节点k之间的俯仰角的测量值，
Figure PCTCN2019083522-appb-000081
为待定位节点i相对于参考节点k之间的方位角的测量值，
Figure PCTCN2019083522-appb-000082
为高斯噪声的协方差矩阵的逆矩阵。
针对分布式方法，如图8所示，步骤“采用梯度下降算法，将所述测量信息代入所述参变量公式对应的代价函数中进行迭代运算”可以包括以下步骤：
S401、将测量信息代入代价函数对应的梯度表达式中进行迭代运算，获取待定位节点第 m次的迭代参数；m为大于等于0的整数。
S402、接收邻居节点广播的第m次的迭代参数。
S403、广播待定位节点第m次的迭代参数。
S404、根据邻居车辆广播的第m次的迭代参数和代价函数，采用梯度表达式，计算待定位节点第m+1次的迭代参数。
在本实施例中，与集中式方法不同的是，待定位节点可以接收其它的邻居节点广播的第m次的迭代参数，根据邻居车辆广播的第m次的迭代参数和代价函数，采用梯度表达式，计算待定位节点第m+1次的迭代参数，也可以广播待定位节点第m次的迭代参数，供其它的邻居节点来定位。
本实施例提供的通信设备的定位信息，将测量信息代入代价函数对应的梯度表达式中进行迭代运算，获取待定位节点第m次的迭代参数，接收邻居节点广播的第m次的迭代参数，广播待定位节点第m次的迭代参数，根据邻居车辆广播的第m次的迭代参数和代价函数，采用梯度表达式，计算待定位 节点第m+1次的迭代参数，可以保证通信设备定位的实时性，满足车辆网的需求。
应该理解的是，虽然图2-8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2-5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。
在一个实施例中，如图9所示，提供了一种通信设备的定位装置，包括：
接收模块11，用于接收待定位节点和至少一种参考节点集合之间的测量信息；测量信息包括待定位节点与参考节点之间的初始伪距和初始朝向角测量信息；参考节点集合包括至少一个参考节点；
处理模块12，用于将测量信息代入预设的参变量公式，得到待定位节点的定位信息；参变量公式用于表征定位信息为预设变量的值的可能性，定位信息包括待定位节点的位置信息、时钟偏差信息和朝向角信息。
在其中一个实施例中，参变量公式为包括节点的位置变量、时钟偏差变量和朝向角变量的关系式。
在其中一个实施例中，位置变量为包含多个节点的位置信息的向量，时钟偏差变量为包含多个节点的时钟偏差信息的向量，朝向角变量为包含多个节点的朝向角信息的向量。
在其中一个实施例中，参变量公式为：
Figure PCTCN2019083522-appb-000083
其中，所有待定位节点i的位置参数向量为
Figure PCTCN2019083522-appb-000084
p i＝[x i,y i,z i] T，待定位节点i与参考节点之间的时钟偏差参数向量为
Figure PCTCN2019083522-appb-000085
待定位节点i与参考节点之间的朝向角参数向量为
Figure PCTCN2019083522-appb-000086
待定位节点集合为N c＝{1,2,...,N c}；N b和N s为不同种类的参考节点集合，N b＝{N c+1,N c+2,...,N c+N b}，N s＝{N c+N b+1,N c+N b+2,...,N c+N b+N s}，λ ik为噪声方差的倒数，待定位节点i与参考节点k之间的伪距
Figure PCTCN2019083522-appb-000087
d ik＝||p i-p k||，p k为参考节点的位置向量，p k＝[x k,y k,z k] T，时钟偏差所引入的距离偏差b i＝c*δ i，c是光速，δ i是待定位节点i与参考锚点之间的时钟同步偏差，ω ik是信号噪声引入的高斯误差，在待定位节点i的坐标系中待定位节点i相对于参考节点k之间的俯仰角
Figure PCTCN2019083522-appb-000088
待定位节点i相对于参考节点k之间的方位角
Figure PCTCN2019083522-appb-000089
为在待定位节点i的坐标系中待定位节点i相对于参考节点k之间的俯仰角的测量值，
Figure PCTCN2019083522-appb-000090
为待定位节点i相对于参考节点k之间的方位角的测量值，
Figure PCTCN2019083522-appb-000091
为高斯噪声的协方差 矩阵的逆矩阵。
在其中一个实施例中，将测量信息代入预设的参变量公式，得到车辆的定位信息，包括：
将测量信息代入参变量公式对应的代价函数中，得到待定位节点的定位信息，代价函数用于表征定位信息为预设变量的值的可能性。
在其中一个实施例中，若待定位节点包括至少两个节点，代价函数为：
Figure PCTCN2019083522-appb-000092
其中，所有待定位节点i的位置参数向量为
Figure PCTCN2019083522-appb-000093
p i＝[x i,y i,z i] T，待定位节点i与参考节点之间的时钟偏差参数向量为
Figure PCTCN2019083522-appb-000094
待定位节点i与参考节点之间的朝向角参数向量为
Figure PCTCN2019083522-appb-000095
待定位节点集合为N c＝{1,2,...,N c}；N b和N s为不同种类的参考节点集合，N b＝{N c+1,N c+2,...,N c+N b}，N s＝{N c+N b+1,N c+N b+2,...,N c+N b+N s}，λ ik为噪声方差的倒数，待定位节点i与参考节点k之间的伪距
Figure PCTCN2019083522-appb-000096
d ik＝||p i-p k||，p k为参考节点的位置向量，p k＝[x k,y k,z k] T时钟偏差所引入的距离偏差b i＝c*δ i，c是光速，δ i是待定位节点i与参考锚点之间的时钟同步偏差，ω ik是信号噪声引入的高斯误差，在待定位节点i的坐标系中待定位节点i相对于参考节点k之间的俯仰角
Figure PCTCN2019083522-appb-000097
待定位节点i相对于参考节点k之间的方位角
Figure PCTCN2019083522-appb-000098
为在待定位节点i的坐标系中待定位节点i相对于参考节点k之间的俯仰角的测量值，
Figure PCTCN2019083522-appb-000099
为待定位节点i相对于参考节点k之间的方位角的测量值，
Figure PCTCN2019083522-appb-000100
为高斯噪声的协方差矩阵的逆矩阵。
在其中一个实施例中，若待定位节点包括一个节点，代价函数为：
Figure PCTCN2019083522-appb-000101
其中，待定位节点i的位置参数p i＝[x i,y i,z i] T，参考节点k的位置参数向量为p k＝[x k,y k,z k] T，待定位节点i与参考节点之间的时钟偏差参数为b i，待定位节点i朝向角参数为φ i，待定位节点i的集合为N c＝{1,2,...,N c}；N b和N s为不同种类的参考节点集合，N b＝{N c+1,N c+2,...,N c+N b}，N s＝{N c+N b+1,N c+N b+2,...,N c+N b+N s}，λ ik为噪声方差的倒数，待定位节点i与参考节点k之间的伪距
Figure PCTCN2019083522-appb-000102
d ik＝||p i-p k||，时钟偏差所引入的距离偏差b i＝c*δ i，c是光速，δ i是待定位节点i与参考锚点之间的时钟同步偏差， ω ik是信号噪声引入的高斯误差，在待定位节点i的坐标系中待定位节点i相对于参考节点k之间的俯仰角
Figure PCTCN2019083522-appb-000103
待定位节点i相对于参考节点k之间的方位角
Figure PCTCN2019083522-appb-000104
Figure PCTCN2019083522-appb-000105
为在待定位节点i的坐标系中待定位节点i相对于参考节点k之间的俯仰角的测量值，
Figure PCTCN2019083522-appb-000106
为待定位节点i相对于参考节点k之间的方位角的测量值，
Figure PCTCN2019083522-appb-000107
为高斯噪声的协方差矩阵的逆矩阵。
在其中一个实施例中，将测量信息代入参变量公式对应的代价函数中，得到车辆的定位信息，包括：
采用梯度下降算法，将测量信息代入参变量公式对应的代价函数中进行迭代运算，直至代价函数满足预设的收敛条件，将满足收敛条件的代价函数对应的迭代参数确定为车辆的定位信息；
其中，收敛条件为代价函数的值最小或者达到最大迭代次数；迭代参数为根据迭代运算得到预设变量的值。
在其中一个实施例中，采用梯度下降算法，将测量信息代入参变量公式对应的代价函数中进行迭代运算，包括：
将测量信息代入代价函数对应的梯度表达式中进行迭代运算，获取待定位节点第m次的迭代参数；m为大于等于0的整数；
根据第m次的迭代参数和代价函数，采用梯度表达式，计算待定位节点第m+1次的迭代参数。
在其中一个实施例中，若待定位节点包括一个节点，接收模块11还用于接收邻居节点广播的第m次的迭代参数；
处理模块12具体用于根据邻居车辆广播的第m次的迭代参数和代价函数，采用梯度表达式，计算待定位节点第m+1次的迭代参数。
在其中一个实施例中，该装置还包括：
发送模块，用于广播待定位节点第m次的迭代参数。
在其中一个实施例中，参考节点集合包括至少四个第一类节点和/或至少一个第二类节点；第一类节点用于发送信号且不接收信号；第二类节点用于发送信号、接收信号和信号阵列处理。
在其中一个实施例中，第一类节点为卫星，第二类节点为基站。
在其中一个实施例中，参考节点集合还包括车辆。
在其中一个实施例中，待定位节点为待定位车辆。
在其中一个实施例中，初始朝向角测量信息包括待定位节点相对于参考节点的俯仰角和/或方位角。
上述实施例提供的一种通信设备的定位装置，其实现原理和技术效果与上述方法实施例类似，在此不再赘述。
关于通信设备的定位装置的具体限定可以参见上文中对于通信设备的定位方法的限定，在此不再赘述。上述通信设备的定位装置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。
在一个实施例中，提供了一种计算机设备，该计算机设备可以是服务器，其内部结构图可以如图10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、网络接口和数据库。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、计算机程序和数据库。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。该计算机设备的数据库用于存储资源查询处理数据。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资源查询处理方法。
本领域技术人员可以理解，图10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。
在一个实施例中，提供了一种计算机设备，包括存储器和处理器，存储器中存储有计算机程序，该处理器执行计算机程序时实现以下步骤：
接收待定位节点和至少一种参考节点集合之间的测量信息；所述测量信息包括待定位节点与参考节点之间的初始伪距和初始朝向角测量信息；所述参考节点集合包括至少一个参考节点；
将所述测量信息代入预设的参变量公式，得到所述待定位节点的定位信息；所述参变量公式用于表征所述定位信息为预设变量的值的可能性，所述定位信息包括所述待定位节点的位置信息、时钟偏差信息和朝向角信息。
上述实施例提供的一种计算机设备，其实现原理和技术效果与上述方法实施例类似，在此不再赘述。
在一个实施例中，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以下步骤：
接收待定位节点和至少一种参考节点集合之间的测量信息；所述测量信息包括待定位节点与参考节点之间的初始伪距和初始朝向角测量信息；所述参考节点集合包括至少一个参考节点；
将所述测量信息代入预设的参变量公式，得到所述待定位节点的定位信息；所述参变量公式用于表征所述定位信息为预设变量的值的可能性，所述定位信息包括所述待定位节点的位置信息、时钟偏差信息和朝向角信息。
上述实施例提供的一种计算机可读存储介质，其实现原理和技术效果与上述方法实施例类似，在此不再赘述。
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申请的几种实施方式，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，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。

Claims (16)

  1. 一种通信设备的定位方法，其特征在于，所述方法包括：
    接收待定位节点和至少一种参考节点集合之间的测量信息；所述测量信息包括待定位节点与参考节点之间的初始伪距和初始朝向角测量信息；所述参考节点集合包括至少一个参考节点；
    将所述测量信息代入预设的参变量公式，得到所述待定位节点的定位信息；所述参变量公式用于表征所述定位信息为预设变量的值的可能性，所述定位信息包括所述待定位节点的位置信息、时钟偏差信息和朝向角信息。
  2. 根据权利要求1所述的方法，其特征在于，所述参变量公式为包括节点的位置变量、时钟偏差变量和朝向角变量的关系式。
  3. 根据权利要求2所述的方法，其特征在于，所述位置变量为包含多个节点的位置信息的向量，所述时钟偏差变量为包含多个节点的时钟偏差信息的向量，所述朝向角变量为包含多个节点的朝向角信息的向量。
  4. 根据权利要求1-3任一项所述的方法，其特征在于，所述将所述测量信息代入预设的参变量公式，得到所述待定位节点的定位信息，包括：
    将所述测量信息代入所述参变量公式对应的代价函数中，得到所述待定位节点的定位信息，所述代价函数用于表征所述定位信息为预设变量的值的可能性。
  5. 根据权利要求4所述的方法，其特征在于，所述将所述测量信息代入所述参变量公式对应的代价函数中，得到所述车辆的定位信息，包括：
    采用梯度下降算法，将所述测量信息代入所述参变量公式对应的代价函数中进行迭代运算，直至所述代价函数满足预设的收敛条件，将满足所述收敛条件的代价函数对应的迭代参数确定为所述车辆的定位信息；
    其中，所述收敛条件为所述代价函数的值最小或者达到最大迭代次数；所述迭代参数为根据所述迭代运算得到所述预设变量的值。
  6. 根据权利要求5所述的方法，其特征在于，所述采用梯度下降算法，将所述测量信息代入所述参变量公式对应的代价函数中进行迭代运算，包括：
    将所述测量信息代入所述代价函数对应的梯度表达式中进行迭代运算，获取所述待定位节点第m次的迭代参数；m为大于等于0的整数；
    根据所述第m次的迭代参数和所述代价函数，采用所述梯度表达式，计算所述待定位节点第m+1次的迭代参数。
  7. 根据权利要求6所述的方法，其特征在于，若所述待定位节点包括一个节点，所述方法还包括：
    接收邻居节点广播的第m次的迭代参数；
    则所述根据所述第m次的迭代参数和所述代价函数，采用所述梯度表达式，计算所述待定位节点第m+1次的迭代参数，包括：
    根据所述邻居车辆广播的第m次的迭代参数和所述代价函数，采用所述梯度表达式，计算所述待定位节点第m+1次的迭代参数。
  8. 根据权利要求7所述的方法，其特征在于，所述方法还包括：
    广播所述待定位节点第m次的迭代参数。
  9. 根据权利要求1或2所述的方法，其特征在于，所述参考节点集合包括至少四个第一类节点和/或至少一个第二类节点；所述第一类节点用于发送信号且不接收信号；所述第二类节点用于发送信号、接收信号和信号阵列处理。
  10. 根据权利要求9所述的方法，其特征在于，所述第一类节点为卫星，所述第二类节点为基站。
  11. 根据权利要求10所述的方法，其特征在于，所述参考节点集合还包括车辆。
  12. 根据权利要求1或2所述的方法，其特征在于，所述待定位节点为待定位车辆。
  13. 根据权利要求1或2所述的方法，其特征在于，所述初始朝向角测量信息包括所述待定位节点相对于所述参考节点的俯仰角和/或方位角。
  14. 一种通信设备的定位装置，其特征在于，包括：
    接收模块，用于接收待定位节点和至少一种参考节点集合之间的测量信息；所述测量信息包括待定位节点与参考节点之间的初始伪距和初始朝向角测量信息；所述参考节点集合包括至少一个参考节点；
    处理模块，用于将所述测量信息代入预设的参变量公式，得到所述待定位节点的定位信息；所述参变量公式用于表征所述定位信息为预设变量的值的可能性，所述定位信息包括所述待定位节点的位置信息、时钟偏差信息和朝向角信息。
  15. 一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，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13中任一项所述方法的步骤。
  16. 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13中任一项所述的方法的步骤。
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CN104157167A (zh) * 2014-08-28 2014-11-19 银江股份有限公司 一种基于协同相对定位技术的车辆防碰撞方法

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