Cooperative positioning method and device and storage medium

Abstract

The invention discloses a cooperative positioning method and device and a storage medium, and relates to the technical field of communication. The specific implementation scheme is as follows: measuring a reference signal sent by a neighbor node in a plurality of nodes, and obtaining a distance measurement value and a carrier phase observation value between a node to be positioned and the neighbor node; receiving a first posterior probability density function and a second posterior probability density function sent by the neighbor node; estimating position coordinates of the node to be positioned according to the distance measurement value, the carrier phase observation value and the first posterior probability density function; and estimating the integer ambiguity of the node to be positioned according to the distance measurement value, the carrier phase observation value and the second posterior probability density function. According to the invention, under the condition that the number of the positioning nodes is small, the positioning precision of all the to-be-positioned nodes can still be improved, and the problem that the positioning error of the terminal equipment is large in a high-precision positioning scene is solved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a collaborative positioning method, device and storage medium. Background Art

[0002] With the development of mobile communication network technology, a large number of positioning requirements have been generated for terminal devices accessing the network. However, due to the limited coverage of wireless access points or signal obstruction by indoor objects, the positioning accuracy of terminal devices is insufficient.

[0003] The existing method of positioning using carrier phase has improved the positioning accuracy to a certain extent, but in high-precision positioning scenarios, there is still a problem of large positioning errors. Summary of the invention

[0004] The present application provides a collaborative positioning method, apparatus and storage medium to solve the problem of large positioning error of terminal devices in high-precision positioning scenarios.

[0005] According to one aspect of the present application, a collaborative positioning method is provided, the method comprising:

[0006] Measuring a reference signal sent by a neighboring node among the multiple nodes to obtain a distance measurement value and a carrier phase observation value between the node to be located and the neighboring node;

[0007] Receiving a first a posteriori probability density function and a second a posteriori probability density function sent by the neighboring node; the first a posteriori probability density function is the a posteriori probability density of the position coordinates of the neighboring node; the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity on the link from the neighboring node to the node to be located;

[0008] Estimate the position coordinates of the node to be located based on the distance measurement value, the carrier phase observation value and the first a posteriori probability density function; and estimate the integer ambiguity of the node to be located based on the distance measurement value, the carrier phase observation value and the second a posteriori probability density function.

[0009] In some possible implementations, the first a posteriori probability density function is the a posteriori probability density of the position coordinates of the neighbor node sent by the neighbor node in the i-1th round, where i is a positive integer; and estimating the position coordinates of the node to be located according to the distance measurement value, the carrier phase observation value and the first a posteriori probability density function includes:

[0010] generating position coordinate update information according to the distance measurement value and the carrier phase observation value;

[0011] Using the position coordinate update information to update the first a posteriori probability density function, obtaining a third a posteriori probability density function of the node to be located in the i-th round; or, according to the distance measurement value, the carrier phase observation value and the position coordinate estimation value of the i-1-th round, obtaining the third a posteriori probability density function of the node to be located in the i-th round;

[0012] Based on the third a posteriori probability density function of the node to be located in the i-th round, the position coordinates of the node to be located are estimated.

[0013] In some possible implementations, there are multiple neighboring nodes, and generating the position coordinate update information according to the distance measurement value and the carrier phase observation value includes:

[0014] For each of the neighbor nodes, constructing a factor node function corresponding to the neighbor node based on the likelihood function set by the distance measurement value and the likelihood function set by the carrier phase observation value;

[0015] The factor node function corresponding to each neighbor node is synthesized with the prior probability density function of the integer ambiguity and the prior probability density function of the neighbor node position coordinates through the sum-product algorithm principle to obtain the position coordinate update information; wherein the prior probability density function is uniformly distributed, or is the third a posteriori probability density function of the i-1th round.

[0016] In some possible implementations, estimating the position coordinates of the node to be located based on the third posterior probability density function of the node to be located in the i-th round includes:

[0017] The third a posteriori probability density function of the node to be located in the i-th round is estimated by using an estimation algorithm to obtain the position coordinates of the node to be located.

[0018] In some possible implementations, the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity sent by the neighbor node in the i-1th round, where i is a positive integer; and estimating the integer ambiguity of the node to be located according to the distance measurement value, the carrier phase observation value and the second a posteriori probability density function includes:

[0019] generating integer ambiguity update information according to the distance measurement value and the carrier phase observation value;

[0020] Using the integer ambiguity update information to update the second a posteriori probability density function, obtaining a fourth a posteriori probability density function of the node to be located in the i-th round; or, according to the distance measurement value, the carrier phase observation value and the integer ambiguity estimate value of the i-1th round, obtaining the fourth a posteriori probability density function of the node to be located in the i-th round;

[0021] Based on the fourth a posteriori probability density function of the node to be located in the i-th round, the integer ambiguity of the node to be located is estimated.

[0022] In some possible implementations, there are multiple neighbor nodes, and generating integer ambiguity update information according to the distance measurement value and the carrier phase observation value includes:

[0023] For each of the neighbor nodes, constructing a factor node function corresponding to the neighbor node based on the likelihood function set by the distance measurement value and the likelihood function set by the carrier phase observation value;

[0024] The factor node function corresponding to each neighbor node, the prior probability density function of the node to be located, and the prior probability density function of the neighbor node position coordinates are synthesized through the sum-product algorithm principle to obtain the integer ambiguity update information; wherein the prior probability density function is uniformly distributed, or is the fourth posterior probability density function of the i-1th round.

[0025] In some possible implementations, the estimating the integer ambiguity of the node to be located based on the fourth a posteriori probability density function of the node to be located in the i-th round includes:

[0026] The fourth a posteriori probability density function of the node to be located in the i-th round is estimated by using an estimation algorithm to obtain the integer ambiguity of the node to be located.

[0027] In some possible implementations, the receiving the first a posteriori probability density function and the second a posteriori probability density function sent by the neighbor node includes:

[0028] Obtaining a joint a posteriori probability density function of each node, wherein the nodes include each node to be located and a neighboring node of each node to be located;

[0029] Decomposing the joint posterior probability density function to obtain a decomposition formula;

[0030] Obtain a marginal probability density function of the decomposition formula with respect to the position coordinates as the first a posteriori probability density function; and / or obtain a marginal probability density function of the decomposition formula with respect to the integer ambiguity of the node to be located as the second a posteriori probability density function.

[0031] In some possible implementations, the method further includes:

[0032] In response to receiving the first a posteriori probability density function and the second a posteriori probability density function of the i-th round sent by the neighbor node, and the i-th round is a preset round, the position coordinates of the node to be located and the estimated result of the integer ambiguity are used as the positioning result, where i is a positive integer.

[0033] According to another aspect of the present application, a collaborative positioning device is provided, including a memory, a transceiver, and a processor:

[0034] A memory for storing a computer program; a transceiver for transmitting and receiving data under the control of the processor; and a processor for reading the computer program in the memory and performing the following operations:

[0035] Measuring a reference signal sent by a neighboring node among the multiple nodes to obtain a distance measurement value and a carrier phase observation value between the node to be located and the neighboring node;

[0036] Receiving a first a posteriori probability density function and a second a posteriori probability density function sent by the neighboring node; the first a posteriori probability density function is the a posteriori probability density of the position coordinates of the neighboring node; the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity on the link from the neighboring node to the node to be located;

[0037] Estimate the position coordinates of the node to be located based on the distance measurement value, the carrier phase observation value and the first a posteriori probability density function; and estimate the integer ambiguity of the node to be located based on the distance measurement value, the carrier phase observation value and the second a posteriori probability density function.

[0038] In some possible implementations, the first a posteriori probability density function is the a posteriori probability density of the position coordinates of the neighbor node sent by the neighbor node in the i-1th round, where i is a positive integer; and estimating the position coordinates of the node to be located according to the distance measurement value, the carrier phase observation value and the first a posteriori probability density function includes:

[0039] generating position coordinate update information according to the distance measurement value and the carrier phase observation value;

[0040] Using the position coordinate update information to update the first a posteriori probability density function, obtaining a third a posteriori probability density function of the node to be located in the i-th round; or, according to the distance measurement value, the carrier phase observation value and the position coordinate estimation value of the i-1-th round, obtaining the third a posteriori probability density function of the node to be located in the i-th round;

[0041] Based on the third a posteriori probability density function of the node to be located in the i-th round, the position coordinates of the node to be located are estimated.

[0042] In some possible implementations, there are multiple neighboring nodes, and generating the position coordinate update information according to the distance measurement value and the carrier phase observation value includes:

[0043] For each of the neighbor nodes, constructing a factor node function corresponding to the neighbor node based on the likelihood function set by the distance measurement value and the likelihood function set by the carrier phase observation value;

[0044] The factor node function corresponding to each neighbor node is synthesized with the prior probability density function of the integer ambiguity and the prior probability density function of the neighbor node position coordinates through the sum-product algorithm principle to obtain the position coordinate update information; wherein the prior probability density function is uniformly distributed, or is the third a posteriori probability density function of the i-1th round.

[0045] In some possible implementations, estimating the position coordinates of the node to be located based on the third posterior probability density function of the node to be located in the i-th round includes:

[0046] The third a posteriori probability density function of the node to be located in the i-th round is estimated by using an estimation algorithm to obtain the position coordinates of the node to be located.

[0047] In some possible implementations, the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity sent by the neighbor node in the i-1th round, where i is a positive integer; and estimating the integer ambiguity of the node to be located according to the distance measurement value, the carrier phase observation value and the second a posteriori probability density function includes:

[0048] generating integer ambiguity update information according to the distance measurement value and the carrier phase observation value;

[0049] Using the integer ambiguity update information to update the second a posteriori probability density function, obtaining a fourth a posteriori probability density function of the node to be located in the i-th round; or, according to the distance measurement value, the carrier phase observation value and the integer ambiguity estimate value of the i-1th round, obtaining the fourth a posteriori probability density function of the node to be located in the i-th round;

[0050] Based on the fourth a posteriori probability density function of the node to be located in the i-th round, the integer ambiguity of the node to be located is estimated.

[0051] In some possible implementations, there are multiple neighbor nodes, and generating integer ambiguity update information according to the distance measurement value and the carrier phase observation value includes:

[0052] For each of the neighbor nodes, constructing a factor node function corresponding to the neighbor node based on the likelihood function set by the distance measurement value and the likelihood function set by the carrier phase observation value;

[0053] The factor node function corresponding to each neighbor node, the prior probability density function of the node to be located, and the prior probability density function of the neighbor node position coordinates are synthesized through the sum-product algorithm principle to obtain the integer ambiguity update information; wherein the prior probability density function is uniformly distributed, or is the fourth posterior probability density function of the i-1th round.

[0054] In some possible implementations, the estimating the integer ambiguity of the node to be located based on the fourth a posteriori probability density function of the node to be located in the i-th round includes:

[0055] The fourth a posteriori probability density function of the node to be located in the i-th round is estimated by using an estimation algorithm to obtain the integer ambiguity of the node to be located.

[0056] In some possible implementations, the receiving the first a posteriori probability density function and the second a posteriori probability density function sent by the neighbor node includes:

[0057] Obtaining a joint a posteriori probability density function of each node, wherein the nodes include each node to be located and a neighboring node of each node to be located;

[0058] Decomposing the joint posterior probability density function to obtain a decomposition formula;

[0059] Obtain a marginal probability density function of the decomposition formula with respect to the position coordinates as the first a posteriori probability density function; and / or obtain a marginal probability density function of the decomposition formula with respect to the integer ambiguity of the node to be located as the second a posteriori probability density function.

[0060] In some possible implementations, the device further includes:

[0061] In response to receiving the first a posteriori probability density function and the second a posteriori probability density function of the i-th round sent by the neighbor node, and the i-th round is a preset round, the position coordinates of the node to be located and the estimated result of the integer ambiguity are used as the positioning result, where i is a positive integer.

[0062] According to another aspect of the present application, a collaborative positioning device is provided, comprising:

[0063] A measuring unit, configured to measure a reference signal sent by a neighboring node among multiple nodes, and obtain a distance measurement value and a carrier phase observation value between the node to be located and the neighboring node;

[0064] A receiving unit, configured to receive a first a posteriori probability density function and a second a posteriori probability density function sent by the neighboring node; the first a posteriori probability density function is the a posteriori probability density of the position coordinates of the neighboring node; the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity on the link from the neighboring node to the node to be located;

[0065] A positioning unit, used to estimate the position coordinates of the node to be located based on the distance measurement value, the carrier phase observation value and the first a posteriori probability density function; and to estimate the integer ambiguity of the node to be located based on the distance measurement value, the carrier phase observation value and the second a posteriori probability density function.

[0066] In some possible implementations, the first a posteriori probability density function is the a posteriori probability density of the position coordinates of the neighbor node sent by the neighbor node in the i-1th round, where i is a positive integer; the positioning unit includes:

[0067] A first generating unit, configured to generate position coordinate update information according to the distance measurement value and the carrier phase observation value;

[0068] A first updating unit is used to update the first posterior probability density function by using the position coordinate update information to obtain a third posterior probability density function of the node to be located in the i-th round; or, according to the distance measurement value, the carrier phase observation value and the position coordinate estimation value of the i-1th round, obtain the third posterior probability density function of the node to be located in the i-th round;

[0069] The first estimating unit is used to estimate the position coordinates of the node to be located based on the third posterior probability density function of the node to be located in the i-th round.

[0070] In some possible implementations, there are multiple neighboring nodes, and the first generating unit includes:

[0071] A first construction unit is used to construct, for each of the neighbor nodes, a factor node function corresponding to the neighbor node based on the likelihood function set by the distance measurement value and the likelihood function set by the carrier phase observation value;

[0072] The first synthesis unit is used to synthesize the factor node function corresponding to each neighbor node, the prior probability density function of the integer ambiguity, and the prior probability density function of the neighbor node position coordinates through the sum-product algorithm principle to obtain the position coordinate update information; wherein the prior probability density function is uniformly distributed, or is the third posterior probability density function of the i-1th round.

[0073] In some possible implementations, the first estimating unit includes:

[0074] The first solving unit is used to estimate the third posterior probability density function of the node to be located in the i-th round by using an estimation algorithm, and solve to obtain the position coordinates of the node to be located.

[0075] In some possible implementations, the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity sent by the neighbor node in the i-1th round, where i is a positive integer; the positioning unit includes:

[0076] A second generating unit, configured to generate integer ambiguity update information according to the distance measurement value and the carrier phase observation value;

[0077] A second updating unit is configured to update the second a posteriori probability density function by using the integer ambiguity update information to obtain a fourth a posteriori probability density function of the node to be located in the i-th round; or, based on the distance measurement value, the carrier phase observation value and the integer ambiguity estimate value of the i-1th round, obtain the fourth a posteriori probability density function of the node to be located in the i-th round;

[0078] The second estimation unit is used to estimate the integer ambiguity of the node to be located based on the fourth a posteriori probability density function of the node to be located in the i-th round.

[0079] In some possible implementations, there are multiple neighboring nodes, and the second generating unit includes:

[0080] A second construction unit is used to construct, for each of the neighbor nodes, a factor node function corresponding to the neighbor node based on the likelihood function set by the distance measurement value and the likelihood function set by the carrier phase observation value;

[0081] The second synthesis unit is used to synthesize the factor node function corresponding to each neighbor node, the prior probability density function of the node to be located, and the prior probability density function of the neighbor node position coordinates through the sum-product algorithm principle to obtain the integer ambiguity update information; wherein the prior probability density function is uniformly distributed, or is the fourth posterior probability density function of the i-1th round.

[0082] In some possible implementations, the second estimating unit includes:

[0083] The second solving unit is used to estimate the fourth a posteriori probability density function of the node to be located in the i-th round by using an estimation algorithm, and solve to obtain the integer ambiguity of the node to be located.

[0084] In some possible implementations, the receiving unit includes:

[0085] A joint a posteriori probability density function acquisition unit is used to acquire a joint a posteriori probability density function of each node, wherein the nodes include each node to be located and a neighboring node of each node to be located;

[0086] A decomposition unit, used to decompose the joint posterior probability density function to obtain a decomposition formula;

[0087] A second a posteriori probability density function acquisition unit is used to obtain the marginal probability density function of the decomposition formula with respect to the position coordinates as the first a posteriori probability density function; and / or to obtain the marginal probability density function of the decomposition formula with respect to the integer ambiguity of the node to be located as the second a posteriori probability density function.

[0088] In some possible implementations, the device further includes:

[0089] A positioning result determination unit is used to respond to receiving the first a posteriori probability density function and the second a posteriori probability density function of the i-th round sent by the neighbor node, and the i-th round is a preset round, and use the position coordinates of the node to be located and the estimated result of the integer ambiguity as the positioning result, wherein i is a positive integer.

[0090] According to another aspect of the present application, a processor-readable storage medium is provided, wherein the processor-readable storage medium stores a computer program, and the computer program is used to enable the processor to execute the steps of the above-mentioned collaborative positioning method.

[0091] The solution provided according to the embodiment of the present application includes the following beneficial effects:

[0092] The collaborative positioning method of the embodiment of the present application measures the reference signal sent by the neighboring nodes among multiple nodes to obtain the distance measurement value and carrier phase observation value between the node to be positioned and the neighboring nodes; receives the first a posteriori probability density function and the second a posteriori probability density function sent by the neighboring nodes; the first a posteriori probability density function is the a posteriori probability density of the position coordinates of the neighboring nodes; the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity on the link from the neighboring nodes to the node to be positioned; estimates the position coordinates of the node to be positioned based on the distance measurement value, the carrier phase observation value and the first a posteriori probability density function; and estimates the integer ambiguity of the node to be positioned based on the distance measurement value, the carrier phase observation value and the second a posteriori probability density function. By utilizing the signal transmitted between the node to be positioned and the neighboring nodes, the carrier phase and distance with higher ranging accuracy are combined with each other. In the case of fewer positioning nodes, the positioning accuracy of all nodes to be positioned can still be improved, solving the problem of large positioning error of terminal devices in high-precision positioning scenarios.

[0093] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present application.

[0095] Figure 1 is a flow chart of a collaborative positioning method according to the first embodiment of the present application;

[0096] Figure 2 is a flow chart of a collaborative positioning method according to the second embodiment of the present application;

[0097] Figure 3 is a subgraph of a factor function graph of a wireless access point-node to be located according to the second embodiment of the present application;

[0098] Figure 4 is a subgraph of a node to be located-node to be located factor function graph according to the second embodiment of the present application;

[0099] Figure 5 is a simulation schematic diagram according to the second embodiment of the present application;

[0100] Figure 6 is a simulation result diagram according to the second embodiment of the present application;

[0101] Figure 7 is a structural schematic diagram of a collaborative positioning device according to a third embodiment of the present application;

[0102] Figure 8 is a schematic structural diagram of a collaborative positioning device according to a fourth embodiment of the present application;

[0103] Fig. 9 It is a schematic structural diagram of a collaborative positioning device according to the fifth embodiment of the present application. DETAILED DESCRIPTION

[0104] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0105] In the embodiments of the present application, the term "and / or" describes the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0106] In the embodiments of the present application, the term "plurality" refers to two or more than two, and other quantifiers are similar.

[0107] In order to facilitate a clearer understanding of the technical solutions of the embodiments of the present application, some technical contents related to the embodiments of the present application are first introduced.

[0108] In wireless communication scenarios, there are two types of nodes: one is a wireless access point with a known and fixed location, and the other is a node to be located with an unknown location. The wireless access point can be used as a reference node to locate the node to be located. However, due to the limited coverage of the wireless access point or the signal being blocked by indoor objects, some nodes to be located may not be able to communicate directly with the wireless access point, resulting in an insufficient number of visible reference nodes, and thus resulting in low positioning accuracy or inability to achieve self-positioning. Although these nodes to be located cannot communicate directly with the wireless access point, they may be able to communicate with other nodes to be located.

[0109] In the prior art, all nodes to be located in the entire network are located by using collaborative positioning technology, for example, centralized collaborative positioning and distributed collaborative positioning.

[0110] Among them, centralized collaborative positioning collects wireless measurement information of all nodes in the entire network and transmits it to a master node. The master node uniformly solves the location information of each node to be located in the entire network and sends the location information to the corresponding node to be located. This collaborative positioning technology has high positioning accuracy, but the calculation overhead of the master node is large and is not suitable for large-scale networks.

[0111] Among them, distributed collaborative positioning is an iterative calculation process of self-positioning and information exchange. Each node to be positioned in the network uses the previously calculated position information and the information it has received to calculate its own position information, and then sends its own position information to the neighboring nodes. After receiving the information, the neighboring nodes update their own positions. The above process is continuously iterated until the positions of all nodes to be positioned in the entire network converge. Distributed collaborative positioning technology has low complexity and is easy to implement, and is suitable for application in actual positioning scenarios.

[0112] Figure 1 is a flow chart of a collaborative positioning method according to the first embodiment of the present application. Figure 1 As shown, the method is applied to any node to be located among multiple nodes, and includes the following steps:

[0113] Step S101, measuring a reference signal sent by a neighboring node among multiple nodes, and obtaining a distance measurement value and a carrier phase observation value between the node to be located and the neighboring node.

[0114] In the embodiment of the present application, for any node to be located, a neighbor node is a node that can directly communicate with the node.

[0115] Optionally, the neighbor node may be a wireless access point or a node to be located.

[0116] In the embodiment of the present application, the reference signal is a known signal provided by the transmitting end to the receiving end for channel estimation or channel detection.

[0117] Optionally, the reference signal may be a positioning reference signal (Positioning Reference Signal, referred to as PRS) or a channel sounding reference signal (Sounding Reference Signal, referred to as SRS).

[0118] For any node to be located, a reference signal sent by a corresponding neighboring node is received, and a distance measurement value and a carrier phase observation value between the node to be located and the neighboring node are obtained by measuring the reference signal.

[0119] Among them, the carrier phase observation value is an important Global Positioning System (GPS) measurement data, which is widely used in high-precision positioning, navigation and other fields.

[0120] In an embodiment of the present application, the distance measurement value can be determined by the position coordinates of the node to be located and the corresponding neighbor node; the carrier phase observation value can be determined by the phase difference between the node to be located and the corresponding neighbor node.

[0121] It should be noted that in different indoor positioning systems, the types of wireless access points, the positioning signals used at the bottom layer, the ranging methods and ranging accuracy may all be different.

[0122] Step S102, receiving a first a posteriori probability density function and a second a posteriori probability density function sent by a neighboring node; the first a posteriori probability density function is the a posteriori probability density of the position coordinates of the neighboring node; the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity on the link from the neighboring node to the node to be located.

[0123] Optionally, the posterior probability density function can be obtained according to the Bayesian formula.

[0124] As a possible implementation method, the position coordinates of the neighbor nodes are used as unknown parameters, and the joint probability distribution is obtained based on the predetermined prior probability distribution of the unknown parameters and sample information. Then, the first posterior probability density distribution corresponding to the position coordinates of the neighbor nodes is obtained through Bayes' theorem.

[0125] As another possible implementation, the integer ambiguity on the link from the neighbor node to the node to be located is taken as the unknown parameter, and the joint probability distribution is obtained according to the predetermined prior probability distribution of the unknown parameter and the sample information. Then, the second posterior probability density distribution corresponding to the position coordinates of the neighbor node is obtained by Bayes' theorem.

[0126] Among them, the integer ambiguity is the integer unknown corresponding to the first observed value of the phase difference between the carrier phase and the reference phase when measuring the carrier phase of GPS technology. The integer ambiguity cannot be obtained through measurement, but can be determined by certain mathematical methods, such as the classic static relative positioning method, the antenna exchange method, the p-code double subtraction method, the filtering method, etc.

[0127] Optionally, the posterior probability density function may be obtained by decomposing a joint posterior probability density function of each node.

[0128] In an embodiment of the present application, after the neighbor node obtains the first a posteriori probability density function and the second a posteriori probability density function, it sends them to the node to be located that directly communicates with the neighbor node.

[0129] Step S103, estimating the position coordinates of the node to be located based on the distance measurement value, the carrier phase observation value and the first a posteriori probability density function; and estimating the integer ambiguity of the node to be located based on the distance measurement value, the carrier phase observation value and the second a posteriori probability density function.

[0130] It should be noted that the distance measurement value reflects the positional relationship between the neighbor node and the node to be located, and the carrier phase observation value reflects the phase relationship between the neighbor node and the node to be located. The combination of the two can accurately characterize the positioning relationship between the distance node and the node to be located.

[0131] In the embodiment of the present application, the first a posteriori probability density function reflects the probability of estimating the position coordinates of the node to be located. Therefore, the position coordinates of the node to be located are jointly estimated by the distance measurement value, the carrier phase observation value and the first a posteriori probability density function.

[0132] In an embodiment of the present application, the second a posteriori probability density function reflects the probability of estimating the integer ambiguity of the node to be located. Therefore, the integer ambiguity of the node to be located is jointly estimated by the distance measurement value, the carrier phase observation value and the second a posteriori probability density function.

[0133] In an embodiment of the present application, a reference signal sent by a neighboring node among multiple nodes is measured to obtain a distance measurement value and a carrier phase observation value between the node to be located and the neighboring node; a first a posteriori probability density function and a second a posteriori probability density function sent by the neighboring node are received; the first a posteriori probability density function is the a posteriori probability density of the position coordinates of the neighboring node; the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity on the link from the neighboring node to the node to be located; the position coordinates of the node to be located are estimated based on the distance measurement value, the carrier phase observation value and the first a posteriori probability density function; and the integer ambiguity of the node to be located is estimated based on the distance measurement value, the carrier phase observation value and the second a posteriori probability density function. By utilizing the signal transmitted between the node to be located and the neighboring node, the carrier phase and distance with higher ranging accuracy are combined with each other. In the case of fewer positioning nodes, the positioning accuracy of all nodes to be located can still be improved, solving the problem of large positioning error of terminal devices in high-precision positioning scenarios.

[0134] exist Figure 1 Based on the embodiment shown, the second embodiment of the present application provides another collaborative positioning method, such as Figure 2 As shown, the method is applied to any node to be located among multiple nodes, and includes the following steps:

[0135] Step S201, measuring a reference signal sent by a neighboring node among multiple nodes, and obtaining a distance measurement value and a carrier phase observation value between the node to be located and the neighboring node.

[0136] In the embodiment of the present application, for any node to be located, a neighbor node is a node that can directly communicate with the node.

[0137] Optionally, the neighbor node may be a wireless access point or a node to be located.

[0138] In the embodiment of the present application, there are A wireless access points in total and M nodes to be located in total, and the wireless access points and the nodes to be located are collectively referred to as nodes.

[0139] In the embodiment of the present application, the reference signal is a known signal provided by the transmitting end to the receiving end for channel estimation or channel detection.

[0140] Optionally, the reference signal may be a positioning reference signal (Positioning Reference Signal, referred to as PRS) or a channel sounding reference signal (Sounding Reference Signal, referred to as SRS).

[0141] For any node to be located, a reference signal sent by a corresponding neighboring node is received, and a distance measurement value and a carrier phase observation value between the node to be located and the neighboring node are obtained by measuring the reference signal.

[0142] In the embodiment of the present application, the position of the node is represented by a two-dimensional coordinate, all nodes are numbered in sequence, and a set A is defined to include the numbers of all wireless access points, and a set M is defined to include the numbers of all nodes to be located. Among them, the position coordinates of the node numbered a can be expressed as X a =(x a ,y a ) T , where x a is the horizontal coordinate of the node numbered a, y a is the ordinate of the node numbered a, () T Represents the transpose of a matrix.

[0143] Take the node numbered a as any node to be located as an example, and receive the reference signal sent by the neighboring node numbered b, which includes the position coordinates X of the neighboring node numbered b. b =(x b ,y b ) T , where x b is the horizontal coordinate of the node numbered b, y b is the ordinate of the node numbered b. The distance measurement value h between the node to be located a and the neighboring node b can be calculated by the following formula b→a :

[0144] h b→a =||X b -X a ||+n b→a

[0145] Among them, ||X b -Xa || represents the Euclidean distance between the position coordinates of the node to be located a and the neighbor node b, n b→a Represents the distance measurement error.

[0146] In the embodiment of the present application, it is assumed that the distance measurement error follows the variance The zero-mean Gaussian distribution is

[0147] It should be noted that in different indoor positioning systems, the types of wireless access points, the positioning signals used at the bottom layer, the ranging methods and ranging accuracy may all be different.

[0148] In the embodiment of the present application, time of arrival (ToA) ranging with relevant accuracy is used, and it is assumed that during the collaborative positioning process, the nodes can perform normal ToA ranging.

[0149] Furthermore, the reference signal sent by the neighboring node b also includes the wavelength λ of the wireless signal. The carrier phase observation value between the node to be located a and the neighboring node b can be calculated by the following formula:

[0150]

[0151] Among them, floor() means rounding down, γ b→a represents the observation error.

[0152] In the embodiment of the present application, it is assumed that the observation error follows the variance The zero-mean Gaussian distribution is

[0153] In the embodiment of the present application, it is also assumed that during the collaborative positioning process, the nodes can perform normal carrier phase observation.

[0154] Optionally, both ToA ranging and carrier phase ranging use a round trip time (RTT) approach to eliminate the influence of clock errors between system nodes.

[0155] Step S202: Receive a first a posteriori probability density function and a second a posteriori probability density function sent by a neighboring node.

[0156] Optionally, obtain the joint a posteriori probability density function of each node, decompose the joint a posteriori probability density function to obtain a decomposition formula; obtain the marginal probability density function of the decomposition formula with respect to the position coordinate as the first a posteriori probability density function; and / or obtain the marginal probability density function of the decomposition formula with respect to the integer ambiguity of the node to be located as the second a posteriori probability density function.

[0157] In the embodiment of the present application, the definition set h includes the distance measurement values ​​h of all nodes to be located. b→a , define the set Contains the carrier phase observation values ​​of all nodes to be located The positioning set X contains the position coordinates of all nodes to be located, and the positioning set N contains the integer ambiguity N of all nodes to be located. b→a .

[0158] Among them, the acquisition of distance measurement value or carrier phase observation value is conditionally independent of each other and is only related to the position coordinates of the two nodes involved. Therefore, the likelihood function of distance measurement value and carrier phase observation value can be written as:

[0159]

[0160]

[0161] Among them, \ means removing an element from a set, for example h\h b→a Indicates removal of h b→a A new set containing all elements of the subsequent set h.

[0162] Due to the distance measurement error n b→a The variance is The zero-mean Gaussian distribution of , the likelihood function of the distance measurement value can be obtained from the properties of the Gaussian distribution:

[0163]

[0164] Due to the observation error γ b→a The variance is The zero-mean Gaussian distribution of , the properties of the Gaussian distribution can obtain the likelihood function of the carrier phase observation value:

[0165]

[0166] Among them, exp represents an exponential function with the natural constant e as the base.

[0167] There are many ways to infer unknown parameters from observed information, which can be divided into Bayesian estimation and non-Bayesian estimation depending on whether the unknown parameters are regarded as random variables.

[0168] In the embodiment of the present application, the position coordinates and integer ambiguity of the node to be located are respectively regarded as random variables for Bayesian estimation to obtain the joint posterior probability density function of each node.

[0169] Taking the position coordinate as the unknown parameter θ as an example, first determine the prior probability distribution p(θ) of the unknown parameter θ in advance, and use the sample information D = {d1, d2, ..., dN}Get the joint probability distribution Through Bayes' theorem, we can obtain the posterior probability distribution of the unknown parameter θ

[0170] Similarly, the corresponding posterior probability distribution is obtained using the integer ambiguity as the unknown parameter.

[0171] Furthermore, the posterior probability distribution of the position coordinates of the node a to be located is recorded as The posterior probability distribution of the integer ambiguity of the link from the neighboring node b to the node a to be located is recorded as The joint posterior probability density function is obtained based on the posterior probability distribution of the position coordinates and the posterior probability distribution of the integer ambiguity.

[0172] In the embodiment of the present application, the joint posterior probability density function can be decomposed according to the Bayesian formula, and the decomposition formula is as follows:

[0173]

[0174] Where ∝ means proportional to, f(X) represents the joint prior probability density function of the position coordinates of all nodes to be located in the entire network, f(N) represents the joint prior probability density function of the integer ambiguity on all links in the entire network, and f(h|x) and Represents the joint likelihood function, and the joint posterior probability density function is proportional to the product of the joint prior probability density function and the joint likelihood function.

[0175] Since the size of the joint posterior probability density function is independent of the joint prior probability density function of the distance measurement value or the carrier phase observation value, it is simplified through a proportional relationship to facilitate subsequent operations.

[0176] The distance measurement and carrier phase observation are independent of each other, so the The joint likelihood function of is decomposed, and the decomposition formula is as follows:

[0177]

[0178] Here, ∪ represents the union of two sets.

[0179] It should be noted that the distance measurement value is only related to the two nodes involved, and the carrier phase observation value is only related to the two nodes involved and the integer ambiguity on the link, so the joint likelihood function can be decomposed according to the different nodes involved.

[0180] Similarly, the joint prior probability density function of the position coordinates of all nodes to be located in the entire network can also be decomposed according to the differences in the nodes to be located.

[0181] As a possible implementation method, the marginal probability density function of the decomposition formula with respect to the position coordinates is obtained as the first posterior probability density function:

[0182]

[0183] As another possible implementation method, the marginal probability density function of the integer ambiguity of the node to be located in the decomposition formula is obtained as the second posterior probability density function:

[0184]

[0185] At this point, the first a posteriori probability density function of the neighbor node position coordinates and the second a posteriori probability density function of the integer ambiguity of the neighbor node are obtained.

[0186] Step S203, the first a posteriori probability density function is the a posteriori probability density of the neighbor node position coordinates sent by the neighbor node in the i-1th round, where i is a positive integer; the position coordinates of the node to be located are estimated based on the distance measurement value, the carrier phase observation value and the first a posteriori probability density function.

[0187] In the embodiment of the present application, positioning estimation is performed based on a factor graph and a sum-product algorithm.

[0188] Since the nodes include wireless access points and nodes to be located, there are two factor graphs accordingly. The subgraphs of the wireless access point-node to be located factor function graph are as follows: Figure 3 As shown, the subgraph of the node to be located-node to be located factor function graph is as follows Figure 4 shown.

[0189] As a possible implementation, the neighbor node is a wireless access point, see Figure 3 , the location coordinates of the wireless access point are known, and no wireless measurement information is needed for location estimation. It is only necessary to estimate the location coordinates of the node to be located and the integer ambiguity on the link based on the location coordinates of the wireless access point and the wireless measurement information between the wireless access point and the node to be located. Therefore, the variable node X representing the location coordinates of the node to be located is required in the factor graph. a , the variable node N of the integer ambiguity on the link from the wireless access point b to the node a to be located b→a , represents variable node X a Factor nodes for prior probability density functions Represents variable node N b→a Factor nodes for prior probability density functions And the factor node function Since the location coordinates of the wireless access point are known, there is no need to use variable nodes in the factor graph to represent the location coordinates of the wireless access point.

[0190] As another possible implementation, the neighbor node is the node to be located. Figure 4 The connection between the nodes to be located is a bidirectional link, and they need to measure each other's wireless information to determine their respective locations. Therefore, the number of nodes in this subgraph is Figure 3 Twice the number of nodes in the subgraph of the graph shown. Figure 4 The variable node X in the factor graph shown needs to represent the position coordinates of the node a to be located. a , the variable node N of the integer ambiguity on the link from the wireless access point b to the node a to be located b→a , represents variable node X a Factor nodes for prior probability density functions Represents variable node N b→a Factor nodes for prior probability density functions Variable node X of the position coordinates of the node b to be located b , variable node N of the integer ambiguity on the link from the node a to be located to the node b to be located a→b , represents variable node X b Factor nodes for prior probability density functions Represents variable node N a→b Factor nodes for prior probability density functions Factor Node Function and factor node functions

[0191] According to the topological structure of the network, the graph subgraphs of each node to be located can be connected into a global factor graph. The information transmitted on the factor graph can be obtained by calculating the prior probability density function of each variable, the likelihood function and the wireless measurement information between nodes. Due to the existence of loops in the factor graph, the positioning process can be iterated for multiple rounds to obtain the final positioning result.

[0192] In the embodiment of the present application, the positioning system is initialized during the 0th round of iteration.

[0193] As a possible implementation method, if there is no prior distribution of the position coordinates X of the nodes to be located in the entire network, f(x) can be initialized to a uniform distribution. Since the 0th iteration is to initialize the position coordinates of the nodes to be located, and no other probability density functions are calculated, the prior distribution at the 0th iteration is the posterior probability density of the 0th iteration.

[0194] As another possible implementation, if there is a priori distribution of the position coordinates X of the node to be located, the priori distribution is used for initialization.

[0195] In an embodiment of the present application, when performing the i-th round of iteration, the first posterior probability density function is the posterior probability density of the neighbor node position coordinates sent by the neighbor node in the i-1th round.

[0196] The posterior probability density function of the position coordinates of the node a to be located can be obtained by combining the marginal distribution of the posterior probability density function. The marginal distribution is only related to the neighboring node b connected to the node a to be located. The integral of other non-neighboring nodes will not affect the shape of the marginal distribution. Therefore, the first posterior probability density function formula is The posterior probability density function of the position coordinates of the node a to be located can be simplified as:

[0197]

[0198] Furthermore, depending on the neighbor nodes, the first posterior probability density function can be decomposed into the form of sub-function integral multiplication:

[0199]

[0200] At this time, the position coordinates of the node to be located are estimated based on the distance measurement value, the carrier phase observation value and the first a posteriori probability density function.

[0201] First, a position coordinate update is generated based on the distance measurements and carrier phase observations.

[0202] In an embodiment of the present application, there are multiple neighbor nodes. For each neighbor node, a factor node function of the corresponding neighbor node is constructed based on the likelihood function set by the distance measurement value and the likelihood function set by the carrier phase observation value; the factor node function corresponding to each neighbor node is synthesized with the prior probability density function of the integer ambiguity and the prior probability density function of the neighbor node position coordinates through the sum-product algorithm principle to obtain position coordinate update information; wherein, the prior probability density function is uniformly distributed, or is the third a posteriori probability density function of the i-1th round.

[0203] As a possible implementation, the likelihood function f(h b→a |X b , X a ) and the likelihood function set by the carrier phase observation value The product of As a factor node function of the corresponding neighbor node.

[0204] In an embodiment of the present application, the factor node function corresponding to each neighbor node is synthesized with the prior probability density function of the integer ambiguity and the prior probability density function of the neighbor node position coordinates through the sum-product algorithm principle to obtain the position coordinate update information.

[0205] For the sum-product algorithm, information transmission in the factor graph follows the following principle: the information transmitted from a node to an edge is the product of the information transmitted to the node by all edges except the edge.

[0206] by Figure 3 The factor node representing the prior probability density function is shown For example, the variable node X a Factor Node The information transmitted is Factor Node To variable node X a The information transmitted is Then the information transfer formula from variable nodes to factor nodes based on the sum-product algorithm can be expressed as: Among them, n(X a ) represents the set of factor node numbers that have edge connections with variable nodes.

[0207] Correspondingly, the information transfer formula from factor nodes to variable nodes based on the sum-product algorithm can be expressed as: Among them, n(f b ) represents the factor node f b A collection of variable node numbers that are connected by edges.

[0208] Through the above analysis, we can obtain the marginal probability density calculation process of the independent variable X, which is essentially the flow into the variable node X. a The information multiplication process, and all the information flowing into the variable node X a The information multiplied by the variable node X a Towards factor node f b The message conveyed therefore,

[0209] Therefore, the factor node function Treat it as a virtual node and synthesize the information transmitted by the virtual node through the sum-product algorithm principle As the position coordinate update information.

[0210] The information transmitted by the virtual node is the non-x a The information that the variable node passes to the virtual factor node. For example Figure 4 In the virtual node The variable nodes include X a 、N b→a and X b , non-X a The variable node is N b→a and X b , then the virtual node The information transmitted is the variable node N b→a and X b The information conveyed.

[0211] It should be noted that factor nodes and virtual nodes are non-physical nodes. a Node represents the node to be located a, X b When the node represents the node b to be located, the variable node N b→a and virtual nodes As a calculation module located on the node to be located b, after the information is calculated, it is transferred to the node to be located a; similarly, the variable node N a→b and virtual nodes As a calculation module located on the node a to be located, after the information is calculated, it is transferred to the node b to be located.

[0212] As a possible implementation, i=1, then the prior probability density function is the prior distribution at the 0th iteration, that is, uniform distribution.

[0213] As another possible implementation, i≠1, then the prior probability density function is the third posterior probability density function of the i-1th round.

[0214] The method for obtaining the third posterior probability density function of the i-1th round may be:

[0215] The posterior probability density of the neighbor node position coordinates sent by the neighbor node in the i-2 round is updated using the position coordinate update information in the i-1 round.

[0216] As a possible implementation, the product of the position coordinate update information in the i-1th round and the posterior probability density of the neighbor node position coordinates sent by the neighbor node in the i-2th round is used as the third posterior probability density function in the i-1th round.

[0217] The method for obtaining the third posterior probability density function of the i-1th round may be:

[0218] It is obtained based on the distance measurement value, carrier phase observation value and the position coordinate estimation value of the i-2th round.

[0219] Secondly, the first a posteriori probability density function is updated using the position coordinate update information to obtain the third a posteriori probability density function of the node to be located in the i-th round; or, the third a posteriori probability density function of the node to be located in the i-th round is obtained based on the distance measurement value, the carrier phase observation value and the position coordinate estimate of the i-1-th round.

[0220] As a possible implementation, the product of the position coordinate update information and the first posterior probability density function is used as the third posterior probability density function of the node to be located in the i-th round.

[0221] As another possible implementation, when obtaining the position coordinate estimation value of the i-1th round, the Dirac delta function is introduced and Replace variable node xb with factor node The information transmitted f(X b ) and variable node N b→a Factor Node The information transmitted f(N b→a ), according to the properties of the Dirac delta function, the first posterior probability density function is decomposed into the integral part in the form of sub-function integral multiplication can be converted to:

[0222]

[0223] The computational complexity of the above integral multiplication algorithm is mainly concentrated in the multiple integral operations where factor nodes transmit information to variable nodes, and the calculation result is still a function, which puts great pressure on the computing power and storage of the device. The introduction of Dirac delta function can convert the integral operation into a simple value transfer, reducing the amount of calculation.

[0224] Furthermore, the probability distribution of the variable nodes is a relatively complex continuous function. In order to reduce the storage pressure, it can be discretized using the grid sampling method.

[0225] Finally, based on the third posterior probability density function of the node to be located in the i-th round, the position coordinates of the node to be located are estimated.

[0226] In the embodiment of the present application, an estimation algorithm is used to estimate the third a posteriori probability density function of the node to be located in the i-th round, and the position coordinates of the node to be located are obtained by solving.

[0227] As a possible implementation, the position coordinates of the node a to be located are estimated based on the Maximum A Posteriori (MAP) estimation algorithm:

[0228]

[0229] As another possible implementation, the position coordinates of the node a to be located are estimated based on the minimum mean square error estimation algorithm (Minimum Mean Squared Error, referred to as MMSE):

[0230]

[0231] The MAP estimation algorithm or the MMSE estimation algorithm is used to estimate the third a posteriori probability density function of the node to be located in the i-th round, and the position coordinates of the node to be located are obtained by solving.

[0232] Further, in response to receiving the first posterior probability density function of the i-th round sent by the neighboring node, and the i-th round is a preset round, the estimated result of the position coordinates of the node to be located is used as the position coordinates in the positioning result.

[0233] If i is the preset round, it means that the iteration is complete, the collaborative positioning algorithm ends, and the estimated position coordinates are the position coordinates in the positioning result.

[0234] If i is less than the preset round, it means that the iteration is insufficient, so let i=i+1 and continue iterating until the preset round is reached.

[0235] Step S204, the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity sent by the neighbor node in the i-1th round; the integer ambiguity of the node to be located is estimated according to the distance measurement value, the carrier phase observation value and the second a posteriori probability density function.

[0236] In the embodiment of the present application, the positioning system is initialized during the 0th round of iteration.

[0237] As a possible implementation method, if there is no prior distribution of the integer ambiguity N on the link of the node to be located in the entire network, f(N) can be initialized to a uniform distribution. Since the 0th iteration is to initialize the integer ambiguity on the link of the node to be located, there is no calculation of other probability density functions, and the prior distribution at the 0th iteration is the posterior probability density of the 0th iteration.

[0238] As another possible implementation, if there is a priori distribution of the integer ambiguity N on the link of the node to be located, the priori distribution is used for initialization.

[0239] In an embodiment of the present application, when performing the i-th round of iteration, the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity sent by the neighbor node in the i-1th round.

[0240] According to the same analysis as step S203, the second posterior probability density function It can be decomposed into the form of sub-function integral multiplication:

[0241]

[0242] Among them, f(h b→a |X b , X a ) is not correct for the shape of the probability density function has no impact, so it is omitted.

[0243] At this time, the integer ambiguity of the node to be located is estimated based on the distance measurement value, the carrier phase observation value and the second a posteriori probability density function.

[0244] First, integer ambiguity updates are generated based on range measurements and carrier phase observations.

[0245] In an embodiment of the present application, there are multiple neighbor nodes. For each neighbor node, a factor node function of the corresponding neighbor node is constructed based on the likelihood function set by the distance measurement value and the likelihood function set by the carrier phase observation value; the factor node function corresponding to each neighbor node is synthesized with the prior probability density function of the node to be located and the prior probability density function of the neighbor node position coordinates through the sum-product algorithm principle to obtain integer ambiguity update information; wherein, the prior probability density function is uniformly distributed, or is the fourth a posteriori probability density function of the i-1th round.

[0246] As a possible implementation, the likelihood function f(h b→a |X b , X a ) and the likelihood function set by the carrier phase observation value The product of As a factor node function of the corresponding neighbor node.

[0247] In an embodiment of the present application, the factor node function corresponding to each neighbor node is synthesized with the prior probability density function of the node to be located and the prior probability density function of the neighbor node position coordinates through the sum-product algorithm principle to obtain integer ambiguity update information.

[0248] According to the same analysis steps as the sum-product algorithm in step S203, the factor node function Treat it as a virtual node and synthesize the information transmitted by the virtual node through the sum-product algorithm principle As the integer ambiguity update information.

[0249] As a possible implementation, i=1, then the prior probability density function is the prior distribution at the 0th iteration, that is, uniform distribution.

[0250] As another possible implementation, i≠1, then the prior probability density function is the fourth posterior probability density function of the i-1th round.

[0251] The method for obtaining the fourth posterior probability density function of the i-1th round may be:

[0252] The integer ambiguity update information of the i-1th round is used to update the posterior probability density of the integer ambiguity label of the neighbor node sent by the neighbor node in the i-2th round.

[0253] As a possible implementation, the product of the integer ambiguity update information of the i-1th round and the posterior probability density of the integer ambiguity of the neighbor node sent by the neighbor node in the i-2th round is used as the fourth posterior probability density function of the i-1th round.

[0254] The method for obtaining the fourth posterior probability density function of the i-1th round may be:

[0255] Obtained based on the distance measurement, carrier phase observation, and integer ambiguity estimate of the i-2th round.

[0256] Secondly, the second a posteriori probability density function is updated using the integer ambiguity update information to obtain the fourth a posteriori probability density function of the node to be located in the i-th round; or, the fourth a posteriori probability density function of the node to be located in the i-th round is obtained based on the distance measurement value, the carrier phase observation value and the integer ambiguity estimate value of the i-1-th round.

[0257] As a possible implementation, the product of the integer ambiguity update information and the second a posteriori probability density function is used as the fourth a posteriori probability density function of the node to be located in the i-th round.

[0258] As another possible implementation, when obtaining the estimated value of the i-1th round, the Dirac delta function is introduced and Replace variable node X respectively b Factor Node The information transmitted f(X b ) and variable node N b→a Factor Node The information transmitted, according to the properties of the Dirac delta function, the second posterior probability density function is decomposed into the integral part in the form of sub-function integral multiplication can be converted to:

[0259]

[0260] The introduction of Dirac delta function can transform the integral operation into a simple value transfer, thus reducing the amount of calculation.

[0261] Furthermore, the probability distribution of the variable nodes is a relatively complex continuous function. In order to reduce the storage pressure, it can be discretized using the grid sampling method.

[0262] Finally, based on the fourth a posteriori probability density function of the node to be located in the i-th round, the integer ambiguity of the node to be located is estimated.

[0263] As a possible implementation method, the integer ambiguity of the node a to be positioned is estimated based on the maximum a posteriori probability estimation algorithm (Maximum A Posteriori, referred to as MAP):

[0264]

[0265] As another possible implementation, the integer ambiguity of the node a to be positioned is estimated based on the minimum mean square error estimation algorithm (Minimum Mean Squared Error, referred to as MMSE):

[0266]

[0267] The fourth a posteriori probability density function of the node to be located in the i-th round is estimated using the MAP estimation algorithm or the MMSE estimation algorithm to obtain the integer ambiguity of the node to be located.

[0268] Further, in response to receiving the second posterior probability density function of the i-th round sent by the neighboring node, and the i-th round is a preset round, the estimation result of the integer ambiguity of the node to be located is used as the integer ambiguity in the positioning result.

[0269] If i is the preset round, it means that the iteration is complete and the collaborative positioning algorithm ends. The estimated integer ambiguity is the integer ambiguity in the positioning result.

[0270] If i is less than the preset round, it means that the iteration is insufficient, so let i=i+1 and continue iterating until the preset round is reached.

[0271] In the embodiment of the present application, the positioning result includes position coordinates and integer ambiguity.

[0272] As a possible implementation, Figure 5 As shown, a 10m×8m indoor area is used as a simulation scene of an embodiment of the present application. A=8 wireless access points with fixed and known positions are set in the simulation scene, as shown by the black dots in the figure, and M=5 nodes to be located are set, as shown by the colored circles in the figure. The real position coordinates of all nodes are marked in the figure.

[0273] Figure 5The two nodes connected by a straight line indicate that they can communicate directly. For example, the node to be located with the real position coordinates of (4, 5) can only communicate directly with the four surrounding nodes to be located whose positions are unknown, but cannot communicate directly with any wireless access point. If a non-cooperative positioning method is used, the positioning of the node to be located cannot be achieved.

[0274] In the embodiment of the present application, the neighboring nodes with which the node to be located can directly communicate remain unchanged, the node can perform normal distance measurement and carrier phase observation, the ToA ranging error standard deviation is 0.5 meters, the carrier phase observation error standard deviation is 0.2π, and the carrier frequency f of the wireless signal c It is 3.5GHz.

[0275] In order to prevent the introduction of large errors in the collaborative positioning process, the propagation principle is set: the node to be located can transmit information to its neighboring nodes only if it receives information from three or more nodes with known locations (including wireless access points and nodes to be located that can estimate their own locations from probability density functions).

[0276] In the figure, the solid unidirectional link is the link for communication in the first round of iteration, the solid unidirectional link plus the dotted unidirectional link is the link for communication in the second round of iteration. The solid unidirectional link and the dotted bidirectional link are the links for communication in subsequent rounds of iteration.

[0277] For the node to be located (4, 5) in this simulation scenario, the positioning system is first initialized, and the probability density of the position coordinates and integer ambiguity of the node to be located is assumed to be uniformly distributed. During the first round of iteration, the distance measurement values ​​and carrier phase measurement values ​​from the neighboring nodes (2, 7) (3, 2) (7, 7) (8, 3) are received, and two likelihood functions between each neighboring node are obtained respectively to form a factor node function. Based on the information transmitted by the factor node function, the position coordinate update information and integer ambiguity update information of each neighboring node are obtained.

[0278] By multiplying the position coordinate update information with the corresponding prior distribution, the posterior probability density function of the position coordinate is obtained, and the integer ambiguity update information is multiplied with the corresponding prior distribution to obtain the posterior probability density function of the integer ambiguity.

[0279] The MAP estimation algorithm is used to solve the posterior probability density function of the position coordinates to obtain the position coordinates of the node (4, 5) to be located; the MAP estimation algorithm is used to solve the posterior probability density function of the integer ambiguity to obtain the integer ambiguity of the node (4, 5) to be located.

[0280] The current round is compared with the preset round. After reaching the preset round, the loop ends and the positioning result of the node to be positioned (4, 5) is obtained.

[0281] In order to verify the effectiveness of the algorithm, the simulation will evaluate the estimated parameters N and X in the joint posterior probability distribution, namely the integer ambiguity on each link in the network and the position coordinates of all nodes to be located. The accuracy of the integer ambiguity estimation is illustrated by the average resolution error of the integer ambiguity on all links in the network. The average resolution error of the integer ambiguity is shown in the following formula:

[0282]

[0283] Among them, card(N) represents the number of elements in the set N, It represents the estimated value of the integer ambiguity on the link from the neighbor node b to the node a to be located after the iteration, N b→a Represents the true value of the integer ambiguity.

[0284] Under the simulation conditions of the embodiment of the present application, the average integer ambiguity resolution error is calculated respectively, and the CDF curve of the error is drawn. The simulation results are as follows: Figure 6 As shown, Figure 6 The horizontal axis represents the average error of the whole cycle ambiguity resolution, and the vertical axis represents the cumulative probability distribution of the error. Figure 6 It can be seen that the average integer ambiguity resolution error has a probability of about 90% being within 1.545 cycles, indicating that the solution of the present application can relatively accurately resolve the integer ambiguity on each link in the network.

[0285] Furthermore, in order to illustrate the superiority of the solution of the present application, simulations are performed from two perspectives to compare the positioning performance of the solution of the present application and the prior art. The positioning performance is measured by the positioning error, and the positioning error e a It can be calculated by the following formula:

[0286]

[0287] in, represents the estimated value of the position coordinates of the node a to be located after the iteration, X a Indicates the true value of the position coordinates of the node a to be located.

[0288] By calculating the positioning error, the positioning error of the solution in the embodiment of the present application is at the sub-meter level, which effectively improves the positioning performance compared to the meter level of the positioning error based only on the ToA collaborative positioning solution. In high-precision scenarios, it can improve the positioning accuracy of all nodes to be located.

[0289] In an embodiment of the present application, a reference signal sent by a neighbor node among multiple nodes is measured to obtain a distance measurement value and a carrier phase observation value between the node to be located and the neighbor node; a first a posteriori probability density function and a second a posteriori probability density function sent by the neighbor node are received; the first a posteriori probability density function is the a posteriori probability density of the position coordinates of the neighbor node sent by the neighbor node in the i-1th round, where i is a positive integer; the position coordinates of the node to be located are estimated based on the distance measurement value, the carrier phase observation value and the first a posteriori probability density function; the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity sent by the neighbor node in the i-1th round; the integer ambiguity of the node to be located is estimated based on the distance measurement value, the carrier phase observation value and the second a posteriori probability density function; in response to receiving the first a posteriori probability density function and the second a posteriori probability density function of the i-th round sent by the neighbor node, and the i-th round is a preset round, the estimation results of the position coordinates and the integer ambiguity of the node to be located are used as the positioning result. By utilizing the signals transmitted between the node to be located and its neighboring nodes, the carrier phase and distance with higher ranging accuracy are combined with each other. When there are fewer positioning nodes, the positioning accuracy of all nodes to be located can still be improved, thus solving the problem of large positioning error of terminal devices in high-precision positioning scenarios.

[0290] Figure 7 is a schematic structural diagram of a collaborative positioning device according to the third embodiment of the present application. Figure 7 As shown, the device comprises:

[0291] The transceiver 700 , the processor 710 and the memory 720 .

[0292] The transceiver 700 is used to receive and send data under the control of the processor 710 .

[0293] Among them, Figure 7 In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically linking together various circuits of one or more processors represented by processor 710 and memory represented by memory 720. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface. The transceiver 700 may be a plurality of components, namely, a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium, such as a wireless channel, a wired channel, an optical cable, and the like. The processor x10 is responsible for managing the bus architecture and general processing, and the memory 720 may store data used by the processor 710 when performing operations.

[0294] The processor 710 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or a complex programmable logic device (CPLD). The processor may also adopt a multi-core architecture.

[0295] The processor 710 calls the computer program stored in the memory and performs the following operations:

[0296] Measure the reference signals sent by neighboring nodes among multiple nodes to obtain the distance measurement value and carrier phase observation value between the node to be located and the neighboring nodes;

[0297] Receive a first a posteriori probability density function and a second a posteriori probability density function sent by a neighboring node; the first a posteriori probability density function is the a posteriori probability density of the position coordinates of the neighboring node; the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity on the link from the neighboring node to the node to be located;

[0298] The position coordinates of the node to be located are estimated based on the distance measurement value, the carrier phase observation value and the first a posteriori probability density function; and the integer ambiguity of the node to be located is estimated based on the distance measurement value, the carrier phase observation value and the second a posteriori probability density function.

[0299] It should be noted here that the above-mentioned collaborative positioning device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned collaborative positioning method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0300] Figure 8 is a schematic structural diagram of a collaborative positioning device according to the fourth embodiment of the present application. Figure 8 As shown, the device comprises:

[0301] Transceiver 800 , processor 810 and memory 820 .

[0302] The transceiver 800 is used to receive and send data under the control of the processor 810 .

[0303] Among them, Figure 8In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically one or more processors represented by processor 810 and various circuits of memory represented by memory 820 are linked together. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface. The transceiver 800 may be a plurality of components, namely, a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium, including transmission media such as wireless channels, wired channels, and optical cables. For different user devices, the user interface 830 may also be an interface capable of externally and internally connecting required devices, and the connected devices include but are not limited to a keypad, a display, a speaker, a microphone, a joystick, and the like.

[0304] The processor 810 is responsible for managing the bus architecture and general processing, and the memory 820 can store data used by the processor 800 when performing operations.

[0305] Optionally, the processor 810 may be a CPU (central processing unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array) or a CPLD (Complex Programmable Logic Device), and the processor may also adopt a multi-core architecture.

[0306] The processor calls the computer program stored in the memory to execute any of the methods provided in the embodiments of the present application according to the obtained executable instructions. The processor and the memory can also be arranged physically separately.

[0307] It should be noted here that the above-mentioned collaborative positioning device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned collaborative positioning method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0308] Fig. 9 is a schematic structural diagram of a collaborative positioning device according to the fifth embodiment of the present application. Fig. 9 As shown, the device comprises:

[0309] Measuring unit 901 , receiving unit 902 and positioning unit 903 .

[0310] The measuring unit 901 is used to measure the reference signal sent by the neighboring node among the multiple nodes, and obtain the distance measurement value and the carrier phase observation value between the node to be located and the neighboring node;

[0311] A receiving unit 902 is configured to receive a first a posteriori probability density function and a second a posteriori probability density function sent by a neighboring node; the first a posteriori probability density function is a posteriori probability density of the position coordinates of the neighboring node; the second a posteriori probability density function is a posteriori probability density of the integer ambiguity on the link from the neighboring node to the node to be located;

[0312] The positioning unit 903 is used to estimate the position coordinates of the node to be located based on the distance measurement value, the carrier phase observation value and the first a posteriori probability density function; and to estimate the integer ambiguity of the node to be located based on the distance measurement value, the carrier phase observation value and the second a posteriori probability density function.

[0313] In some possible implementations, the first posterior probability density function is the posterior probability density of the neighbor node position coordinates sent by the neighbor node in the i-1th round, where i is a positive integer; the positioning unit 903 includes:

[0314] A first generating unit, configured to generate position coordinate update information according to the distance measurement value and the carrier phase observation value;

[0315] A first updating unit is used to update the first posterior probability density function using the position coordinate update information to obtain a third posterior probability density function of the node to be located in the i-th round; or, based on the distance measurement value, the carrier phase observation value and the position coordinate estimation value of the i-1th round, obtain the third posterior probability density function of the node to be located in the i-th round;

[0316] The first estimation unit is used to estimate the position coordinates of the node to be located based on the third posterior probability density function of the node to be located in the i-th round.

[0317] In some possible implementations, there are multiple neighbor nodes, and the first generation unit includes:

[0318] A first construction unit is used to construct, for each neighbor node, a factor node function corresponding to the neighbor node based on a likelihood function set by a distance measurement value and a likelihood function set by a carrier phase observation value;

[0319] The first synthesis unit is used to synthesize the factor node function corresponding to each neighbor node with the prior probability density function of the integer ambiguity and the prior probability density function of the neighbor node position coordinates through the sum-product algorithm principle to obtain the position coordinate update information; wherein the prior probability density function is uniformly distributed, or is the third a posteriori probability density function of the i-1th round.

[0320] In some possible implementations, the first estimation unit includes:

[0321] The first solving unit is used to estimate the third posterior probability density function of the node to be located in the i-th round by using an estimation algorithm, and solve to obtain the position coordinates of the node to be located.

[0322] In some possible implementations, the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity sent by the neighbor node in the i-1th round, where i is a positive integer; the positioning unit includes:

[0323] A second generating unit, configured to generate integer ambiguity update information according to the distance measurement value and the carrier phase observation value;

[0324] A second updating unit is used to update the second a posteriori probability density function by using the integer ambiguity update information to obtain a fourth a posteriori probability density function of the node to be located in the i-th round; or, according to the distance measurement value, the carrier phase observation value and the integer ambiguity estimate value of the i-1th round, obtain the fourth a posteriori probability density function of the node to be located in the i-th round;

[0325] The second estimation unit is used to estimate the integer ambiguity of the node to be located based on the fourth a posteriori probability density function of the node to be located in the i-th round.

[0326] In some possible implementations, there are multiple neighbor nodes, and the second generation unit includes:

[0327] A second construction unit is used to construct a factor node function corresponding to each neighbor node based on the likelihood function set by the distance measurement value and the likelihood function set by the carrier phase observation value;

[0328] The second synthesis unit is used to synthesize the factor node function corresponding to each neighbor node, the prior probability density function of the node to be located, and the prior probability density function of the neighbor node position coordinates through the sum-product algorithm principle to obtain the integer ambiguity update information; wherein the prior probability density function is uniformly distributed, or is the fourth a posteriori probability density function of the i-1th round.

[0329] In some possible implementations, the second estimation unit includes:

[0330] The second solving unit is used to estimate the fourth a posteriori probability density function of the node to be located in the i-th round by using an estimation algorithm, and solve to obtain the integer ambiguity of the node to be located.

[0331] In some possible implementations, the receiving unit 902 includes:

[0332] A joint a posteriori probability density function acquisition unit is used to acquire the joint a posteriori probability density function of each node, where the nodes include each node to be located and a neighboring node of each node to be located;

[0333] A decomposition unit, used for decomposing the joint posterior probability density function to obtain a decomposition formula;

[0334] The second a posteriori probability density function acquisition unit is used to obtain the marginal probability density function of the decomposition formula with respect to the position coordinates as the first a posteriori probability density function; and / or to obtain the marginal probability density function of the decomposition formula with respect to the integer ambiguity of the node to be located as the second a posteriori probability density function.

[0335] In some possible implementations, the device further includes:

[0336] A positioning result determination unit is used to respond to the first a posteriori probability density function and the second a posteriori probability density function of the i-th round sent by the receiving neighbor node, and the i-th round is a preset round, and the position coordinates of the node to be located and the estimated result of the integer ambiguity are used as the positioning result, wherein i is a positive integer.

[0337] Among them, the method and the device are based on the same application concept. Since the method and the device solve the problem in a similar principle, the implementation of the device and the method can refer to each other, and the repeated parts will not be repeated.

[0338] It should be noted that the division of units in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional unit in each embodiment of the present application may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0339] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0340] It should be noted here that the above-mentioned device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as those in the method embodiment will not be described in detail here.

[0341] An embodiment of the present application further provides a processor-readable storage medium, which stores a computer program, and the computer program is used to enable a processor to execute the collaborative positioning method of the present application.

[0342] The processor-readable storage medium can be any available medium or data storage device that can be accessed by the processor, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid-state drive (SSD)), etc.

[0343] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0344] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer executable instructions. These computer executable instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0345] These processor-executable instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0346] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A collaborative positioning method, characterized in that: Applied to any node to be located among multiple nodes, the method comprises: Measuring a reference signal sent by a neighboring node among the multiple nodes to obtain a distance measurement value and a carrier phase observation value between the node to be located and the neighboring node; Receiving a first a posteriori probability density function and a second a posteriori probability density function sent by the neighboring node; the first a posteriori probability density function is the a posteriori probability density of the position coordinates of the neighboring node; the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity on the link from the neighboring node to the node to be located; Estimate the position coordinates of the node to be located based on the distance measurement value, the carrier phase observation value and the first a posteriori probability density function; and estimate the integer ambiguity of the node to be located based on the distance measurement value, the carrier phase observation value and the second a posteriori probability density function.

2. The method according to claim 1, characterized in that The first a posteriori probability density function is the a posteriori probability density of the position coordinates of the neighbor node sent by the neighbor node in the i-1th round, where i is a positive integer; estimating the position coordinates of the node to be located according to the distance measurement value, the carrier phase observation value and the first a posteriori probability density function, comprises: generating position coordinate update information according to the distance measurement value and the carrier phase observation value; Using the position coordinate update information to update the first a posteriori probability density function, obtaining a third a posteriori probability density function of the node to be located in the i-th round; or, according to the distance measurement value, the carrier phase observation value and the position coordinate estimation value of the i-1-th round, obtaining the third a posteriori probability density function of the node to be located in the i-th round; Based on the third a posteriori probability density function of the node to be located in the i-th round, the position coordinates of the node to be located are estimated.

3. The method according to claim 2, characterized in that There are multiple neighboring nodes, and generating position coordinate update information according to the distance measurement value and the carrier phase observation value includes: For each of the neighbor nodes, constructing a factor node function corresponding to the neighbor node based on the likelihood function set by the distance measurement value and the likelihood function set by the carrier phase observation value; The factor node function corresponding to each neighbor node is synthesized with the prior probability density function of the integer ambiguity and the prior probability density function of the neighbor node position coordinates through the sum-product algorithm principle to obtain the position coordinate update information; wherein the prior probability density function is uniformly distributed, or is the third a posteriori probability density function of the i-1th round.

4. The method according to claim 2, characterized in that: The estimating the position coordinates of the node to be located based on the third posterior probability density function of the node to be located in the i-th round includes: The third a posteriori probability density function of the node to be located in the i-th round is estimated by using an estimation algorithm to obtain the position coordinates of the node to be located.

5. The method according to claim 1, characterized in that The second a posteriori probability density function is the a posteriori probability density of the integer ambiguity sent by the neighbor node in the i-1th round, where i is a positive integer; and estimating the integer ambiguity of the node to be located according to the distance measurement value, the carrier phase observation value and the second a posteriori probability density function, comprising: generating integer ambiguity update information according to the distance measurement value and the carrier phase observation value; Using the integer ambiguity update information to update the second a posteriori probability density function, obtaining a fourth a posteriori probability density function of the node to be located in the i-th round; or, according to the distance measurement value, the carrier phase observation value and the integer ambiguity estimate value of the i-1th round, obtaining the fourth a posteriori probability density function of the node to be located in the i-th round; Based on the fourth a posteriori probability density function of the node to be located in the i-th round, the integer ambiguity of the node to be located is estimated.

6. The method according to claim 5, characterized in that There are multiple neighboring nodes, and generating integer ambiguity update information according to the distance measurement value and the carrier phase observation value includes: For each of the neighbor nodes, constructing a factor node function corresponding to the neighbor node based on the likelihood function set by the distance measurement value and the likelihood function set by the carrier phase observation value; The factor node function corresponding to each neighbor node, the prior probability density function of the node to be located, and the prior probability density function of the neighbor node position coordinates are synthesized through the sum-product algorithm principle to obtain the integer ambiguity update information; wherein the prior probability density function is uniformly distributed, or is the fourth posterior probability density function of the i-1th round.

7. The method according to claim 5, characterized in that The estimating the integer ambiguity of the node to be located based on the fourth a posteriori probability density function of the node to be located in the i-th round includes: The fourth a posteriori probability density function of the node to be located in the i-th round is estimated by using an estimation algorithm to obtain the integer ambiguity of the node to be located.

8. The method according to claim 1, characterized in that The receiving the first a posteriori probability density function and the second a posteriori probability density function sent by the neighbor node comprises: Obtaining a joint a posteriori probability density function of each node, wherein the nodes include each node to be located and a neighboring node of each node to be located; Decomposing the joint posterior probability density function to obtain a decomposition formula; Obtain a marginal probability density function of the decomposition formula with respect to the position coordinates as the first a posteriori probability density function; and / or obtain a marginal probability density function of the decomposition formula with respect to the integer ambiguity of the node to be located as the second a posteriori probability density function.

9. The method according to claim 1, characterized in that: The method further comprises: In response to receiving the first a posteriori probability density function and the second a posteriori probability density function of the i-th round sent by the neighbor node, and the i-th round is a preset round, the position coordinates of the node to be located and the estimated result of the integer ambiguity are used as the positioning result, where i is a positive integer.

10. A collaborative positioning device, characterized in that: Including memory, transceiver, processor: A memory for storing a computer program; a transceiver for transmitting and receiving data under the control of the processor; and a processor for reading the computer program in the memory and performing the following operations: Measuring a reference signal sent by a neighboring node among multiple nodes to obtain a distance measurement value and a carrier phase observation value between the node to be located and the neighboring node; Receiving a first a posteriori probability density function and a second a posteriori probability density function sent by the neighboring node; the first a posteriori probability density function is the a posteriori probability density of the position coordinates of the neighboring node; the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity on the link from the neighboring node to the node to be located; Estimate the position coordinates of the node to be located based on the distance measurement value, the carrier phase observation value and the first a posteriori probability density function; and estimate the integer ambiguity of the node to be located based on the distance measurement value, the carrier phase observation value and the second a posteriori probability density function.

11. The device according to claim 10, characterized in that The first a posteriori probability density function is the a posteriori probability density of the position coordinates of the neighbor node sent by the neighbor node in the i-1th round, where i is a positive integer; estimating the position coordinates of the node to be located according to the distance measurement value, the carrier phase observation value and the first a posteriori probability density function, comprises: generating position coordinate update information according to the distance measurement value and the carrier phase observation value; Using the position coordinate update information to update the first a posteriori probability density function, obtaining a third a posteriori probability density function of the node to be located in the i-th round; or, according to the distance measurement value, the carrier phase observation value and the position coordinate estimation value of the i-1-th round, obtaining the third a posteriori probability density function of the node to be located in the i-th round; Based on the third a posteriori probability density function of the node to be located in the i-th round, the position coordinates of the node to be located are estimated.

12. The device according to claim 11, characterized in that There are multiple neighboring nodes, and generating position coordinate update information according to the distance measurement value and the carrier phase observation value includes: For each of the neighbor nodes, constructing a factor node function corresponding to the neighbor node based on the likelihood function set by the distance measurement value and the likelihood function set by the carrier phase observation value; The factor node function corresponding to each neighbor node is synthesized with the prior probability density function of the integer ambiguity and the prior probability density function of the neighbor node position coordinates through the sum-product algorithm principle to obtain the position coordinate update information; wherein the prior probability density function is uniformly distributed, or is the third a posteriori probability density function of the i-1th round.

13. The device according to claim 11, characterized in that The estimating the position coordinates of the node to be located based on the third posterior probability density function of the node to be located in the i-th round includes: The third a posteriori probability density function of the node to be located in the i-th round is estimated by using an estimation algorithm to obtain the position coordinates of the node to be located.

14. The device according to claim 10, characterized in that The second a posteriori probability density function is the a posteriori probability density of the integer ambiguity sent by the neighbor node in the i-1th round, where i is a positive integer; and estimating the integer ambiguity of the node to be located according to the distance measurement value, the carrier phase observation value and the second a posteriori probability density function, comprising: generating integer ambiguity update information according to the distance measurement value and the carrier phase observation value; Using the integer ambiguity update information to update the second a posteriori probability density function, obtaining a fourth a posteriori probability density function of the node to be located in the i-th round; or, according to the distance measurement value, the carrier phase observation value and the integer ambiguity estimate value of the i-1th round, obtaining the fourth a posteriori probability density function of the node to be located in the i-th round; Based on the fourth a posteriori probability density function of the node to be located in the i-th round, the integer ambiguity of the node to be located is estimated.

15. The device according to claim 14, characterized in that There are multiple neighboring nodes, and generating integer ambiguity update information according to the distance measurement value and the carrier phase observation value includes: For each of the neighbor nodes, constructing a factor node function corresponding to the neighbor node based on the likelihood function set by the distance measurement value and the likelihood function set by the carrier phase observation value; The factor node function corresponding to each neighbor node, the prior probability density function of the node to be located, and the prior probability density function of the neighbor node position coordinates are synthesized through the sum-product algorithm principle to obtain the integer ambiguity update information; wherein the prior probability density function is uniformly distributed, or is the fourth posterior probability density function of the i-1th round.

16. The device according to claim 14, characterized in that The estimating the integer ambiguity of the node to be located based on the fourth a posteriori probability density function of the node to be located in the i-th round includes: The fourth a posteriori probability density function of the node to be located in the i-th round is estimated by using an estimation algorithm to obtain the integer ambiguity of the node to be located.

17. The device according to claim 10, characterized in that The receiving the first a posteriori probability density function and the second a posteriori probability density function sent by the neighbor node comprises: Obtaining a joint a posteriori probability density function of each node, wherein the nodes include each node to be located and a neighboring node of each node to be located; Decomposing the joint posterior probability density function to obtain a decomposition formula; Obtain a marginal probability density function of the decomposition formula with respect to the position coordinates as the first a posteriori probability density function; and / or obtain a marginal probability density function of the decomposition formula with respect to the integer ambiguity of the node to be located as the second a posteriori probability density function.

18. The device according to claim 10, characterized in that The device also includes: In response to receiving the first a posteriori probability density function and the second a posteriori probability density function of the i-th round sent by the neighbor node, and the i-th round is a preset round, the position coordinates of the node to be located and the estimated result of the integer ambiguity are used as the positioning result, where i is a positive integer.

19. A collaborative positioning device, characterized in that: include: A measuring unit, configured to measure a reference signal sent by a neighboring node among multiple nodes, and obtain a distance measurement value and a carrier phase observation value between the node to be located and the neighboring node; A receiving unit, configured to receive a first a posteriori probability density function and a second a posteriori probability density function sent by the neighboring node; the first a posteriori probability density function is the a posteriori probability density of the position coordinates of the neighboring node; the second a posteriori probability density function is the a posteriori probability density of the integer ambiguity on the link from the neighboring node to the node to be located; A positioning unit, used to estimate the position coordinates of the node to be located based on the distance measurement value, the carrier phase observation value and the first a posteriori probability density function; and to estimate the integer ambiguity of the node to be located based on the distance measurement value, the carrier phase observation value and the second a posteriori probability density function.

20. The device according to claim 19, characterized in that The first posterior probability density function is the posterior probability density of the position coordinates of the neighbor node sent by the neighbor node in the i-1th round, where i is a positive integer; The positioning unit comprises: A first generating unit, configured to generate position coordinate update information according to the distance measurement value and the carrier phase observation value; A first updating unit is used to update the first posterior probability density function by using the position coordinate update information to obtain a third posterior probability density function of the node to be located in the i-th round; or, according to the distance measurement value, the carrier phase observation value and the position coordinate estimation value of the i-1th round, obtain the third posterior probability density function of the node to be located in the i-th round; The first estimating unit is used to estimate the position coordinates of the node to be located based on the third posterior probability density function of the node to be located in the i-th round.

21. The device according to claim 20, characterized in that There are multiple neighboring nodes, and the first generating unit includes: A first construction unit is used to construct, for each of the neighbor nodes, a factor node function corresponding to the neighbor node based on the likelihood function set by the distance measurement value and the likelihood function set by the carrier phase observation value; The first synthesis unit is used to synthesize the factor node function corresponding to each neighbor node, the prior probability density function of the integer ambiguity, and the prior probability density function of the neighbor node position coordinates through the sum-product algorithm principle to obtain the position coordinate update information; wherein the prior probability density function is uniformly distributed, or is the third posterior probability density function of the i-1th round.

22. The device according to claim 20, characterized in that The first estimation unit comprises: The first solving unit is used to estimate the third posterior probability density function of the node to be located in the i-th round by using an estimation algorithm, and solve to obtain the position coordinates of the node to be located.

23. The device according to claim 19, characterized in that The second a posteriori probability density function is the a posteriori probability density of the integer ambiguity sent by the neighbor node in the i-1th round, where i is a positive integer; The positioning unit comprises: A second generating unit, configured to generate integer ambiguity update information according to the distance measurement value and the carrier phase observation value; A second updating unit is configured to update the second a posteriori probability density function by using the integer ambiguity update information to obtain a fourth a posteriori probability density function of the node to be located in the i-th round; or, based on the distance measurement value, the carrier phase observation value and the integer ambiguity estimate value of the i-1th round, obtain the fourth a posteriori probability density function of the node to be located in the i-th round; The second estimation unit is used to estimate the integer ambiguity of the node to be located based on the fourth a posteriori probability density function of the node to be located in the i-th round.

24. The device according to claim 23, characterized in that There are multiple neighboring nodes, and the second generating unit includes: A second construction unit is used to construct, for each of the neighbor nodes, a factor node function corresponding to the neighbor node based on the likelihood function set by the distance measurement value and the likelihood function set by the carrier phase observation value; The second synthesis unit is used to synthesize the factor node function corresponding to each neighbor node, the prior probability density function of the node to be located, and the prior probability density function of the neighbor node position coordinates through the sum-product algorithm principle to obtain the integer ambiguity update information; wherein the prior probability density function is uniformly distributed, or is the fourth posterior probability density function of the i-1th round.

25. The device according to claim 23, characterized in that The second estimation unit comprises: The second solving unit is used to estimate the fourth a posteriori probability density function of the node to be located in the i-th round by using an estimation algorithm, and solve to obtain the integer ambiguity of the node to be located.

26. The device according to claim 19, characterized in that The receiving unit comprises: A joint a posteriori probability density function acquisition unit is used to acquire a joint a posteriori probability density function of each node, wherein the nodes include each node to be located and a neighboring node of each node to be located; A decomposition unit, used to decompose the joint posterior probability density function to obtain a decomposition formula; A second a posteriori probability density function acquisition unit is used to obtain the marginal probability density function of the decomposition formula with respect to the position coordinates as the first a posteriori probability density function; and / or to obtain the marginal probability density function of the decomposition formula with respect to the integer ambiguity of the node to be located as the second a posteriori probability density function.

27. The device according to claim 19, characterized in that The device also includes: A positioning result determination unit is used to respond to receiving the first a posteriori probability density function and the second a posteriori probability density function of the i-th round sent by the neighbor node, and the i-th round is a preset round, and use the position coordinates of the node to be located and the estimated result of the integer ambiguity as the positioning result, wherein i is a positive integer.

28. A processor-readable storage medium, characterized in that: The processor-readable storage medium stores a computer program, and the computer program is used to enable the processor to execute the method according to any one of claims 1 to 9.

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