CN110312306B - Unmanned aerial vehicle cluster cooperative positioning method and device based on asynchronous information - Google Patents

Unmanned aerial vehicle cluster cooperative positioning method and device based on asynchronous information Download PDF

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CN110312306B
CN110312306B CN201910537244.6A CN201910537244A CN110312306B CN 110312306 B CN110312306 B CN 110312306B CN 201910537244 A CN201910537244 A CN 201910537244A CN 110312306 B CN110312306 B CN 110312306B
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CN110312306A (en
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丛一睿
王祥科
尹栋
贺光
刘志宏
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National University of Defense Technology
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
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    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/18502—Airborne stations
    • H04B7/18506—Communications with or from aircraft, i.e. aeronautical mobile service
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention discloses an unmanned aerial vehicle cluster cooperative positioning method and device based on asynchronous information, wherein the method comprises the following steps: s1, correspondingly configuring a database for storing information required by node positioning for each non-anchor node in an unmanned aerial vehicle cluster; s2, when the target non-anchor node measures or receives the state of the anchor node or receives messages sent by other nodes, updating the database corresponding to the target non-anchor node, and executing the step S3 after the updating is finished; and S3, the target non-anchor node generates broadcast information according to the information in the corresponding database so as to broadcast the broadcast information to the adjacent nodes for updating the state information, and updates the position information of the target non-anchor node, so that the positioning of the unmanned aerial vehicle node is completed. The method is suitable for large-scale unmanned aerial vehicle clusters, and has the advantages of simple implementation method, small communication bandwidth and calculation overhead, high positioning accuracy and the like.

Description

Unmanned aerial vehicle cluster cooperative positioning method and device based on asynchronous information
Technical Field
The invention relates to the technical field of unmanned aerial vehicle cluster positioning, in particular to an unmanned aerial vehicle cluster cooperative positioning method and device based on asynchronous information.
Background
The unmanned aerial vehicle cluster receives unprecedented attention in the global scope with unique advantages, and has great application and economic value in the military and civil fields. The cooperative positioning of the unmanned aerial vehicle cluster can enable the cluster to better adapt to complex environments, so that the unmanned aerial vehicle cluster has great application prospect. The drone is a kind of mobile node, and an effective solution is not available at present for cooperative positioning of the mobile node, which has a series of problems such as network synchronization and delay problems.
In the prior art, a positioning method for a mobile node mainly adopts a BP (Belief Propagation) positioning method, which is based on a Factor Graph (Factor Graph) and uses a Sum-Product algorithm to propagate a confidence probability in the whole Factor Graph, when no loop (Cycle) exists in the Factor Graph, the Sum-Product algorithm can accurately obtain an edge probability, but for the positioning algorithm, the Factor Graph extends to two directions of time and space, even if the space dimension does not contain a loop, the time dimension necessarily contains a loop, otherwise, no mutual measurement between nodes exists, cooperative positioning cannot be realized, and when the Factor Graph contains a loop, the Sum-Product method cannot obtain an accurate result, iteration can be performed only by repeatedly transmitting information, and the more the number of iterations is, the more the result is accurate.
At present, the mainstream BP positioning method cannot be effectively exerted in the cooperative positioning of the unmanned aerial vehicle cluster, and has the following defects:
(1) huge communication cost and computational complexity. The BP positioning method requires repeated interaction iterations, each interaction iteration corresponds to the overhead of communication and calculation resources, and the overhead is increased with the increase of the scale of the positioning network. Even if only the delay caused by the communication architecture is considered (i.e. the communication bandwidth constraint and the constraint of the computing resource are ignored), the BP positioning method can hardly support the real-time positioning of more than 10 mobile nodes, which is worse if the limitation of the communication resource of the actual system is considered. However, for a drone cluster, the number of drone nodes will be much greater than 10. Therefore, the existing traditional BP algorithm cannot solve the problem of slightly scaled unmanned aerial vehicle cluster cooperative positioning.
(2) The dependence on clock synchronization. The BP positioning method needs all nodes in a network to have synchronous clocks, and the essential reason is that a moving node discretization model is established based on an information propagation clock. Once the clocks are out of synchronization, ambiguity occurs in time between nodes, and the ambiguity gradually accumulates along with the increase of the network scale, so that the motion information is wrong.
In summary, in the existing unmanned aerial vehicle cluster positioning method, because the adopted filter is based on the discrete time, the clocks of the networks need to be accurately aligned, and the communication in the whole unmanned aerial vehicle cluster needs to be strictly synchronized, so the synchronization of network communication needs to be relied on, and meanwhile, because the adopted filter is based on the discrete time, the synchronization of measurement needs to be performed, the measurement time deviation of different unmanned aerial vehicles causes inaccuracy of the positioning result, so the synchronization still needs to be relied on, while the main stream BP positioning method needs to be iterated repeatedly, and the communication cost is high, so the current cooperative positioning method can only be applied to an extremely small unmanned aerial vehicle cluster, and cannot be adapted to the cooperative positioning of the slightly-scaled unmanned aerial vehicle cluster.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides the unmanned aerial vehicle cluster cooperative positioning method and device based on asynchronous information, which can be suitable for large-scale unmanned aerial vehicle clusters, and has the advantages of simple implementation method, low communication bandwidth and calculation cost and high positioning precision.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
an unmanned aerial vehicle cluster cooperative positioning method based on asynchronous information comprises the following steps:
s1, correspondingly configuring a database for storing information required by node positioning for each non-anchor node in an unmanned aerial vehicle cluster, wherein the non-anchor node is an unmanned aerial vehicle node which is unknown in position and needs to be positioned;
s2, when the target non-anchor node measures or receives the state of the anchor node or receives messages sent by other nodes, updating the database corresponding to the target non-anchor node, and executing the step S3 after the updating is finished;
and S3, generating broadcast information by the target non-anchor node according to the corresponding information in the database so as to broadcast the broadcast information to the adjacent nodes for updating state information, updating the position information of the target non-anchor node, and completing the positioning of the unmanned aerial vehicle node.
As a further improvement of the method of the present invention, the data recorded at each time in the database of the non-anchor node includes four parts, namely, a local prior probability, a local posterior probability, a local measurement set, and a local anchor node state set, where the local prior probability includes prior joint probability density information of a node related to positioning and is continuously updated, the local posterior probability includes posterior joint probability density information of a node related to positioning and is continuously updated, the local measurement set includes all anchor node state information related to positioning and is continuously selected or updated, the local anchor node state set includes all anchor node state information related to positioning and is continuously selected or updated, and the broadcast information includes three parts, namely, the local prior probability, the local measurement set, and the local anchor node state set.
As a further improvement of the method of the present invention, the database of the non-anchor node is specifically:
Figure BDA0002101554760000021
wherein the content of the first and second substances,
Figure BDA0002101554760000022
a database corresponding to the ith non-anchor node,
Figure BDA0002101554760000023
in order to update the database after the update,
Figure BDA0002101554760000024
is a set of update times for the database,
Figure BDA0002101554760000025
updating for the k time of the database;
will be provided with
Figure BDA0002101554760000026
Is defined as
Figure BDA0002101554760000027
And:
Figure BDA0002101554760000028
Figure BDA0002101554760000029
wherein the content of the first and second substances,
Figure BDA0002101554760000031
for the database in
Figure BDA0002101554760000032
The recording of the time of day is carried out,
Figure BDA0002101554760000033
for the set of all the recording moments,
Figure BDA0002101554760000034
is the local prior probability that the current probability is,
Figure BDA0002101554760000035
is the local a-posteriori probability,
Figure BDA0002101554760000036
is a local measurement set;
Figure BDA0002101554760000037
a local anchor node state set; t is the current time of day and t is,
Figure BDA0002101554760000038
the recording time of the s-th time in the k-th updating is shown, wherein k and s are positive integers respectively;
the broadcast information is specifically:
Figure BDA0002101554760000039
Figure BDA00021015547600000310
wherein the content of the first and second substances,
Figure BDA00021015547600000311
for the broadcast information generated at the kth update of the ith non-anchor node,
Figure BDA00021015547600000312
is that make
Figure BDA00021015547600000313
Set of established time instants.
As a further improvement of the method of the present invention, in step S2, a local time axis is updated first, and a distributed prior segmentation method is adopted to update a local measurement set, a local anchor node state set, a local prior probability and a local posterior probability in the database based on the updated time axis, and the specific steps include:
s21, judging whether the current time is the database time which is the latest time in the local time axis, if so, executing the step S22, and if not, exiting;
s22, local measurement set and local anchor node state set updating: judging whether the updating time corresponds to the database time, if so, adding a new measurement set in the local measurement set to obtain an updated local measurement set, and adding a new anchor node state set in the local anchor node state set to obtain an updated local anchor node state set, otherwise, adding the measurement set in the interaction information in the local measurement set to obtain an updated local measurement set, and adding the anchor node state set in the interaction information in the local anchor node state set to obtain an updated local anchor node state set;
s23, priori fusion: respectively searching out a neighbor node j with the minimum prior variance for each node l' contained in a database of the node l, and splicing the prior distributions of all the searched nodes to form combined prior distribution;
s24, prior segmentation: subset based on local measurement set
Figure BDA00021015547600000314
Local prior state set
Figure BDA00021015547600000315
And a subset of a local anchor node state set
Figure BDA00021015547600000316
Defining a triple SNCAMST, selecting the triple SNCAMST with the minimum segmentation difference and calculating the edge probability;
s25, posterior updating and prior forecasting: updating the local posterior probability using the calculated marginal probability, and predicting the local prior probability using the updated local posterior probability at the last moment of the database.
As a further improvement of the method of the invention, said updating of the local timeline uses
Figure BDA00021015547600000317
To limit the maximum number of moments contained in the database, wherein
Figure BDA00021015547600000318
Is a maximum memory constraint (e.g. of
Figure BDA00021015547600000319
Meaning that the database of node l can contain up to 3 past times of information), i.e., the updated local timeline
Figure BDA00021015547600000320
Is a summary time axis
Figure BDA00021015547600000321
The aggregate timeline
Figure BDA00021015547600000322
Is an old database timeline
Figure BDA00021015547600000323
Union with the new incoming message timeline.
As a further improvement of the method of the present invention, the step S23 specifically includes: defining local prior variance
Figure BDA0002101554760000041
To reflect the local prior probability of node l
Figure BDA0002101554760000042
Uncertainty of wherein
Figure BDA0002101554760000043
In order to be the state of the node l',
Figure BDA0002101554760000044
selecting the node corresponding to the minimum local prior variance from other nodes l' stored in the database for the s-th recording time in the k-th updating and j is the serial number of the neighbor node
Figure BDA0002101554760000045
As
Figure BDA0002101554760000046
The sequence number of the smallest local prior variance is specifically as follows:
Figure BDA0002101554760000047
wherein
Figure BDA0002101554760000048
For the returned optimal node sequence number corresponding to the l', namely the sequence number of the minimum local prior variance,
Figure BDA0002101554760000049
for inclusion of local prior variance in the database
Figure BDA00021015547600000410
A set of all nodes of (a);
and then carrying out prior splicing according to the following formula:
Figure BDA00021015547600000411
wherein the content of the first and second substances,
Figure BDA00021015547600000412
and (4) forming a set by the optimal node serial numbers corresponding to all l'.
As a further improvement of the method of the present invention, the step S24 specifically includes: defining is based on a set of local measurements
Figure BDA00021015547600000413
The local measurement map of (1) is:
Figure BDA00021015547600000414
wherein
Figure BDA00021015547600000415
For measuring maps locally
Figure BDA00021015547600000416
Corresponding vertex set as local measurement map
Figure BDA00021015547600000417
A corresponding set of edges;
defining the triple SNCAMST as
Figure BDA00021015547600000418
Wherein
Figure BDA00021015547600000419
And
Figure BDA00021015547600000420
are respectively
Figure BDA00021015547600000421
And
Figure BDA00021015547600000422
is selected from the group consisting of (a) a subset of,
Figure BDA00021015547600000423
is the local prior probability,
Figure BDA00021015547600000424
Is the local posterior probability,
Figure BDA00021015547600000425
In order to measure the set of measurements locally,
Figure BDA00021015547600000426
a local anchor node state set;
the segmentation difference is as follows:
Figure BDA00021015547600000427
wherein the content of the first and second substances,
Figure BDA00021015547600000428
as a priori probabilities for the location after segmentation,
Figure BDA00021015547600000429
the prior probability which is left after segmentation and is not used for positioning;
order to
Figure BDA00021015547600000430
Is the maximum number of states of
Figure BDA00021015547600000431
Said triplet SNCAMST, denoted as
Figure BDA00021015547600000432
All segmentation differences will constitute a set:
Figure BDA00021015547600000433
the SNCAMST that yields the smallest segmentation difference is:
Figure BDA00021015547600000434
calculating the edge probability by adopting the following formula to the SNCAMST with the minimum segmentation difference
Figure BDA00021015547600000435
Figure BDA00021015547600000436
Wherein the content of the first and second substances,
Figure BDA0002101554760000051
is a subset of the local prior state set, and
Figure BDA0002101554760000052
has a probability density function of
Figure BDA0002101554760000053
As a further improvement of the method of the present invention, the step S25 specifically includes:
s251, judging whether the local prior set and the local anchor node state set are empty, if the local prior set or the local anchor node state set is not empty, executing the step S252, otherwise, executing the step S253;
s252, updating the local posterior probability by using the calculated marginal probability through a Bayesian method, and executing a step S253;
and S253, judging whether the current database time is the last time in the database, if so, predicting the local posterior probability by using the current local posterior probability and adopting a Kolmogorov equation.
As a further improvement of the method of the present invention, the formula for updating the local posterior probability by using the bayesian method is as follows:
Figure BDA0002101554760000054
wherein the content of the first and second substances,
Figure BDA0002101554760000055
for the set of local a-priori states,
Figure BDA0002101554760000056
in order to be the edge probability,
Figure BDA0002101554760000057
is that
Figure BDA0002101554760000058
Is selected from the group consisting of (a) a subset of,
Figure BDA0002101554760000059
is a local measurement set;
the formula for predicting the local posterior probability by adopting the Kolmogorov equation is as follows:
Figure BDA00021015547600000510
wherein the content of the first and second substances,
Figure BDA00021015547600000511
for a set of local prior states
Figure BDA00021015547600000512
The corresponding support set (i.e., the set with non-zero probability density).
An unmanned aerial vehicle cluster cooperative positioning apparatus based on asynchronous information comprises a computer device programmed to execute the steps of the unmanned aerial vehicle cluster cooperative positioning method based on asynchronous information.
Compared with the prior art, the invention has the advantages that:
1. the invention configures a database for each unmanned aerial vehicle node needing positioning in the unmanned aerial vehicle cluster, updates the database when a non-anchor node measures or receives the state of the anchor node or receives messages sent by other nodes, broadcasts state information to adjacent nodes according to the updated database, and updates the position of the node, can realize multi-unmanned aerial vehicle cluster cooperative positioning based on asynchronous information, does not need to rely on clock synchronization, can still describe correct node motion information during network asynchronous communication and asynchronous measurement, has communication volume and calculation amount independent of the scale of the network, and can realize large-scale unmanned aerial vehicle cluster cooperative positioning.
2. The method can be suitable for asynchronous communication and asynchronous measurement in a large-scale unmanned aerial vehicle cluster, can accurately position the unmanned aerial vehicle by self on the premise of occupying a small amount of communication bandwidth, solves the problem that the traditional mainstream BP-based positioning method cannot process asynchronous communication and asynchronous measurement information, is not limited by network scale, has small communication and calculation overhead when a large-scale dynamic network is positioned, and effectively solves the problem that huge communication and calculation overhead is needed when the large-scale dynamic network is positioned in the prior art.
3. The invention further realizes the database updating based on a distributed prior segmentation method, can ensure the correct acceptance and rejection of partial local prior states based on the continuity of the prior segmentation difference and the positioning estimation difference, and can effectively reduce the positioning difference and improve the positioning efficiency compared with the traditional method based on global information.
Drawings
Fig. 1 is a schematic flow chart of an implementation process of the unmanned aerial vehicle cluster cooperative positioning method based on asynchronous information according to the present embodiment.
Fig. 2 is a schematic diagram of measurement between an anchor node and a non-anchor node in the present embodiment.
Fig. 3 is a schematic diagram of the communication broadcast time of the non-anchor node in the embodiment.
Fig. 4 is a diagram illustrating the packet reception time in this embodiment.
Fig. 5 is a schematic flow chart of a specific implementation of non-anchor node location in this embodiment.
Fig. 6 is a detailed implementation flow diagram of implementing database update based on a distributed prior segmentation method in this embodiment.
Fig. 7 is a detailed implementation flow diagram of the local posterior update and the prior prediction in this embodiment.
Detailed Description
The invention is further described below with reference to the drawings and specific preferred embodiments of the description, without thereby limiting the scope of protection of the invention.
As shown in fig. 1, the method for cooperatively positioning a cluster of unmanned aerial vehicles based on asynchronous information in this embodiment includes the steps of:
s1, correspondingly configuring a database for storing information required by node positioning for each non-anchor node in an unmanned aerial vehicle cluster, wherein the non-anchor node is an unmanned aerial vehicle node which is unknown in position and needs to be positioned;
s2, when the target non-anchor node measures or receives the state of the anchor node or receives messages sent by other nodes, updating the database corresponding to the target non-anchor node, and executing the step S3 after the updating is finished;
and S3, generating broadcast information by the target non-anchor node according to the corresponding information in the database so as to broadcast the broadcast information to the adjacent nodes for updating state information, updating the position information of the target non-anchor node, and completing the positioning of the unmanned aerial vehicle node.
In this embodiment, a database is configured for each unmanned aerial vehicle node (non-anchor node) that needs to be positioned in an unmanned aerial vehicle cluster, the database is updated whenever the non-anchor node measures or receives the state of the anchor node or receives messages sent by other nodes, state information is broadcast to adjacent nodes according to the updated database, and the position of the unmanned aerial vehicle node is updated at the same time.
Different from a discrete time node motion model used in a traditional BP positioning method, the node motion model in the embodiment is based on continuous time, so that measurement information and communication interaction information are not limited by time slot synchronization any more, and subsequent asynchronous information processing is facilitated. The concrete model is as follows (the system specifically comprises I nodes, and the set is used
Figure BDA00021015547600000711
To represent):
dxi(t)＝fi(xi(t),t)dt+dβi(t), (1)
yi(t)＝gi(xi(t)), (2)
wherein, formula (1) is a system equation, formula (2) is a position equation, and states x of the nodes i are described respectivelyi(t) evolution law and position yi(t) law of change, fiAnd giRespectively a system function and a position function, betai(t) is Brownian motion.
The measurement information used in this embodiment is distance measurement information, which can be obtained by TOA (time of arrival ranging method), TDOA (time difference of arrival ranging method), or RSS (received signal strength ranging method). When node i is at node j
Figure BDA0002101554760000071
When measurement is carried out at any moment, the measurement equation is as follows:
Figure BDA0002101554760000072
wherein
Figure BDA0002101554760000073
In order to be a measure of the noise,
Figure BDA0002101554760000074
to measure noise.
In a specific application embodiment, the inter-measurement information between unmanned aerial vehicle nodes is shown in fig. 2, where a1,a2,a3For an anchor node, the state of the anchor node is precisely known,/1,...,l5The states of the non-anchor nodes obey a certain random distribution for the non-anchor nodes, and the hatched portions bounded by the dotted lines in the figure represent regions where the non-anchor nodes may appear with high probability. By way of measurement, the high probability region of non-anchor nodes has an overall decreasing trend.
In this embodiment, communication information is only transferred between non-anchor nodes, and the content includes distance measurement information and location prior information of a node set. The communication information is delivered in the form of broadcast packets, the broadcast time of which is shown in fig. 3, taking node 1 as an example,
Figure BDA0002101554760000075
representing the first packet broadcast by node 1, with a start time of
Figure BDA0002101554760000076
The end time is
Figure BDA0002101554760000077
The time when the data packet is received in this embodiment is shown in fig. 4, where there is a processing delay between the broadcast stop time and the received time. For the anchor node, only the passive response of the anchor node state information sending request sent by the non-anchor node is needed.
This embodiment specifies each non-anchor node
Figure BDA0002101554760000078
Comprising a database
Figure BDA0002101554760000079
The database stores the information needed by the node l for self positioning; after measurement, receiving the state of the anchor node, or receiving the message sent by other nodes, the non-anchor node l will correspondingly update its own database(ii) a Once the database is updated, the non-anchor node l designs broadcast information according to the existing information in the database to help the neighbor node to update corresponding state information; once the database is updated, the non-anchor node l updates the position information of the non-anchor node l by using the information in the database, and all unmanned planes in the unmanned plane cluster can estimate the real-time position of the non-anchor node l through mutual cooperation.
As shown in fig. 5, the detailed steps of implementing the non-anchor node location by the unmanned aerial vehicle cluster cooperative location algorithm based on asynchronous information in this embodiment are as follows:
step 1: initializing state prior distribution of non-anchor nodes l
Figure BDA00021015547600000710
Step 2: if measuring other nodes or receiving the state information of the anchor node or receiving the information sent by other nodes, executing the step 3;
and step 3: updating a database
Figure BDA0002101554760000081
And 4, step 4: database with a plurality of databases
Figure BDA0002101554760000082
After updating, the broadcast information is designed and pressed
Figure BDA0002101554760000083
Calculating state distribution;
and 5: by using
Figure BDA0002101554760000084
The position estimation information is updated.
In this embodiment, positioning of a non-anchor node is specifically realized by configuring an unmanned aerial vehicle cluster cooperative positioning algorithm based on asynchronous information, and a specific flow of the algorithm is shown as the following algorithm 1:
Figure BDA0002101554760000085
in this embodiment, the data recorded at each time in the database of the non-anchor node includes four parts of data, namely, a local prior probability, a local posterior probability, a local measurement set, and a local anchor node state set, where the local prior probability is
Figure BDA0002101554760000086
The prior joint probability density information containing nodes related to positioning is continuously updated, and the local posterior probability is
Figure BDA0002101554760000087
The posterior joint probability density information containing the nodes related to the positioning is continuously updated, and the local measurement set is
Figure BDA0002101554760000088
Containing all measurements related to positioning and continuously accepting or updating, the local anchor node state set is
Figure BDA0002101554760000089
Including all anchor node state information related to positioning and continuously accepting or updating, t being the current time,
Figure BDA00021015547600000810
the recording time of the s-th time in the k-th updating is shown, wherein k and s are positive integers respectively; the broadcast information comprises three parts of information of local prior probability, the local measurement set and a local anchor node state set.
In this embodiment, the database of the non-anchor node
Figure BDA00021015547600000811
Updates are only made after making measurements, receiving anchor node status, or receiving messages sent by other nodes, and so
Figure BDA00021015547600000812
The change is made at discrete moments, namely:
Figure BDA00021015547600000813
wherein the content of the first and second substances,
Figure BDA00021015547600000814
a database corresponding to the ith non-anchor node,
Figure BDA00021015547600000815
in order to update the database after the update,
Figure BDA00021015547600000816
is a set of update times for the database,
Figure BDA0002101554760000091
for the kth update of the database.
For the sake of simplifying the description, it will be
Figure BDA0002101554760000092
Is defined as
Figure BDA0002101554760000093
It has the following form:
Figure BDA0002101554760000094
Figure BDA0002101554760000095
wherein the content of the first and second substances,
Figure BDA0002101554760000096
for the database in
Figure BDA0002101554760000097
The recording of the time of day is carried out,
Figure BDA0002101554760000098
for the set of all the recording moments,
Figure BDA0002101554760000099
is the local prior probability,
Figure BDA00021015547600000910
Is the local posterior probability,
Figure BDA00021015547600000911
In order to measure the set of measurements locally,
Figure BDA00021015547600000912
is a local anchor node state set, t is the current time,
Figure BDA00021015547600000913
is the s-th recording time in the k-th update (wherein k and s are positive integers respectively). For recording
Figure BDA00021015547600000914
It is composed of four parts, namely a local prior
Figure BDA00021015547600000915
Local posterior
Figure BDA00021015547600000916
Local measurement set
Figure BDA00021015547600000917
And local anchor node state set
Figure BDA00021015547600000918
In this embodiment, there is only local prior
Figure BDA00021015547600000919
Local measurement set
Figure BDA00021015547600000920
And local anchor node state set
Figure BDA00021015547600000921
Participating in information interaction between nodes, local posteriori
Figure BDA00021015547600000922
The interaction is not participated, that is, the broadcast information only contains the above three kinds of information, and the specific form is:
Figure BDA00021015547600000923
wherein the content of the first and second substances,
Figure BDA00021015547600000924
is that make
Figure BDA00021015547600000925
Set of established moments, i.e.
Figure BDA00021015547600000926
Only the different parts of the current database from the previous database are included.
In view of the actual communication conditions, not all parts of the broadcast information can be successfully received by other nodes, and thus the received information may be all or only a portion of the broadcast information. In this embodiment are
Figure BDA00021015547600000927
At this time, the specific form of receiving information from node j by non-anchor node l is as follows:
Figure BDA00021015547600000928
wherein
Figure BDA00021015547600000929
Consisting of three parts, i.e. local priors
Figure BDA00021015547600000930
Local measurement set
Figure BDA00021015547600000931
Local anchor node state set
Figure BDA00021015547600000932
The concrete form is as follows:
Figure BDA00021015547600000933
in step S2 of this embodiment, a local time axis is updated first, and a distributed prior segmentation method is used to update a local measurement set, a local anchor node state set, a local prior probability, and a local posterior probability in a database based on the updated time axis, and the specific steps include:
s21, judging whether the current time is the database time, wherein the database time is the latest time in the local time axis, if so, executing the step S22, and if not, exiting;
s22, local measurement set and local anchor node state set updating: judging whether the updating time corresponds to the database time, if so, adding a new measurement set in the local measurement set to obtain an updated local measurement set, and adding a new anchor node state set in the local anchor node state set to obtain an updated local anchor node state set, otherwise, adding the measurement set in the interaction information in the local measurement set to obtain an updated local measurement set, and adding the anchor node state set in the interaction information in the local anchor node state set to obtain an updated local anchor node state set;
s23, priori fusion: respectively searching out a neighbor node j with the minimum prior variance for each node l' contained in a database of the node l, and splicing the prior distributions of all the searched nodes to form combined prior distribution;
s24, prior segmentation: subset based on local measurement set
Figure BDA0002101554760000101
Local prior state set
Figure BDA0002101554760000102
Andsubsets of local anchor node state sets
Figure BDA0002101554760000103
Defining a triple SNCAMST, selecting the triple SNCAMST with the minimum segmentation difference and calculating the edge probability;
s25, posterior updating and prior forecasting: and updating the local posterior probability by using the calculated marginal probability, and predicting the local prior probability by using the updated local posterior probability at the last moment of the database.
In the embodiment, the database is updated by the distributed prior segmentation method, and based on the continuity of the prior segmentation difference and the positioning estimation difference, the correct accepting or rejecting of the local prior partial state can be ensured.
In this embodiment, at each database update time
Figure BDA0002101554760000104
The non-anchor node l will have its database from
Figure BDA0002101554760000105
Is updated to
Figure BDA0002101554760000106
The update of the database comprises five parts: a local timeline, a local measurement set, a local anchor node state set, a local prior, and a local posterior. The local time axis is updated, and then other parts are updated based on the updated local time axis. Due to the limited computational and memory capabilities of the nodes,
Figure BDA0002101554760000107
it cannot contain data at all past times, especially when k becomes very large over time, and this embodiment uses
Figure BDA0002101554760000108
To limit
Figure BDA0002101554760000109
The maximum number of time instants contained in (a),
Figure BDA00021015547600001010
is a maximum memory constraint (e.g. of
Figure BDA00021015547600001011
Meaning that the database of node l can contain up to 3 past times of information), i.e., the updated local timeline
Figure BDA00021015547600001012
Is a summary time axis
Figure BDA00021015547600001013
Wherein the timeline is aggregated
Figure BDA00021015547600001014
Is an old database timeline
Figure BDA00021015547600001015
Union with the new incoming message timeline.
In this example, the local measurement set
Figure BDA00021015547600001016
And local anchor node state set
Figure BDA00021015547600001017
When updating, if
Figure BDA00021015547600001018
And
Figure BDA00021015547600001019
correspondingly (i.e. the updated data time corresponds to the latest time in the local time axis), the updated local measurement set is added to the new measurement set on the original basis
Figure BDA00021015547600001020
At the moment, the updated local anchor node state set is added into the new anchor node state set on the original basis
Figure BDA00021015547600001021
If it is not
Figure BDA00021015547600001022
And
Figure BDA00021015547600001023
if the local measurement sets are not equal, the updated local measurement set is added with a new measurement set contained in the interactive information on the original basis
Figure BDA00021015547600001024
At this time, the updated local anchor node state set is added to the new anchor node state set contained in the interactive information on the original basis
Figure BDA00021015547600001025
Since the prior distribution of each node l 'contained in the database of the node l is multiple, that is, each neighbor j of the node l provides different prior information of the node l', however, due to the limitation of information, the prior information given by the neighbor is good or bad. In the embodiment, prior fusion is to find out the neighbor node j with the smallest prior variance (i.e. the best estimation effect) for each l', and record the neighbor node j as
Figure BDA0002101554760000111
Finally, all the l' prior distributions are spliced together to form a joint prior distribution. During splicing/fusion, coupling between different neighbor information is ignored, that is, assuming that prior information given by different j is independent, joint distribution is equal to the product of distribution given by different j.
In this embodiment, step S23 returns the fused prior
Figure BDA0002101554760000112
The method comprises the following specific steps: first, a local prior variance is defined
Figure BDA0002101554760000113
To reflect that node l' is locally a priori
Figure BDA0002101554760000114
Uncertainty of wherein
Figure BDA0002101554760000115
In order to be the state of the node l',
Figure BDA0002101554760000116
is the s-th recording time in the k-th updating, and j is the serial number of the neighbor node. Local prior variance
Figure BDA0002101554760000117
The smaller, the
Figure BDA0002101554760000118
The smaller the uncertainty there is, the embodiment chooses the one corresponding to the smallest local prior variance
Figure BDA0002101554760000119
And use it as
Figure BDA00021015547600001110
According to a probability of
Figure BDA00021015547600001111
A priori stitching is performed, wherein,
Figure BDA00021015547600001112
for inclusion of local prior variance in the database
Figure BDA00021015547600001113
A set of all nodes.
In this embodiment, the specific step of step S24 includes: first, defining a measurement state triplet with a state quantity constraintSNCAMST. SNCAMST relies on local measurement maps, based on local measurement sets
Figure BDA00021015547600001114
The local measurement map of (a) is defined as:
Figure BDA00021015547600001115
wherein
Figure BDA00021015547600001116
For measuring maps locally
Figure BDA00021015547600001117
Corresponding vertex set as local measurement map
Figure BDA00021015547600001118
A corresponding set of edges;
on the basis, a state constraint measurement state triple (SNCAMST) is defined
Figure BDA00021015547600001119
Wherein
Figure BDA00021015547600001120
And
Figure BDA00021015547600001121
are respectively
Figure BDA00021015547600001122
And
Figure BDA00021015547600001123
a subset of (a).
The estimated variance (estimation gap) is a segmentation gap
Figure BDA00021015547600001124
In which
Figure BDA00021015547600001125
For the priors used in the positioning algorithm after segmentation,
Figure BDA00021015547600001126
representing the residual priors after segmentation (i.e. the priors not used by the localization algorithm), the present embodiment can obtain better localization effect by selecting a prior with the minimum segmentation difference.
Order to
Figure BDA00021015547600001127
Is the maximum number of states of
Figure BDA00021015547600001128
SNCAMST of (D)
Figure BDA00021015547600001129
All segmentation differences will constitute a set:
Figure BDA00021015547600001130
the SNCAMST that yields the smallest segmentation difference is:
Figure BDA00021015547600001131
on the basis, the SNCAMST with the minimum segmentation difference is calculated by adopting the following marginal probability formula
Figure BDA00021015547600001132
Figure BDA00021015547600001133
Wherein the content of the first and second substances,
Figure BDA00021015547600001134
is a subset of the local prior state set, and
Figure BDA00021015547600001135
has a probability density function of
Figure BDA00021015547600001136
In this embodiment, the step S25 includes the following steps:
s251, judging whether the local prior set and the local anchor node state set are empty, if the local prior set or the local anchor node state set is not empty, executing the step S252, otherwise, executing the step S253;
s252, updating the local posterior probability by using the calculated marginal probability and adopting a Bayesian method, and executing a step S253;
and S253, judging whether the current database time is the last time in the database, if so, predicting the local posterior probability by using the current local posterior probability and adopting a Kolmogorov equation.
In this embodiment, specifically, if the local prior set or the local anchor node state set is not empty, the local posterior is updated by using a bayesian method:
Figure BDA0002101554760000121
if it is not
Figure BDA0002101554760000122
Is not provided with
Figure BDA0002101554760000123
The updated local a posteriori is used as the computed local a priori
Figure BDA0002101554760000124
Local posterior probabilities were predicted using the Kolmogorov equation:
Figure BDA0002101554760000125
wherein the content of the first and second substances,
Figure BDA0002101554760000126
for a set of local prior states
Figure BDA0002101554760000127
The corresponding support set (i.e., the set with non-zero probability density).
In the specific application embodiment, the time axis is updated based on
Figure BDA0002101554760000128
Local measurement set, local anchor node state set, local prior and local posterior update are realized by configuring a distributed prior segmentation algorithm, and the specific process of the distributed prior segmentation method is shown as algorithm 2.
Figure BDA0002101554760000129
Figure BDA0002101554760000131
In the above algorithm 2, the database
Figure BDA0002101554760000132
According to time set
Figure BDA0002101554760000133
The time in the database is updated sequentially from element to element in the order from small to large, and the elements in the database are updated sequentially from element to element
Figure BDA0002101554760000134
The updating comprises five parts of local measurement set updating, local anchor node state set updating, prior fusion, prior segmentation, posterior updating and prior prediction.
As shown in fig. 6, the detailed flow of the distributed prior segmentation algorithm for implementing the local measurement set, the local anchor node state set, the local prior and the local posterior update is as follows:
step 1: judging whether the current time is the database time, namely whether the current time is satisfied
Figure BDA0002101554760000135
If yes, the step 2 is executed, otherwise, the operation is ended;
step 2: judging whether the updating time is the corresponding database time, namely whether the updating time meets the requirements
Figure BDA0002101554760000136
If yes, executing step 3, otherwise executing step 4;
and step 3: push button
Figure BDA0002101554760000137
Adding a new measurement set in the local database, and pressing
Figure BDA0002101554760000138
Adding a new anchor node state set into a local database, and executing the step 5;
and 4, step 4: push button
Figure BDA0002101554760000139
Joining measurement sets in the interaction information at the local database, and
Figure BDA00021015547600001310
adding a new anchor node state set in the interactive information into the local database, and executing the step 5;
and 5: for other nodes l' stored in each database, the minimum local prior variance sequence number is taken
Figure BDA0002101554760000141
Step 6: according to
Figure BDA0002101554760000142
Carrying out priori splicing;
and 7: push button
Figure BDA0002101554760000143
Selecting SNCAMST with minimized segmentation difference;
step (ii) of8: push type
Figure BDA0002101554760000144
Calculating the edge probability;
and step 9: the local posteriori is updated and the prior probability is predicted.
The foregoing is considered as illustrative of the preferred embodiments of the invention and is not to be construed as limiting the invention in any way. Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical spirit of the present invention should fall within the protection scope of the technical scheme of the present invention, unless the technical spirit of the present invention departs from the content of the technical scheme of the present invention.

Claims (7)

1. An unmanned aerial vehicle cluster cooperative positioning method based on asynchronous information is characterized by comprising the following steps:
s1, correspondingly configuring a database for storing information required by node positioning for each non-anchor node in an unmanned aerial vehicle cluster, wherein the non-anchor node is an unmanned aerial vehicle node which is unknown in position and needs to be positioned;
s2, when the target non-anchor node measures or receives the state of the anchor node or receives messages sent by other nodes, updating the database corresponding to the target non-anchor node, and executing the step S3 after the updating is finished;
s3, the target non-anchor node generates broadcast information according to the corresponding information in the database to broadcast the broadcast information to adjacent nodes for state information updating and updating the position information of the target non-anchor node, and positioning of the unmanned aerial vehicle node is completed;
the data recorded at each moment in the database of the non-anchor node comprises four parts of data of a local prior probability, a local posterior probability, a local measurement set and a local anchor node state set, wherein the local prior probability comprises prior joint probability density information of a node related to positioning and is continuously updated, the local posterior probability comprises posterior joint probability density information of the node related to positioning and is continuously updated, the local measurement set comprises all anchor node state information related to positioning and is continuously selected or updated, the local anchor node state set comprises all anchor node state information related to positioning and is continuously selected or updated, and the broadcast information comprises three parts of information of the local prior probability, the local measurement set and the local anchor node state set;
in step S2, a local time axis is updated first, and a distributed prior segmentation method is used to update the local measurement set, the local anchor node state set, the local prior probability, and the local posterior probability in the database based on the updated time axis, and the specific steps include:
s21, judging whether the current time is the database time which is the latest time in the local time axis, if so, executing the step S22, and if not, exiting;
s22, local measurement set and local anchor node state set updating: judging whether the updating time corresponds to the database time, if so, adding a new measurement set in the local measurement set to obtain an updated local measurement set, and adding a new anchor node state set in the local anchor node state set to obtain an updated local anchor node state set, otherwise, adding the measurement set in the interaction information in the local measurement set to obtain an updated local measurement set, and adding the anchor node state set in the interaction information in the local anchor node state set to obtain an updated local anchor node state set;
s23, priori fusion: respectively searching out a neighbor node j with the minimum prior variance for each node l' contained in a database of the node l, and splicing the prior distributions of all the searched nodes to form combined prior distribution;
s24, prior segmentation: subset based on local measurement set
Figure FDA0002631872600000011
Local prior state set
Figure FDA0002631872600000012
And a subset of a local anchor node state set
Figure FDA0002631872600000013
Defining a triple SNCAMST, selecting the triple SNCAMST with the minimum segmentation difference and calculating the edge probability;
s25, posterior updating and prior forecasting: updating the local posterior probability using the calculated marginal probability, and predicting the local prior probability using the updated local posterior probability at the last moment of the database.
2. The unmanned aerial vehicle cluster cooperative positioning method based on asynchronous information as claimed in claim 1, wherein the database of the non-anchor node is specifically:
Figure FDA0002631872600000021
wherein the content of the first and second substances,
Figure FDA0002631872600000022
a database corresponding to the ith non-anchor node,
Figure FDA0002631872600000023
in order to update the database after the update,
Figure FDA0002631872600000024
is a set of update times for the database,
Figure FDA0002631872600000025
updating for the k time of the database;
will be provided with
Figure FDA0002631872600000026
Is defined as
Figure FDA0002631872600000027
And:
Figure FDA0002631872600000028
Figure FDA0002631872600000029
wherein the content of the first and second substances,
Figure FDA00026318726000000210
for the database in
Figure FDA00026318726000000211
The recording of the time of day is carried out,
Figure FDA00026318726000000212
the collection of all recording moments;
Figure FDA00026318726000000213
is the local prior probability that the current probability is,
Figure FDA00026318726000000214
is the local a-posteriori probability,
Figure FDA00026318726000000215
in order to measure the set of measurements locally,
Figure FDA00026318726000000216
a local anchor node state set; t is the current time of day and t is,
Figure FDA00026318726000000217
the recording time of the s-th time in the k-th updating is shown, wherein k and s are positive integers respectively;
the broadcast information is specifically:
Figure FDA00026318726000000218
Figure FDA00026318726000000219
wherein the content of the first and second substances,
Figure FDA00026318726000000220
for the broadcast information generated at the kth update of the ith non-anchor node,
Figure FDA00026318726000000221
is that make
Figure FDA00026318726000000222
Set of established time instants.
3. The cooperative positioning method for unmanned aerial vehicle cluster based on asynchronous information as claimed in claim 1, wherein the updating of local time axis uses
Figure FDA00026318726000000223
To limit the maximum number of moments contained in the database, wherein
Figure FDA00026318726000000224
For maximum memory constraint, i.e. updated local time axis
Figure FDA00026318726000000225
Is a summary time axis
Figure FDA00026318726000000226
The aggregate timeline
Figure FDA00026318726000000227
Is an old database timeline
Figure FDA00026318726000000228
Union with the new incoming message timeline.
4. The unmanned aerial vehicle cluster cooperative positioning method based on asynchronous information as claimed in claim 1, wherein the step S23 specifically comprises: defining local prior variance
Figure FDA00026318726000000229
To reflect the local prior probability of node l
Figure FDA00026318726000000230
Uncertainty of wherein
Figure FDA00026318726000000231
In order to be the state of the node l',
Figure FDA00026318726000000232
selecting the node corresponding to the minimum local prior variance from other nodes l' stored in the database for the s-th recording time in the k-th updating and j is the serial number of the neighbor node
Figure FDA00026318726000000233
As
Figure FDA00026318726000000234
The sequence number of the smallest local prior variance is specifically as follows:
Figure FDA00026318726000000235
wherein
Figure FDA00026318726000000236
For the returned optimal node sequence number corresponding to the l', namely the sequence number of the minimum local prior variance,
Figure FDA00026318726000000237
for inclusion of local prior variance in the database
Figure FDA00026318726000000238
A set of all nodes of (a);
and then carrying out prior splicing according to the following formula:
Figure FDA0002631872600000031
wherein the content of the first and second substances,
Figure FDA0002631872600000032
and (4) forming a set by the optimal node serial numbers corresponding to all l'.
5. The unmanned aerial vehicle cluster cooperative positioning method based on asynchronous information as claimed in claim 1, wherein the step S24 specifically comprises: defining is based on a set of local measurements
Figure FDA0002631872600000033
The local measurement map of (1) is:
Figure FDA0002631872600000034
wherein
Figure FDA0002631872600000035
For measuring maps locally
Figure FDA0002631872600000036
Corresponding vertex set as local measurement map
Figure FDA0002631872600000037
A corresponding set of edges;
defining the triple SNCAMST as
Figure FDA0002631872600000038
Wherein
Figure FDA0002631872600000039
And
Figure FDA00026318726000000310
are respectively
Figure FDA00026318726000000311
And
Figure FDA00026318726000000312
is selected from the group consisting of (a) a subset of,
Figure FDA00026318726000000313
for the set of local a-priori states,
Figure FDA00026318726000000314
is the local prior probability,
Figure FDA00026318726000000315
Is the local posterior probability,
Figure FDA00026318726000000316
In order to measure the set of measurements locally,
Figure FDA00026318726000000317
a local anchor node state set;
the segmentation difference is as follows:
Figure FDA00026318726000000318
wherein the content of the first and second substances,
Figure FDA00026318726000000319
as a priori probabilities for the location after segmentation,
Figure FDA00026318726000000320
is left after divisionLower prior probability not used for positioning;
order to
Figure FDA00026318726000000321
Is the maximum number of states of
Figure FDA00026318726000000322
Said triplet SNCAMST, denoted as
Figure FDA00026318726000000323
All segmentation differences will constitute a set:
Figure FDA00026318726000000324
the SNCAMST that yields the smallest segmentation difference is:
Figure FDA00026318726000000325
calculating the edge probability by adopting the following formula to the SNCAMST with the minimum segmentation difference
Figure FDA00026318726000000326
Figure FDA00026318726000000327
Wherein the content of the first and second substances,
Figure FDA00026318726000000328
is a subset of the local prior state set, and
Figure FDA00026318726000000329
has a probability density function of
Figure FDA00026318726000000330
6. The unmanned aerial vehicle cluster cooperative positioning method based on asynchronous information as claimed in claim 1, wherein the step S25 comprises the following steps:
s251, judging whether the local prior set and the local anchor node state set are empty, if the local prior set or the local anchor node state set is not empty, executing the step S252, otherwise, executing the step S253;
s252, updating the local posterior probability by using the calculated marginal probability through a Bayesian method, and executing a step S253;
and S253, judging whether the current database time is the last time in the database, if so, predicting the local posterior probability by using the current local posterior probability and adopting a Kolmogorov equation.
7. The cluster cooperative positioning method for unmanned aerial vehicles based on asynchronous information as claimed in claim 6, wherein the formula for updating the local posterior probability by Bayesian method is as follows:
Figure FDA0002631872600000041
wherein the content of the first and second substances,
Figure FDA0002631872600000042
for the set of local a-priori states,
Figure FDA0002631872600000043
in order to be the edge probability,
Figure FDA0002631872600000044
is that
Figure FDA0002631872600000045
Is selected from the group consisting of (a) a subset of,
Figure FDA0002631872600000046
is a local measurement set;
the formula for predicting the local posterior probability by adopting the Kolmogorov equation is as follows:
Figure FDA0002631872600000047
wherein the content of the first and second substances,
Figure FDA0002631872600000048
for a set of local prior states
Figure FDA0002631872600000049
A corresponding support set.
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