CN112508277B - Underwater multi-target positioning method, terminal equipment and storage medium - Google Patents
Underwater multi-target positioning method, terminal equipment and storage medium Download PDFInfo
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Abstract
The invention relates to an underwater multi-target positioning method, terminal equipment and a storage medium, wherein the method comprises the following steps: s1: constructing a dynamic factor graph for multi-target real-time cooperative positioning; s2: each node in the dynamic factor graph is evolved and updated in real time along with the change of time; s3: and acquiring the real-time position of each target according to the dynamic factor graph. The method can decompose a complex global optimization problem into a plurality of simple local optimization problems for distributed solving, has the characteristics of low algorithm complexity and the like, gives consideration to the distributed characteristics of the network, can make proper adjustment along with the change of the network state, and has obvious advantages for solving the underwater multi-target real-time cooperative positioning problem.
Description
Technical Field
The invention relates to the field of underwater acoustic communication, in particular to an underwater multi-target positioning method, terminal equipment and a storage medium.
Background
With the development of underwater acoustic communication technology, research on real-time positioning technology for underwater acoustic sensor networks is gradually increasing in recent years. For example, in underwater applications such as early warning systems, underwater real-time monitoring, network routing protocols and MAC protocol real-time control, providing real-time location information of underwater targets is important for system control and decision making.
The traditional underwater sound sensor network moving target positioning technology mostly needs a target to frequently communicate with a known reference anchor node, and therefore the traditional underwater sound sensor network moving target positioning technology is a great challenge to the communication overhead and energy consumption of a network. In a multi-target positioning occasion, multiple targets can be decomposed into multiple independent individuals in principle, and the relation among the targets is not considered, so that the single-target positioning technology is directly expanded and applied to the occasion. In order to meet the precision requirement, the non-cooperative positioning method needs frequent communication among nodes, so that the communication overhead and energy consumption of a network are increased, and meanwhile, a target node is required to be loaded with an expensive precision sensor. And by using the cooperative positioning scheme, additional relative position information, such as constraint conditions of distance, connectivity, direction and the like, can be obtained through cooperation among multiple targets, so that the positioning performance of the system can be obviously improved. On the other hand, in the conventional technology, only spatial constraint relations such as distance measurement and the like are used in the positioning process, and the motion information of network nodes and the real-time application requirements are not considered, so that the algorithm positioning performance is obviously reduced in a target moving occasion or a real-time positioning scene.
The problem of multi-target real-time cooperative positioning in the underwater acoustic sensor network is a complex multi-dimensional space-time estimation problem, a large number of parameters need to be optimized simultaneously, and the parameters are interdependent and difficult to solve directly. Therefore, based on the underwater acoustic sensor network application environment, the research of a reliable and effective underwater multi-target real-time positioning method aiming at the characteristics of complex and variable marine environment becomes a key problem to be solved urgently.
Disclosure of Invention
In order to solve the problems, the invention provides an underwater multi-target positioning method, terminal equipment and a storage medium, which can realize the real-time positioning of underwater moving multi-targets with high precision and low complexity.
The specific scheme is as follows:
an underwater multi-target positioning method comprises the following steps:
s1: constructing a dynamic factor graph for multi-target real-time cooperative positioning;
the dynamic factor graph comprises two types of nodes, namely variable nodes and function nodes; the variable nodes represent the positions of targets, and the variable nodes of the same target at different continuous moments are sequentially arranged on the same horizontal line to form a horizontal link; the function nodes comprise time constraints and space constraints, the time constraints are used for describing the instantaneous speed of the target at each moment, the space constraints comprise first space constraints and second space constraints, the first space constraints are used for describing the relative position relationship between the reference anchor nodes and the target nodes, and the second space constraints are used for describing the relative position relationship between different target nodes;
s2: each node in the dynamic factor graph is evolved and updated in real time along with the change of time;
s3: and acquiring the real-time position of each target according to the dynamic factor graph.
Further, the real-time evolution and update of the dynamic factor graph in step S2 includes: when a new moment comes, a new variable node and a new function node are created, specifically:
(1) when a new moment comes, a new variable node is created for each target and is connected with the variable node of the target at the previous moment to form a new horizontal link;
(2) and when a new moment comes, if the variable nodes corresponding to the two targets are communicated, constructing a function node corresponding to a second space constraint between the two variable nodes.
Further, when a new variable node is created, a function node corresponding to the time constraint is constructed between the variable nodes at the new moment and the variable nodes at the previous moment, and the variable nodes at the new moment and the variable nodes at the previous moment are connected through the constructed function node; the expression for the time constraint is as follows:
wherein, Pm(tK) And Pm(tK+1) Respectively representing the position of the target at time K and at time K +1, tKAnd tK+1Respectively representing the K time and the K +1 time, vK+1Representing the speed of the target at time K +1,
indicating a speed measurement error.
Further, the expression of the function node corresponding to the second spatial constraint is as follows:
wherein d isK+1Represents the distance, P, between two targets at time K +1m(tK+1) And Pn(tK+1) Respectively representing the positions of target m and target n at time K +1, 
Indicating distance measurement errorsAnd (4) poor.
Further, the real-time evolving and updating the dynamic factor graph in step S2 further includes: and performing state estimation on the variable node at the current moment according to the function node acquired by the target at the current moment and the information of all nodes of the target at each past moment, and updating the state estimation of all nodes of the target at each past moment.
Further, each past time is all the times in the time window length before the current time, the time window length is calculated by using the convergence condition of the KL divergence, and the specific calculation formula is as follows:
wherein, wkRepresents a weight value, VkDenotes the vector dimension, N denotes the total number of samples of the discrete distribution, k denotes the kth sample, and H (q) denotes the KL divergence.
Further, the real-time evolving and updating the dynamic factor graph in step S2 further includes: when the target communicates with other targets, the distributed iterative transfer of the message is carried out in the dynamic factor graph through the sum-product algorithm, and then the state estimation of all nodes of other targets which communicate is updated.
Further, in the sum-product algorithm, the continuous probability of the position estimation when the node message is transmitted is subjected to binarization processing.
An underwater multi-object positioning terminal device comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the method of the embodiment of the invention.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method as described above for embodiments of the invention.
By adopting the technical scheme, the invention can decompose the complex global optimization problem into a plurality of simple local optimization problems for distributed solving, has the characteristics of low algorithm complexity and the like, gives consideration to the distributed characteristics of the network, can make proper adjustment along with the change of the network state, and has obvious advantages for solving the underwater multi-target real-time cooperative positioning problem.
Drawings
Fig. 1 is a flowchart of a first embodiment of the invention.
FIG. 2 is a diagram showing an initial model of the dynamic factor graph in this embodiment.
Fig. 3 is a schematic diagram illustrating dynamic changes of network topology caused by target movement in this embodiment.
Fig. 4 is a schematic diagram illustrating real-time messaging and updating of the network in this embodiment.
Detailed Description
To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures.
The invention will now be further described with reference to the accompanying drawings and detailed description.
The first embodiment is as follows:
the embodiment of the invention provides an underwater multi-target positioning method, as shown in fig. 1, which is a flow chart of the underwater multi-target positioning method, the method comprises the following steps:
s1: and constructing a dynamic factor graph for multi-target real-time cooperative positioning.
In this embodiment, the positions of the targets are abstracted into variable nodes, and the obtained distance measurement and speed measurement and the interdependence between the nodes are abstracted into function nodes to represent the position distribution state of the targets in the network. Specifically, as shown in fig. 2 (in this embodiment, tracking 3 moving objects is taken as an example for explanation), the current time t isKThe dynamic factor graph comprises two types of nodes, namely a variable node p and a function node f. Variables of The node p represents the position of the target, and the variable nodes of the same target at different continuous time are sequentially arranged on the same horizontal line to form a horizontal link, i.e. the moving target moves from t1Time to tKThe motion trajectory at the moment. The function node f links the related variable nodes together according to a known constraint relationship, and the constraint relationship is divided into two types: time constraint f1And spatial, temporal constraints f1For describing the instantaneous speed of the target at various times, the speed measurement in this embodiment is converted from IMU sensor measurement data. The spatial constraints comprise a first spatial constraint f2And a second spatial constraint f3First spatial constraint f2For describing the relative positional relationship between the reference anchor node and the target node, a second spatial constraint f3For describing the relative position relationship between different target nodes. Wherein the distance measurement is obtained by ToA ranging.
The specific expressions of the time constraint and the space constraint corresponding to the function node are as follows:
f1(Pm((i+1)·Δt),Pm(i·Δt),vm(i·Δt))＝p(Pm((i+1)·Δt)|Pm(i·Δt),vm(i·Δt))
s2: and each node in the dynamic factor graph is evolved and updated in real time along with the change of time.
And (4) considering the real-time positioning requirement of the system, and constructing a multi-target real-time cooperative positioning system based on a dynamic factor graph and capable of automatically evolving along with time. When a new time comes, or a new measurement occurs, the dynamic factor graph automatically creates a new state and updates the current time position state estimate as well as the past time position state. In addition, in order to ensure the real-time performance of the algorithm, the system adaptively determines the length of a time window participating in the operation according to the positioning precision requirement.
In the real-time positioning scene, when the new time tK+1The dynamic factor graph should evolve and update over time as new measurements come in, or as new measurements occur. The specific evolution and update process includes:
(1) when the new time tK+1When coming, a new variable node and a new function node are created, specifically, the following two situations are created:
first, when the new time tK+1And when the variable node comes, a new variable node is created at a corresponding position at the moment in the dynamic factor graph and is connected with the variable node of the target at the previous moment to form a new horizontal link.
When a new variable node is created, at a new time tK+1And the previous time tKThe function nodes corresponding to the time constraint are constructed among the variable nodes, and the new time tK+1And the previous time tKThe variable nodes are connected through the constructed function nodes; time constrained to new time tK+1The corresponding speed is expressed as follows:
wherein, Pm(tK) And Pm(tK+1) Respectively representing the position of the target at time K and at time K + 1, tKAnd tK+1Respectively representing the K time and the K +1 time, vK+1Representing the speed of the target at time K + 1,
indicating a speed measurement error.
Second, when two target nodes are at a new time tK+1When communication occurs, a distance measurement between the two will be generated, which should be taken at a new time t in the dynamic factor graph K+1The upper connection of the two corresponding horizontal links specifically comprises: and constructing a function node corresponding to a second space constraint between the two nodes for describing distance measurement between the two nodes, wherein the specific expression is as follows:
wherein d isK+1Represents the distance, P, between two targets at time K +1m(tK+1) And Pn(tK+1) Respectively representing the positions of target m and target n at time K + 1,
indicating a distance measurement error.
The function node will provide new information available for the iteration and updating of the algorithm.
(2) And performing state estimation on the variable node at the current moment according to the function node acquired by the target at the current moment and the information of all nodes of the target at each past moment, and updating the state estimation of all nodes of the target at each past moment.
Function node obtained at present time as at previous time tKThe last measurement (distance measurement or velocity measurement) was obtained. In real-time positioning, the next time t is determined by the state estimation of the current time and the state update of the past timeK+1The position estimation accuracy of (1).
In this embodiment, the past times are set to all times within the time window length T before the current time. Since the state estimation at the current moment needs to use the node information at the past moment, the longer the length of T, the more the past available information, the better the algorithm performance, but at the same time, the operation overhead and the communication overhead will also be increased. To select a suitable time window length T, the time window length in this embodiment is calculated using the convergence condition of the KL divergence, and the specific calculation formula is:
Wherein wkRepresents a weight value, VkRepresenting the vector dimension, N the total number of discretely distributed samples, and k the kth sampleSimilarly, H (q) represents KL divergence. In this embodiment, T is set to 320 s.
(3) When a target communicates with other targets, the state estimates of all nodes of the other targets (i.e., other horizontal links) with which communication is taking place are updated.
In the embodiment, distributed iterative transfer of messages is performed in the dynamic factor graph through a sum-product algorithm, so that state estimates of all nodes of other targets where communication occurs are updated. The specific implementation process is as follows:
as shown in FIG. 3 as t1And t4The network topology changes between the three target nodes A, B, C at two different times. t is t1At the moment, B and A are in communication range, so B can share the current position status update P to AB(t1) Then A updates itself at t with the message1The position status of the time of day. And at a subsequent t4At time, B communicates with C to obtain new measurements available and updates its past time t1Position state P ofB(t1) As shown in the following formula:
wherein the content of the first and second substances,
in the real-time positioning scenario shown in fig. 4, the left diagram represents messaging and updating on moving object a and the left diagram represents messaging and updating on moving object B. t is t 4Time B updates its past time t by a new measurement available1Position state P ofB(t1) To ensure positioning performance, B should pass the update to a, but since a and B are not in communication range at this time, a cannot get the message update in time. Considering the limitation of underwater acoustic environment on energy and communication overhead, the nodes are set to be updated regularly through the multi-hop sharing state only at a longer time interval L in the embodiment, so that error accumulation is limited, and the positioning accuracy is improved; in this embodimentSet L to 3600 s. At other times, the node only shares updates with neighboring nodes to achieve a balance of positioning performance and overhead.
The sum-product algorithm is transmitted in an iterative mode on a distributed network to ensure that the nodes can complete message updating in time, message iterative operation is divided into two types, and the message updating from variable nodes to function nodes is represented as follows:
the function node to variable node message update is represented as:
furthermore, the probability of the position estimation transmitted during the node message transmission is a continuous probability of 0-1, such as 0.1 and 0.2 …, and the computation complexity and the cost required by the corresponding subsequent computation are both large. Therefore, in order to reduce the data volume of node message transmission and optimize the network communication overhead, in the network message fusion, in the sum-product algorithm in this embodiment, the binarization processing is performed on the continuous probability of the position estimation during node message transmission, and the binary probability is converted into 0 or 1, and only the position state distribution of non-zero values is retained during iterative update, thereby effectively reducing the data volume of message sharing. Meanwhile, the sum and product algorithm is converted into binary logic operation, the summary operation of the sum and product algorithm is simplified into logic or sum logic and operation, and the operation complexity can be reduced.
In addition, according to the requirement of positioning accuracy, the bit number represented by the message is adaptively adjusted in the message iteration process of the network node, so that the operation and communication overhead of the network is further reduced.
S3: and acquiring the real-time position of each target according to the dynamic factor graph.
The method and the device have the advantages that the complex global optimization problem of underwater multi-target positioning is decomposed into the plurality of simple local optimization problems to be solved in a distributed mode, the method and the device have the characteristics of low algorithm complexity and the like, the distributed characteristic of the network is considered, the algorithm can be properly adjusted along with the change of the network state, and the method and the device have obvious advantages for solving the problem of underwater multi-target real-time cooperative positioning.
The second embodiment:
the invention also provides underwater multi-target positioning terminal equipment which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the computer program to realize the steps of the method embodiment of the first embodiment of the invention.
Further, as an executable scheme, the underwater multi-target positioning terminal device may be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The underwater multi-target positioning terminal equipment can comprise, but is not limited to, a processor and a memory. It is understood by those skilled in the art that the above-mentioned structure of the underwater multi-target positioning terminal device is only an example of the underwater multi-target positioning terminal device, and does not constitute a limitation to the underwater multi-target positioning terminal device, and may include more or less components than the above, or combine some components, or different components, for example, the underwater multi-target positioning terminal device may further include an input/output device, a network access device, a bus, etc., which is not limited in this embodiment of the present invention.
Further, as an executable solution, the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and the like. The general processor can be a microprocessor or the processor can be any conventional processor and the like, the processor is a control center of the underwater multi-target positioning terminal device, and various interfaces and lines are utilized to connect various parts of the whole underwater multi-target positioning terminal device.
The memory can be used for storing the computer program and/or the module, and the processor realizes various functions of the underwater multi-target positioning terminal equipment by operating or executing the computer program and/or the module stored in the memory and calling data stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system and an application program required by at least one function; the storage data area may store data created according to the use of the mobile phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The present invention also provides a computer-readable storage medium, which stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method of an embodiment of the present invention.
The modules/units integrated with the underwater multi-target positioning terminal equipment can be stored in a computer readable storage medium if the modules/units are realized in the form of software functional units and sold or used as independent products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), software distribution medium, and the like.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. An underwater multi-target positioning method is characterized by comprising the following steps:
s1: constructing a dynamic factor graph for multi-target real-time cooperative positioning;
the dynamic factor graph comprises two types of nodes, namely variable nodes and function nodes; the variable nodes represent the positions of targets, and the variable nodes of the same target at different continuous moments are sequentially arranged on the same horizontal line to form a horizontal link; the function nodes comprise time constraints and space constraints, the time constraints are used for describing the instantaneous speed of the target at each moment, the space constraints comprise first space constraints and second space constraints, the first space constraints are used for describing the relative position relationship between the reference anchor nodes and the target nodes, and the second space constraints are used for describing the relative position relationship between different target nodes; s2: each node in the dynamic factor graph is evolved and updated in real time along with the change of time; the real-time evolution and updating of the dynamic factor graph comprises the following steps: when a new moment comes, a new variable node and a new function node are created, specifically:
(1) When a new moment comes, a new variable node is created for each target and is connected with the variable node of the target at the previous moment to form a new horizontal link;
(2) when a new moment comes, if the variable nodes corresponding to the two targets are communicated, a function node corresponding to a second space constraint is constructed between the variable nodes and the function node;
when a new variable node is created, a function node corresponding to time constraint is constructed between the variable nodes at the new moment and the previous moment, and the variable nodes at the new moment and the previous moment are connected through the constructed function node; the expression for the time constraint is as follows:
wherein, Pm(tK) And Pm(tK+1) Respectively representing the position of the target at time K and at time K +1, tKAnd tK+1Respectively representing the K time and the K +1 time, vK+1Representing the speed of the target at time K +1,
indicating a speed measurement error;
the expression of the function node corresponding to the second spatial constraint is as follows:
wherein d isK+1Represents the distance, P, between two targets at time K +1m(tK+1) And Pn(tK+1) Respectively representing the positions of target m and target n at time K +1,
indicating a distance measurement error; s3: and acquiring the real-time position of each target according to the dynamic factor graph.
2. The underwater multi-object positioning method according to claim 1, characterized in that: the real-time evolving and updating the dynamic factor graph in step S2 further includes: and performing state estimation on the variable node at the current moment according to the function node acquired by the target at the current moment and the information of all nodes of the target at each past moment, and updating the state estimation of all nodes of the target at each past moment.
3. The underwater multi-target positioning method according to claim 2, characterized in that: the past moments are all moments in the time window length before the current moment, the time window length is calculated by using the convergence condition of the KL divergence, and the specific calculation formula is as follows:
wherein, wkRepresents a weight value, VkDenotes the vector dimension, N denotes the total number of samples of the discrete distribution, k denotes the kth sample, and H (q) denotes the KL divergence.
4. The underwater multi-object positioning method according to claim 1, characterized in that: the real-time evolving and updating the dynamic factor graph in step S2 further includes: when the target communicates with other targets, the distributed iterative transfer of the message is carried out in the dynamic factor graph through the sum-product algorithm, and then the state estimation of all nodes of other targets which communicate is updated.
5. The underwater multi-object positioning method of claim 4, wherein: in the sum-product algorithm, the continuous probability of position estimation during node message transmission is subjected to binarization processing.
6. An underwater multi-target positioning terminal device is characterized in that: comprising a processor, a memory and a computer program stored in the memory and running on the processor, the processor implementing the steps of the method according to any of claims 1 to 5 when executing the computer program.
7. A computer-readable storage medium storing a computer program, the computer program characterized in that: the computer program when executed by a processor implementing the steps of the method as claimed in any one of claims 1 to 5.
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