CN112954590A - Node positioning method and device and computer readable storage medium - Google Patents
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Abstract
The invention provides a node positioning method, a node positioning device and a computer readable storage medium, which are applied to a node positioning system, wherein the system consists of a node to be positioned and N anchor nodes which are connected in a wireless communication manner, and the method comprises the following steps: determining N first estimated distances between a node to be positioned and N anchor nodes based on a phase ranging technology, wherein N is an integer greater than or equal to 3; carrying out median filtering processing on the N first estimated distances to obtain N second estimated distances; traversing a plurality of preset nodes by using a preset lookup table to obtain N ideal distance data between each preset node and N anchor nodes; and calculating the similarity between each preset node and the node to be positioned based on the N second estimated distances and the N ideal distance data corresponding to each preset node, and determining a target positioning node from the plurality of preset nodes according to the similarity. By the method, higher node positioning accuracy can be realized with less calculation amount.
Description
Technical Field
The invention belongs to the field of positioning, and particularly relates to a node positioning method and device and a computer readable storage medium.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
In a radio network, it is desirable that nodes of the radio network can be located. In the prior art, radio ranging technology is generally adopted to measure distances between a wireless node to be positioned and a plurality of wireless nodes with known positions respectively, and the position of the wireless node to be positioned can be determined according to the known positions of the plurality of wireless nodes and ranging results.
However, due to various factors, such as interference of wireless signals like WIFI and bluetooth, or signals may be transmitted through multiple paths (such as obstacles in the space, such as walls, from which the signals may be reflected into the wireless node, thereby falsely measuring the refracted or reflected path as a direct path), the above ranging result may have a relatively large error.
Therefore, how to guarantee the node positioning accuracy in the wireless node positioning is an urgent problem to be solved.
Disclosure of Invention
In view of the above problems in the prior art, a node positioning method, a node positioning device, and a computer-readable storage medium are provided.
The present invention provides the following.
In a first aspect, a node positioning method is provided, which is applied to a node positioning system, where the node positioning system includes a node to be positioned and N anchor nodes with known positions, and the N anchor nodes are connected to the node to be positioned through wireless signal communication, and the method includes: determining N first estimated distances between a node to be positioned and N anchor nodes based on a phase ranging technology, wherein N is an integer greater than or equal to 3; carrying out median filtering processing on the N first estimated distances to obtain N second estimated distances; traversing a plurality of preset nodes by using a preset lookup table to obtain N ideal distance data between each preset node and N anchor nodes; and calculating the similarity between each preset node and the node to be positioned based on the N second estimated distances and the N ideal distance data corresponding to each preset node, and determining a target positioning node from the plurality of preset nodes according to the similarity.
In a possible implementation, the method further includes a step of determining a preset lookup table, specifically including: determining a preset boundary range of a node to be positioned, and setting M preset nodes in the preset boundary range; setting a preset lookup table based on an ideal distance from each preset node to each anchor node; and the ideal distance or the mathematical variation of the ideal distance between each preset node and each anchor node is prestored in the preset lookup table.
In a possible implementation, performing median filtering on the N first estimated distances to obtain N second estimated distances further includes: and after carrying out median filtering processing on the N first estimation distances, carrying out Kalman filtering processing to obtain N second estimation distances.
In one possible embodiment, the method further comprises: calculating the similar distance between each preset node and the node to be positioned by using any one of the following formulas:
alternatively, the first and second electrodes may be,
alternatively, the first and second electrodes may be,
wherein m is the mark number of the preset node, DmThe method is characterized in that N ideal distances between the mth preset node and N anchor nodes and similar distances between N second estimated distances are included, the similar distances and the similarity are in inverse proportion, and R isnIs a second estimated distance, dist, between the node to be positioned and the nth anchor nodemnRefers to the ideal distance between the mth preset node and the nth anchor node, where N is 1, 2.
In one possible embodiment, the predetermined lookup table has a square value of the ideal distance between each predetermined node and each anchor node.
In a possible implementation manner, determining a target positioning node from a plurality of preset nodes according to the similarity further includes: determining one preset node with the highest similarity with the node to be positioned from the M preset nodes as a target positioning node; and/or determining at least two preset nodes from the M preset nodes according to the similarity ranking, and determining a target positioning node between the at least two preset nodes according to the similarity between each of the at least two preset nodes and the node to be positioned.
In a second aspect, a node positioning apparatus is provided, which is applied to a node positioning system, where the node positioning system includes a node to be positioned and N anchor nodes with known positions, and the N anchor nodes and the node to be positioned are connected through wireless signal communication, and the apparatus further includes: the positioning unit is used for positioning the node to be positioned and N anchor nodes in the network according to the first estimated distances, wherein N is an integer greater than or equal to 3; the filtering unit is used for carrying out median filtering processing on the N first estimated distances so as to obtain N second estimated distances; the searching unit is used for traversing the preset nodes by utilizing a preset searching table so as to obtain N ideal distance data between each preset node and N anchor nodes; and the calculating unit is used for calculating the similarity between each preset node and the node to be positioned based on the N second estimated distances and the N ideal distance data corresponding to each preset node, and determining the target positioning node from the plurality of preset nodes according to the similarity.
In a possible implementation, the node positioning apparatus further includes a look-up table setting unit, configured to: determining a preset boundary range of a node to be positioned, and setting M preset nodes in the preset boundary range; setting a preset lookup table based on an ideal distance from each preset node to each anchor node; and the ideal distance or the mathematical variation of the ideal distance between each preset node and each anchor node is prestored in the preset lookup table.
In one possible embodiment, the filter unit is configured to: and after carrying out median filtering processing on the N first estimation distances, carrying out Kalman filtering processing to obtain N second estimation distances.
In a possible embodiment, the computing unit is further configured to: calculating the similar distance between each preset node and the node to be positioned by using any one of the following formulas:
or the like, or, alternatively,
or the like, or, alternatively,
wherein m is the mark number of the preset node, DmThe method is characterized in that N ideal distances between the mth preset node and N anchor nodes and similar distances between N second estimated distances are included, the similar distances and the similarity are in inverse proportion, and R isnIs a second estimated distance, dist, between the node to be positioned and the nth anchor nodemnRefers to the ideal distance between the mth preset node and the nth anchor node, where N is 1, 2.
In one possible embodiment, the predetermined lookup table has a square value of the ideal distance between each predetermined node and each anchor node.
In a possible embodiment, the computing unit is further configured to: determining one preset node with the highest similarity with the node to be positioned from the M preset nodes as a target positioning node; and/or determining at least two preset nodes from the M preset nodes according to the similarity ranking, and determining a target positioning node between the at least two preset nodes according to the similarity between each of the at least two preset nodes and the node to be positioned.
In a third aspect, a node positioning apparatus is provided, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform: the method of the first aspect.
In a fourth aspect, there is provided a computer readable storage medium storing a program which, when executed by a multicore processor, causes the multicore processor to perform the method of the first aspect.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects: in this embodiment, by setting a node positioning system including a plurality of anchor nodes and using the phase ranging technique to determine the estimated distance between the node to be positioned and the plurality of anchor nodes, a relatively accurate estimated distance can be obtained, and the median filtering is used to process the ranging result obtained according to the phase ranging technique, so that the ranging error caused by signal interference and the like can be reduced, and the node positioning accuracy is further enhanced. In addition, the space search can be realized based on less calculation amount by adopting a lookup table method, and the target positioning node is determined from the preset nodes with known positions, so that the calculation is simpler.
It should be understood that the above description is only an overview of the technical solutions of the present invention, so as to clearly understand the technical means of the present invention, and thus can be implemented according to the content of the description. In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
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The advantages and benefits described herein, as well as other advantages and benefits, will be apparent to those of ordinary skill in the art upon reading the following detailed description of the exemplary embodiments. The drawings are only for purposes of illustrating exemplary embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like elements throughout. In the drawings:
FIG. 1 is a schematic diagram of an exemplary node location system in accordance with an embodiment of the present invention;
fig. 2 is a flowchart illustrating a node locating method according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a node locating device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a node locating device according to another embodiment of the present invention.
In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In the present invention, it is to be understood that terms such as "including" or "having," or the like, are intended to indicate the presence of the disclosed features, numbers, steps, behaviors, components, parts, or combinations thereof, and are not intended to preclude the possibility of the presence of one or more other features, numbers, steps, behaviors, components, parts, or combinations thereof.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 is a schematic diagram of a node location system 100 according to an embodiment of the present application, where the node location system 100 includes a node D to be located and 3 anchor nodes A, B, C with known locations, and each anchor node is connected to the node D to be located through wireless signal communication. It is understood that the node location system may include more anchor nodes, and the embodiment is described by taking 3 anchor nodes as an example, but is not limited thereto.
Those skilled in the art will appreciate that the described application scenario is only one example in which an embodiment of the present invention may be implemented. The scope of applicability of the embodiments of the present invention is not limited in any way. Having described the general principles of the invention, various non-limiting embodiments of the invention are described in detail below.
Fig. 2 is a schematic flowchart of a node location method 200 according to an embodiment of the present application, where the method may be applied to the node location system 100 shown in fig. 1, where the node location system 100 includes a node to be located and N (N is exemplarily equal to 3 in fig. 1) anchor nodes with known positions, and the N anchor nodes and the node to be located are connected by wireless signal communication, and in the flowchart, from a device perspective, an execution subject may be one or more electronic devices; from the program perspective, the execution main body may accordingly be a program loaded on these electronic devices.
As shown in FIG. 2, the method 200 may include steps 201 and 204.
In step 201, determining N first estimated distances between a node to be positioned and N anchor nodes based on a phase ranging technique, where N is an integer greater than or equal to 3;
the phase ranging technique is a radio ranging technique that uses phase deflection of a radio signal for radio ranging, and ideally, can obtain a ranging result with very high accuracy.
An anchor node refers to a wireless node of known location for transmitting or receiving wireless signals.
A node to be positioned refers to a wireless node of unknown location that is also used to transmit or receive wireless signals. The node to be positioned may be a moving or stationary wireless transceiver device. In one possible example, the node to be located may be any mobile terminal or fixed terminal with a function of sending and receiving signals wirelessly, such as a mobile phone, a wearable device, a tablet computer, and the like, which is not limited in this application.
In one possible example, successive transmissions with frequency f, such as at a node, are madeaAnd having a frequency fbThe carrier signal of (a). The two frequencies differ only by a small frequency difference Δ f, the carrier signal is received in a further node, the phase of the received wave is evaluated and stored as a phase measurement
And
thereby, the distance between the nodes can be calculated based on the phase difference and the frequency difference
Where c is the speed of light. The above is only one possible example of a phase ranging technique given, it being understood that any possibility based on a phase ranging technique is possibleAll of the variations of (2) can be applied to the present solution.
In an ideal situation, a very high accuracy of the ranging result can be obtained based on the phase ranging technique. However, the above ranging result has a relatively large error due to interference of wireless signals such as WIFI and bluetooth. In addition, in the case of indoor scenes or obstacles, the phase ranging technique described above may take the wrong refracted or reflected path as a direct path, which may also adversely affect the ranging result.
In step 202, performing median filtering processing on the N first estimated distances to obtain N second estimated distances;
it can be understood that, due to interference of wireless signals such as WIFI and bluetooth, the ranging result between the node to be located and the anchor node determined based on the phase ranging technique in step 201 has a relatively large error. This effect is evenly distributed over the entire span rather than the traditional gaussian distribution caused by noise, and therefore, the use of median filtering in this embodiment can reduce these non-gaussian distributed noises.
In step 203, the preset lookup table is further utilized to traverse the plurality of preset nodes to obtain N ideal distance data between each preset node and the N anchor nodes.
The preset nodes refer to node positions with a plurality of known positions, and since the positions of the anchor nodes are also known, the real distance between each preset node and each anchor node, namely the ideal distance, can be calculated in advance. The preset lookup table may then be pre-created based on the ideal distances between the plurality of preset nodes and the anchor node.
The preset lookup table is pre-stored with ideal distance data, which may be the ideal distance or a mathematical variation of the ideal distance.
In one example, the preset lookup table has preset ideal distances dist between M preset nodes and N anchor nodesmnN is 1,2,. N, M is 1,2,. M, N is the anchor node index, and the total number is N. M is the label of the preset node, and the total number is M. Wherein distmnMeans the m-th preset node and the n-th preset nodeThe desired distance between anchor nodes.
In another example, the preset lookup table has preset ideal distances dist between M preset nodes and N anchor nodesmnAny of the mathematical variants of (1), such as distmnAny mathematical variant such as a square value, a multiple square value, a square root value and the like.
In step 204, the similarity between each preset node and the node to be positioned is further calculated based on the N second estimated distances and the N ideal distances corresponding to each preset node, and the target positioning node is determined from the plurality of preset nodes according to the similarity.
It can be understood that if the positions of the node to be positioned and a certain preset node are closer, the similarity distance between the second estimated distance between the node to be positioned and each anchor node and the ideal distance between the preset node and each anchor node is smaller, that is, the similarity is higher. As can be seen from the reverse reasoning, the similarity between each preset node and the node to be positioned may be calculated based on the N second estimated distances and the N ideal distances corresponding to each preset node, and one or more preset nodes with high similarity may be selected to determine the target positioning node.
In one example, referring to fig. 1, there are 3 anchor nodes a (0, a, 0), B (0, 0, 0), C (C, 0, 0) of known location and 1 node D (x, y, z) to be located. Obtaining a first estimated distance (r) between a node (D) to be positioned and each anchor node (A, B, C) based on a phase ranging techniqueA，rB，rC). Further to the first estimated distance (r)A，rB，rC) Is median filtered, resulting in a second estimated distance (R)A，RB，RC). A plurality of preset nodes can be preset, for example, M preset nodes can be uniformly arranged in a length range capable of supporting ranging based on the position of an anchor node and a wireless ranging technology, an ideal distance from each preset node to each anchor node or a mathematical variant thereof is calculated in advance and stored in a preset lookup table. Traversing each preset node using a preset look-up table to obtain 3 ideal distances (di) between each preset node and 3 anchor nodes (A, B, C)stmA，distmB，distmC) Where M is the index of the preset node, M is 1,2A，RB，RC) And 3 ideal distances (dist) corresponding to each preset nodemA，distmB，distmC) And calculating the similarity between each preset node and the node to be positioned. In one example, a modulus of the difference between the corresponding second estimated distance and the ideal distance may be calculated, and it may be understood that the smaller the modulus of the difference is, the higher the similarity is, so that the similarity between each preset node and the node to be located may be determined. And finally, determining a preset node with the highest similarity from the M preset nodes as a target positioning node according to the result of the similarity calculation. Optionally, two or more preset nodes with the highest similarity may also be determined from the M preset nodes, and the position of the target positioning node between the two or more preset nodes may also be determined.
In this embodiment, a node positioning system including a plurality of anchor nodes is provided, and the estimated distance between the node to be positioned and the plurality of anchor nodes is determined by using a phase ranging technique, so that a more accurate estimated distance can be obtained. And the median filtering is used for processing the ranging result obtained according to the phase ranging technology, so that the ranging error caused by signal interference and the like can be reduced, and the accuracy of the estimated distance is further enhanced. In addition, the target positioning node can be determined from a plurality of preset nodes with known positions based on less calculation amount by adopting a lookup table method, and the calculation is simpler.
In a possible implementation manner, before step 203, in order to obtain the preset lookup table, the method 200 may further include a step of determining the preset lookup table, specifically including: determining a preset boundary range of a node to be positioned, setting M preset nodes in the preset boundary range, and setting a preset lookup table based on an ideal distance from each preset node to each anchor node. And the ideal distance or the mathematical variation of the ideal distance between each preset node and each anchor node is prestored in the preset lookup table.
The preset boundary range may be determined based on the actual range of the node to be located. For example, if the position of the node to be located is determined to be in a certain room, a preset boundary range may be set according to the boundary of the indoor space (such as a wall, a roof, etc.). It is understood that the preset boundary range can be freely set based on the actual application scenario, and the purpose is to reduce the search space range, reduce the amount of calculation, and improve the positioning accuracy.
A virtual spatial grid may be set within a preset boundary range, and each grid node of the spatial grid may be set as a preset node. It can be understood that if higher positioning accuracy is required, a grid with higher density can be set, and accordingly, more preset nodes are also set. If the positioning accuracy is required to be low, a grid with sparse density can be set, so that the data volume of the lookup table can be saved, and the data volume for subsequently performing similarity calculation can be reduced.
Spatial grids with unequal density distribution can be set within a preset boundary range, and each grid node of the spatial grids is set as a preset node. It can be understood that although the node to be located may be located at any position within the preset boundary range in theory, the probability of different positions is likely to be the same, and therefore, spatial grids with unequal density distributions may be set, and a spatial grid with a higher density is set in an area with a higher location probability, that is, more preset nodes are set, and conversely, fewer preset nodes are set in an area with a lower location probability. Therefore, the calculation resource required by positioning can be reduced while the positioning accuracy is guaranteed to the maximum extent. For example, in one possible example, there may be obstacles such as furniture, pillars, etc. indoors, and the probability that the node to be located is at the obstacle position is minimal, so fewer preset nodes may be set at these obstacles, or even no preset nodes may be set at these obstacle positions.
The predetermined look-up table is an M × N table in which the ideal distance or mathematical variant of the ideal distance from each predetermined node to each anchor node is pre-stored. Alternatively, the mathematical variation of the ideal distance may be a mathematical variation such as a square value, a square root value, or the like of the ideal distance, which is not particularly limited in the present application.
In a possible implementation manner, in order to further reduce the error in the phase ranging process and achieve more accurate node positioning, step 202 may further include: and after carrying out median filtering processing on the N first estimation distances, carrying out Kalman filtering processing to obtain N second estimation distances.
Kalman (kalman) filtering the ranging results may eliminate some errors and obtain better results. It can be understood that Kalman filtering is performed on the premise that noise is gaussian distributed, and noise of the median-filtered data is gaussian distributed, so that the Kalman filtering can be used to further optimize the median-filtered data, and a better effect can be obtained. Based on the characteristics of Kalman filtering, the positioning effect of the Kalman filtering, particularly on the node to be positioned in motion, is better.
In one possible embodiment, the method further comprises: calculating the similar distance between each preset node and the node to be positioned by using the following formula (1):
(1)
wherein m is the mark number of the preset node, DmThe method is characterized in that N ideal distances between the mth preset node and N anchor nodes and similar distances between N second estimated distances are included, the similar distances and the similarity are in inverse proportion, and R isnIs a second estimated distance, dist, between the node to be positioned and the nth anchor nodemnRefers to the ideal distance between the mth preset node and the nth anchor node, where N is 1, 2.
In this embodiment, R may be based onn 2Finding the closest distmn 2. And by adopting sqart square root operation, the adverse effect of points with large error deviation can be weakened.
Optionally, the following formula (2) may also be used to calculate the similar distance between each preset node and the node to be positioned:
(2)
wherein m is the mark number of the preset node, DmThe method is characterized in that N ideal distances between the mth preset node and N anchor nodes and similar distances between N second estimated distances are included, the similar distances and the similarity are in inverse proportion, and R isnIs a second estimated distance, dist, between the node to be positioned and the nth anchor nodemnRefers to the ideal distance between the mth preset node and the nth anchor node, where N is 1, 2.
Optionally, the following formula (3) may also be used to calculate the similar distance between each preset node and the node to be positioned:
(3)
wherein m is the mark number of the preset node, DmThe method is characterized in that N ideal distances between the mth preset node and N anchor nodes and similar distances between N second estimated distances are included, the similar distances and the similarity are in inverse proportion, and R isnIs a second estimated distance, dist, between the node to be positioned and the nth anchor nodemnRefers to the ideal distance between the mth preset node and the nth anchor node, where N is 1, 2.
In one possible embodiment, the method further comprises: the lookup table is set based on a square of an ideal distance of each preset node to each anchor node. The calculation such as the above formula (1), (2) or (3) can thus be performed by directly obtaining the square value of the ideal distance from the preset lookup table. Avoiding solving the square value in the calculation process.
In a possible implementation manner, the determining the target positioning node from the plurality of preset nodes according to the similarity in step 104 may further include: and determining one preset node with the highest similarity with the node to be positioned from the M preset nodes as a target positioning node.
In another possible implementation manner, the determining the target positioning node from the plurality of preset nodes according to the similarity in step 104 may further include: and determining at least two preset nodes from the M preset nodes according to the similarity ranking, and determining a target positioning node between the at least two preset nodes according to the similarity between each of the at least two preset nodes and the node to be positioned.
In one example, if the similarity between the neighboring preset node p, the neighboring preset node q, and the node to be positioned is calculated to be the highest, and the difference between the two similarities is smaller than the preset value, the central point between the preset nodes p and q may be set as the target positioning node, or an appropriate position may be selected as the target positioning node between the preset nodes p and q based on the similarity ratio. In another example, three or more preset nodes with the highest similarity can be determined according to the similarity ranking, and the position of the node to be positioned is determined in a small-granularity area formed by the three or more preset nodes according to the similarity calculation result. Thereby, node positioning can be performed more accurately.
Based on the same technical concept, an embodiment of the present invention further provides a node positioning apparatus, which is applied to a node positioning system shown in fig. 1, where the node positioning system includes a node to be positioned and N anchor nodes with known positions, and the N anchor nodes are connected to the node to be positioned through wireless signal communication, and the node positioning apparatus is further configured to execute the node positioning method provided in any of the embodiments. Fig. 3 is a schematic structural diagram of a node positioning apparatus according to an embodiment of the present invention.
As shown in fig. 3, the apparatus 300 further comprises:
a ranging unit 301, configured to determine N first estimated distances between a node to be located and N anchor nodes based on a phase ranging technique, where N is an integer greater than or equal to 3;
a filtering unit 302, configured to perform median filtering processing on the N first estimated distances to obtain N second estimated distances;
a searching unit 303, configured to traverse a plurality of preset nodes by using a preset lookup table to obtain N ideal distance data between each preset node and N anchor nodes;
a calculating unit 304, configured to calculate a similarity between each preset node and the node to be positioned based on the N second estimated distances and the N ideal distance data corresponding to each preset node, and determine a target positioning node from the plurality of preset nodes according to the similarity.
In a possible implementation, the node positioning apparatus further includes a look-up table setting unit, configured to: determining a preset boundary range of a node to be positioned, and setting M preset nodes in the preset boundary range; setting a preset lookup table based on an ideal distance from each preset node to each anchor node; and the ideal distance or the mathematical variation of the ideal distance between each preset node and each anchor node is prestored in the preset lookup table.
In one possible embodiment, the filter unit is configured to: and after carrying out median filtering processing on the N first estimation distances, carrying out Kalman filtering processing to obtain N second estimation distances.
In a possible embodiment, the computing unit is further configured to: calculating the similar distance between each preset node and the node to be positioned by using any one of the following formulas:
or the like, or, alternatively,
or the like, or, alternatively,
wherein m is the mark number of the preset node, DmMeans that the m-th preset node and the N anchor nodes RnN ideal distances therebetween and N second estimated distances therebetween, the similarity distance and the similarity degree being inversely proportional, RnIs a second estimated distance, dist, between the node to be positioned and the nth anchor nodemnRefers to the ideal distance between the mth preset node and the nth anchor node, where N is 1, 2.
In one possible embodiment, the predetermined lookup table has a square value of the ideal distance between each predetermined node and each anchor node.
In a possible embodiment, the computing unit is further configured to: determining one preset node with the highest similarity with the node to be positioned from the M preset nodes as a target positioning node; and/or determining at least two preset nodes from the M preset nodes according to the similarity ranking, and determining a target positioning node between the at least two preset nodes according to the similarity between each of the at least two preset nodes and the node to be positioned.
It should be noted that the node positioning apparatus in the embodiment of the present application may implement each process of the foregoing embodiment of the node positioning method, and achieve the same effect and function, which is not described herein again.
Fig. 4 is a node positioning apparatus according to an embodiment of the present application, configured to perform the node positioning method shown in fig. 2, where the apparatus includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the above embodiments.
According to some embodiments of the present application, there is provided a non-transitory computer storage medium of a node location method having stored thereon computer-executable instructions configured to, when executed by a processor, perform: the method of the above embodiment.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus, device, and computer-readable storage medium embodiments, the description is simplified because they are substantially similar to the method embodiments, and reference may be made to some descriptions of the method embodiments for their relevance.
The apparatus, the device, and the computer-readable storage medium provided in the embodiment of the present application correspond to the method one to one, and therefore, the apparatus, the device, and the computer-readable storage medium also have advantageous technical effects similar to those of the corresponding method.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. Moreover, while the operations of the method of the invention are depicted in the drawings in a particular order, this does not require or imply that the operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
While the spirit and principles of the invention have been described with reference to several particular embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, nor is the division of aspects, which is for convenience only as the features in such aspects may not be combined to benefit. The invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (14)
1. A node positioning method is applied to a node positioning system, the node positioning system comprises a node to be positioned and N anchor nodes with known positions, and the N anchor nodes are connected with the node to be positioned through wireless signal communication, and the method comprises the following steps:
determining N first estimated distances between a node to be positioned and N anchor nodes based on a phase ranging technology, wherein N is an integer greater than or equal to 3;
performing median filtering processing on the N first estimated distances to obtain N second estimated distances;
traversing a plurality of preset nodes by using a preset lookup table to obtain N ideal distance data between each preset node and the N anchor nodes;
and calculating the similarity between each preset node and the node to be positioned based on the N second estimated distances and the N ideal distance data corresponding to each preset node, and determining a target positioning node from the plurality of preset nodes according to the similarity.
2. The method according to claim 1, further comprising the step of determining the preset lookup table, specifically comprising:
determining a preset boundary range of the node to be positioned, and setting M preset nodes in the preset boundary range;
setting the preset lookup table based on the ideal distance from each preset node to each anchor node;
wherein the ideal distance or a mathematical variant of the ideal distance between each of the preset nodes and each of the anchor nodes is pre-stored in the preset lookup table.
3. The method of claim 1, wherein median filtering the N first estimated distances to obtain N second estimated distances further comprises:
and after performing median filtering processing on the N first estimation distances, performing Kalman filtering processing to obtain N second estimation distances.
4. The method of claim 1, further comprising:
calculating the similarity distance between each preset node and the node to be positioned by using any one of the following formulas:
wherein m is the label of the preset node, DmIs the similar distance between the N ideal distances between the mth preset node and the N anchor nodes and the N second estimated distances, the similar distance and the similarity are in inverse proportion, R isnIs the second estimated distance, dist, between the node to be positioned and the nth anchor nodemnIs the ideal distance between the mth preset node and the nth anchor node, wherein N is 1, 2.
5. The method of claim 4, wherein the predetermined lookup table has a square of the desired distance between each of the predetermined nodes and each of the anchor nodes.
6. The method of claim 1, wherein determining a target positioning node from the plurality of preset nodes according to the similarity further comprises:
determining a preset node with the highest similarity with the node to be positioned from the M preset nodes as the target positioning node; and/or the presence of a gas in the gas,
and determining at least two preset nodes from the M preset nodes according to the similarity ranking, and determining the target positioning node between the at least two preset nodes according to the similarity between each of the at least two preset nodes and the node to be positioned.
7. A node positioning apparatus, for use in a node positioning system, the node positioning system including a node to be positioned and N anchor nodes of known locations, the N anchor nodes and the node to be positioned being connected by wireless signal communication, the apparatus comprising:
the positioning unit is used for positioning the node to be positioned and N anchor nodes in the network according to the first estimated distances, wherein N is an integer greater than or equal to 3;
a filtering unit, configured to perform median filtering processing on the N first estimated distances to obtain N second estimated distances;
the searching unit is used for traversing a plurality of preset nodes by utilizing a preset searching table so as to obtain N ideal distance data between each preset node and the N anchor nodes;
a calculating unit, configured to calculate a similarity between each of the preset nodes and the node to be positioned based on the N second estimated distances and the N ideal distance data corresponding to each of the preset nodes, and determine a target positioning node from the plurality of preset nodes according to the similarity.
8. The apparatus of claim 7, further comprising a look-up table setting unit configured to:
determining a preset boundary range of the node to be positioned, and setting M preset nodes in the preset boundary range;
setting the preset lookup table based on the ideal distance from each preset node to each anchor node;
wherein the ideal distance or a mathematical variant of the ideal distance between each of the preset nodes and each of the anchor nodes is pre-stored in the preset lookup table.
9. The apparatus of claim 7, wherein the filtering unit is configured to:
and after performing median filtering processing on the N first estimation distances, performing Kalman filtering processing to obtain N second estimation distances.
10. The apparatus of claim 7, wherein the computing unit is further configured to: calculating the similarity distance between each preset node and the node to be positioned by using any one of the following formulas:
wherein m is the label of the preset node, DmIs the similar distance between the N ideal distances between the m-th preset node and the N anchor nodes and the N second estimated distancesThe similarity distance and the similarity are in inverse proportion, RnIs the second estimated distance, dist, between the node to be positioned and the nth anchor nodemnIs the ideal distance between the mth preset node and the nth anchor node, wherein N is 1, 2.
11. The apparatus of claim 10, wherein the predetermined lookup table has a square of the desired distance between each of the predetermined nodes and each of the anchor nodes.
12. The apparatus of claim 7, wherein the computing unit is further configured to:
determining a preset node with the highest similarity with the node to be positioned from the M preset nodes as the target positioning node; and/or the presence of a gas in the gas,
and determining at least two preset nodes from the M preset nodes according to the similarity ranking, and determining the target positioning node between the at least two preset nodes according to the similarity between each of the at least two preset nodes and the node to be positioned.
13. A node positioning apparatus, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform: the method of any one of claims 1-6.
14. A computer-readable storage medium storing a program that, when executed by a multi-core processor, causes the multi-core processor to perform the method of any one of claims 1-6.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101435867A (en) * | 2008-12-17 | 2009-05-20 | 西安交通大学 | Wireless sensor network node positioning method based on phase measurement |
| CN104678353A (en) * | 2015-01-30 | 2015-06-03 | 天津大学 | Anchor node layout based on detection signal wavelength information and ranging and positioning method thereof |
| CN108307301A (en) * | 2018-01-29 | 2018-07-20 | 华侨大学 | Indoor orientation method based on RSSI rangings and track similitude |
| CN108490392A (en) * | 2018-04-16 | 2018-09-04 | 哈尔滨工业大学（威海） | A kind of least square 3-D positioning method based on range estimation screening |
-
2021
- 2021-02-07 CN CN202110177115.8A patent/CN112954590A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101435867A (en) * | 2008-12-17 | 2009-05-20 | 西安交通大学 | Wireless sensor network node positioning method based on phase measurement |
| CN104678353A (en) * | 2015-01-30 | 2015-06-03 | 天津大学 | Anchor node layout based on detection signal wavelength information and ranging and positioning method thereof |
| CN108307301A (en) * | 2018-01-29 | 2018-07-20 | 华侨大学 | Indoor orientation method based on RSSI rangings and track similitude |
| CN108490392A (en) * | 2018-04-16 | 2018-09-04 | 哈尔滨工业大学（威海） | A kind of least square 3-D positioning method based on range estimation screening |
Non-Patent Citations (1)
| Title |
|---|
| 田小林等: "船舶电力推进及动力定位控制系统", pages: 239 - 47 * |
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Application publication date: 20210611 |