CN114383618A

CN114383618A - Positioning method and device based on graph model and electronic equipment

Info

Publication number: CN114383618A
Application number: CN202210109509.4A
Authority: CN
Inventors: 董元友; 盛敏; 连振中; 金勇�; 张永新
Original assignee: Nanjing North Road Intelligent Control Technology Co ltd
Current assignee: Nanjing North Road Intelligent Control Technology Co ltd
Priority date: 2022-01-29
Filing date: 2022-01-29
Publication date: 2022-04-22

Abstract

The invention provides a positioning method, a positioning device and electronic equipment based on a graph model, wherein the method comprises the following steps: constructing a state space graph model based on map information, acquiring motion state data and wireless data sent by a wireless node, carrying out initialization operation when the motion state data and the wireless data are acquired for the first time, updating the state of the particles according to the acquired motion state data, updating the weight values of the particles according to the acquired motion state data, carrying out fusion weighting calculation on a coordinate vector determined according to the motion direction of the particles and the weight values of the particles to obtain an estimated coordinate, calculating an accurate position as a positioning position of a positioning terminal according to the estimated coordinate and the state space graph model, and improving the positioning accuracy, meanwhile, most of the existing inertial positioning utilizes wireless signals to calibrate accumulated errors, and the wireless signals are often unstable due to various problems, and the positioning method can still carry out accurate positioning when the wireless signals are unstable, has high robustness.

Description

Positioning method and device based on graph model and electronic equipment

Technical Field

The present invention relates to the field of positioning technologies, and in particular, to a positioning method and apparatus based on a graph model, and an electronic device.

Background

At present, outdoor positioning is based on a satellite positioning system for positioning, and the indoor positioning cannot be applied to indoor positioning because satellite signals cannot be effectively received indoors. The position data in the indoor environment is an important attribute of sensing application, and the position information can be reliably and effectively acquired, so that more application scene requirements can be created and met. With the rapid development of internet of things technology and various applications based on location services, the demand for realizing accurate and real-time location positioning service for indoor personnel becomes stronger and stronger.

Compared with an open outdoor space, wireless positioning in an indoor environment is more complicated than that in an outdoor environment, for example, the presence of walls and obstacles makes indoor wireless propagation generally non-line-of-sight, signals are often scattered in the process of propagating between a transmitter and a receiver, and the multipath propagation causes additional signal strength damage and additional transmission time, thereby causing the reduction of positioning accuracy and errors.

Present indoor location is mainly based on the location of equipment, mainly fixes a position the equipment that people carried, and is common like: smart phones, tablet computers, sensors, tag cards, and the like.

Currently, indoor positioning methods based on wireless signal technologies such as WiFi, bluetooth, UWB, infrared, and RFID generally include: triangulation location method, proximity perception location method and signal fingerprint location method. The nature of the positioning method based on the wireless technology is that calculation modeling is carried out by using RSSI data, positioning is carried out based on the change rule of RSSI signal values and distances, the precision of the positioning method is limited by the stability of RSSI signals, and due to the complexity of practical application environments, the RSSI values of wireless signals can be influenced to a large extent in the transmission process due to wall bodies, metal, glass, other electromagnetic signal interference, multipath effects and the like, so that the RSSI values of the signals are unstable, the positioning method is low in positioning precision and poor in stability under certain environments, and the usability and the practicability of wireless positioning are influenced. Meanwhile, a series of problems such as huge construction and deployment workload, high cost and the like exist during the actual popularization and application of the concrete.

The Pedestrian Dead Reckoning (PDR) is a method which does not depend on any external force for assistance, and can detect a user motion mode through a given initial position by using inertial sensors, magnetometers, pressure sensors and the like which are carried by personnel carrying equipment to carry out pedestrian dead reckoning, so that the position change track of personnel in a room is obtained. This method exists in practical applications: the problem of accumulated errors and the problem of initial positions are solved, the PDR can only give out relative positions but cannot give out absolute physical space positions, and the situation that a person simply depends on a walking position exists in partial indoor scenes, and the scenes of sitting, riding and the like also exist, so that the PDR cannot normally work, the independent PDR positioning technology cannot meet the indoor positioning requirement, at present, indoor positioning is mostly carried out in a mode of combining the PDR and wireless signals, the initial position positioning is carried out by combining the wireless signals, and the accumulated errors of the PDR are calibrated. However, in the actual use process, the indoor environment is complex, so that the wireless signal is unstable, and the robustness of the positioning system adopting the method is not strong.

Disclosure of Invention

Based on the problems, the invention provides a positioning system based on a graph model, which constructs a state space graph model based on map information, samples the motion state of particles based on the obtained motion state data, updates the weight of the particles based on the state space graph model and the motion state of the particles, combines the particle coordinate vector determined by the motion state data with the weight of the particles to calculate estimated coordinates, and performs fitting filtering fusion on the calculated estimated coordinates and the graph model to obtain the final estimated value of the particle state, namely the personnel position information. The positioning method has the characteristics of high precision, high robustness and the like.

In order to achieve the above purpose, the invention provides the following technical scheme:

the first aspect of the present application provides a positioning method based on a graph model, including:

setting nodes and edges for a graph model based on spatial information conversion of a target area, wherein the pitch distances among the nodes are the same and are preset values, the edges are connecting lines between adjacent nodes, and a state space graph model and state model data are constructed according to the nodes and the edges, wherein the state model data comprise node data and edge data;

acquiring motion state data and wireless data acquired by a positioning terminal, wherein the motion state data comprises a step length and a motion direction, and the wireless data is sent by a wireless node arranged in a target area;

performing an initialization operation when the motion state data and the wireless data are acquired for the first time, including: determining an initial positioning position of a positioning terminal according to the wireless data, determining initial positions of particles according to the initial positioning positions, setting a node with the minimum distance from the initial positioning positions in the state space graph model as a correlation node of the particles, setting initial weights of all the particles to be 1/N, and performing Gaussian sampling on step length, wherein N is the number of the sampled particles, and the states of the particles comprise the correlation node and the moving direction;

the method comprises the steps of carrying out Gaussian sampling on the motion direction in the motion state data, carrying out edge sampling on the particles, setting the edge sampling probability to meet the condition that the mean value is Gaussian distribution of the included angle between the motion direction of the particles and the sampling edge, selecting the edge with the smallest included angle with the motion direction of the particles as a correction edge when the associated node of the particles is connected with a plurality of edges, carrying out Gaussian sampling on the step length in the received motion state data, carrying out node sampling on the particles to determine the associated node of the particles, and setting the node sampling probability as the ratio of the step length to the side length;

updating the weight of the particle according to the acquired motion state data, comprising: the smaller the included angle between the motion direction of the particle and the correction edge is, the larger the weight value given to the particle is;

fusing and weighting the coordinate vector determined according to the motion direction of the particles and the weight of the particles to calculate estimated coordinates;

and calculating an accurate position as the positioning position of the positioning terminal according to the estimated coordinates and the state space diagram model.

Preferably, the setting of nodes and edges for the graph model transformed based on the spatial information of the target region includes: and the node and the edge are both in an area which needs to carry out positioning monitoring on a positioning terminal in the target area.

Preferably, the updating the weight of the particle according to the acquired motion state data includes: if the included angles of all edges connected by the motion direction of the particle and the associated node of the particle are detected to be larger than 90 degrees, setting the weight of the particle as 0; and setting a resampling threshold value, and re-sampling when the number of the particles with the weight value larger than 0 is smaller than the resampling threshold value.

Preferably, the initialization operation is performed when the motion state data and the wireless data are acquired for the first time, and includes: presetting a calibration period and a calibration threshold value, if the difference value between the motion directions acquired by the positioning terminal in the calibration period is smaller than the calibration threshold value, taking the direction of the associated node of the particle when the associated node of the particle points to the end of the calibration period at the initial time of the calibration period as the calibration direction, and the calibration direction is used for calibrating the motion direction acquired by the positioning terminal.

Preferably, the acquiring motion state data and wireless data collected by the positioning terminal, where the motion state data includes a step length and a motion direction, and the wireless data is sent by a wireless node arranged in a target area, includes:

the wireless node is a Ubeacon beacon, and the wireless data comprises Bluetooth signal intensity and UWB beacon measuring distance.

Preferably, the updating the weight of the particle according to the acquired motion state data includes:

and if the positioning terminal detects the Ubeacon beacon, updating the weight of the particle according to the ranging result calculated by the wireless data, and defining that the smaller the distance between the particle and the ranging result is, the larger the weight given to the particle is.

A second aspect of the present application provides a positioning apparatus based on a graph model, including:

the state target area module is used for setting nodes and edges for a graph model converted based on space information of a target area, wherein the pitch distances among the nodes are the same and preset values, the edges are connecting lines among adjacent nodes, and a state space graph model and state model data are constructed according to the nodes and the edges, wherein the state model data comprise node data and edge data;

a particle state sampling detection module for detecting the state of particles,

the wireless node positioning system comprises a wireless node and a positioning terminal, wherein the wireless node positioning system is used for acquiring motion state data and wireless data acquired by the positioning terminal, the motion state data comprises a step length and a motion direction, and the wireless data is sent by the wireless node arranged in a target area;

the method is used for carrying out initialization operation when the motion state data and the wireless data are acquired for the first time, and comprises the following steps: determining an initial positioning position of a positioning terminal according to the wireless data, determining an initial position of a particle according to the initial positioning position, performing particle sampling, setting a node with the minimum distance from the initial positioning position in the state space graph model as a correlation node of the particle, setting initial weights of all the particles to be 1/N, and performing Gaussian sampling on a step length, wherein N is the number of the particle samples, and the state of the particle comprises the correlation node and a motion direction;

the particle weight correction module is used for updating the weight of the particle according to the acquired motion state data, and comprises the following steps that the smaller the included angle between the motion direction of the particle and the correction edge is, the larger the weight given to the particle is;

the particle fitting filter module is used for performing fusion weighting calculation on a coordinate vector determined according to the motion direction of the particles and the weight of the particles to obtain an estimated coordinate, and is also used for calculating an accurate position as a positioning position of the positioning terminal according to the estimated coordinate and a state space diagram model;

and the direction calibration module is used for presetting a calibration period and a calibration threshold value, if the difference value between the motion directions acquired by the positioning terminal in the calibration period is smaller than the calibration threshold value, the direction of the associated node of the particle when the associated node of the particle points to the tail of the calibration period at the initial time of the calibration period is used as the calibration direction, and the calibration direction is used for calibrating the motion direction acquired by the positioning terminal.

Preferably, the particle weight correction module is further configured to update the weight of the particle according to the ranging result calculated through the wireless data if the positioning terminal detects the Ubeacon beacon, and define that the smaller the distance between the particle and the ranging result is, the larger the weight given to the particle is.

A third aspect of the present application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus; the memory is used for storing a computer program; the processor is configured to implement the graph model-based positioning method according to any one of the first aspect of the present application when executing the program stored in the memory.

A fourth aspect of the present application provides a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements the steps of the graph model-based positioning method according to any one of the first aspects of the present application.

Has the advantages that:

it can be known from the above technical solutions that the technical solution of the present invention provides a positioning method based on a graph model, which constructs a state space graph model by setting nodes and edges based on the graph model, obtains motion state data and wireless data, performs initialization operation to determine an initial positioning position and an initial position of particle sampling, determines a state of a particle according to the motion state data, updates a weight of the particle by combining the state space graph model and the state of the particle, so that an estimated coordinate calculated by a coordinate vector and the weight of the particle is more accurate, performs fitting filtering calculation on the estimated coordinate and the state space graph model to obtain an accurate position as a positioning position, effectively reduces an influence of an accumulated error of PDR position estimation on positioning by this way, and when the wireless data is unstable, a positioning system using the method can also operate effectively, the high precision and the high robustness of the positioning method are ensured.

It should be understood that all combinations of the foregoing concepts and additional concepts described in greater detail below can be considered as part of the inventive subject matter of this disclosure unless such concepts are mutually inconsistent.

The foregoing and other aspects, embodiments and features of the present teachings can be more fully understood from the following description taken in conjunction with the accompanying drawings. Additional aspects of the present invention, such as features and/or advantages of exemplary embodiments, will be apparent from the description which follows, or may be learned by practice of specific embodiments in accordance with the teachings of the present invention.

Drawings

The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. Embodiments of various aspects of the present invention will now be described, by way of example, with reference to the accompanying drawings, in which:

fig. 1 is a schematic flowchart of a positioning method based on a graph model according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of a positioning apparatus based on a graph model according to an embodiment of the present invention;

FIG. 3 is a schematic flow chart of constructing a state space diagram model according to an embodiment of the present invention;

fig. 4 is a schematic diagram of a positioning terminal structure according to an embodiment of the present invention;

fig. 5 is a schematic diagram of a Ubeacon beacon structure provided in the embodiment of the present invention;

FIG. 6 is a diagram illustrating a state space diagram model according to an embodiment of the present invention;

fig. 7 is a diagram of a real-time positioning result provided by the embodiment of the invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention without any inventive step, are within the scope of protection of the invention. Unless defined otherwise, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs.

The use of "first," "second," and similar terms in the description and claims of the present application do not denote any order, quantity, or importance, but rather the terms are used to distinguish one element from another. Similarly, the singular forms "a," "an," or "the" do not denote a limitation of quantity, but rather denote the presence of at least one, unless the context clearly dictates otherwise. The terms "comprises," "comprising," or the like, mean that the elements or items listed before "comprises" or "comprising" encompass the features, integers, steps, operations, elements, and/or components listed after "comprising" or "comprising," and do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. "upper", "lower", "left", "right", and the like are used only to indicate relative positional relationships, and when the absolute position of the object to be described is changed, the relative positional relationships may also be changed accordingly.

As shown in fig. 1, a flowchart of a positioning method based on a graph model provided in an embodiment of the present invention includes:

step S101, nodes and edges are arranged on a graph model converted based on space information of a target area, the distances between the nodes are the same, the distances between the nodes are preset values, the edges are connecting lines between adjacent nodes, a state space graph model and state model data are constructed according to the nodes and the edges, and the state model data comprise node data and edge data.

It should be noted that, the nodes and the edges are all in the region of the target region where the positioning terminal needs to be located and monitored, as shown in fig. 3, in this embodiment, on the basis of considering the characteristics of the pedestrian moving in the indoor space and the characteristics of the indoor space structure, when the nodes and the edges are set, the narrow and limited passing region in the indoor is expressed in a one-dimensional space manner, and the wide region is expressed in a two-dimensional grid diagram, so as to express a discrete state space, and the state space diagram model in this embodiment is shown in fig. 6. In addition, nodes and edges can be set according to actual positioning accuracy requirements, when some areas have no more accurate positioning requirements, the areas are expressed by a one-dimensional space, namely a line segment consisting of the nodes and the edges, and when the positioning accuracy is higher, the areas are expressed by a grid graph.

The pitch distance of the nodes, i.e. the preset value, is selected from the human step size mean, i.e. 0.7 m. This state space diagram model is denoted by G in this embodiment. As shown in fig. 6, the state space graph model G can express the state space by nodes and edges, and can express the state space graph model G by G =<

,

>And n in G represents a node,

representing the set of all nodes, e represents an edge,

representing all edge sets. Where i represents an ID identification. Each node n can be represented by (i, x, y) three sets of data, where i represents a node number, an x coordinate, and a y coordinate; edges can be expressed by nodes of the G model<start-n，end-n,length>Respectively representing an initial node, a termination node and a side length.

The nodes in the graph model G are added with classification attributes, and the classification attributes mainly have the following types: the system comprises a corridor C, a hall H, a stair S, an elevator L, a door D, an electromagnetic abnormal point M, a wireless node deployment point U and the like, wherein the same node can be of multiple types;

adding reachable direction attributes to the nodes n in the graph model G, and expressing 8 directions by character strings, wherein the directions are northwest, north, northeast, east, southeast, south, southwest and west;

and converting the graph model G information into a data mode which can be stored by a computer for storage based on the information. The data attributes of the n nodes in the graph model G include: < Point type, relative coordinate x, relative coordinate y, relative height, reachable Point Direction, reachable Point sequence number K (K is derived from reachable Point Direction) >. The design provides convenience for determining the positioning position through the n nodes subsequently.

Step S104, acquiring motion state data and wireless data acquired by a positioning terminal, wherein the motion state data comprises a step length and a motion direction, and the wireless data is sent by a wireless node arranged in a target area;

it should be noted that, as shown in fig. 4, the positioning terminal is worn by a positioning person or is disposed on a mobile machine, the positioning terminal is mainly used for PDR data acquisition and acquisition processing of wireless data of the wireless node, and the positioning terminal has a data return function and returns data according to a set acquisition period. The motion state data comprises step length and motion direction theta, and the step length and the motion direction are calculated through data of the inertial sensor. The wireless data may be bluetooth signals, WIFI signals, or radio frequency signals.

In this embodiment, the wireless node is an Ubeacon beacon, and the wireless data includes a bluetooth signal strength and a UWB beacon measurement distance.

It should be noted that, in the embodiment of the present application, the Ubeacon beacon is deployed in the target area, and the deployment site location information is stored in the node data of the corresponding graph model G.

As shown in fig. 5, the Ubeacon beacon integrates the functions of the bluetooth beacon and the UWB beacon, and is a multi-source beacon, and when the UWB is turned on, the power consumption of the Ubeacon beacon is large, so the Ubeacon beacon only defaults to turn on the function of the bluetooth beacon, and the positioning terminal selects the BLE/UWB mode of the Ubeacon beacon according to whether the UWB ranging condition is satisfied;

the Ubeacon data comprises BLE data (BLE-ID, BLE-RSSI) and UWB data (UWB-ID, RSSI,

) Wherein BLE-ID and UWB-ID belong to the same Ubeacon number ID, BLE _ RSSI is Bluetooth signal strength,

namely, the positioning terminal measures the distance from the UWB beacon of Ubeacon.

Step S107, performing an initialization operation when the motion state data and the wireless data are acquired for the first time, including: determining an initial positioning position of a positioning terminal according to the wireless data, determining an initial position of a particle according to the initial positioning position, sampling the particle, setting a node with the minimum distance from the initial positioning position in the state space graph model as a related node n of the particle, and setting initial weights of all the particles as initial weights of the particle

=1/N andgaussian sampling of step size𝞴~N(

σ), where N is the number of particle samples, the state of the particle includes the associated node and the direction of motion.

When initialization operation is carried out, a calibration period and a calibration threshold value are preset, if the difference value between the motion directions acquired by the positioning terminal in the calibration period is smaller than the calibration threshold value, the direction of the associated node of the particle when the associated node of the particle points to the tail of the calibration period at the initial time of the calibration period is used as a calibration direction, and the calibration direction is used for calibrating the motion direction acquired by the positioning terminal. By determining the calibration direction, the accuracy of the subsequently acquired movement direction is improved. In addition, when the positioning terminal is activated for use for the first time, the positioning terminal can move linearly, so that the accuracy of the movement direction of the positioning terminal can be calibrated by setting the calibration direction.

S110, updating the state of the particle according to the acquired motion state data, including performing Gaussian sampling on the motion direction in the motion state data, performing edge sampling on the particle, setting the edge sampling probability to meet the mean value as Gaussian distribution of an included angle between the motion direction of the particle and a sampling edge, when the associated node of the particle is connected with a plurality of edges, selecting the edge with the smallest included angle with the motion direction of the particle as a correction edge, performing Gaussian sampling on the step length in the received motion state data, performing node sampling on the particle to determine the associated node of the particle, and setting the node sampling probability as the ratio of the step length to the side length.

It should be noted that, after receiving the step length and the movement direction obtained by the PDR, the probability distribution of the next state of the particle for prediction is calculated, and the particle distribution position is calculated by detecting the update of the particle state, where the particle state includes two attributes, namely, the particle state sampling detection model can be defined as

~

（

|

，

,G）

Wherein represents

The associated node and the direction of motion at a time on the particle;

representing the estimation result (including step length and motion direction) of the current PDR method; g represents a graph model;

represents the current associated node of the particle,

representing the current direction of motion of the particle.

According to the obtained updated position of the particle state, the state of the particle needs to be switched from the current state to the next state to be determined by two factors, namely nodes and edges in the graph model G, the selection of the edges mainly depends on the estimation direction theta of the PDR, and the selection of the nodes mainly depends on the step length, so that the sampling edges of the PDR particle state sampling detection model and the sampling nodes can be expressed:

（

|

，

,G）=

（

|

，

,G）.

（

|

，

，

,G）

wherein

（

|

，

G) is samplingEdge model:

when the associated node of the particle is a node of a plurality of edges, the next edge is sampled according to the walking characteristics of the person, and the selection of the edge is generally only related to the moving direction of the person, namely the moving probability of the particle towards the moving direction and the direction with the minimum included angle of the sampled edge is the maximum, and accordingly, the corrected edge is selected.

Therefore, after receiving the estimation result of the PDR method, firstly, the direction of the reported particles is subjected to Gaussian sampling for one time

（

）~N（

，

）

Then, sampling each particle at the same time, and assuming that the sampling probability satisfies the mean value as the gaussian distribution of the included angle between the particle direction and the sampling edge, that is, the edge sampling model can be defined as:

（

|

，

,G）=-

exp[

]

wherein

（

|

，

，

And G) is a node sampling model.

Under the condition that the correction edge is determined, the updating of the associated node in the particle state has two conditions, namely, no change is made, and the updating is carried out to be a node of the corresponding edge, and the state change is determined by the step length and the side length together. After the step length of the estimation result of the PDR method is detected, Gaussian sampling of the step length is firstly carried out

（

）~N（

，

）

Subsequently sampling each particle state node, and expressing the node sampling probability as the ratio of the step length to the side length:

（

|

，

，

,G） =

s113, updating the weight of the particle according to the obtained motion state data, including: the smaller the included angle between the moving direction of the particle and the correction edge is, the larger the weight given to the particle is.

After the correction edge is determined, the particle weight is updated based on the estimation result of the PDR method, the more the motion direction of the particle is consistent with the edge of the sample, namely the direction of the correction edge, the larger the particle weight is, and the weight updating process mode is as follows:

=

.

（

）

（

）=

（

|

，

,G）

the coordinates of the representative particles are updated in such a way, so that the particles which are more consistent with the pedestrian motion rule have higher weights, and the deviation of the motion direction in the estimation result of the PDR method is reduced.

Meanwhile, if the positioning terminal detects the Ubeacon beacon, the weight of the particle is updated according to the distance measurement result calculated by the wireless data, and the smaller the distance between the particle and the distance measurement result is, the larger the weight given to the particle is.

It should be noted that, in the embodiment of the present application, the Ubeacon beacon deployed in the positioning area is used as a model calibration point to correct the accumulated error existing in the PDR. After the positioning terminal detects the Ubeacon beacon, a larger weight is given to a point near a ranging result based on the Ubeacon beacon, and the distance between the Ubeacon and the point is assumed to be the mean value of the weight

The Gaussian distribution, the particle weight correction method based on Ubeacon is

=

.

（

）

（

）=

exp[

]

Wherein

Representing current particle coordinates;

is a positioning result coordinate based on Ubeacon;

the Ubeacon data estimation coordinates are divided into the following according to the interaction mode of the positioning terminal and the Ubeacon: position estimation based on Bluetooth RSSI intensity, position estimation based on UWB ranging, and position estimation based on three-point positioning of a partial area.

It should be noted that before updating the weight value of the particle, if it is detected that the included angles between the motion direction of the particle and all edges connected by the associated nodes of the particle are greater than 90 degrees, the weight value of the particle is set to 0; and setting a resampling threshold value, and re-sampling when the number of the particles with the weight value larger than 0 is smaller than the resampling threshold value.

By eliminating invalid particles through the method, the accuracy of the estimated coordinate can be effectively improved, and the accuracy of the finally determined positioning position is further improved.

And S116, fusing and weighting the coordinate vector determined according to the motion direction of the particles and the weight of the particles to calculate estimated coordinates. The particle weighted summation is as follows:

（

，

）=

.

wherein

Is the current coordinate vector of particle i, ((ii))

，

) The estimated coordinates resulting from the current weighted sum.

And S119, calculating an accurate position as the positioning position of the positioning terminal according to the estimated coordinate and the state space diagram model.

After the estimated coordinates are obtained, fitting the result with the graph model G, and selecting the node closest to the estimated coordinates as the state estimation value in this embodiment, in the following manner:

=arg min(sqrt((

.x-

)²+（

.y-

）²))，

。

in the embodiment of the invention, nodes and edges are set through a graph model converted based on a target area, then a state space graph model is constructed through the nodes and the edges, particle sampling is carried out according to the acquired data information, and the weight of the particles is updated by combining the state space graph model, so that the particles meeting the pedestrian activity specification obtain higher weight, and further, the estimated coordinate obtained through particle weighted fusion calculation is closer to a real positioning position. And finally, performing fitting filtering operation on the estimated coordinates and the nodes closest to the estimated coordinates to obtain an accurate position as a final positioning position, and continuously calculating to obtain the positioning position and the pedestrian track, wherein the calculation result of each time is obtained by combining a state space diagram model in the process.

In the embodiment of the present invention, the sequence number of each step is not a specific limitation on the execution sequence of each step, and a step at a previous stage only needs to be executed before a corresponding lower stage.

In the embodiment of the invention, the state space diagram model and the inertial sensor are combined for positioning, and the wireless technology is used for assisting in correction, so that the positioning with high precision, continuity, stability and low power consumption is realized. Compare in wiFi, Ubeacon has two kinds of modes of bluetooth and UWB to fix a position supplementary, when the bluetooth signal is unstable, can obtain accurate location through UWB and correct PDR's accumulative error. As shown in fig. 7, the dotted line beyond the wall is the action track without using the inertial positioning method of this embodiment, and the action track of the positioning method does not conform to the real action track because no correction is performed, even a wall-through behavior occurs. The dotted line that does not extend beyond the wall is substantially a two-color dotted line, wherein one dotted line is the state space diagram model in fig. 6, and the other dotted line is the action track using the positioning method according to the embodiment of the present invention. As can be seen from the figure, the positioning method is substantially consistent with the real action trajectory represented by the solid line in the figure, and it can be seen that the positioning method according to the embodiment of the present invention reduces the accumulated error generated by the PDR data information, and can realize high-precision, continuous, stable, and low-power-consumption positioning.

As shown in fig. 2, the schematic diagram of a positioning apparatus based on a graph model provided in this embodiment includes:

the particle state sampling detection module is used for acquiring motion state data and wireless data acquired by a positioning terminal, wherein the motion state data comprises a step length and a motion direction, and the wireless data is sent by a wireless node arranged in a target area;

It should be noted that the particle weight correction module is further configured to update the weight of the particle according to the ranging result calculated through the wireless data if the positioning terminal detects the Ubeacon beacon, and define that the smaller the distance between the particle and the ranging result is, the larger the weight given to the particle is.

The embodiment provides an electronic device, which comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus;

the memory is used for storing a computer program;

the processor is used for realizing any one of the positioning methods based on the graph model when executing the program stored in the memory.

These computer programs 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, and corresponding steps may be implemented by different modules.

The programs described above may be run on a processor or may also be stored in memory (or referred to as computer-readable media), which includes both non-transitory and non-transitory, removable and non-removable media, that 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. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

The present embodiment provides a computer-readable storage medium, in which a computer program is stored, and the computer program is executed by a processor to implement any one of the above-mentioned map model-based positioning methods.

For the embodiment of the positioning apparatus/electronic device/computer-readable storage medium based on the graph model, since it is substantially similar to the embodiment of the positioning method based on the graph model, the description is simple, and the relevant points can be referred to the description of the embodiment of the positioning method based on the graph model.

The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.

Claims

1. A positioning method based on a graph model is characterized in that,

performing an initialization operation when the motion state data and the wireless data are acquired for the first time, including: determining an initial positioning position of a positioning terminal according to the wireless data, determining an initial position of a particle according to the initial positioning position, performing particle sampling, setting a node with the minimum distance from the initial positioning position in the state space graph model as a correlation node of the particle, setting initial weights of all the particles to be 1/N, and performing Gaussian sampling on a step length, wherein N is the number of the particle samples, and the state of the particle comprises the correlation node and a motion direction;

2. The method of claim 1, wherein the setting of nodes and edges for the graph model based on the spatial information transformation of the target region comprises: and the node and the edge are both in an area which needs to carry out positioning monitoring on a positioning terminal in the target area.

3. The method according to claim 1, wherein the updating the weight of the particle according to the obtained motion state data comprises: if the included angles of all edges connected by the motion direction of the particle and the associated node of the particle are detected to be larger than 90 degrees, setting the weight of the particle as 0;

and setting a resampling threshold value, and re-sampling when the number of the particles with the weight value larger than 0 is smaller than the resampling threshold value.

4. The method of claim 1, wherein the initializing operation when the motion state data and the wireless data are first obtained comprises: presetting a calibration period and a calibration threshold value, if the difference value between the motion directions acquired by the positioning terminal in the calibration period is smaller than the calibration threshold value, taking the direction of the associated node of the particle when the associated node of the particle points to the end of the calibration period at the initial time of the calibration period as the calibration direction, and the calibration direction is used for calibrating the motion direction acquired by the positioning terminal.

5. The method of claim 1, wherein: the wireless node is a Ubeacon beacon, and the wireless data comprises Bluetooth signal intensity and UWB beacon measuring distance.

6. The method according to claim 5, wherein the updating the weight of the particle according to the obtained motion state data comprises:

7. A positioning device based on a graph model is characterized in that: comprises that

8. The map model-based positioning apparatus of claim 7, wherein: and the particle weight correction module is used for updating the weight of the particle according to the distance measurement result calculated by the wireless data if the positioning terminal detects the Ubeacon beacon, and defining that the smaller the distance between the particle and the distance measurement result, the larger the weight given to the particle.

9. The electronic equipment is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory finish mutual communication through the communication bus;

the memory is used for storing a computer program;

the processor, when executing the program stored in the memory, implementing the method steps of any of claims 1-6.

10. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 6.