An Indoor Dynamic Positioning Method and Device Based on Adaptive Hierarchical Resampling

Abstract

The present application relates to an indoor dynamic positioning method and device based on adaptive hierarchical resampling, belonging to the technical field of indoor positioning. The method includes: dividing an indoor map into at least two sub-map regions; initializing the particle distribution to obtain initial particles; performing position transfer on the initial particles to obtain a first subset of sub-particles that exceed the boundary of the sub-map region and a second subset of sub-particles that do not exceed the boundary of the sub-map region; performing position correction on the first subset of sub-particles to obtain a third subset of sub-particles; updating the weights of the second subset of sub-particles and the third subset of sub-particles; determining at least two hierarchical intervals based on the particle weights of the particles in the third subset of sub-particles and the second subset of sub-particles; collecting target particles based on the hierarchical intervals and the particle weights of the respective particles to obtain a target particle set; determining the positioning position of the positioning tag based on the target particle set; the present application can improve the accuracy of indoor positioning and achieve real-time positioning.

Description

Technical Field

[0001] This application belongs to the technical field of indoor positioning, and in particular, relates to an indoor dynamic positioning method and device based on adaptive hierarchical resampling. Background Art

[0002] The rapid development of technology has led to an increasing demand for location information services. Traditional location information services mainly focus on outdoor positioning research. In outdoor environments, satellite positioning can achieve sub-meter positioning accuracy, basically meeting the positioning needs of users. However, in complex indoor environments, due to the obstruction of buildings, satellite signals will rapidly attenuate or even completely interrupt, resulting in a significant decrease in the accuracy of indoor positioning and unable to meet people's needs. Indoor positioning technology makes up for the lack of positioning accuracy of satellite positioning in indoor environments and is widely used in shopping mall navigation, intelligent warehousing and logistics systems, robot navigation, Augmented Reality (AR), and Virtual Reality (VR), and can provide accurate location information services.

[0003] The particle filter algorithm has been widely applied and studied in the field of indoor positioning, mainly due to its superiority in dealing with positioning problems in non-linear, non-Gaussian noise, and dynamic environments. The particle filter algorithm has high flexibility and scalability and can adapt to dynamic positioning scenarios.

[0004] However, the positioning accuracy of the particle filter positioning algorithm is still affected by sensor noise and errors, dynamic changes in the environment, etc., resulting in an increase in positioning errors, and thus it is difficult to achieve real-time positioning in actual application scenarios. Summary of the Invention

[0005] In order to improve the positioning accuracy of indoor positioning and achieve real-time positioning, this application provides an indoor dynamic positioning method and device based on adaptive hierarchical resampling.

[0006] In a first aspect, this application provides an indoor dynamic positioning method based on adaptive hierarchical resampling, including:

[0007] Dividing the constructed indoor map into at least two sub-map regions;

[0008] Setting at least two positioning beacons at fixed positions within the indoor map, where the positioning beacons are used to communicate with positioning tags, and the positioning tags are configured on moving objects;

[0009] Initializing the particle distribution within the sub-map region to obtain initial particles;

[0010] During the movement of the positioning tag, perform position transfer on the initial particles to obtain a first candidate particle set, where the first candidate particle set includes a first sub-particle set and a second sub-particle set. The first sub-particle set is the particle set that exceeds the boundary of the sub-map area, and the second sub-particle set is the particle set that does not exceed the boundary of the sub-map area;

[0011] Perform position correction on the first sub-particle set to obtain a third sub-particle set;

[0012] During the communication between the positioning beacon and the positioning tag, perform weight update on the second sub-particle set and the third sub-particle set. The third sub-particle set and the second sub-particle set form a second candidate particle set;

[0013] Based on the particle weights of the particles in the second candidate particle set, determine at least two stratification intervals, where the interval size of the stratification interval is negatively correlated with the particle weight;

[0014] Based on the stratification interval and the particle weights of the particles in the second candidate particle set, collect target particles from the second candidate particle set to obtain a target particle set;

[0015] Based on the target particle set, determine the positioning position of the positioning tag.

[0016] By adopting the above technical solution, after updating the weights of the particles in each sub-map area on the indoor map, at least two stratification intervals are determined according to the particle weights. Among them, the interval size of the stratification interval is negatively correlated with the particle weight, so that the interval size of the stratification interval can automatically adapt to the updated and changing particle weights, that is, the stratification size can be dynamically adjusted according to the weight distribution, making the stratification in the high-weight area finer, increasing the sampling opportunity of high-weight particles, making the stratification in the low-weight area coarser, reducing the sampling opportunity of low-weight particles, and moreover, the above technical solution can perform position correction on out-of-bounds particles, realizing dynamic real-time high-precision positioning.

[0017] In a specific feasible implementation, the determining at least two stratification intervals based on the particle weights of the particles in the second candidate particle set includes:

[0018] Based on the particle weights of the particles in the second candidate particle set, determine the weight density corresponding to each particle, where the weight density is positively correlated with the particle weight;

[0019] Based on the weight density corresponding to each particle, determine the interval size of each stratification interval, where the interval size is negatively correlated with the weight density;

[0020] Based on the interval sizes of at least two of the stratification intervals, determine at least two of the stratification intervals.

[0021] By adopting the above technical solution, the weight density corresponding to each particle in the second candidate particle set is determined, so as to determine the distribution of particle weights in the entire particle set. Compared with simply determining the interval size according to the numerical size of particle weights, first determining the interval size according to the weight density and then determining the stratified intervals can avoid inaccurate stratification caused by the overall low or high numerical value of particle weights, making the stratified intervals adapt to the dynamic changes of the particle weights of each particle in the particle set.

[0022] In a specific feasible implementation, determining the weight density corresponding to each particle based on the particle weights of each particle in the second candidate particle set includes:

[0023] Normalize the particle weights of each particle in the second candidate particle set to obtain the normalized weights of each particle;

[0024] Calculate the weight density corresponding to each particle according to formula (1):

[0025] ;

[0026] where is the weight density of the th particle, is the normalized weight of the th particle, is the maximum normalized weight.

[0027] In the above technical solution, taking the maximum normalized weight as the denominator in calculating the weight density and taking the normalized weight of a single particle as the numerator in calculating the weight density can increase the difference between high-weight particles and low-weight particles and improve the influence degree of high-weight particles on the stratified intervals compared with taking the cumulative sum of weights as the denominator in calculating the weight density.

[0028] In a specific feasible implementation, the number of the stratified intervals is equal to the total number of particles in the second candidate particle set;

[0029] Determining the interval size of each stratified interval based on the weight density corresponding to each particle includes:

[0030] Sort each particle according to the particle weight, where a single particle corresponds to a single stratified interval;

[0031] Calculate the interval size of the stratified interval corresponding to each particle according to formula (2):

[0032] ;

[0033] where is the interval size of the hierarchical interval corresponding to the th particle, is the weight density of the th particle, and

[0034] is the total number of particles. In the above technical solution, sorting by particle weight makes the high-weight particles concentrated. During the stratified resampling process, since there is a negative correlation between the weight density and the interval size in formula (2), the greater the weight density, the smaller the corresponding interval size, and the denser the stratified sampling. By adopting the above technical solution, the determination method of the interval size of the stratified interval is clarified, which is beneficial to improving the stratification rationality so as to increase the sampling opportunity of high-weight particles.

[0035] In a specific feasible implementation, dividing the constructed indoor map into at least two sub-map regions includes:

[0036] Dividing the indoor map into the at least two sub-map regions according to the shape. The regional shapes of the at least two sub-map regions include arc-shaped regions, rectangular regions, and triangular regions. The arc-shaped regions are used to represent arc-shaped corridors, the rectangular regions are used to represent rooms or rectangular corridors, and the combined regions of the triangular regions and the rectangular regions are used to represent the rooms adjacent to the arc-shaped corridors. Among them, the triangular regions are adjacent to the arc-shaped regions;

[0037] The position correction of the first subset of sub-particles includes:

[0038] When the sub-map region is the arc-shaped region, removing the particle positions of the first subset of sub-particles that exceed the arc-shaped region;

[0039] When the sub-map region is the triangular region, correcting the particle positions of the first subset of sub-particles back to the boundary of the triangular region;

[0040] When the sub-map region is the rectangular region, correcting the particle positions of the first subset of sub-particles back to the boundary of the rectangular region.

[0041] By adopting the above technical solution, the indoor map is divided according to arc-shaped regions, rectangular regions, and triangular regions. Among them, different regions represent different indoor scenes. For example, arc-shaped regions represent arc-shaped corridors, which is beneficial to realizing the position correction of particles exceeding the sub-map region in different indoor scenes according to sub-map regions of different shapes, and improving the accuracy when estimating the possible positioning positions of the particle set.

[0042] In a specific feasible implementation, there are connection nodes on the adjacent boundaries between the at least two sub-map regions, which are used to connect the indoor spaces represented by the adjacent sub-map regions;

[0043] The correcting the particle positions of the first subset of particles back to the boundary of the rectangular region includes:

[0044] When the distance between the particle positions of the first subset of particles and the connection node is less than the distance threshold, correcting the particle positions of the first subset of particles to the connection node of the rectangular region;

[0045] The correcting the particle positions of the first subset of particles back to the boundary of the triangular region includes:

[0046] When the distance between the particle positions of the first subset of particles and the connection node is less than the distance threshold, correcting the particle positions of the first subset of particles to the connection node of the triangular region.

[0047] In the above technical solution, considering that there are connection nodes between different indoor spaces, for example, a room door can connect a corridor and a room. On the boundary where there are connection nodes, the probability that a particle is located at the connection node is relatively high. By correcting the out-of-bounds particles around the connection node to the connection node, the positioning accuracy at the connection node can be improved.

[0048] In a specific feasible implementation, the sub-map region includes an oriented rectangular region, and the oriented rectangular region represents the region corresponding to the one-way movement path on the indoor map;

[0049] When the sub-map region is the oriented rectangular region, after the weights of the second subset of particles and the third subset of particles are updated during the communication between the positioning beacon and the positioning tag, the method further includes:

[0050] Determining a one-way movement coefficient according to the particle position at the current moment, the particle position at the previous moment, and the one-way movement direction of the oriented rectangular region, where the one-way movement coefficient is used to reduce the particle weights of the particles moving in the reverse direction and increase the particle weights of the particles moving in the forward direction;

[0051] Quadratically updating the updated particle weights using the one-way movement coefficient.

[0052] In the above technical solution, the particle weights are updated twice for the indoor directional movement scenario. For example, in the scenario of moving in a single-line corridor or on an escalator, obviously, the probability of moving in a specific direction is higher than that of moving in other directions. By determining the one-way movement coefficient, the secondary update of the particle weights is realized, the influence of reverse-moving particles on the result is suppressed, and the influence of forward-moving particles on the result is enhanced, which is beneficial to improving the positioning accuracy in the directional movement scenario.

[0053] In a second aspect, the present application provides an indoor dynamic positioning system based on adaptive hierarchical resampling, adopting the following technical solution:

[0054] An indoor dynamic positioning system based on adaptive hierarchical resampling includes:

[0055] A partitioning module, configured to partition the constructed indoor map into at least two sub-map regions;

[0056] A setting module, configured to set at least two positioning beacons at fixed positions in the indoor map, where the positioning beacons are used to communicate with positioning tags, and the positioning tags are configured on moving objects;

[0057] An initialization module, configured to initialize the particle distribution in the sub-map region to obtain initial particles;

[0058] A transfer module, configured to transfer the positions of the initial particles during the movement of the positioning tag to obtain a first candidate particle set, where the first candidate particle set includes a first sub-particle set and a second sub-particle set, the first sub-particle set is the particle set that exceeds the boundary of the sub-map region, and the second sub-particle set is the particle set that does not exceed the boundary of the sub-map region;

[0059] A correction module, configured to correct the positions of the first sub-particle set to obtain a third sub-particle set;

[0060] An update module, configured to update the weights of the second sub-particle set and the third sub-particle set during the communication between the positioning beacon and the positioning tag, and the third sub-particle set and the second sub-particle set form a second candidate particle set;

[0061] An interval determination module, configured to determine at least two hierarchical intervals based on the particle weights of the particles in the second candidate particle set, and the interval size of the hierarchical interval is negatively correlated with the particle weight;

[0062] An acquisition module, configured to acquire target particles from the second candidate particle set based on the hierarchical intervals and the particle weights of the particles in the second candidate particle set to obtain a target particle set;

[0063] A position determination module, configured to determine the positioning position of the positioning tag based on the target particle set.

[0064] In a third aspect, the present application provides an electronic device, adopting the following technical solution:

[0065] An electronic device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory, so that the electronic device executes the method described in the above aspect.

[0066] In a fourth aspect, the present application provides a computer-readable storage medium, adopting the following technical solution:

[0067] A computer-readable storage medium stores multiple instructions. The instructions are loaded and executed by a processor to implement the method described in the above aspect.

[0068] In summary, the present application includes at least one of the following beneficial technical effects:

[0069] 1. Perform position correction on out-of-bounds particles and dynamically adjust the hierarchical size according to the weight distribution, making the high-weight area more finely divided, increasing the sampling opportunity of high-weight particles, and achieving dynamic real-time high-precision positioning. Among them, by changing the weight density calculation method, the difference between high-weight particles and low-weight particles is increased, and the influence degree of high-weight particles on the hierarchical interval is improved, realizing the dynamic determination of the hierarchical interval size.

[0070] 2. For sub-map areas of different shapes, perform position correction on particles that exceed the sub-map area, improving the accuracy when the particle set estimates the possible positioning position. Among them, by correcting the out-of-bounds particles around the connected nodes to the connected nodes, the positioning accuracy at the connected nodes can be improved. By determining the one-way movement coefficient, the secondary update of the particle weight is realized, suppressing the influence of reverse-moving particles on the result, enhancing the influence of forward-moving particles on the result, and being beneficial to improving the positioning accuracy in the directional movement scenario. Description of the Drawings

[0071] Figure 1 is a flowchart of an indoor dynamic positioning method based on adaptive hierarchical resampling provided by an exemplary embodiment of the present application;

[0072] Figure 2 is a schematic diagram of an indoor map provided by an exemplary embodiment of the present application;

[0073] Figure 3 is a flowchart of the determination process of the hierarchical interval provided by an exemplary embodiment of the present application;

[0074] Figure 4 It is a schematic diagram of the positioning position determined based on the target particle set provided by an exemplary embodiment of the present application;

[0075] Figure 5 It is a schematic diagram of the path positioning result provided by an exemplary embodiment of the present application;

[0076] Figure 6 A schematic diagram of an indoor map divided by shape provided by another exemplary embodiment of the present application;

[0077] Figure 7 It is a structural block diagram of an indoor dynamic positioning system based on adaptive hierarchical resampling provided by an exemplary embodiment of the present application;

[0078] Figure 8 It is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present application. Detailed implementation manners

[0079] To make the objectives, technical solutions, and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the accompanying drawings.

[0080] First, the nouns involved in the embodiments of the present application are introduced.

[0081] Indoor map: An indoor map is a top view showing the indoor space distribution. The indoor map may include the boundary division and annotation of different indoor spaces.

[0082] Particle set: The particle set on the indoor map is used to describe the possibility of the positioning position at each particle position. Each particle position corresponds to a particle weight, and the particle weight can be used to characterize the probability of the positioning position at that particle position.

[0083] Positioning tag: A positioning tag refers to a component or device configured on a moving object that can communicate with a positioning beacon to determine its own position.

[0084] Positioning beacon: A device with a fixed position as a reference marker that can communicate with a positioning tag to determine the current positioning position on the indoor map according to the relative position relationship between the positioning tag and the positioning beacon.

[0085] In related indoor positioning technologies, the positioning accuracy of the particle filter positioning algorithm is still affected by sensor noise and errors, dynamic changes in the environment, etc., resulting in an increase in positioning errors, and further resulting in real-time problems in actual application scenarios.

[0086] The present application provides an indoor dynamic positioning method that can dynamically adjust the size of the stratification interval during stratified resampling according to particle weights. Among them, the stratification interval can automatically adapt to changes in particle weights, and in subsequent resampling processes, the resampling opportunities for high-weight particles are increased, thereby improving indoor positioning accuracy.

[0087] See Figure 1 , Figure 1 FIG. is a flowchart of an indoor dynamic positioning method based on adaptive stratified resampling provided by an exemplary embodiment of the present application. This method is executed by an electronic device. The electronic device includes devices with particle computing capabilities such as smartphones, smart watches, tablets, personal computers, smart vehicle consoles, workstations, etc. This method includes the following steps.

[0088] Step 101, divide the constructed indoor map into at least two sub-map regions.

[0089] Optionally, the indoor map can be a pre-constructed two-dimensional map, which can include the zoning of different indoor spaces and the markings of each indoor space. The indoor map can also be used to represent a single connected indoor space, such as a connected corridor.

[0090] In a possible implementation manner, the electronic device divides the indoor map according to its shape. For example, the electronic device divides the indoor map into at least two sub-map regions in a rectangular shape. For another example, the electronic device divides the corridor into at least two connected sub-map regions in a rectangular shape and an arc shape.

[0091] In another possible implementation manner, the electronic device divides the indoor map according to the region type. For example, the indoor map is marked with indoor regions such as office areas, corridor areas, and rest areas according to the region type, and the electronic device divides each indoor region into a sub-map region.

[0092] Step 102, set at least two positioning beacons at fixed positions within the indoor map. The positioning beacons are used to communicate with positioning tags, and the positioning tags are configured on moving objects.

[0093] In some embodiments, the positioning tags are configured on moving objects. The moving object can be an electronic device or a movable object such as a pedestrian, a pet, or a remote control device carrying the positioning tag. The electronic device can be connected to the positioning tag through Bluetooth, Wi-Fi (Wireless Fidelity), and UWB (Ultra-Wideband) to achieve information interaction.

[0094] A positioning beacon is a device with a fixed position used as a reference marker. Communication can be carried out between the positioning beacon and the positioning tag. Optionally, the positioning beacon can be a Bluetooth beacon, a UWB beacon, and a Wi-Fi beacon, that is, communication can be carried out between the positioning beacon and the positioning tag via Bluetooth, UWB, and Wi-Fi.

[0095] Step 103: Initialize the particle distribution within the sub-map area to obtain initial particles.

[0096] In some embodiments, among at least two sub-map areas, the number of particles initialized on different sub-map areas is different. Optionally, the number of particles can be determined according to the area shape and size of the sub-map area. For example, 150 particles are initialized in a rectangular area, and 100 particles are initialized in an arc-shaped area.

[0097] Optionally, the number of particles initialized in the sub-map area can also be supplemented according to the characteristics of the indoor map and the requirements of calculation efficiency. For example, in an indoor map with a starting point and an ending point, it is easy for the random sampling distribution to be sparse, resulting in a long convergence time for the algorithm. To improve the calculation efficiency, particles can be supplemented at the starting point and the ending point of the indoor map when initializing the particle distribution.

[0098] Optionally, within sub-map areas with different shapes, the particle distributions of the initial particles are different. For example, for the initial positions of the particles in the arc-shaped area, in the polar coordinate system, both the distance and the angle of the particles from the center of the circle conform to a uniform distribution; the initial positions of the particles in the rectangular area conform to a Laplace distribution.

[0099] See Figure 2 , Figure 2 is a schematic diagram of an indoor map provided by an exemplary embodiment of the present application.

[0100] Next, taking an arc-shaped corridor in a 25-meter * 18-meter two-dimensional plane as an example, steps 101 to 103 will be described.

[0101] First, divide the arc-shaped corridor map into areas according to the shape, which are sub-map area 201 ( ), sub-map area 202 ( ), and sub-map area 203 ( ) from bottom to top. Among them, sub-map area 201 is an arc-shaped area, sub-map area 202 is a rectangular area, and sub-map area 203 is an arc-shaped area. Among them, the edge line segment (bold part) of sub-map area 201 represents the starting point position 204, and the edge line segment (bold part) of sub-map area 203 represents the ending point position 205.

[0102] Then, at intervals along both arc segments of the corridor A Bluetooth beacon is set every 2.5 meters in the arc and a Bluetooth beacon is set every 2.5 meters in the straight line.

[0103] Initialize the cth area in the map particles. Among them, , , , add 65 supplementary particles at the map starting position 204 and the end position 205 respectively.

[0104] Then, the particle distribution is initialized in the corridors represented by different sub-map areas. The particle distribution initialized in sub-map areas of different shapes is different.

[0105] The initial position of the particle in the arc region is obtained by converting the polar coordinate system into the rectangular coordinate system. In the polar coordinate system, the distance and angle between the particle and the center of the circle are uniformly distributed, and the probability density function formula is as follows:

[0106] ;

[0107] ;

[0108] in, is the distance of the particle from the center of the circle, is the angle in polar coordinates, and The lower bound of the corresponding uniform distribution is, and The upper bound of the uniform distribution is obtained in the sub-map area 201. 、 、 、 , take the sub-map area 203 、 、 、 .

[0109] The initial position of the particle in the rectangular area conforms to the Laplace distribution, and the probability density function formula is as follows:

[0110] ;

[0111] ;

[0112] in, and is the position coordinate variable, and is the mean of the Laplace distribution, which determines the center position of the distribution. and is the scale parameter of the Laplace distribution, which determines the width of the distribution. , , , .

[0113] To improve the calculation efficiency, the electronic device adds 65 supplementary particles at the map starting position 204 and the ending position 205 respectively. The initial positions of the particles conform to the Gaussian distribution, and the probability density function formula is as follows:

[0114] ;

[0115] ;

[0116] where, and are the position coordinate variables, and are the means of the Gaussian distribution, which determine the central position of the distribution, and are the variances of the Gaussian distribution, which determine the degree of dispersion of the distribution. The starting position takes , , and , and the ending position takes , , and .

[0117] Calculate the total number of particles , is the total number of regions, and this total number of particles includes the number of supplementary particles at the starting position and the terminal position; initialize the initial weights of all particles, and the initial weights of each particle are uniformly set to .

[0118] Step 104, during the movement of the positioning label, perform position transfer on the initial particles to obtain the first candidate particle set. The first candidate particle set includes a first subset of particles and a second subset of particles. The first subset of particles is the set of particles that exceed the boundary of the sub-map region, and the second subset of particles is the set of particles that do not exceed the boundary of the sub-map region.

[0119] where, the movement amount of the particle position transfer follows the Gaussian distribution, and the formula is as follows:

[0120] ;

[0121] ;

[0122] ;

[0123] where, and respectively At time the position of the -axis and -axis of the and respectively At time the position of the -axis and -axis of the and respectively represent the coordinate movement amounts of the particles in the -axis and -axis directions, both of which conform to the Gaussian distribution, is its probability density function, and are the variances of the Gaussian distribution, which determine the degree of dispersion of the distribution, and are the means of the Gaussian distribution, which determine the central position of the distribution, and are respectively taken as , , , .

[0124] Step 105: Perform position correction on the first subset of sub-particles to obtain a third subset of sub-particles.

[0125] Optionally, the electronic device first determines whether the particles exceed the boundary of the sub-map area. In the case of exceeding the boundary of the sub-map area, according to the shape of the sub-map area, the position correction method is determined, including the following two correction methods:

[0126] Method 1: Correct the particle positions of the first subset of sub-particles back to the boundary of the sub-map area.

[0127] Method 2: Remove the particle positions of the first subset of sub-particles that exceed the sub-map area.

[0128] Step 106: During the communication between the positioning beacon and the positioning tag, update the weights of the second subset of sub-particles and the third subset of sub-particles. The third subset of sub-particles and the second subset of sub-particles form a second candidate particle set.

[0129] The following first describes the implementation process of updating the weights of the second subset of sub-particles.

[0130] The electronic device first converts the RSSI (Received Signal Strength Indication) data measured by the positioning tag into the distance between the positioning tag and the positioning beacon. The formula is as follows:

[0131] ;

[0132] Among them, is the received signal strength data obtained by measurement, is the loss factor, is the distance between the positioning tag and the beacon, is the signal strength received by the positioning tag at a distance of from the beacon, is the measurement noise random variable, which follows a Gaussian distribution with a mean of 0 and a variance of . After measurement and calculation, we get:

[0133] ;

[0134] Subsequently, the electronic device updates the particle weights according to the distance between the positioning tag and the positioning beacon. The formula is as follows:

[0135] ;

[0136] ;

[0137] Among them, is the weight of the th particle at the th moment, and are the th beacon's axis and axis coordinates, and are the th particle's th moment's axis and axis coordinates, is the distance from the th particle to the th beacon, is the distance from the positioning tag to the th beacon, is the RSSI measurement noise variance, is the total number of beacons that can receive RSSI, and the RSSI measurement noise variance is taken as . is a constant.

[0138] The weight update of the third sub-particle set corresponds to the position correction method in step 105. Among them, in method 1, the particle weights are recalculated in the same way as the second sub-particle set. In method 2, the particle weights are set to 0.

[0139] Step 107: Based on the particle weights of the particles in the second candidate particle set, determine at least two stratification intervals, and the interval size of the stratification interval is negatively correlated with the particle weight.

[0140] See Figure 3 , Figure 3 which is a flowchart of the determination process of the stratification interval provided by an exemplary embodiment of the present application. This process includes the following sub-steps.

[0141] Sub-step 107A: Based on the particle weights of the particles in the second candidate particle set, determine the weight density corresponding to each particle, and the weight density is positively correlated with the particle weight.

[0142] The electronic device first normalizes the particle weights of the particles in the second candidate particle set to obtain the normalized weights of the particles. The formula is as follows:

[0143] ;

[0144] where is the weight of the th particle at the th moment, is the normalized weight of the th particle, is the total number of particles.

[0145] Then, calculate the weight density corresponding to each particle according to the following formula:

[0146] ;

[0147] where is the weight density of the th particle, is the normalized weight of the th particle, is the maximum normalized weight.

[0148] Exemplarily, the particle weights of the particles in the second candidate particle set include {0.2, 0.4, 0.2, 0.5, 0.3}, and the normalized weights obtained after normalization are {0.125, 0.25, 0.125, 0.31, 0.19}. Among them, the maximum normalized weight is 0.31, and the weight density is {0.4, 0.8, 0.4, 1, 0.6}.

[0149] Sub-step 107B: Based on the weight density corresponding to each particle, determine the interval size of each stratification interval, and the interval size is negatively correlated with the weight density.

[0150] The electronic device first sorts each particle according to the particle weight, so that the high-weight particles are concentratedly arranged, where a single particle corresponds to a single hierarchical interval. The number of hierarchical intervals is equal to the total number of particles in the second candidate particle set.

[0151] Then, calculate the interval size of the hierarchical interval corresponding to each particle according to the following formula:

[0152] ;

[0153] Where, is the interval size of the hierarchical interval corresponding to the th particle, is the weight density of the th particle, is the total number of particles.

[0154] Subsequently, the electronic device normalizes the interval size of the hierarchical interval, and the formula is as follows:

[0155] ;

[0156] Where, is the interval size of the hierarchical interval of the th particle before normalization, is the interval size of the th particle after normalization, is the total number of particles.

[0157] Sub-step 107C, determine at least two hierarchical intervals based on the interval sizes of at least two hierarchical intervals.

[0158] The electronic device first calculates the cumulative sum of the particle weights, and the formula is as follows:

[0159] ;

[0160] Where, is the cumulative sum of the weights of the first particles, is the normalized weight of the th particle, is the total number of particles.

[0161] Subsequently, the electronic device normalizes all the cumulative sums of the weights, and the formula is as follows:

[0162] ;

[0163] Where, is the cumulative sum of the weights of the first particles, is the cumulative sum of the weights of the first particles after normalization, is the total number of particles.

[0164] Step 108: Based on the hierarchical intervals and the particle weights of each particle in the second candidate particle set, collect target particles from the second candidate particle set to obtain a target particle set.

[0165] The electronic device performs stratified resampling according to the normalized interval size and randomly samples a value within each stratified interval , finds the index of the first one greater than or equal to of , copies the th particle as the target particle after resampling, and repeats times to obtain the particle set after resampling, that is, the target particle set. After resampling, the weight of each particle is uniformly updated to .

[0166] Step 109: Based on the target particle set, determine the positioning position of the positioning label.

[0167] The electronic device estimates the position of the positioning label through the particle position coordinates and particle weights. The formula is as follows:

[0168] ;

[0169] where is the positioning position of the positioning label at moment, which is composed of the axis coordinate and the axis coordinate , is the coordinate of the th particle at moment, which is composed of the axis coordinate and the axis coordinate , is the total number of particles.

[0170] See Figure 4 , Figure 4 is a schematic diagram of the positioning position determined based on the target particle set provided by an exemplary embodiment of this application. This figure takes an arc-shaped corridor map in a 25-meter * 18-meter two-dimensional indoor map as an example to illustrate the positioning position finally determined by the electronic device.

[0171] Among them, the arc-shaped corridor is divided into single rectangular segments and two arc segments according to its shape. As Figure 4 shown, at both sides of the corridor, arc segments are provided at intervals of Set a positioning beacon 401 at a radian, and set a positioning beacon 401 every 2.5 meters on the straight line segment. The positioning tag is located at the real position 402. The electronic device determines the target particle set by performing the above steps. The target particle set gathers around the real position 402. The position of the positioning tag is estimated through the particle position coordinates and the particle weight, and the estimated position 403 is determined as the positioning position of the positioning tag, and the matching degree between the positioning position and the real position 402 is improved.

[0172] The electronic device can obtain the path positioning result by concatenating the positioning positions of the positioning tags determined in real time. See Figure 5 , Figure 5 Figure 5 is a schematic diagram of the path positioning result provided by an exemplary embodiment of the present application. This figure takes an arc-shaped corridor map in a 25-meter * 18-meter two-dimensional plane of an indoor map as an example for illustration. Similarly, Figure 4 , the beacon setting method and shape of this arc-shaped corridor will not be elaborated here.

[0173] Among them, the solid line connection part is the estimated position 501, and the position marked by the dotted line is the real position 502. When the matching degree between the positioning position and the real position 502 is improved, the matching degree between the estimated path and the real path is improved, and the accuracy of real-time positioning is improved.

[0174] In the above embodiment, after updating the weights of the particles in each sub-map area on the indoor map, at least two hierarchical intervals are determined according to the particle weights. Among them, the interval size of the hierarchical interval is negatively correlated with the particle weight, so that the interval size of the hierarchical interval can automatically adapt to the updated and changing particle weights, that is, the hierarchical size can be dynamically adjusted according to the weight distribution, so that the high-weight area is stratified more finely, increasing the sampling opportunity of high-weight particles, making the low-weight area stratified coarser, reducing the sampling opportunity of low-weight particles, and moreover, the above technical solution can correct the positions of out-of-bounds particles, realizing dynamic real-time high-precision positioning.

[0175] Among them, by determining the weight density corresponding to each particle in the second candidate particle set, the distribution of the particle weights in the entire particle set is determined. Compared with simply determining the interval size according to the numerical size of the particle weights, first determining the interval size according to the weight density and then determining the hierarchical interval can avoid inaccurate stratification caused by the overall low or high numerical value of the particle weights, so that the hierarchical interval can adapt to the dynamic changes of the particle weights of each particle in the particle set.

[0176] By using the maximum normalized weight as the denominator when calculating the weight density and the normalized weight of a single particle as the numerator, compared with using the cumulative sum of weights as the denominator when calculating the weight density, the difference between high-weight particles and low-weight particles can be increased, and the influence degree of high-weight particles on the stratified interval can be improved.

[0177] By sorting the particles according to their weights, the high-weight particles are concentrated. During the stratified resampling process, since the weight density is negatively correlated with the interval size, the larger the weight density, the smaller the corresponding interval size, and the denser the stratified sampling. By adopting the above technical solution, the determination method of the interval size of the stratified interval is clarified, which is beneficial to improving the stratification rationality so as to increase the sampling opportunity of high-weight particles.

[0178] The process of correcting the positions of the first subset of particles in step 105 will be further described below. The content shown in the indoor map includes each room and the corridor outside the room. Among them, the corridor can be divided into sub-map areas according to rectangles and arcs, and the rooms on both sides of the corridor can be divided into sub-map areas according to rectangles and triangles.

[0179] In some embodiments, the electronic device divides the indoor map into at least two sub-map areas according to the shape. Among them, the area shapes of the at least two sub-map areas include arc-shaped areas, rectangular areas, and triangular areas. Among them, the arc-shaped area is used to represent the arc-shaped corridor, the rectangular area is used to represent the room or the rectangular corridor, and the combined area of the triangular area and the rectangular area is used to represent the room adjacent to the arc-shaped corridor, where the triangular area is adjacent to the arc-shaped area. It should be noted that the triangular area can also be an area approximately in the shape of a triangle adjacent to the arc-shaped area.

[0180] See Figure 6 , Figure 6 is a schematic diagram of an indoor map divided by shape provided by another exemplary embodiment of the present application. Among them, the arc-shaped area 601 is a section of the arc-shaped path of the corridor, and the combined area of the triangular area 602 and the rectangular area 603 is used to represent the room adjacent to the arc-shaped area 601. The rectangular area 604 is a section of the rectangular path of the corridor. Among them, the triangular area 602 is adjacent to the arc-shaped area 601.

[0181] During the process of correcting the positions of the first subset of particles, the electronic device adopts the following correction methods according to different sub-map area shapes and the meanings of the sub-map areas:

[0182] Method 1: In the case where the sub-map area is an arc-shaped area, remove the particle positions of the first subset of particles that exceed the arc-shaped area.

[0183] In some embodiments, the boundaries between adjacent sub-map regions can be divided into two types: connected boundaries and closed boundaries. Among them, the connected boundary is used to represent that the combined figure of two adjacent sub-map regions is the same indoor space, and the closed boundary is used to represent that the two adjacent sub-map regions represent two different indoor spaces. For the particles beyond the closed boundary, the particle positions can be removed, and for the particles beyond the connected boundary, the particle positions can be corrected to the connected boundary.

[0184] Considering that the two arcs of the circular arc region are closed boundaries, the electronic device removes the particle positions of the first subset of particles beyond the circular arc region.

[0185] Such as Figure 2 shown, the path center of the circle in sub-map region 201 , , the path center of the circle in sub-map region 203 , . Similarly, Figure 6 the middle map constructs the coordinate system in the same way, and the centers of the circular arc paths are respectively , , and , .

[0186] The electronic device calculates the distances between all particles in the circular arc region and the center of the circular arc , and the formula is as follows:

[0187] ;

[0188] Among them, and are the th particle's axis and axis coordinates at time, and are the axis and axis coordinates of the center of the circular arc path.

[0189] Subsequently, in the polar coordinate system, the electronic device determines whether the particle exceeds the path boundary according to the distance and the upper and lower bound information of the radius of the circular arc path and , optimizes the particles that exceed the boundary, and sets the weights of these particles to 0.

[0190] Method 2: In the case where the sub-map region is a triangular region, the particle positions of the first subset of particles are corrected back to the boundary of the triangular region.

[0191] In some embodiments, when the sub-map area is a triangular area or a rectangular area, since the area boundary includes a connected boundary, the positioning position may be located on the connected boundary. Therefore, the particle positions of the first subset of particles are corrected back to the boundary of the triangular area.

[0192] Exemplarily, Figure 6 In Figure 6 , the triangular area 602 is adjacent to the rectangular area 603, and the combined figure of the triangular area 602 and the rectangular area 603 represents a room.

[0193] Optionally, the boundary between the triangular area 602 and the rectangular area 603 is hereinafter referred to as the first adjacent side. The first adjacent side belongs to the connected boundary. When the electronic device exceeds this first adjacent side, the particle positions of the first subset of particles are corrected back to the first adjacent side.

[0194] In some embodiments, the positioning tag moves in the corridor and stays in the room. It can be known that the probability that the positioning tag stays by the inner wall of the room is relatively high, and the probability that it stays by the wall of the corridor is relatively low. Therefore, the electronic device can also correct the particle positions of the first subset of particles back to the closed boundary of the triangular area.

[0195] Method 3: When the sub-map area is a rectangular area, the particle positions of the first subset of particles are corrected back to the boundary of the rectangular area.

[0196] Similarly, since the rectangular area boundary includes a connected boundary, the positioning position may be located on the connected boundary. Therefore, the particle positions of the first subset of particles are corrected back to the connected boundary of the triangular area. Also, the probability that the positioning tag stays by the inner wall of the room is relatively high, and the probability that it stays by the wall of the corridor is relatively low. The electronic device can also correct the particle positions of the first subset of particles back to the closed boundary of the rectangular area.

[0197] Exemplarily, Figure 6 In Figure 6 , the triangular area 602 is adjacent to the rectangular area 603, and the rectangular area 604 is adjacent to the arc-shaped area Figure 6 . The combined figure of the triangular area 602 and the rectangular area 603 represents a room. The rectangular area 604 and the arc-shaped area Figure 6 represent a section of the same corridor, that is, the combined figure of the rectangular area and the arc-shaped area can represent the corridor.

[0198] Optionally, the boundary between the rectangular area 604 and the arc-shaped area Figure 6 is hereinafter referred to as the second adjacent side. When the electronic device exceeds this second adjacent side, the particle positions of the first subset of particles are corrected back to the second adjacent side.

[0199] In some embodiments, there may be connected nodes on the closed boundary.

[0200] Optionally, there are connection nodes on the adjacent boundaries between at least two sub-map regions for connecting the indoor spaces between adjacent sub-map regions, such as connecting corridors and rooms, where the rooms are represented by triangular regions and rectangular regions.

[0201] When the electronic device corrects the particle positions of the first subset of particles back to the boundary of the rectangular region, if the distance between the particle positions of the first subset of particles and the connection node is less than the distance threshold, the particle positions of the first subset of particles can be corrected to the connection node of the rectangular region; when the particle positions of the first subset of particles are corrected back to the boundary of the triangular region, if the distance between the particle positions of the first subset of particles and the connection node is less than the distance threshold, the particle positions of the first subset of particles can be corrected to the connection node of the triangular region.

[0202] Exemplarily, as Figure 6 shown, there is a connection node 606 between the rectangular region 604 and the rectangular region 605, representing the door from the corridor to the interior of the room. Hereinafter, the boundary between the rectangular region 604 and the rectangular region 605 is referred to as the third adjacent boundary. Since the positioning position may be at the connection node 606 or on the closed boundary where the connection node 606 is located, such as staying at the edge of the room, the electronic device can first correct the particle positions that exceed the third adjacent boundary around the connection node and have a distance less than the distance threshold from the connection node to the connection node 606, and then correct the other particles that exceed the third adjacent boundary to the third adjacent boundary.

[0203] Optionally, the sub-map region includes an oriented rectangular region, which represents the region corresponding to the one-way movement path on the indoor map, such as the escalator and one-way passage in the mall.

[0204] In the case where the sub-map region is an oriented rectangular region, after updating the weights of the second subset of particles and the third subset of particles during the communication between the positioning beacon and the positioning tag, the electronic device can further perform the following steps:

[0205] Step 1, determine a one-way movement coefficient according to the particle positions at the current moment, the particle positions at the previous moment, and the one-way movement direction of the oriented rectangular region. The one-way movement coefficient is used to reduce the particle weights of the particles moving in the reverse direction and increase the particle weights of the particles moving in the forward direction.

[0206] In a possible implementation manner, the electronic device determines the particle movement direction according to the particle positions at the current moment and the particle positions at the previous moment, and determines the direction angle according to the one-way movement direction of the oriented rectangular region and the particle movement direction. Subsequently, the one-way movement coefficient is determined by using the direction angle, where the angle is negatively correlated with the one-way movement coefficient. For example, the direction angle is , , the one-way movement coefficient is , a constant greater than 0. For another example, the included angle is , , the one-way movement coefficient is , a constant greater than 0. For yet another example, when the included angle is greater than 0, .

[0207] Optionally, the electronic device can adjust the determination function of the one-way movement coefficient according to actual needs, such as according to the demand for the change trend of the one-way movement coefficient . For example, for particles, a larger one-way movement coefficient is adopted, and the change trend of the one-way movement coefficient is relatively slow. For particles, a smaller one-way movement coefficient is adopted, and the change trend of the one-way movement coefficient shows a sharp decline first and then a steady decline. The electronic device can fit a corresponding .

[0208] Step 2, use the one-way movement coefficient to perform a secondary update on the updated particle weights.

[0209] The electronic device performs the secondary update according to the following formula:

[0210] ;

[0211] where refers to the particle weight of the -th particle at the -th moment after the secondary update, is the normalized weight of the -th particle, and the one-way movement coefficient is .

[0212] Optionally, the secondary update can be performed synchronously with the primary update. When the electronic device executes step 106, the following formula can be used to update the particle weights of the second subset of particles:

[0213] ;

[0214] where is the weight of the -th particle at the -th moment, is the distance from the -th particle to the -th beacon, is the distance from the positioning tag to the -th beacon, is the RSSI measurement noise variance, is the total number of beacons that can receive RSSI. Take the RSSI measurement noise variance . as a constant. The unidirectional movement coefficient is .

[0215] By adopting the above technical solution, the indoor map is divided according to the circular arc area, rectangular area and triangular area. Among them, different areas represent different indoor scenes. For example, the circular arc area represents a circular arc corridor, which is beneficial to correct the positions of particles beyond the sub-map area in different indoor scenes according to the sub-map areas of different shapes, and improve the accuracy when the particle set estimates the possible positioning positions.

[0216] In the above technical solution, considering that there are connection nodes between different indoor spaces, for example, a room door can connect the corridor and the room. On the boundary where there are connection nodes, the probability that the particle is located at the connection node is relatively high. By correcting the out-of-bounds particles around the connection node to the connection node, the positioning accuracy at the connection node can be improved.

[0217] In the above technical solution, the particle weight is updated twice in the indoor directional movement scenario. For example, in the scenario of moving in a single-line passage or on an escalator, obviously, the probability of moving in a specific direction is higher than that of moving in other directions. By determining the unidirectional movement coefficient to realize the secondary update of the particle weight, the influence of reverse-moving particles on the result is suppressed, and the influence of forward-moving particles on the result is enhanced, which is beneficial to improving the positioning accuracy in the directional movement scenario.

[0218] Based on the above same inventive concept, the embodiment of the present application also discloses an indoor dynamic positioning system based on adaptive hierarchical resampling, see Figure 7 , including:

[0219] A division module 701, configured to divide the constructed indoor map into at least two sub-map areas;

[0220] A setting module 702, configured to set at least two positioning beacons at fixed positions in the indoor map, and the positioning beacons are used for communicating with positioning tags;

[0221] An initialization module 703, configured to initialize the particle distribution in the sub-map area to obtain initial particles;

[0222] A transfer module 704, configured to transfer the positions of the initial particles during the movement of the positioning tag to obtain a first candidate particle set, where the first candidate particle set includes a first sub-particle set and a second sub-particle set, the first sub-particle set is a particle set that exceeds the boundary of the sub-map area, and the second sub-particle set is a particle set that does not exceed the boundary of the sub-map area;

[0223] A correction module 705 for correcting the positions of the first subset of sub-particles to obtain a third subset of sub-particles;

[0224] An update module 706 for updating the weights of the second subset of sub-particles and the third subset of sub-particles during the communication between the positioning beacon and the positioning tag, and the third subset of sub-particles and the second subset of sub-particles form a second candidate particle set;

[0225] An interval determination module 707 for determining at least two hierarchical intervals based on the particle weights of the particles in the second candidate particle set, and the interval size of the hierarchical interval is negatively correlated with the particle weight;

[0226] A collection module 708 for collecting target particles from the second candidate particle set based on the hierarchical intervals and the particle weights of the particles in the second candidate particle set to obtain a target particle set;

[0227] A position determination module 709 for determining the positioning position of the positioning tag based on the target particle set.

[0228] Optionally, the interval determination module 707 is further configured to:

[0229] Determine the weight density corresponding to each particle based on the particle weights of the particles in the second candidate particle set, and the weight density is positively correlated with the particle weight;

[0230] Determine the interval size of each hierarchical interval based on the weight density corresponding to each particle, and the interval size is negatively correlated with the weight density;

[0231] Determine at least two of the hierarchical intervals based on the interval sizes of at least two of the hierarchical intervals.

[0232] Optionally, the interval determination module 707 is further configured to:

[0233] Perform normalization processing on the particle weights of the particles in the second candidate particle set to obtain the normalized weights of the particles;

[0234] Calculate the weight density corresponding to each particle according to formula (1):

[0235] ;

[0236] Wherein, The weight density of the th particle, is the normalized weight of the th particle, is the maximum normalized weight.

[0237] Optionally, the number of the hierarchical intervals is equal to the total number of particles in the second candidate particle set;

[0238] The interval determination module 707 is further configured to:

[0239] Sort each particle according to the particle weight, where a single particle corresponds to a single hierarchical interval;

[0240] Calculate the interval size of the hierarchical interval corresponding to each particle according to formula (2):

[0241] ;

[0242] Wherein, is the interval size of the hierarchical interval corresponding to the th particle, is the weight density of the th particle, is the total number of particles.

[0243] Optionally, the partitioning module 701 is further configured to:

[0244] Partition the indoor map into the at least two sub-map regions according to the shape, the region shapes of the at least two sub-map regions include an arc-shaped region, a rectangular region, and a triangular region, the arc-shaped region is used to represent an arc-shaped corridor, the rectangular region is used to represent a room or a rectangular corridor, and the combined region of the triangular region and the rectangular region is used to represent a room adjacent to the arc-shaped corridor, wherein the triangular region is adjacent to the arc-shaped region;

[0245] The correction module 705 is further configured to:

[0246] When the sub-map region is the arc-shaped region, remove the particle positions of the first subset of particles that exceed the arc-shaped region;

[0247] When the sub-map region is the triangular region, correct the particle positions of the first subset of particles back to the boundary of the triangular region;

[0248] When the sub-map region is the rectangular region, correct the particle positions of the first subset of particles back to the boundary of the rectangular region.

[0249] Optionally, there are connection nodes on the adjacent boundaries between the at least two sub-map regions for connecting the indoor spaces between the adjacent sub-map regions;

[0250] The correction module 705 is further configured to:

[0251] When the distance between the particle positions of the first subset of particles and the connected node is less than the distance threshold, correct the particle positions of the first subset of particles to the connected node in the rectangular region;

[0252] When the distance between the particle positions of the first subset of particles and the connected node is less than the distance threshold, correct the particle positions of the first subset of particles to the connected node in the triangular region.

[0253] Optionally, the sub-map region includes an oriented rectangular region, and the oriented rectangular region represents the region corresponding to the one-way movement path on the indoor map;

[0254] When the sub-map region is the oriented rectangular region, the update module 706 is further configured to:

[0255] Determine a one-way movement coefficient according to the particle position at the current moment, the particle position at the previous moment, and the one-way movement direction of the oriented rectangular region, where the one-way movement coefficient is used to reduce the particle weight of the particles moving in the reverse direction and increase the particle weight of the particles moving in the forward direction;

[0256] Perform a secondary update on the updated particle weights using the one-way movement coefficient.

[0257] Based on the same inventive concept as above, the present application also discloses an electronic device. Please refer to Figure 8 , Figure 8 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application. The electronic device 800 may include: a processor 801, a communication bus 802, a user interface 803, a network interface 804, and a memory 805.

[0258] Among them, the communication bus 802 is used to realize the connection and communication between these components.

[0259] Among them, the user interface 803 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 803 may further include a standard wired interface and a wireless interface.

[0260] Among them, the network interface 804 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0261] Among them, the processor 801 may include one or more processing cores. The processor 801 connects various parts within the entire server through various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 805, and by calling the data stored in the memory 805, it executes various functions of the server and processes data. Optionally, the processor 801 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 801 may integrate one or a combination of several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 801 and may be implemented separately by a single chip.

[0262] Among them, the memory 805 may include random access memory (RAM), and may also include read-only memory (ROM). Optionally, the memory 805 includes a non-transitory computer-readable medium. The memory 805 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 805 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory 805 may also be at least one storage system located far from the aforementioned processor 801.

[0263] Among them, the memory 805 is used to store instructions, the user interface 803 and the network interface 804 are used to communicate with other devices, and the processor 801 is used to execute the instructions stored in the memory 805 so that the electronic device executes the method provided in the above embodiment.

[0264] Based on the same inventive concept as described above, an embodiment of the present application further discloses a computer-readable storage medium. Multiple instructions are stored in the computer-readable storage medium, and the instructions are loaded and executed by a processor to implement the method described in the above embodiment.

[0265] Referring to Figure 8 , in a memory 805 as a computer storage medium, an operating system, a network communication module, a user interface module, and application programs may be included.

[0266] In Figure 8 the electronic device 800 shown, the user interface 803 is mainly used to provide an interface for the user to input data and obtain the data input by the user; and the processor 801 may be used to call the application program for the road evaluation method stored in the memory 805. When executed by one or more processors 801, the electronic device 800 executes the method as described in one or more of the above embodiments. It should be noted that, for the foregoing method embodiments, for simplicity of description, they are all described as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps may be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application. In the above embodiments, each embodiment is described with emphasis. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0267] It should be understood that "a plurality of" mentioned herein refers to two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0268] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above embodiments can be completed by hardware, or can be completed by a program instructing relevant hardware. The program can be stored in a computer-readable storage medium. The storage media mentioned above include, for example: various media such as USB flash drives, mobile hard disks, read-only memory ROM, random access memory RAM, magnetic disks, or optical discs that can store program codes.

[0269] The above are only optional embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An indoor dynamic positioning method based on adaptive hierarchical resampling, characterized in that, Comprising: Dividing the constructed indoor map into at least two sub - map regions; Setting at least two positioning beacons at fixed positions within the indoor map, where the positioning beacons are used to communicate with positioning tags, and the positioning tags are configured on moving objects; Initializing the particle distribution within the sub - map region to obtain initial particles; During the movement of the positioning tag, performing position transfer on the initial particles to obtain a first candidate particle set, where the first candidate particle set includes a first sub - particle set and a second sub - particle set. The first sub - particle set is the set of particles that exceed the boundary of the sub - map region, and the second sub - particle set is the set of particles that do not exceed the boundary of the sub - map region; Performing position correction on the first sub - particle set to obtain a third sub - particle set; During the communication between the positioning beacon and the positioning tag, performing weight update on the second sub - particle set and the third sub - particle set. The third sub - particle set and the second sub - particle set form a second candidate particle set; Based on the particle weights of each particle within the second candidate particle set, determining at least two stratification intervals, where the interval size of the stratification interval is negatively correlated with the particle weight; Based on the stratification intervals and the particle weights of each particle within the second candidate particle set, collecting target particles from the second candidate particle set to obtain a target particle set; Based on the target particle set, determining the positioning position of the positioning tag; The determining at least two stratification intervals based on the particle weights of each particle within the second candidate particle set includes: Based on the particle weights of each particle within the second candidate particle set, determining the weight density corresponding to each particle, where the weight density is positively correlated with the particle weight; Based on the weight density corresponding to each particle, determining the interval size of each stratification interval, where the interval size is negatively correlated with the weight density; Based on the interval sizes of at least two of the stratification intervals, determining at least two of the stratification intervals; The determining the weight density corresponding to each particle based on the particle weights of each particle within the second candidate particle set includes: Performing normalization processing on the particle weights of each particle within the second candidate particle set to obtain the normalized weight of each particle; Calculating the weight density corresponding to each particle according to formula (1); ； Among them, is the weight density of the th particle, is the maximum normalized weight; The number of the stratification intervals is equal to the total number of particles in the second candidate particle set; The determining the interval size of each stratification interval based on the weight density corresponding to each particle includes: Sorting each particle according to the particle weight, where a single particle corresponds to a single stratification interval; Calculating the interval size of the stratification interval corresponding to each particle according to formula (2); ； Among them, is the interval size of the hierarchical interval corresponding to the th particle, is the weight density of the th particle, is the total number of particles.

2. The method according to claim 1, wherein The dividing the constructed indoor map into at least two sub - map regions includes: The indoor map is divided into the at least two sub - map regions according to the shape. The regional shapes of the at least two sub - map regions include arc - shaped regions, rectangular regions, and triangular regions. The arc - shaped regions are used to represent arc - shaped corridors, the rectangular regions are used to represent rooms or rectangular corridors, and the combined region of the triangular region and the rectangular region is used to represent the room adjacent to the arc - shaped corridor. Among them, the triangular region is adjacent to the arc - shaped region; The position correction of the first subset of particles includes: When the sub - map region is the arc - shaped region, removing the particle positions of the first subset of particles that exceed the arc - shaped region; When the sub - map region is the triangular region, correcting the particle positions of the first subset of particles back to the boundary of the triangular region; When the sub - map region is the rectangular region, correcting the particle positions of the first subset of particles back to the boundary of the rectangular region.

3. The method according to claim 2, characterized in that, There are connection nodes on the adjacent boundaries between the at least two sub - map regions, which are used to connect the indoor spaces represented by the adjacent sub - map regions; The correcting the particle positions of the first subset of particles back to the boundary of the rectangular region includes: When the distance between the particle position of the first subset of particles and the connection node is less than the distance threshold, correcting the particle position of the first subset of particles to the connection node of the rectangular region; The correcting the particle positions of the first subset of particles back to the boundary of the triangular region includes: When the distance between the particle position of the first subset of particles and the connection node is less than the distance threshold, correcting the particle position of the first subset of particles to the connection node of the triangular region.

4. The method according to claim 1, characterized in that, The sub - map region includes an oriented rectangular region, and the oriented rectangular region represents the region corresponding to the one - way movement path on the indoor map; When the sub - map region is the oriented rectangular region, after the weight update of the second subset of particles and the third subset of particles during the communication between the positioning beacon and the positioning tag, the method further includes: Determining a one - way movement coefficient according to the particle position at the current moment, the particle position at the previous moment, and the one - way movement direction of the oriented rectangular region. The one - way movement coefficient is used to reduce the particle weight of the particles moving in the reverse direction and increase the particle weight of the particles moving in the forward direction; Performing a secondary update on the updated particle weights using the one - way movement coefficient.

5. An indoor dynamic positioning system based on adaptive hierarchical resampling, characterized in that, Including: A division module for dividing the constructed indoor map into at least two sub - map regions; A setting module for setting at least two positioning beacons at fixed positions in the indoor map. The positioning beacons are used to communicate with the positioning tags, and the positioning tags are configured on the moving objects; An initialization module for initializing the particle distribution in the sub - map region to obtain initial particles; A transfer module, configured to perform position transfer on the initial particles during the movement of the positioning tag, so as to obtain a first candidate particle set, where the first candidate particle set includes a first sub-particle set and a second sub-particle set, the first sub-particle set is a particle set that exceeds the boundary of the sub-map area, and the second sub-particle set is a particle set that does not exceed the boundary of the sub-map area; A correction module, configured to perform position correction on the first sub-particle set to obtain a third sub-particle set; An update module, configured to perform weight update on the second sub-particle set and the third sub-particle set during the communication between the positioning beacon and the positioning tag, where the third sub-particle set and the second sub-particle set form a second candidate particle set; An interval determination module, configured to determine at least two hierarchical intervals based on the particle weights of the particles in the second candidate particle set, where the interval size of the hierarchical interval is negatively correlated with the particle weight; An acquisition module, configured to acquire target particles from the second candidate particle set based on the hierarchical intervals and the particle weights of the particles in the second candidate particle set to obtain a target particle set; A position determination module, configured to determine the positioning position of the positioning tag based on the target particle set; The interval determination module is further configured to determine the weight density corresponding to each particle based on the particle weights of the particles in the second candidate particle set, where the weight density is positively correlated with the particle weight; determine the interval size of each hierarchical interval based on the weight density corresponding to each particle, where the interval size is negatively correlated with the weight density; and determine at least two of the hierarchical intervals based on the interval sizes of at least two of the hierarchical intervals; The interval determination module is further configured to perform normalization processing on the particle weights of the particles in the second candidate particle set to obtain the normalized weights of the particles; and calculate the weight density corresponding to each particle according to formula (1): ； Among them, is the weight density of the th particle, is the maximum normalized weight; The number of the hierarchical intervals is equal to the total number of particles in the second candidate particle set; The interval determination module is further configured to sort each particle according to the particle weight, where a single particle corresponds to a single hierarchical interval; and calculate the interval size of the hierarchical interval corresponding to each particle according to formula (2): ； Among them, is the interval size of the hierarchical interval corresponding to the th particle, is the weight density of the th particle, is the total number of particles.

6. An electronic device, characterized in that, It includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, Multiple instructions are stored in the computer-readable storage medium, and the instructions are loaded and executed by the processor to implement the method according to any one of claims 1 to 4.

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