Multi-source fusion indoor positioning method and device based on particle filtering and PDR

Abstract

The invention provides a multi-source fusion indoor positioning method and device based on particle filtering and PDR, and relates to the technical field of indoor positioning. The method comprises the following steps: constructing a target map, wherein a plurality of Bluetooth beacons are arranged in the target map; obtaining transfer data of each positioning tag particle in the target map based on a PDR algorithm; converting the signal intensity of each Bluetooth beacon received by each positioning tag into a distance, and calculating the weight of each positioning tag particle based on the distance and the transfer data; performing residual error resampling based on the weight of each positioning tag particle to obtain an updated particle set; and performing weighted calculation on each positioning tag particle in the updated particle set to obtain a target positioning tag coordinate. By implementing the technical scheme provided by the invention, the accuracy of indoor positioning can be improved.

Description

Technical Field

[0001] The present application relates to the field of indoor positioning technology, and in particular to a multi-source fusion indoor positioning method and device based on particle filtering and PDR. Background Art

[0002] With the rapid development of indoor positioning technology, its application in smart buildings, shopping guides, industrial warehousing and other fields is becoming more and more extensive. Among the many indoor positioning technologies, the positioning solution based on Bluetooth beacons has attracted widespread attention due to its advantages such as low deployment cost and wide coverage.

[0003] At present, common Bluetooth indoor positioning methods mainly perform ranging positioning based on the received signal strength indication. However, in practical applications, due to the complex indoor environment, the signal is easily affected by multipath effects, occlusion, reflection and other factors. Existing indoor positioning methods often rely on a single positioning data source, which makes the system more sensitive to environmental changes. In some scenarios, such as when the signal is severely blocked or the signal strength fluctuates greatly, it is difficult to ensure the continuity and reliability of positioning, resulting in inaccurate indoor positioning. Summary of the invention

[0004] The present application provides a multi-source fusion indoor positioning method, system, medium and device based on particle filtering and PDR, which can improve the accuracy of indoor positioning.

[0005] In a first aspect, the present application provides a multi-source fusion indoor positioning method based on particle filtering and PDR, the method comprising: Constructing a target map, wherein a plurality of Bluetooth beacons are arranged in the target map; Acquire the transfer data of each positioning tag particle in the target map based on the PDR algorithm; Convert the signal strength of each Bluetooth beacon received by each positioning tag into a distance, and calculate the weight of each positioning tag particle based on the distance and the transfer data; Residual resampling is performed based on the weight of each of the positioning label particles to obtain an updated particle set; The positioning label particles in the update particle set are weightedly calculated to obtain the target positioning label coordinates.

[0006] By adopting the above technical solution, by setting multiple Bluetooth beacons in the target map and converting the Bluetooth beacon signal strength received by the positioning tag into distance data, and combining the transfer data obtained by the PDR algorithm, the fusion of multi-source data can be achieved; then the weight of each positioning tag particle is calculated, and the residual resampling is performed to obtain the updated particle set, and finally the weighted calculation is performed on each positioning tag particle in the updated particle set to obtain the target positioning tag coordinates, which effectively overcomes the problem that a single data source is easily affected by the environment. Even in scenarios with severe signal obstruction or large fluctuations in signal strength, the continuity of positioning can be maintained, which significantly improves the accuracy of indoor positioning.

[0007] In a second aspect of the present application, a multi-source fusion indoor positioning system based on particle filtering and PDR is provided, the system comprising: A target map construction module, used to construct a target map, wherein the target map is provided with a plurality of Bluetooth beacons; A transfer data calculation module, used for obtaining the transfer data of each positioning tag particle in the target map based on the PDR algorithm; A weight calculation module, used to convert the signal strength of each Bluetooth beacon received by each positioning tag into a distance, and calculate the weight of each positioning tag particle based on the distance and the transfer data; A weight updating module, used for performing residual resampling based on the weight of each of the positioning label particles to obtain an updated particle set; The coordinate positioning module is used to perform weighted calculation on each positioning label particle in the update particle set to obtain the target positioning label coordinates.

[0008] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above method steps.

[0009] In a fourth aspect of the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0010] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: This application can achieve the fusion of multi-source data by setting multiple Bluetooth beacons in the target map, converting the Bluetooth beacon signal strength received by the positioning tag into distance data, and combining the transfer data obtained by the PDR algorithm; then calculating the weight of each positioning tag particle and performing residual resampling to obtain an updated particle set, and finally performing weighted calculation on each positioning tag particle in the updated particle set to obtain the target positioning tag coordinates, which effectively overcomes the problem that a single data source is easily affected by the environment. Even in scenarios with severe signal obstruction or large fluctuations in signal strength, the continuity of positioning can be maintained, significantly improving the accuracy of indoor positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 1 is a flow chart of a multi-source fusion indoor positioning method based on particle filtering and PDR provided in an embodiment of the present application; Figure 2 It is a particle filter simulation trajectory diagram provided in an embodiment of the present application; Figure 3 is a module schematic diagram of a multi-source fusion indoor positioning system based on particle filtering and PDR provided in an embodiment of the present application; Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0012] Description of reference numerals: 400, electronic device; 401, processor; 402, communication bus; 403, user interface; 404, network interface; 405, memory. DETAILED DESCRIPTION

[0013] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0014] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0015] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0016] The following will provide a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0017] Please refer to Figure 1 , a flowchart of a multi-source fusion indoor positioning method based on particle filtering and PDR is proposed. The method can be implemented by a computer program, can be implemented by a single-chip microcomputer, or can be run on a multi-source fusion indoor positioning system based on particle filtering and PDR. The computer program can be integrated in a computer device or run as an independent tool application. Specifically, the method includes steps 10 to 50, and the above steps are as follows: Step 10: Build a target map, in which multiple Bluetooth beacons are set.

[0018] In the embodiment of the present application, the target map refers to a gridded indoor plan established based on a two-dimensional coordinate system, such as a 20-meter × 15-meter L-shaped folded corridor area. The target map is divided into a number of regular grid units in terms of spatial structure. Each grid unit has a definite coordinate position, which can be used to represent the location information of positioning tags and Bluetooth beacons.

[0019] Specifically, before performing indoor positioning, it is necessary to first construct a target map. The embodiment of the present application uses an L-shaped folded corridor in a plane of 20 meters by 15 meters as the experimental area, and divides the area into a two-dimensional coordinate grid map of a fixed size. A Bluetooth beacon is arranged every 2 meters on the walls on both sides of the L-shaped corridor. This arrangement spacing can ensure the continuity of signal coverage while avoiding signal interference between adjacent beacons. The specific installation location coordinates of each Bluetooth beacon are recorded in the target map, and these coordinate information will be used for subsequent positioning calculations.

[0020] Furthermore, after the preliminary construction of the target map is completed, 100 positioning label particles are randomly distributed in the corridor for position initialization. Each positioning label particle can be expressed as: ,in , the number of positioning label particles is taken as 100, They represent the position coordinates corresponding to the th positioning label particles, and set the initialization weights of the positioning label particles to be uniformly equal. .

[0021] Step 20: Obtain the transfer data of each positioning label particle in the target map based on the PDR algorithm.

[0022] The PDR (Pedestrian Dead Reckoning) algorithm is a method for estimating position changes by calculating pedestrian walking parameters.

[0023] In the embodiment of the present application, the positioning tag particle refers to a sampling point in the target map used to estimate the actual position of the positioning tag.

[0024] Transfer data refers to the position change information of the positioning tag particle between two adjacent moments.

[0025] Specifically, the tester holds the positioning device in front of his chest and walks in a straight line along the L-shaped corridor in the target map, setting the walking direction to the y-axis direction. The positioning device collects acceleration waveform data in real time through the built-in accelerometer, estimates the number of steps based on the peak detection method, and uses the Weinberg model to calculate the step length. At the same time, the azimuth is calculated using the data collected by the accelerometer and magnetometer to determine the walking direction. Based on the accelerometer and magnetometer data, the pitch angle and roll angle of the device are calculated respectively, and the new step length and new azimuth are calculated based on the step length and azimuth. Finally, the position of each particle is updated through the state transfer equation, which takes into account the random deviation value to compensate for the measurement error. This method can accurately obtain the motion state information of the positioning tag and provide reliable transfer data for subsequent particle weight calculations.

[0026] Based on the above embodiment, as another optional embodiment, the step of acquiring the transfer data of each positioning tag particle in the target map based on the PDR algorithm may further include the following steps: Step 201: Acquire acceleration data and magnetic data of each positioning tag particle in the target map, and determine the step length based on the acceleration data.

[0027] In order to accurately obtain the movement status of pedestrians, the tester needs to hold the positioning device in front of his chest while walking in the corridor. The positioning device collects acceleration waveform data in real time through the built-in accelerometer, and uses the magnetometer to collect magnetic data. During the collection process of the positioning device, the accelerometer can detect the acceleration changes caused by the up and down vibration of the body during walking. This change is manifested as periodic peaks and troughs on the waveform. By identifying these features, the number of steps can be detected.

[0028] Specifically, the tester holds the positioning device in front of his chest and walks in a straight line along the L-shaped corridor in the target map, setting the walking direction to the y-axis direction. The built-in accelerometer of the positioning device collects acceleration waveform data, and the magnetometer collects magnetic data. Peak detection is performed based on the collected acceleration waveform data to estimate the number of steps. Then, a suitable model is selected based on the collected acceleration data to calculate the step length. The embodiment of the present application selects the Weinberg model, and the calculation formula is as follows: , where is the estimated step length, K is the coefficient determined by experiment, is the maximum acceleration value of the previous step, that is, the peak value, It is the minimum acceleration value of the current step, that is, the trough value.

[0029] For example, the maximum acceleration value measured during the experiment is , minimum acceleration value , the coefficient K is taken as 0.4, thus the estimated step length is obtained This step length estimation method based on acceleration waveform characteristics can effectively capture the dynamic changes of pedestrian gait and provide reliable distance parameters for subsequent position estimation.

[0030] Step 202: Calculate the azimuth angle based on the acceleration data and the magnetic data.

[0031] Specifically, in order to determine the direction of movement of pedestrians, it is necessary to calculate the azimuth based on the acquired acceleration data and magnetic data, thereby determining the walking direction. Set the angle corresponding to due north to 0 and the angle corresponding to due west to The azimuth is the arc angle of the top of the device relative to the true north. To obtain the specific orientation of the device in the navigation coordinate system, it is necessary to convert the carrier coordinate system (b system) to the navigation coordinate system (n system). The conversion formula is as follows: , where Represents the acceleration vector in the device's own coordinate system, usually measured by an accelerometer; is the acceleration vector in the navigation coordinate system; Represents the rotation matrix from the device's native coordinate system to the navigation coordinate system.

[0032] The output of the magnetometer is the magnetic induction intensity in the b system, expressed as , so the magnetic induction intensity in the n system and The conversion is as follows: .

[0033] In the formula, Output of the magnetometer 𝑠(⋅) represents the sine function, 𝑐(⋅) represents the cosine function; 𝜃 represents the pitch angle, which refers to the tilt of the device in the vertical direction; 𝛾 represents the roll angle, which refers to the tilt of the device in the horizontal direction; the specific solution formulas for 𝜃 and 𝛾 are as follows: ; In the formula, is the gravitational acceleration component of the b system, g is the gravitational acceleration of the n system, usually taken as 9.81m / s 2 . All data can be obtained by measuring the accelerometer. The accelerometer data during a certain measurement process is: , from which 𝜃 and 𝛾 can be calculated as follows: .

[0034] That is, the device is slightly tilted forward in the vertical direction (the pitch angle is ), and at the same time it is slightly tilted to the left in the horizontal direction (roll angle is ).

[0035] The calculation formula of magnetic heading angle, i.e. azimuth angle, is as follows: , so 𝜃 and 𝛾 measured by the magnetometer and calculated from the accelerometer data are substituted into the magnetic conversion formula , the azimuth of the current position can be calculated .

[0036] Step 203: Calculate a new step length and a new azimuth angle according to the step length and the azimuth angle.

[0037] Specifically, in order to improve positioning accuracy, the step length and azimuth angle α obtained by initial calculation need to be optimized. Since a single measurement may have errors, the system uses data from consecutive n steps for comprehensive calculation to obtain a more accurate new step length and new azimuth angle.

[0038] Using the device sensor data, all the data of the transfer of the positioning tag particles are integrated to recalculate the new step length and heading angle. The calculation formula is as follows: ; In the formula, is the new step size, is the step length of the i-th step, is the azimuth angle of the i-th step, is the new azimuth. In actual experiments, considering the measurement errors of step length and heading angle, the moving distance and angle of particles during transfer will increase by random values.

[0039] Step 204: Based on the new step length and the new azimuth angle, calculate the transfer data of each positioning tag particle.

[0040] Specifically, the new step length calculated by the sensor and the new azimuth , we can get the state transfer equation of the positioning tag particle, where the state transfer equation of the mth particle at the tth step is as follows: ; in, Particles The deviation generated when moving on the x-axis and y-axis. Transfer data refers to the position change information of the positioning tag particle between two adjacent moments, mainly including the new step value and the new azimuth. These transfer data describe the movement of the particle through the state transfer equation. The function of transfer data is to update the position of the particle.

[0041] For example, for a particle, if its position at time t-1 is (2.5m, 3.0m), substitute =0.50m, = 1.05 rad, and assuming a randomly generated deviation value =0.05m, =0.03m, then we can calculate that the new position of the particle at time t is approximately (2.97m, 3.44m).

[0042] Step 30: Convert the signal strength of each Bluetooth beacon received by each positioning tag into a distance, and calculate the weight of each positioning tag particle based on the distance and transfer data.

[0043] Specifically, in order to accurately estimate the actual position of the positioning tag particle, it is necessary to calculate the distance between the positioning tag particle and each Bluetooth beacon based on the Bluetooth signal strength, and calculate the weight of each positioning tag particle in combination with the transfer data. First, the received Bluetooth signal strength RSSI value is converted into distance through the logarithmic distance path loss model.

[0044] The positioning tag receives RSSI from several Bluetooth beacons, and each RSSI can be converted into the distance from the positioning tag to the Bluetooth signal, which is called the measured distance. All positioning tag particles in the target map can calculate the distance between themselves and each beacon. By comparing the similarity between the calculated distance and the measured distance, different weights can be assigned to each particle. The conversion between RSSI and distance usually uses the logarithmic distance path loss model, and the formula is as follows: ; In the formula, RSSI is the received signal strength, in dBm; 𝑛 is the loss factor related to the wireless transmission environment; d is the distance between the receiver and the transmitter, that is, the distance between the positioning tag particle and the Bluetooth beacon, in meters; 𝐴 is the signal strength received by the receiver at a distance of d0 from the transmitter, in dBm, is the reference distance, generally set to 1 meter; 𝜎 is subject to mean 0 and variance 𝜎 2The formula of the path loss model can be simplified as follows: , the parameters can be obtained by detecting different RSSIs, and finally the loss factor n=3 and the parameter a=-55 are obtained, from which the measured distance can be obtained: .

[0045] In order to enhance the weight of particles close to the real position during the particle filtering process and effectively reduce the influence of noise particles, the threshold increase and decrease item is added when the weight is updated, giving particles close to the device position a higher weight and particles far from the device position a lower weight. The distance difference is selected as the judgment condition for setting the threshold, that is: , diff is the distance error. When the distance error is less than 1.5 meters, it means that the particle position is close to the device position, and these particles will be given a higher weight. Taking the reception of a single RSSI as an example, the weight of the particle can be determined by the following formula: ,in, is the distance from the positioning tag particle to the Bluetooth beacon converted by RSSI, is the distance between the positioning tag particle and the Bluetooth beacon, 𝜎 is the noise variance, and in this experiment, 𝜎=2. When the distance difference is greater than 1.5 meters, the particle is given a smaller weight, reducing its weight to one tenth of the original, that is, .

[0046] Since the positioning tag particle will receive the RSSI of multiple different Bluetooth beacons, they all need to be taken into account when calculating the weight. The weight of each received beacon is calculated by the above weight formula, and the weight of the particle under the joint action of multiple beacons can be obtained by multiplying the obtained weights: .

[0047] Step 40: Residual resampling is performed based on the weight of each positioning label particle to obtain an updated particle set.

[0048] Specifically, in order to prevent particle degradation and maintain the diversity of the particle swarm, the current particle set needs to be resampled for residuals. In the actual positioning process, the weights of some particles may become very small. If resampling is not performed, these low-weight particles will gradually disappear in subsequent positioning, resulting in too concentrated particle distribution and affecting positioning accuracy. First, the number of integers that should appear in each positioning label particle in the new particle set is calculated, that is, the weight of each positioning label particle is multiplied by the total number of positioning label particles and rounded; then the residual weight of each positioning label particle is calculated, that is, the weight minus the proportion of the integer copy number; then the number of particles that need to be supplemented is calculated, that is, the total number of positioning label particles minus the sum of all frame copy numbers; the particles are repeated using the integer copy number to form a new particle set. The form of the number of supplemented particles is determined by the residual. If the residuals of all particles are zero, uniform random sampling is performed; if the residuals are not zero, random sampling is performed according to the residual weight. The supplemented particles are added to the new particle set formed by the integer copy particles to form a complete resampled particle set, that is, the updated particle set is obtained.

[0049] Based on the above embodiment, as another optional embodiment, the step of performing residual resampling based on the weight of each positioning tag particle to obtain an updated particle set may also include the following steps: Step 401: Calculate the integer copy number of each positioning tag particle in the new particle set, where the integer copy number is the integer part of the product of the weight and the total number of positioning tag particles.

[0050] Specifically, in order to avoid particle degradation and maintain the diversity of the particle swarm, it is necessary to perform residual resampling on the particle set after the weight update. First, calculate the integer number of each positioning label particle that should appear in the new particle set. For example, in a system containing 100 positioning label particles, assuming that the weight of a positioning label particle is 0.0234, the number of its appearances is 2.34, and the integer copy number after rounding is 2, indicating that the particle should be copied at least twice in the new particle set. Similarly, for particles with a weight of 0.0456, the integer copy number is 4. In this way, the system prioritizes the number of copies of particles with larger weights.

[0051] Step 402: Calculate the residual weight of each positioning tag particle, where the residual weight is the weight minus the proportion of the integer copy number.

[0052] Specifically, the residual weight of each positioning tag particle is calculated. For example, a particle with a weight of 0.0234 has an integer copy number of 2 accounting for 0.02 of the total number of particles, so the residual weight of the particle is 0.0034 (i.e. 0.0234-0.02). For a particle with a weight of 0.0456, its integer copy number of 4 accounts for 0.04, and the residual weight is 0.0056. These residual weights will be used to determine how to supplement additional particles.

[0053] Step 403: Residual resampling is performed based on each residual weight to obtain an updated particle set.

[0054] Specifically, residual resampling is performed based on the calculated residual weights. Assuming that 85 particles are obtained after integer copying, 15 positioning label particles need to be added to maintain the total number of 100. At this time, the system will check the residual weights of all positioning label particles. If the residual weights of all positioning label particles are zero, it means that the weight of each positioning label particle corresponds to an integer number of copies. In this case, the system will perform uniform random sampling from the existing particles to supplement the remaining 15 particles. This ensures that the system can maintain the diversity of the distribution of positioning label particles even in special circumstances.

[0055] But in more common cases, the positioning label particles will have non-zero residual weights. At this time, the system performs random sampling based on the normalized residual weights to supplement the positioning label particles. For example, if the normalized residual weight of a positioning label particle is large, such as 0.15, then when supplementing 15 particles, the particle may be selected 2-3 additional times. In contrast, particles with smaller residual weights, such as 0.02, may not be selected. This residual weight-based supplementation method not only takes into account the importance of positioning label particles, but also introduces randomness, which helps maintain the diversity of the particle swarm.

[0056] Finally, the 85 particles obtained by integer copying and the 15 positioning label particles supplemented by residual sampling are merged to form a complete updated particle set. The advantages of this residual resampling method are: integer copying ensures the basic number of copies of high-weight particles, and residual sampling introduces moderate randomness. The combination of the two maintains the representativeness of the particle group and avoids the uniformity of the sample.

[0057] Many practices have shown that this residual resampling method can effectively improve positioning accuracy. For example, in a certain indoor positioning test, the particle distribution after using this method is more reasonable, the number of effective particles is increased from 45 to 95, and the positioning error is reduced by about 20%. This shows that this method solves the particle degradation problem well and improves the overall performance of the particle filter algorithm. Through this sophisticated residual resampling process, the system can better balance the diversity and convergence of the particle swarm, providing a more reliable reference for subsequent position estimation.

[0058] Step 50: Perform weighted calculation on each positioning label particle in the update particle set to obtain the target positioning label coordinates.

[0059] Specifically, after completing the particle set update, in order to accurately determine the actual position of the target positioning tag, a series of calculation steps are required to integrate the information of all particles. This process first requires obtaining the specific coordinate values ​​of each positioning tag particle in the updated particle set, and then through reasonable weight allocation and weighted calculation, the accurate position of the target is finally obtained.

[0060] In practical applications, the coordinate information of each positioning tag particle is first extracted from the updated particle set. For example, in a system containing 100 positioning tag particles, each positioning tag particle contains its X coordinate and Y coordinate on a two-dimensional plane. For example, in a system containing 100 positioning tag particles, each positioning tag particle carries its coordinate information in space. Suppose that in an indoor scene, 35 particles are distributed near the coordinates (4.2 meters, 2.8 meters), 30 particles are distributed near the coordinates (4.3 meters, 2.9 meters), and the remaining particles are scattered in the surrounding area. These location information together constitute multiple possible estimates of the user's location. Then calculate the weight value of each particle. Due to the resampling process, the weights of all positioning tag particles in the particle set have been reset. For example, in a system of 100 particles, the weight of each particle is 0.01.

[0061] It should be noted that before resampling, the weight of each location label particle represents the degree of conformity between the location label particle and the actual observation value. Particles with larger weights indicate that their position estimates are closer to the true position, while particles with smaller weights indicate that their position estimates may have larger deviations. This weight calculation process is crucial for screening and evaluating particles. During the residual resampling process, particles with larger weights (such as particles with a weight of 0.15) may be replicated multiple times (for example, 15 times), while particles with smaller weights (such as particles with a weight of 0.01) may be replicated only once or eliminated. In this way, the new particle set contains more copies of particles in "good" positions. Through the number of replications, the weight information has been converted into a particle number distribution. High-weight particles occupy a larger share in the new particle set through multiple replications. The weights are reset to the starting point to prepare for the next round of weight updates to avoid the weight accumulation effect. It also ensures that all particles have the same starting position in the next round of estimation to prevent historical weights from having an excessive impact on new observations.

[0062] This uniform weight distribution ensures that each particle has an appropriate influence on the final position estimate and avoids individual particles from having too large an influence.

[0063] Furthermore, the system performs a weighted average operation on the coordinates of all particles. Specifically, the X coordinate of each particle is multiplied by its weight, and all the results are added to obtain the weighted average in the X direction; the weighted average in the Y direction is calculated in the same way. In the above example, through this weighted average calculation, the final target positioning tag coordinate estimate may be (4.25 meters, 2.85 meters). This result well reflects the overall characteristics of the particle distribution, taking into account the influence of the main particle concentration area without completely ignoring the information carried by the dispersed particles.

[0064] See also Figure 2 , which is a particle filter simulation trajectory diagram provided in an embodiment of the present application.

[0065] like Figure 2 As shown in the figure, it contains three key trajectories: the blue dots mark the actual measurement trajectory based on the Bluetooth beacon, the green dotted line represents the actual movement trajectory of the target, and the red solid line is the positioning trajectory estimated by the particle filter algorithm of this application solution. The pink circle in the figure marks the starting position of the movement. As can be seen from the coordinate axis, the test scene covers an area of ​​about 20 meters × 15 meters. Figure 2 It can be seen that the estimated trajectory (red solid line) obtained after processing by the particle filter algorithm of the present application scheme is obviously smoother than the original measurement and closer to the true trajectory (green dotted line), which also verifies the effectiveness of the particle weight update and residual resampling steps in this scheme.

[0066] See also Figure 3 , is a module schematic diagram of a multi-source fusion indoor positioning system based on particle filtering and PDR provided in an embodiment of the present application, the multi-source fusion indoor positioning system based on particle filtering and PDR includes: A target map construction module is used to construct a target map, in which a plurality of Bluetooth beacons are set; A transfer data calculation module is used to obtain the transfer data of each positioning tag particle in the target map based on the PDR algorithm; A weight calculation module, used to convert the signal strength of each Bluetooth beacon received by each positioning tag into a distance, and calculate the weight of each positioning tag particle based on the distance and transfer data; The weight updating module is used to perform residual resampling based on the weight of each positioning label particle to obtain an updated particle set; The coordinate positioning module is used to perform weighted calculation on each positioning label particle in the update particle set to obtain the target positioning label coordinates.

[0067] Optionally, the transfer data calculation module is further used to obtain acceleration data and magnetic data of each positioning tag particle in the target map, and determine the step length based on the acceleration data; Calculating an azimuth angle based on the acceleration data and the magnetic data; Calculate a new step length and a new azimuth according to the step length and the azimuth; Based on the new step value and the new azimuth angle, the transfer data of each positioning tag particle is calculated.

[0068] Optionally, the weight calculation module is further used to obtain the signal strength of each Bluetooth beacon received by each positioning tag; Based on a preset path loss model, the signal strength is converted into the distance between each positioning tag particle and each Bluetooth beacon, wherein the path loss model includes a logarithmic relationship between signal strength and distance.

[0069] Optionally, the weight calculation module is further used to obtain the theoretical distance between each of the positioning tag particles and each of the Bluetooth beacons; Calculating the error between the theoretical distance and the distance; A weight value of each positioning tag particle is calculated based on the error, wherein the weight value is inversely proportional to the error.

[0070] Optionally, the weight updating module is further used to calculate the integer copy number of each positioning tag particle in the new particle set, where the integer copy number is the integer part of the product of the weight and the total number of positioning tag particles; Calculating the residual weight of each positioning tag particle, wherein the residual weight is the weight minus the proportion of the integer copy number; Residual resampling is performed based on the residual weights to obtain an updated particle set.

[0071] Optionally, the weight updating module is further used to determine the number of particles that need to be supplemented, where the number of particles that need to be supplemented is the difference between the total number of positioning tag particles and the sum of the integer copy number; Based on the integer copy number, each of the positioning tag particles is replicated to obtain an initial particle set; Randomly sampling particles that need to be supplemented according to the residual weight to obtain supplementary particles; The supplementary particles are added to the initial particle set to obtain the updated particle set.

[0072] Optionally, the coordinate positioning module is further used to obtain the coordinate value of each positioning tag particle in the updated particle set; Calculating the weighted mean of each of the positioning tag particles; A weighted average calculation is performed based on the coordinate value and the weight mean to obtain the target positioning tag coordinates.

[0073] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0074] An embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executing a multi-source fusion indoor positioning method based on particle filtering and PDR of the above embodiment. The specific execution process can be found in the specific description of the above embodiment, which will not be repeated here.

[0075] Please refer to Figure 4 The application also discloses an electronic device. Figure 4 The electronic device 400 may include: at least one processor 401 , at least one network interface 404 , a user interface 403 , a memory 405 , and at least one communication bus 402 .

[0076] The communication bus 402 is used to realize the connection and communication between these components.

[0077] The user interface 403 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 403 may also include a standard wired interface and a wireless interface.

[0078] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0079] Among them, the processor 401 may include one or more processing cores. The processor 401 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 405. Optionally, the processor 401 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 401 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 401, and it can be implemented separately through a chip.

[0080] Among them, the memory 405 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 405 includes a non-transitory computer-readable storage medium. The memory 405 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 405 may optionally also be at least one storage device located away from the aforementioned processor 401. Refer to Figure 4 The memory 405 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a multi-source fusion indoor positioning method based on particle filtering and PDR.

[0081] exist Figure 4In the electronic device 400 shown, the user interface 403 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 401 can be used to call the application program stored in the memory 405 for a multi-source fusion indoor positioning method based on particle filtering and PDR. When executed by one or more processors 401, the electronic device 400 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0082] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0083] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0084] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0086] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0087] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0088] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A multi-source fusion indoor positioning method based on particle filtering and PDR, characterized in that: The method comprises: Constructing a target map, wherein a plurality of Bluetooth beacons are arranged in the target map; Acquire the transfer data of each positioning tag particle in the target map based on the PDR algorithm; Convert the signal strength of each Bluetooth beacon received by each positioning tag particle into a distance, and calculate the weight of each positioning tag particle based on the distance and the transfer data; Residual resampling is performed based on the weight of each of the positioning label particles to obtain an updated particle set; The positioning label particles in the updated particle set are weighted to obtain the target positioning label coordinates.

2. The multi-source fusion indoor positioning method based on particle filtering and PDR according to claim 1 is characterized in that: The step of obtaining the transfer data of each positioning tag particle in the target map based on the PDR algorithm includes: Acquire acceleration data and magnetic data of each of the positioning tag particles in the target map, and determine a step length based on the acceleration data; Calculating an azimuth angle based on the acceleration data and the magnetic data; Calculate a new step length and a new azimuth according to the step length and the azimuth; Based on the new step size and the new azimuth angle, the transfer data of each positioning tag particle is calculated.

3. The multi-source fusion indoor positioning method based on particle filtering and PDR according to claim 1 is characterized in that: The converting the signal strength of each Bluetooth beacon received by each positioning tag particle into a distance comprises: Obtaining the signal strength of each Bluetooth beacon received by each positioning tag particle; Based on a preset path loss model, the signal strength is converted into the distance between each positioning tag particle and each Bluetooth beacon, wherein the path loss model includes a logarithmic relationship between signal strength and distance.

4. The multi-source fusion indoor positioning method based on particle filtering and PDR according to claim 1 is characterized in that: The calculating the weight of each of the positioning tag particles based on the distance and the transfer data comprises: Obtaining the theoretical distance between each of the positioning tag particles and each of the Bluetooth beacons; Calculating the error between the theoretical distance and the distance; A weight value of each positioning tag particle is calculated based on the error, wherein the weight value is inversely proportional to the error.

5. The multi-source fusion indoor positioning method based on particle filtering and PDR according to claim 1 is characterized in that: The residual resampling is performed based on the weight of each of the positioning label particles to obtain an updated particle set, including: Calculating the integer copy number of each positioning tag particle in the new particle set, where the integer copy number is the integer part of the product of the weight and the total number of positioning tag particles; Calculating the residual weight of each positioning tag particle, wherein the residual weight is the weight minus the proportion of the integer copy number; Residual resampling is performed based on the residual weights to obtain an updated particle set.

6. The multi-source fusion indoor positioning method based on particle filtering and PDR according to claim 5 is characterized in that: The residual resampling is performed based on each residual weight to obtain an updated particle set, including: Determining the number of particles that need to be supplemented, where the number of particles that need to be supplemented is the difference between the total number of positioning tag particles and the sum of the integer copy number; Based on the integer copy number, each of the positioning tag particles is replicated to obtain an initial particle set; Randomly sampling particles that need to be supplemented according to the residual weight to obtain supplementary particles; The supplementary particles are added to the initial particle set to obtain the updated particle set.

7. The multi-source fusion indoor positioning method based on particle filtering and PDR according to claim 1 is characterized in that: The step of performing weighted calculation on each positioning label particle in the updated particle set to obtain the target positioning label coordinates includes: Obtaining the coordinate value of each positioning tag particle in the updated particle set; Calculating the weighted mean of each of the positioning tag particles; A weighted average calculation is performed based on the coordinate value and the weight mean to obtain the target positioning tag coordinates.

8. A multi-source fusion indoor positioning system based on particle filtering and PDR, characterized in that: The system comprises: A target map construction module, used to construct a target map, wherein the target map is provided with a plurality of Bluetooth beacons; A transfer data calculation module, used for obtaining the transfer data of each positioning tag particle in the target map based on the PDR algorithm; A weight calculation module, used to convert the signal strength of each Bluetooth beacon received by each positioning tag particle into a distance, and calculate the weight of each positioning tag particle based on the distance and the transfer data; A weight updating module, used for performing residual resampling based on the weight of each of the positioning label particles to obtain an updated particle set; The coordinate positioning module is used to perform weighted calculation on each positioning label particle in the updated particle set to obtain the target positioning label coordinates.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 7.

10. 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, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

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