Positioning method, system, computer storage medium, and electronic device

Abstract

The application discloses a positioning method and system, a computer storage medium and an electronic device, and relates to the field of financial technologies. The method comprises the following steps: acquiring the position coordinates of a plurality of reference base stations, the position coordinates of a target object to be detected in a previous time period, the strength of the radio frequency signals of the measuring devices identified by each reference base station, and the pose data measured by the measuring devices in each time period; calculating the local estimation value of the target object according to the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the strength of the radio frequency signals in the current time period and the pose data in the current time period in each time period, and obtaining a plurality of groups of local estimation values; determining the global position information of the target object in each time period according to each group of local estimation values, and thus determining the moving track of the target object. The application solves the problems of poor flexibility and low positioning accuracy of devices in the prior art.

Description

Technical Field

[0001] This application relates to the field of financial technology, and more specifically, to a positioning method, system, computer storage medium, and electronic device. Background Technology

[0002] To ensure the security of bank vaults, it is necessary to locate the equipment, personnel, and other objects within the vault. Accurate location tracking is a fundamental requirement for achieving information-based management and intelligent inventory operations in vaults. Current technologies typically employ fingerprint signal positioning based on wireless routers, which is simple to install and relatively inexpensive. However, this method requires the prior collection of large amounts of data to establish a fingerprint database, resulting in poor flexibility, long positioning times, poor robustness, and low positioning accuracy.

[0003] To address the problem of low positioning accuracy, engineers developed a radar positioning method with high accuracy and good timeliness. However, because radar signals are highly susceptible to interference from media such as metal, water, and the human body, and because the installation and calibration process for radar base stations is cumbersome and costly, this method has significant limitations in application.

[0004] There is currently no effective solution to the problems of poor flexibility and low positioning accuracy of the devices in related technologies. Summary of the Invention

[0005] This application provides a positioning method, system, computer storage medium, and electronic device to solve the problems of poor flexibility and low positioning accuracy of related devices.

[0006] According to one aspect of this application, a positioning method is provided. The method includes: acquiring, in each time period, the position coordinates of multiple reference base stations, the position coordinates of a target object to be detected in the previous time period, the intensity of the radio frequency signal of a measuring device identified by each reference base station, and the pose data measured by the measuring device, wherein the target object carries the measuring device, and the pose data refers to the pose data of the target object in the current time period; calculating, in each time period, local estimates of the target object based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data in the current time period, obtaining multiple sets of local estimates, wherein each set of local estimates includes local estimates associated with each reference base station in one time period; determining the global position information of the target object in each time period based on each set of local estimates, and determining the movement trajectory of the target object based on the global position information in each time period.

[0007] Optionally, the measuring device includes an RFID tag and an inertial measurement unit (IMU). The IMU is used to measure pose data. An RFID reader is installed on the reference base station. The RFID reader is used to identify the strength of the radio frequency signal when the RFID tag is close. Local estimates of the target object are calculated based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the strength of the radio frequency signal in the current time period, and the pose data in the current time period. Multiple sets of local estimates are obtained, including: for each time period, the distance between the reference base station and the target object is calculated based on the strength of the radio frequency signal identified by the RFID reader of each base station; for each distance, local estimates are calculated based on the distance, the position coordinates of the previous time period, and the pose data, resulting in multiple local estimates, which constitute a set of local estimates.

[0008] Optionally, calculating the local estimate based on the distance, the position coordinates of the previous time period, and the pose data includes: constructing a state equation based on the position coordinates and pose data of the previous time period; constructing a measurement equation based on the distance and pose data; forming a system observation model from the state equation and the measurement equation; updating the parameters in the system observation model using a volumetric filtering algorithm to obtain the state estimate, the information matrix, and the information vector, and determining the information vector as the local estimate.

[0009] Optionally, before calculating local estimates of the target object based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data in the current time period in each time period, and obtaining multiple sets of local estimates, the method includes: obtaining an intensity threshold; comparing the intensity of the radio frequency signal obtained by multiple reference base stations with the intensity threshold in each time period to obtain multiple comparison results; controlling the reference base station to remain in a sleep state if any comparison result indicates that the intensity of the radio frequency signal obtained by the reference base station is less than the intensity threshold; waking up the reference base station and putting it into a triggered state if any comparison result indicates that the intensity of the radio frequency signal obtained by the reference base station is greater than or equal to the intensity threshold; and performing the steps of calculating local estimates of the target object based on the intensity of the radio frequency signal obtained by the reference base station in the triggered state, respectively, based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data in the current time period.

[0010] Optionally, determining the global location information of the target object in each time period based on each set of local estimates includes: for a set of local estimates in the same time period, calculating the average of multiple local estimates to obtain a mean parameter; calculating the absolute value of the difference between each local estimate and the mean parameter to obtain multiple initial fusion weights; and normalizing the multiple initial fusion weights to obtain the fusion weight corresponding to each local estimate.

[0011] Optionally, after normalizing multiple initial fusion weights to obtain the fusion weight corresponding to each local estimate, the method includes: calculating the fusion weight corresponding to each local estimate in each group of local estimates; and calculating the global position information of the current time period by weighted summing of each fusion weight with the corresponding local estimate.

[0012] According to another aspect of this application, a positioning system is provided. The system includes: a measuring device disposed on a target object to be detected and moving with the target object, for measuring the pose data of the target object; a plurality of reference base stations, each reference base station for identifying the strength of the radio frequency signal of the measuring device; and a controller communicatively connected to the plurality of reference base stations for executing a positioning method.

[0013] Optionally, the measuring device includes an RFID tag and an inertial measurement unit (IMU), the IMU being used to measure the pose data of the target object; each reference base station is equipped with an RFID reader, the RFID reader being used to identify the strength of the radio frequency signal when the RFID tag is close.

[0014] According to another aspect of this application, a positioning device is provided. The device includes: a first acquisition unit, configured to acquire, in each time period, the position coordinates of multiple reference base stations, the position coordinates of a target object to be detected in the previous time period, the intensity of the radio frequency signal of a measuring device identified by each reference base station, and the pose data measured by the measuring device, wherein the target object carries the measuring device, and the pose data refers to the pose data of the target object in the current time period; a first calculation unit, configured to calculate, in each time period, a local estimate of the target object based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data in the current time period, obtaining multiple sets of local estimates, wherein each set of local estimates includes local estimates associated with each reference base station in one time period; and a determination unit, configured to determine the global position information of the target object in each time period based on each set of local estimates, and determine the movement trajectory of the target object based on the global position information in each time period.

[0015] According to another aspect of the present invention, a computer storage medium is also provided for storing a program, wherein the program, when running, controls the device where the non-volatile storage medium is located to execute a positioning method.

[0016] According to another aspect of the present invention, an electronic device is also provided, comprising a processor and a memory; the memory stores computer-readable instructions, and the processor is configured to execute the computer-readable instructions, wherein the computer-readable instructions execute a positioning method when executed.

[0017] This application employs the following steps: In each time period, the position coordinates of multiple reference base stations, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal of the measuring device identified by each reference base station, and the pose data measured by the measuring device are acquired. The target object carries the measuring device, and the pose data refers to the pose data of the target object in the current time period. In each time period, local estimates of the target object are calculated based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data in the current time period, resulting in multiple sets of local estimates. Each set of local estimates includes the local estimates associated with each reference base station in one time period. The global position information of the target object in each time period is determined based on each set of local estimates. The movement trajectory of the target object is determined from the global position information in each time period. This solves the problems of poor device flexibility and low positioning accuracy in related technologies. By weighted summing of the local estimates of the target object identified by multiple reference base stations with corresponding fusion weights, the flexibility and positioning accuracy of the device are improved. Attached Figure Description

[0018] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 This is a schematic diagram of the positioning method provided according to an embodiment of this application;

[0020] Figure 2 This is a flowchart of the system observation model provided according to the embodiments of this application;

[0021] Figure 3 This is a flowchart of a positioning system provided according to an embodiment of this application;

[0022] Figure 4 This is a schematic diagram of an optional positioning method provided according to an embodiment of this application;

[0023] Figure 5 This is a schematic diagram of multi-level data fusion provided in the embodiments of this application;

[0024] Figure 6 This is a schematic diagram of a positioning device provided according to an embodiment of this application;

[0025] Figure 7 This is a schematic diagram of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0026] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0027] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] It should be noted that all information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are information and data authorized by the user or fully authorized by all parties.

[0030] According to an embodiment of this application, a positioning method is provided.

[0031] Figure 1 This is a schematic diagram of the positioning method provided according to the embodiments of this application, such as... Figure 1 As shown, the method includes the following steps:

[0032] Step S102: In each time period, the position coordinates of multiple reference base stations, the position coordinates of the target object to be detected in the previous time period, the intensity of the radio frequency signal of the measuring device identified by each reference base station, and the pose data measured by the measuring device are obtained respectively. The target object carries the measuring device, and the pose data refers to the pose data of the target object in the current time period.

[0033] Before accurately locating the target object, multiple pieces of information need to be acquired to calculate the system observation model, and then the local estimated value of the target object is determined through the system observation model. Specifically, it is necessary to acquire the position coordinates of the reference base station, the position coordinates of the target object in the previous time period, the pose data, and the signal strength of the carrying measurement equipment. If the target object has not moved, the position coordinates in the previous time period are represented as the initial position coordinates of the target object.

[0034] It should be noted that pose data may include the target object's step size l and heading angle data in the current time period. The pose data is obtained by the measuring equipment carried by the target object; the distance between the target object and the base station is determined by identifying the strength of the radio frequency signal emitted by the measuring equipment carried by the target object at different positions on the ceiling of the vault, thereby determining the specific location of the target object.

[0035] Step S104: In each time period, calculate the local estimated value of the target object based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the strength of the radio frequency signal in the current time period, and the pose data in the current time period, to obtain multiple sets of local estimated values. Each set of local estimated values ​​includes the local estimated values ​​associated with each reference base station in a time period.

[0036] Specifically, by using a volumetric filtering algorithm to input data acquired in multiple current time periods into the system observation model, multiple local estimates of the target object in the current time period can be calculated. Furthermore, the system observation model is iterated according to the time period. One local estimate is calculated using the strength of the radio frequency signal identified by a reference base station, while multiple local estimates are estimates of the target object's position coordinates during its movement in multiple different time periods.

[0037] Step S106: Determine the global position information of the target object in each time period based on the local estimates of each group, and determine the movement trajectory of the target object based on the global position information in each time period.

[0038] Specifically, by using multiple local estimates from different time periods, the weight parameters corresponding to each local estimate can be calculated. Then, by weighted calculation, a more accurate location information of the target object in each time period can be obtained, that is, the global location information.

[0039] Furthermore, by sorting the global location information of different time periods in chronological order, the specific movement trajectory of the target object can be obtained.

[0040] The positioning method provided in this application acquires the position coordinates of multiple reference base stations, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal of the measuring device identified by each reference base station, and the pose data measured by the measuring device in each time period. The target object carries the measuring device, and the pose data refers to the pose data of the target object in the current time period. In each time period, local estimates of the target object are calculated based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data in the current time period, resulting in multiple sets of local estimates. Each set of local estimates includes the local estimates associated with each reference base station in one time period. The global position information of the target object in each time period is determined based on each set of local estimates, and the movement trajectory of the target object is determined from the global position information in each time period. This solves the problems of poor device flexibility and low positioning accuracy in related technologies. By weighted summing of the local estimates of the target object identified by multiple reference base stations with corresponding fusion weights, the global position information is obtained, thereby improving the flexibility of the device and the positioning accuracy.

[0041] The strength of radio frequency (RF) signals can be obtained through an RF identification device, which may include an RF tag and an RF reader. Optionally, in the positioning method provided in this application embodiment, the measuring device includes an RF tag and an inertial measurement unit (IMU). The IMU is used to measure pose data. An RF reader is installed on the reference base station. The RF reader is used to identify the strength of the RF signal when the RF tag is close. Local estimates of the target object are calculated based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the strength of the RF signal in the current time period, and the pose data in the current time period. Multiple sets of local estimates are obtained, including: for each time period, calculating the distance between the reference base station and the target object based on the strength of the RF signal identified by the RF reader of each base station; for each distance, calculating local estimates based on the distance, the position coordinates of the previous time period, and the pose data, obtaining multiple local estimates, which constitute a set of local estimates.

[0042] Because different reference base stations are at different distances from the target object, the RFID reader installed on the reference base station will identify RFID signals of different intensities P. The farther the reference base station is from the target object, the weaker the identified RFID signal. By identifying RFID signals of different intensities, the distance between different reference base stations and the target object can be obtained. Specifically, the distance between the reference base station and the target object is determined using the ranging and positioning formula: P(d r ) = P c -10n lg(d r )+ζ, where P(d r ) represents the strength of the radio frequency signal; P c ζ represents the radio frequency signal strength when the reference base station is 1m away from the target object; n is the path loss parameter indicating the degree of signal energy loss; ζ is represented as the shading factor, which is a normal variable with a mean of 0.

[0043] Furthermore, by obtaining multiple distances, and utilizing the target object's position coordinates in the previous time period of the current time period, as well as the measured displacement step size l and heading angle data, Multiple local estimates are obtained in the current time period, and all local estimates are grouped together as a set of local estimates in the current time period. By arranging multiple sets of local estimates according to the time period, the movement trajectory of the target object can be determined.

[0044] It should be noted that an inertial measurement unit (IMU) and a radio frequency identification (RFID) device are used to form a wireless sensor network. The wireless sensor network acquires relevant data to achieve low-cost and high-precision positioning of the target. The RFID device includes an RFID reader and an RFID tag.

[0045] The local estimate can be obtained using a volumetric filtering algorithm. Optionally, in the positioning method provided in this application embodiment, calculating the local estimate based on distance, position coordinates of the previous time period, and pose data includes: constructing a state equation based on the position coordinates and pose data of the previous time period; constructing a measurement equation based on the distance and pose data; forming a system observation model from the state equation and the measurement equation; updating the parameters in the system observation model using a volumetric filtering algorithm to obtain the state estimate, information matrix, and information vector, and determining the information vector as the local estimate.

[0046] Specifically, Figure 2 This is a flowchart of the system observation model provided according to the embodiments of this application, such as... Figure 2As shown, the system observation model is the target object localization model. The system observation model is composed of a state equation and a measurement equation. The state equation is constructed from the target object's pose data and local estimates in the current time period. Specifically, the state equation can be constructed by obtaining the pose data and the target object's position coordinates in the previous time period. The state equation is expressed as: Where (e, n) represents the local estimated value of the target object at time period k and time period k-1 respectively, and l is the displacement step size of the target object in the pose data of the current time period. This represents the heading angle data of the target object in the pose data of the current time period, where k represents a specific time period. The measurement equation is constructed from the intensity of the radio frequency signal and the pose data obtained from the base station in the current time period. Specifically, the measurement equation is: in, (e s ,n s ) Let w represent the coordinates of the location of the reference base station S, and let E represent the set of reference base stations that satisfy the state equation and the measurement equation. k-1 and It is represented as a Gaussian white noise vector.

[0047] Furthermore, based on the volumetric filtering algorithm, namely the CIF (Cubature Information Filtering) algorithm, and the credibility-based consensus-based Cubature Information Filtering algorithm, namely the CCIF (Credibility-based Consensus-based Cubature Information Filtering) algorithm, a two-level fusion calculation is performed. This involves calculating the local estimate and the fusion weights to obtain the global position coordinates of the target object in the current time period. The position coordinates of the local estimate are (e, n). These coordinates are then updated in the localization model to obtain the information vector. Information matrix Y s and state estimates in, The information vector is determined as the local estimate, and the state estimate is determined as the state information of the target object at the coordinate point where the local estimate is located.

[0048] To improve the accuracy of global location information, it is necessary to filter the intensity of the radio frequency signals acquired by the reference base stations. Optionally, in the positioning method provided in this application embodiment, before calculating the local estimated value of the target object based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data of the current time period in each time period, and obtaining multiple sets of local estimated values, the method includes: acquiring an intensity threshold; comparing the intensity of the radio frequency signals acquired by multiple reference base stations with the intensity threshold in each time period to obtain multiple comparison results; if any comparison result indicates that the intensity of the radio frequency signal acquired by the reference base station is less than the intensity threshold, controlling the reference base station to remain in a sleep state; if any comparison result indicates that the intensity of the radio frequency signal acquired by the reference base station is greater than or equal to the intensity threshold, waking up the reference base station and putting the reference base station into a triggered state; and based on the intensity of the radio frequency signal acquired by the reference base station in the triggered state, performing the steps of calculating the local estimated value of the target object based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data of the current time period.

[0049] It should be noted that the reliability of each local estimate is positively correlated with the strength of the radio frequency (RF) signal. The weaker the RF signal, i.e., the farther the distance between the target object and the reference base station, the lower the reliability of the calculated local estimate. Lower reliability local estimates will negatively impact the accuracy of the final global location information. Therefore, it is necessary to filter the acquired RF signal strength, which means filtering the reference base stations.

[0050] Specifically, the reference base station can be in two states: dormant and active, also known as triggered. Before operation, the ceiling-mounted reference base station is in a dormant state, except for neighboring reference base stations. Neighboring base stations are those that are always in the triggered state. When the controller sends an identification command to the reference base station, the reference base station begins to identify the strength of the radio frequency signal emitted by the measuring device. Then, it compares the strength of all radio frequency signals with a strength threshold, obtaining multiple comparison results. The strength threshold is the minimum power required to trigger the reference base station, and it needs to be pre-assigned before the reference base station operates, ensuring that at least three reference base stations are in the triggered state in each time period.

[0051] When the strength of the radio frequency signal identified by the reference base station is less than the strength threshold, the reference base station remains in a dormant state and deletes the strength of the radio frequency signal identified by the reference base station; conversely, if the strength of the radio frequency signal is greater than or equal to the strength threshold, the reference base station is triggered, and the strength of the radio frequency signal identified by the triggered reference base station is input into the filtering algorithm for calculation, thereby improving the accuracy of global location information.

[0052] Calculating fusion weights can assign lower weights to local estimates with low confidence to reduce their impact on determining global location information. Optionally, in the positioning method provided in this application embodiment, determining the global location information of the target object in each time period based on each group of local estimates includes: for a group of local estimates in the same time period, calculating the average of multiple local estimates to obtain a mean parameter; calculating the absolute value of the difference between each local estimate and the mean parameter to obtain multiple initial fusion weights; and normalizing the multiple initial fusion weights to obtain the fusion weight corresponding to each local estimate.

[0053] It should be noted that the global location information is obtained by weighted summation of multiple local estimates within the same time period. The weight of each local estimate varies depending on its reliability. Assigning lower weights to local estimates with lower reliability can reduce adverse effects during the fusion calculation.

[0054] Specifically, the average value of multiple local estimates for the same time period is calculated, which is the mean parameter of each local estimate, also known as the set center. The set center refers to the coordinates of the center position of all local estimates. For example, if there are three local estimates that form an equilateral triangle in a two-dimensional plane, the set center refers to the incenter of the equilateral triangle.

[0055] The obtained mean parameter is compared with each local estimate using norm calculation, which is the absolute value of the difference obtained after subtracting the two coordinates. in, J s The estimated mean, dev s J s The total number of nodes in J s It is represented as a set of multiple local estimates, and K represents a specific time period, thereby obtaining multiple initial fusion weights.

[0056] Furthermore, the initial fusion weights are normalized, that is... This allows us to obtain the fusion weights corresponding to each local estimate.

[0057] Optionally, in the positioning method provided in the embodiments of this application, after normalizing multiple initial fusion weights to obtain the fusion weight corresponding to each local estimate, the method includes: calculating the fusion weight corresponding to each local estimate in each group of local estimates; and calculating the global position information of the current time period by weighted summing of each fusion weight with the corresponding local estimate.

[0058] Specifically, after obtaining the corresponding fusion weights, the weights are weighted and summed with the local estimates, that is... This yields the global location information y, where w refers to the fusion weight of the various local estimates. This refers to the estimated values ​​of each local part.

[0059] After obtaining global location information for multiple time periods, the global location information for multiple time periods is distributed in chronological order to obtain the movement trajectory of the target object.

[0060] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0061] According to an embodiment of this application, a positioning system is provided.

[0062] Figure 3 This is a flowchart of a positioning system provided according to an embodiment of this application, such as... Figure 3 As shown, the system includes:

[0063] The measuring device 301 is set on the target object to be detected and moves with the target object to measure the pose data of the target object.

[0064] Specifically, the personnel are located in the vault. The target can be the personnel, who carry a measuring device 301, which may include an RFID tag and an inertial measurement unit. When the reader approaches the RFID tag, it can identify the radio frequency signal. The inertial measurement unit is used to detect the personnel's displacement step length and heading angle data, as well as other pose data.

[0065] Multiple reference base stations 302, each reference base station 302 is used to identify the strength of the radio frequency signal of the measuring device 301.

[0066] Specifically, the reference base station 302 can be installed in the ceiling or wall of the vault. The location of the reference base station 302 is fixed. The reference base station 302 is equipped with a reader for identifying signals. When the measuring device 301 sends a radio frequency signal, the strength of the radio frequency signal can be identified.

[0067] Multiple controllers 303 are respectively installed in the reference base station 302 to execute the positioning method in the above embodiments.

[0068] Specifically, the controller 303 is used to control the working state of the reference base station 302. When a positioning command is detected, the controller controls the reference base station 302 to change from a dormant state to a triggered state and controls the reference base station 302 in the triggered state to perform the operation of identifying radio frequency signals.

[0069] Furthermore, the identified signal strength, the acquired target object pose data, and the position coordinates of the previous time period are used to calculate multiple local estimates using a filtering algorithm. These local estimates are then synchronized with adjacent base stations, i.e., the local estimates are exchanged. By calculating the average parameter of the multiple local estimates, the corresponding fusion weight can be calculated. Finally, the weight and the local estimates are weighted and summed to obtain the global position information of the target object.

[0070] Optionally, in the positioning system provided in this application embodiment, the measuring device 301 includes a radio frequency identification tag and an inertial measurement unit. The inertial measurement unit is used to measure the pose data of the target object. Each reference base station 302 is provided with a radio frequency identification reader, which is used to identify the strength of the radio frequency signal when the radio frequency identification tag is close.

[0071] Specifically, the RFID tag and RFID reader together form an RFID device, which is used to send and identify the strength of the RFID signal. The pose data can include the displacement step size and heading angle data of the target object. By measuring the various data information measured by the measuring device 301, a local estimate of the target object can be obtained. By weighted summation of the various local estimates, a more accurate global position information can be obtained.

[0072] The positioning system provided in this application embodiment uses a measuring device 301, which is set on the target object to be detected and moves with the target object to measure the pose data of the target object; multiple reference base stations 302, each of which is used to identify the strength of the radio frequency signal of the measuring device 301; and multiple controllers 303, which are respectively set in the reference base stations 302, to execute the positioning method in the above embodiment. This system solves the problems of poor device flexibility and low positioning accuracy in related technologies. By weighted summing of the local estimates of the target object identified by multiple reference base stations with the corresponding fusion weights, the system achieves the effect of improving the flexibility of the device and the positioning accuracy.

[0073] This application also provides an optional positioning method in its embodiments. Figure 4 This is a schematic diagram of an optional positioning method provided according to an embodiment of this application, such as... Figure 4 As shown, the method includes:

[0074] Before accurately locating the target object, multiple pieces of information need to be acquired for calculation of the system observation model. The system observation model then determines the local estimated value of the target object, and this information is obtained through reference base stations. To simplify the model, the target object is denoted as a blind node, and the reference base station is denoted as an anchor node. Anchor nodes include both untriggered and triggered anchor nodes.

[0075] On one hand, the RFID reader on the triggered anchor node identifies the RFID tags carried by the blind node, obtaining RFID signals of different intensities P. On the other hand, the blind node also carries an inertial measurement unit (IMU) for acquiring gait data, which may include stride displacement l and heading angle data.

[0076] Figure 5 This is a schematic diagram of multi-level data fusion provided in the embodiments of this application, such as... Figure 5 As shown, by inputting the intensity, step displacement, and heading angle data of the radio frequency signal measured by an anchor node into the Local CIF calculation module, that is, inputting the data information into the first-level fusion filter for data-level fusion, the local estimate value of the blind node measured by an anchor node in the current time period can be obtained; further, the multiple local estimates obtained by multiple anchor nodes are input into the Consensus CIF calculation module to obtain the fusion weight corresponding to each local estimate value.

[0077] The fusion weight corresponding to each local estimate is weighted and summed with each local estimate. In other words, the weights and first-level local estimates are input into the second-level fusion filter for feature-level fusion. Finally, the global position coordinates of the blind node in the current time period can be obtained.

[0078] By arranging the global position coordinates of multiple time periods in chronological order, the motion trajectory of the blind node can be obtained.

[0079] By constructing a heterogeneous wireless sensor network using RFID equipment systems and inertial measurement units (IMUs), relevant data can be identified through the wireless sensor network, and the corresponding data can be fused using data fusion technology to achieve low-cost and high-precision positioning.

[0080] This application also provides a positioning device. It should be noted that the positioning device of this application can be used to execute the positioning method provided in this application. The positioning device provided in this application is described below.

[0081] Figure 6This is a schematic diagram of a positioning device provided according to an embodiment of this application, such as... Figure 6 As shown, the device includes: a first acquisition unit 60, a first calculation unit 61, and a determination unit 62.

[0082] The first acquisition unit 60 is used to acquire, in each time period, the position coordinates of multiple reference base stations, the position coordinates of the target object to be detected in the previous time period, the intensity of the radio frequency signal of the measuring device identified by each reference base station, and the pose data measured by the measuring device. The target object carries the measuring device, and the pose data refers to the pose data of the target object in the current time period.

[0083] The first calculation unit 61 is used to calculate the local estimated value of the target object in each time period based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data in the current time period, to obtain multiple sets of local estimated values. Each set of local estimated values ​​includes the local estimated values ​​associated with each reference base station in a time period.

[0084] The determining unit 62 is used to determine the global position information of the target object in each time period based on each group of local estimates, and to determine the movement trajectory of the target object based on the global position information in each time period.

[0085] Optionally, in the positioning device provided in the embodiments of this application, the first calculation unit 61 includes: a first calculation module, used to calculate the distance between the reference base station and the target object for each time period based on the strength of the radio frequency signal identified by the radio frequency identification reader of each base station; and a second calculation module, used to calculate local estimates for each distance based on the distance, the position coordinates of the previous time period, and the pose data, to obtain multiple local estimates, and the multiple local estimates constitute a set of local estimates.

[0086] Optionally, in the positioning device provided in the embodiments of this application, the first calculation unit 61 includes: a first construction module, used to construct a state equation based on the position coordinates and pose data of the previous time period; a second construction module, used to construct a measurement equation based on the distance and pose data; a composition module, used to construct a system observation model from the state equation and the measurement equation; and an update module, used to update the parameters in the system observation model through a volumetric filtering algorithm to obtain a state estimate, an information matrix, and an information vector, and to determine the information vector as a local estimate.

[0087] Optionally, in the positioning device provided in this application embodiment, the device includes: a second acquisition unit, configured to calculate a local estimate of the target object based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data in the current time period in each time period, and acquire an intensity threshold before obtaining multiple sets of local estimates; a comparison unit, configured to compare the intensity of the radio frequency signals acquired by multiple reference base stations with the intensity threshold in each time period, and obtain multiple comparison results; a control unit, configured to control the reference base station to remain in a sleep state when any comparison result indicates that the intensity of the radio frequency signal acquired by the reference base station is less than the intensity threshold; a wake-up unit, configured to wake up the reference base station and put the reference base station into a triggered state when any comparison result indicates that the intensity of the radio frequency signal acquired by the reference base station is greater than or equal to the intensity threshold; and an execution unit, configured to execute the steps of calculating the local estimate of the target object based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data in the current time period, based on the intensity of the radio frequency signal acquired by the reference base station in the triggered state.

[0088] Optionally, in the positioning device provided in this application embodiment, the determining unit 62 includes: a third calculation module, used to calculate the average value of multiple local estimates for a set of local estimates in the same time period to obtain a mean parameter; a fourth calculation module, used to calculate the absolute value of the difference between each local estimate and the mean parameter to obtain multiple initial fusion weights; and a processing module, used to normalize the multiple initial fusion weights to obtain the fusion weight corresponding to each local estimate.

[0089] Optionally, in the positioning device provided in the embodiments of this application, the device includes: a second calculation unit, used to calculate the fusion weight corresponding to each local estimate in each group of local estimates after normalizing multiple initial fusion weights to obtain the fusion weight corresponding to each local estimate; and a third calculation unit, used to calculate the global position information of the current time period by weighted summing of each fusion weight and the corresponding local estimate.

[0090] The positioning device provided in this application embodiment, through a first acquisition unit 60, is used to acquire, in each time period, the position coordinates of multiple reference base stations, the position coordinates of the target object to be detected in the previous time period, the intensity of the radio frequency signal of the measuring device identified by each reference base station, and the pose data measured by the measuring device. The target object carries the measuring device, and the pose data refers to the pose data of the target object in the current time period. A first calculation unit 61 is used, in each time period, to calculate, based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data in the current time period. The local estimates of the target object are calculated to obtain multiple sets of local estimates, each set of local estimates containing the local estimates associated with each reference base station in a time period. The determination unit 62 is used to determine the global location information of the target object in each time period based on each set of local estimates. The movement trajectory of the target object is determined by the global location information in each time period. This solves the problems of poor device flexibility and low positioning accuracy in related technologies. By weighted summing of the local estimates of the target object identified by multiple reference base stations with the corresponding fusion weights, the global location information is obtained, thereby improving the flexibility of the device and the positioning accuracy.

[0091] The aforementioned positioning device includes a processor and a memory. The first acquisition unit 60, the first calculation unit 61, and the determination unit 62 are all stored in the memory as program units. The processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0092] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can address issues such as poor device flexibility and low positioning accuracy in related technologies.

[0093] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0094] This application also provides a computer storage medium for storing a program, wherein the program, when running, controls the device where the non-volatile storage medium is located to execute a positioning method.

[0095] This application also provides an electronic device. Figure 7 This is a schematic diagram of an electronic device provided according to an embodiment of this application, such as... Figure 7As shown, the electronic device 70 includes a processor and a memory; the memory stores computer-readable instructions, and the processor executes the computer-readable instructions, wherein the computer-readable instructions, when executed, perform a positioning method. The electronic device in this document can be a server, PC, PAD, mobile phone, etc.

[0096] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0098] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0099] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0100] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0101] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0102] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0103] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0104] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A positioning method, characterized in that, include: In each time period, the position coordinates of multiple reference base stations, the position coordinates of the target object to be detected in the previous time period, the intensity of the radio frequency signal of the measuring device identified by each reference base station, and the pose data measured by the measuring device are acquired respectively. The target object carries the measuring device, and the pose data refers to the pose data of the target object in the current time period. In each time period, the local estimated value of the target object is calculated based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data in the current time period, to obtain multiple sets of local estimated values. Each set of local estimated values ​​includes the local estimated values ​​associated with each reference base station in a time period. The global position information of the target object in each time period is determined based on the local estimates of each group, and the movement trajectory of the target object is determined by the global position information in each time period. The process involves calculating local estimates of the target object based on the location coordinates of each reference base station, the location coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data in the current time period, resulting in multiple sets of local estimates. This includes: for each time period, calculating the distance between the reference base station and the target object based on the intensity of the radio frequency signal detected by the RFID reader of each base station; for each distance, calculating local estimates based on the distance, the location coordinates of the previous time period, and the pose data, including: constructing a state equation based on the location coordinates of the previous time period and the pose data; constructing a measurement equation based on the distance and the pose data; forming a system observation model from the state equation and the measurement equation; updating the parameters in the system observation model using a volumetric filtering algorithm to obtain the state estimate, information matrix, and information vector, and determining the information vector as the local estimate.

2. The method according to claim 1, characterized in that, The measuring device includes an RFID tag and an inertial measurement unit (IMU). The IMU measures the pose data. An RFID reader is installed on the reference base station. The RFID reader identifies the intensity of the radio frequency signal when the RFID tag is close. Based on the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data in the current time period, local estimates of the target object are calculated, resulting in multiple sets of local estimates, including: For each time period, the distance between the reference base station and the target object is calculated based on the strength of the radio frequency signal identified by the radio frequency identification reader of each base station; For each distance, a local estimate is calculated based on the distance, the position coordinates of the previous time period, and the pose data to obtain multiple local estimates, which together form a set of local estimates.

3. The method according to claim 1, characterized in that, In each time period, before calculating local estimates of the target object based on the location coordinates of each reference base station, the location coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data in the current time period, and obtaining multiple sets of local estimates, the method includes: Obtain the intensity threshold; In each time period, the intensity of the radio frequency signal acquired by the multiple reference base stations is compared with the intensity threshold to obtain multiple comparison results; If any of the comparison results indicate that the strength of the radio frequency signal acquired by the reference base station is less than the strength threshold, the reference base station is controlled to remain in a sleep state. If any of the comparison results indicate that the strength of the radio frequency signal obtained by the reference base station is greater than or equal to the strength threshold, the reference base station is woken up and put into a triggered state. Based on the intensity of the radio frequency signal obtained by the reference base station in the triggered state, the steps of calculating the local estimated value of the target object according to the position coordinates of each reference base station, the position coordinates of the target object in the previous time period, the intensity of the radio frequency signal in the current time period, and the pose data in the current time period are executed respectively.

4. The method according to claim 1, characterized in that, Determining the global location information of the target object in each time period based on each set of local estimates includes: For a set of local estimates within the same time period, calculate the average of the multiple local estimates to obtain the mean parameter; Calculate the absolute value of the difference between each local estimate and the mean parameter to obtain multiple initial fusion weights; The initial fusion weights are normalized to obtain the fusion weights corresponding to each local estimate.

5. The method according to claim 4, characterized in that, After normalizing multiple initial fusion weights to obtain the fusion weights corresponding to each local estimate, the method includes: Calculate the fusion weight for each local estimate in each set of local estimates; The global location information for the current time period is calculated by weighting and summing each fusion weight with its corresponding local estimate.

6. A positioning system, characterized in that, include: A measuring device is set on the target object to be detected and moves with the target object to measure the pose data of the target object; Multiple reference base stations, each used to identify the strength of the radio frequency signal of the measuring device; The controller is communicatively connected to the plurality of reference base stations and is used to perform the positioning method according to any one of claims 1 to 5.

7. The positioning system according to claim 6, characterized in that: The measuring device includes a radio frequency identification tag and an inertial measurement unit, the inertial measurement unit being used to measure the pose data of the target object; Each reference base station is equipped with an RFID reader, which is used to identify the strength of the radio frequency signal when the RFID tag is close.

8. A computer storage medium, characterized in that, The computer storage medium is used to store a program, wherein the program, when running, controls the device where the computer storage medium is located to execute the positioning method according to any one of claims 1 to 5.

9. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the positioning method according to any one of claims 1 to 5 through the computer program.

Images