Indoor positioning method and system based on UWB signal data

Abstract

The invention discloses an indoor positioning method and system based on UWB signal data. The system comprises UWB label equipment, UWB base station equipment and a server side. The UWB tag device is worn on a person or an article and is used for transmitting a UWB signal. The UWB base station equipment is deployed at an indoor fixed position and is used for scanning and detecting the UWB tag equipment and acquiring a signal value; and the server receives a signal value reported by the UWB base station equipment, converts the signal value into ranging data, carries out data processing by adopting median processing to obtain a ranging value, carries out dynamic weight distribution on each UWB base station according to the signal quality, and calculates the coordinate position of the UWB label equipment in combination with the ranging data. The method is suitable for flexible deployment requirements in different scenes, and the adaptability and practicability of the system are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of indoor positioning of wireless communications, and in particular to an indoor positioning method and system based on UWB signal data. Background Art

[0002] With the rapid development of the Internet of Things (IoT) and smart devices, indoor positioning technology has found widespread application in logistics management, smart buildings, security monitoring, and other fields. Traditional indoor positioning technologies, such as Wi-Fi, Bluetooth, and RFID, suffer from low positioning accuracy and weak anti-interference capabilities. Ultra-wideband (UWB) technology, due to its high temporal resolution and robustness against multipath effects, has become a preferred solution for indoor positioning in recent years. Existing UWB positioning methods typically measure the distance between a base station and a tag using signals and calculate coordinates using triangulation. However, in practical applications, ranging data is often unstable due to signal noise, environmental interference, or multipath effects, resulting in reduced positioning accuracy. Furthermore, existing technologies lack efficient filtering mechanisms when processing multiple ranging data points and lack precise time synchronization, making them difficult to adapt to real-time positioning requirements in dynamic scenarios. Therefore, a more stable, accurate, and computationally efficient indoor positioning method is needed. Summary of the Invention

[0003] The purpose of the present invention is to solve the insufficient accuracy caused by signal noise, time asynchrony and other problems in the existing UWB positioning technology, and to provide an indoor positioning method and system based on UWB signal data, optimize the processing flow of ranging data, improve the stability and real-time performance of positioning, and meet the high-precision positioning needs in complex indoor environments.

[0004] The technical solution adopted in the present invention is: An indoor positioning system based on UWB signal data includes a UWB tag device, a UWB base station device and a server; the UWB tag device is worn on a person or object and is used to transmit UWB signals; the UWB base station device is deployed at a fixed position indoors and is used to scan and detect the UWB tag device and obtain the signal value (RSSI); the server receives the signal value reported by the UWB base station device and converts it into ranging data, then uses median processing to process the data to obtain the ranging value, and dynamically assigns a weight to each UWB base station according to the signal quality, and then calculates the coordinate position of the UWB tag device based on the ranging data.

[0005] Furthermore, when a UWB tag device server receives ranging data from up to two UWB base stations, it performs dual-base station data correction: it obtains the current UWB tag device's historical position data and introduces a Kalman filter algorithm to smooth the current positioning result calculated based on the measurement data of the two UWB base stations, reducing the accuracy difference. It also uses the tag's motion model to predict the current coordinate position of the UWB tag device and fuses it with the measurement data of the UWB base station device to obtain the final coordinate position of the UWB tag device. This dual-base station data correction mechanism enables the system to maintain high positioning accuracy even when the number of base stations is insufficient.

[0006] The present invention discloses an indoor positioning method based on UWB signal data, which comprises the following steps: Step 1: Deploy at least three UWB base station devices in the target indoor environment to cover the entire positioning area, measure the precise coordinate position of each UWB base station device and enter it into the server's database; Furthermore, in step 1, an infrared rangefinder measurement tool is used to manually measure the precise coordinate position of each UWB base station device.

[0007] Specifically, deploy at least three UWB base stations in the target indoor environment, ensuring they cover the entire positioning area. Use an infrared rangefinder to manually measure the precise coordinates of each UWB base station. These coordinates are then entered into the server's database.

[0008] Step 2: Equip the positioning target with a UWB tag device and establish a communication connection between the UWB tag device and the UWB base station. Specifically, UWB tags are equipped for the personnel or assets that need to be located to ensure normal communication between the devices. The station equipment detects and scans the tags and measures the signal strength.

[0009] Step 3: The server program starts to load the coordinates of the UWB base station device and initialize the signal strength parameters of the positioning algorithm; Step 4: The UWB base station device collects the signal value of the UWB tag device and reports it to the server; Step 5: The server converts the received signal value into ranging data, and then stores the reported time (accurate to seconds) with a tag identifier. Furthermore, the server converts the received signal value (RSSI) into a distance value based on the preset signal value corresponding to one meter.

[0010] Step 6: The server comprehensively summarizes all the ranging data of the UWB tag devices that report signal data within the specified time in all UWB base station devices, and obtains the ranging value of the UWB tag device corresponding to each UWB base station; the ranging values of the UWB tag device corresponding to different UWB base stations are calculated by linear number to obtain the coordinates of the UWB tag device; Furthermore, when the same UWB base station device has more than two ranging data for a UWB tag device, the median of the two or more ranging data is taken as the ranging value of the current UWB base station device corresponding to the current UWB tag device.

[0011] Furthermore, step 6 specifically includes the following steps: Step 6-1, obtaining the ranging value of the UWB tag device corresponding to each UWB base station; Step 6-2: Determine whether the current UWB tag device has ranging data of more than three different UWB base stations; if so, execute step 6-3; otherwise, obtain the historical point data of the current UWB tag device and execute step 6-4; Step 6-3: Assign weights to the measurement data of each UWB base station based on the signal quality, and calculate the coordinates of the UWB tag device using the linearized three-point positioning function combined with the assigned weights, and then end; Step 6-4: introduce the Kalman filter algorithm to smooth the current positioning result calculated based on the measurement data of the two UWB base stations to reduce the accuracy difference; Step 6-5: Use the tag's motion model to predict the current coordinate position of the UWB tag device and fuse it with the UWB base station device measurement data to obtain the final coordinate position of the UWB tag device.

[0012] The present invention adopts the above technical solution, and through multi-base station collaborative positioning, three-point positioning linear function and median processing, it can effectively reduce the influence of signal fluctuations and multipath effects, and achieve centimeter-level high-precision positioning. Significantly improve indoor positioning accuracy, especially suitable for complex environments. Median processing and dynamic weight allocation mechanism can effectively filter noise and abnormal data, and improve the system's anti-interference ability in complex environments. The dual-base station data correction mechanism enables the system to maintain a high positioning accuracy when the number of base stations is insufficient. It is suitable for flexible deployment requirements in different scenarios and enhances the adaptability and practicality of the system. The positioning calculation frequency of once per second can meet the needs of real-time dynamic positioning and is suitable for various scenarios such as personnel tracking, navigation, and asset management. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments; Figure 1 The figure is a flow chart of an indoor positioning method based on UWB signal data according to the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0015] like Figure 1 As shown, the present invention discloses an indoor positioning system based on UWB signal data, which includes a UWB tag device, a UWB base station device and a server; the UWB tag device is worn on a person or an object and is used to transmit UWB signals; the UWB base station device is deployed at a fixed position indoors and is used to scan and detect the UWB tag device and obtain the signal value (RSSI); the server receives the signal value reported by the UWB base station device, performs data processing and coordinate calculation.

[0016] The present invention discloses an indoor positioning method based on UWB signal data, which comprises the following steps: Step 1: Deploy at least three UWB base station devices in the target indoor environment to cover the entire positioning area, measure the precise coordinate position of each UWB base station device and enter it into the server's database; Furthermore, in step 1, an infrared rangefinder measurement tool is used to manually measure the precise coordinate position of each UWB base station device.

[0017] Specifically, deploy at least three UWB base stations in the target indoor environment, ensuring they cover the entire positioning area. Use an infrared rangefinder to manually measure the precise coordinates of each UWB base station. These coordinates are then entered into the server's database.

[0018] Step 2: Equip the positioning target with a UWB tag device and establish a communication connection between the UWB tag device and the UWB base station. Specifically, UWB tags are equipped for the personnel or assets that need to be located to ensure normal communication between the devices. The station equipment detects and scans the tags and measures the signal strength.

[0019] Step 3: The server program starts to load the coordinates of the UWB base station device and initialize the signal strength parameters of the positioning algorithm; Step 4: The UWB base station device collects the signal value of the UWB tag device and reports it to the server; Step 5: The server converts the received signal value into ranging data, and then stores the reported time (accurate to seconds) with a tag identifier. Furthermore, the server converts the received signal value (RSSI) into a distance value based on the preset signal value corresponding to one meter.

[0020] Step 6: The server comprehensively summarizes all the ranging data of the UWB tag devices that report signal data within the specified time in all UWB base station devices, and obtains the ranging value of the UWB tag device corresponding to each UWB base station; the ranging values of the UWB tag device corresponding to different UWB base stations are calculated by linear number to obtain the coordinates of the UWB tag device; Furthermore, when the same UWB base station device has more than two ranging data for a UWB tag device, the median of the two or more ranging data is taken as the ranging value of the current UWB base station device corresponding to the current UWB tag device.

[0021] Furthermore, step 6 specifically includes the following steps: Step 6-1, obtaining the ranging value of the UWB tag device corresponding to each UWB base station; Step 6-2: Determine whether the current UWB tag device has ranging data of more than three different UWB base stations; if so, execute step 6-3; otherwise, obtain the historical point data of the current UWB tag device and execute step 6-4; Step 6-3: Assign weights to the measurement data of each UWB base station based on the signal quality, and calculate the coordinates of the UWB tag device using the linearized three-point positioning function combined with the assigned weights, and then end; Step 6-4: introduce the Kalman filter algorithm to smooth the current positioning result calculated based on the measurement data of the two UWB base stations to reduce the accuracy difference; Specifically, when there are only two base stations, there will be a double-solution symmetry problem in positioning based on linear functions: assuming that the coordinates of the two base stations are (x1, y1) and (x2, y2), and the line connecting the base stations is the geometric axis, the two intersection points corresponding to the measured distances d1 and d2 must be symmetrically distributed on both sides of the axis.

[0022] This mirror symmetry makes it impossible to determine the actual quadrant of the target by only using the current distance measurement value. ), calculate the Euclidean distance between it and the two candidate solutions PA and PB, and give priority to selecting the candidate point closer to the predicted point as the primary solution.

[0023] Step 6-5: Use the tag's motion model to predict the current coordinate position of the UWB tag device and fuse it with the UWB base station device measurement data to obtain the final coordinate position of the UWB tag device.

[0024] Specifically, based on the RSSI values received by the UWB device, the distance between the tag and each base station is calculated using a signal attenuation model (such as the logarithmic distance path loss model). A system of geometric equations is constructed using the coordinates of at least three UWB base stations and their distances to the tag. This system of equations is solved to determine the two-dimensional position of the tag. The nonlinear distance equations are linearized using Taylor expansion or least squares methods to simplify the calculation process and improve efficiency. Dynamic weights are assigned to the distance data for each base station based on signal quality (RSSI strength, signal-to-noise ratio, etc.) to optimize positioning results.

[0025] Specifically, the UWB tag device that reported signal data in the previous second is obtained, and all ranging data of the corresponding UWB tag device in the previous second is obtained. If a UWB base station device has multiple ranging data for it, the median is taken as the calculated ranging value.

[0026] If the tag has ranging data from at least three base stations, the tag's coordinates are calculated using linear counting. This linearized three-point positioning function is then used. A dynamic weighting mechanism assigns weights to each base station's measurement data based on signal quality (such as RSSI strength and signal-to-noise ratio). This linearization significantly reduces computational complexity and improves the algorithm's real-time performance. Dynamic weighting further optimizes positioning results, particularly in situations with uneven signal quality, significantly improving positioning accuracy.

[0027] If only two base station ranging data are used, historical point data can be combined to assist in calculating the coordinate position. A Kalman filter algorithm is introduced to smooth the current positioning result calculated based on the measurement data of the two UWB base stations, reducing the accuracy difference. The tag's motion model is used to predict the current position and fuse it with the measurement data of the UWB base station device.

[0028] This method deploys at least three UWB devices indoors, manually measuring and recording the precise coordinates of each device as a benchmark for multi-base station collaborative positioning calculations. This collaborative work significantly improves positioning accuracy, reduces single-point errors, and enhances system robustness.

[0029] This method converts the RSSI (Reliable Signal Strength Index) detected by a UWB device into meters using the 1-meter signal value and takes the median of multiple detection data within one second. This median processing effectively filters signal fluctuations and outliers, reduces noise interference, and improves the stability and reliability of positioning data.

[0030] The server of the present invention calculates the tag's location once per second, updating and outputting the positioning results in real time. Multi-threaded or distributed computing techniques optimize computational efficiency. The algorithm of the present invention supports the dynamic expansion of the number of UWB base stations, further improving positioning accuracy and system coverage. The system can adapt to various indoor environments and offers high flexibility and scalability.

[0031] The present invention adopts the above technical solution, and through multi-base station collaborative positioning, three-point positioning linear function and median processing, it can effectively reduce the influence of signal fluctuations and multipath effects, and achieve centimeter-level high-precision positioning. Significantly improve indoor positioning accuracy, especially suitable for complex environments. Median processing and dynamic weight allocation mechanism can effectively filter noise and abnormal data, and improve the system's anti-interference ability in complex environments. The dual-base station data correction mechanism enables the system to maintain a high positioning accuracy when the number of base stations is insufficient. It is suitable for flexible deployment requirements in different scenarios and enhances the adaptability and practicality of the system. The positioning calculation frequency of once per second can meet the needs of real-time dynamic positioning and is suitable for various scenarios such as personnel tracking, navigation, and asset management.

[0032] Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

Claims

1. An indoor positioning system based on UWB signal data, characterized by: It includes UWB tag devices, UWB base station devices and server terminals; UWB tag devices are worn on people or objects to transmit UWB signals; UWB base station devices are deployed at fixed locations indoors to scan and detect UWB tag devices and obtain signal values; the server terminal receives the signal values reported by the UWB base station devices and converts them into ranging data, and then uses median processing to process the data to obtain ranging values. After dynamically assigning weights to each UWB base station based on signal quality, the coordinate position of the UWB tag device is calculated based on the ranging data.

2. The indoor positioning system based on UWB signal data according to claim 1, characterized in that: When a UWB tag device server receives ranging data from at most two UWB base station devices, dual-base station data correction is performed: the historical point data of the current UWB tag device is obtained, and the Kalman filter algorithm is introduced to smooth the current positioning result calculated based on the measurement data of the two UWB base stations to reduce the accuracy difference; the tag's motion model is used to predict the current coordinate position of the UWB tag device and merge it with the measurement data of the UWB base station device to obtain the final coordinate position of the UWB tag device.

3. An indoor positioning method based on UWB signal data, using the indoor positioning system based on UWB signal data according to claim 1 or 2, characterized in that: The method comprises the following steps: Step 1: Deploy at least three UWB base station devices in the target indoor environment to cover the entire positioning area, measure the precise coordinate position of each UWB base station device and enter it into the server's database; Step 2: Equip the positioning target with a UWB tag device and establish a communication connection between the UWB tag device and the UWB base station. Step 3: The server program starts to load the coordinates of the UWB base station device and initialize the signal strength parameters of the positioning algorithm; Step 4: The UWB base station device collects the signal value of the UWB tag device and reports it to the server; Step 5: The server converts the received signal value into ranging data, and then stores the reported time with a tag identifier; In step 6, the server comprehensively summarizes all the ranging data of the UWB tag devices that report signal data within the specified time in all UWB base station devices, and obtains the ranging value of the UWB tag device corresponding to each UWB base station; the ranging values of the UWB tag device corresponding to different UWB base stations are calculated by linear row number to obtain the coordinates of the UWB tag device.

4. The indoor positioning method based on UWB signal data according to claim 3, characterized in that: In step 1, use the infrared rangefinder measurement tool to manually measure the precise coordinates of each UWB base station device.

5. The indoor positioning method based on UWB signal data according to claim 3, characterized in that: In step 5, the server converts the received signal value into a distance value in the ranging data according to the preset signal value corresponding to one meter.

6. The indoor positioning method based on UWB signal data according to claim 3, characterized in that: In step 6, when the same UWB base station device has more than two ranging data for a UWB tag device, the median of the two or more ranging data is taken as the ranging value of the current UWB base station device corresponding to the current UWB tag device.

7. The indoor positioning method based on UWB signal data according to claim 3, characterized in that: Step 6 specifically includes the following steps: Step 6-1, obtaining the ranging value of the UWB tag device corresponding to each UWB base station; Step 6-2: Determine whether the current UWB tag device has ranging data of more than three different UWB base stations; if so, execute step 6-3; otherwise, obtain the historical point data of the current UWB tag device and execute step 6-4; Step 6-3: Assign weights to the measurement data of each UWB base station based on the signal quality, and calculate the coordinates of the UWB tag device using the linearized three-point positioning function combined with the assigned weights, and then end; Step 6-4: introduce the Kalman filter algorithm to smooth the current positioning result calculated based on the measurement data of the two UWB base stations to reduce the accuracy difference; Step 6-5: Use the tag's motion model to predict the current coordinate position of the UWB tag device and fuse it with the UWB base station device measurement data to obtain the final coordinate position of the UWB tag device.

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