Decentralized indoor positioning system and positioning method

Abstract

The invention provides a decentralized indoor positioning system, which comprises a plurality of mobile devices with IMU (Inertial Measurement Unit) sensors and UWB (Ultra Wideband) communication capability, and is characterized in that when entering an indoor space, the mobile devices construct a decentralized communication network through an UWB technology; whenever the mobile devices enter a room after obtaining an initial accurate position outdoors through a real-time dynamic technology, the high-precision positioning data of the mobile devices are used as a calibration reference of the whole system; the mobile device uses an IMU sensor to position in an indoor environment without GPS signals.

Description

Technical Field

[0001] The present invention relates to the field of communications, and in particular to a decentralized indoor positioning system and positioning method. Background Art

[0002] Traditional indoor positioning systems usually rely on stable reference points (such as Wi-Fi access points, Bluetooth beacons, etc.) to ensure positioning accuracy. According to different technical means, traditional indoor positioning methods are mainly divided into the following forms:

[0003] 1. Positioning based on wireless signals, including Wi-Fi positioning and Bluetooth positioning.

[0004] Wi-Fi positioning: Use the signal strength (RSSI) of Wi-Fi access points for positioning. Common methods include fingerprint matching and triangulation. The main problem is that the signal strength is greatly affected by obstacles such as walls and furniture, so the positioning accuracy may not be high.

[0005] Bluetooth positioning: Use Bluetooth beacons (such as iBeacon) for positioning, similar to Wi-Fi positioning. Bluetooth signals are more stable, but the coverage range is smaller and a large number of beacons need to be deployed.

[0006] 2. Vision-based positioning, including image matching positioning and lidar positioning.

[0007] Image matching positioning: The camera obtains the environment image, and then matches it with the pre-stored image database to determine the location. This method is prone to failure when the lighting and environment change.

[0008] LiDAR: LiDAR sensors use laser beams to generate a three-dimensional map of the surrounding environment and receive reflected beams for positioning. The disadvantage is that the equipment is expensive and may not perform well in environments with glass or mirror reflections.

[0009] 3. Positioning based on inertial measurement unit (IMU)

[0010] IMU positioning: IMU sensors calculate displacement and estimate position by detecting motion information such as acceleration and angular velocity. The advantage of this method is that it is independent of external reference points and can achieve seamless positioning. However, since IMU sensors accumulate measurement errors (such as zero drift and noise), the error continues to increase over time, eventually leading to positioning deviation.

[0011] However, there is an error accumulation problem when using the IMU sensor positioning method. For example, the drift accumulation error: the drift and noise of the IMU sensor cause the position estimation error to accumulate with the increase of time and movement distance. This accumulated error is called drift. The accumulation of drift reduces the accuracy of IMU positioning during long-term operation. On this basis, the traditional error elimination method is to fuse other sensor data: In order to reduce the IMU cumulative error, a common method is to fuse IMU data with other sensor data. For example, a Kalman filter is used to fuse IMU data with GPS, Wi-Fi and other data to correct the drift error of the IMU. Or reset algorithm: In some cases, known reference points (such as Wi-Fi hotspots or visual markers) can be used to periodically reset the IMU's position estimate to reduce the accumulated error.

[0012] This situation has the following problems: 1. Sensor dependency: Fusion of data from multiple sensors depends on the stability and accuracy of other sensors. If the external sensor signal is interfered with (such as Wi-Fi signal fluctuations), it will affect the effect of error correction. 2. Computational complexity: Multi-sensor fusion methods often require complex algorithms (such as Kalman filters or particle filters), which increases the computational burden, especially on resource-constrained devices (such as smartphones). 3. Errors are not completely eliminated: Although errors can be reduced through multi-sensor fusion, IMU errors may still accumulate over long periods of time and in complex environments and cannot be completely eliminated. Summary of the invention

[0013] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is the limited signal strength, low positioning accuracy, high cost, strong sensor dependence, high computational complexity and error headlight problems of the existing indoor positioning methods. Therefore, the present invention provides a decentralized indoor positioning system and positioning method to improve positioning accuracy, enhance the stability and robustness of the system, reduce the cumulative error of IMU sensors, and provide a wide coverage of high-precision positioning services.

[0014] To achieve the above-mentioned purpose, the present invention provides a decentralized indoor positioning system, including multiple mobile devices with IMU sensors and UWB communication modules. When the mobile devices enter the indoor space, a decentralized communication network is constructed through ultra-wideband technology, and each mobile device serves as a node in the decentralized communication network; whenever the mobile devices enter the indoor space after obtaining the initial precise position outdoors through real-time dynamic technology, their high-precision positioning data is used as a calibration reference for the entire system; the mobile devices use IMU sensors and / or UWB communication modules to perform positioning in an indoor environment without GPS signals.

[0015] Furthermore, through the decentralized P2P network, the newly entered mobile device communicates with the existing mobile devices in the room through UWB, sharing location data to achieve mutual calibration between devices.

[0016] Furthermore, a decentralized P2P network communication protocol is included to coordinate the exchange of positioning data and calibration instructions between devices.

[0017] Another preferred embodiment of the present invention provides a positioning method for a decentralized indoor positioning system, comprising the following steps:

[0018] When a new mobile device enters the room and connects to the decentralized network, it first scans the surrounding environment through UWB communication technology, identifies and connects to other devices already in the network;

[0019] New mobile devices broadcast their current location via high-precision outdoor RTK positioning data and join the decentralized network;

[0020] Existing devices in the decentralized network receive high-precision location information from new mobile devices through UWB communication modules and calculate relative position relationships;

[0021] After calculating the relative position relationship, the existing device uses the high-precision position information of the new mobile device as a benchmark, calculates its own positioning deviation, and then performs error correction;

[0022] The error-corrected existing device broadcasts its updated location information to other devices in the decentralized network;

[0023] As more new mobile devices join the network, the above process is repeated iteratively, providing positioning information for each device while improving the positioning accuracy of the entire decentralized network.

[0024] Furthermore, existing devices in the decentralized network receive high-precision location information of the new mobile device through the UWB communication module and calculate the relative position relationship. Specifically, the existing devices receive the high-precision location information of the new device through the UWB communication module, and after each device receives the location information of the new mobile device, it confirms the relative position relationship with the new mobile device through the timestamp and UWB signal strength.

[0025] Furthermore, the existing device calculates the current relative position through its own IMU sensor data, and then converts the UWB signal strength into a distance to determine the relative position between the existing device and the new mobile device;

[0026] The relative position relationship between the existing device and the new mobile device is calculated through triangulation or multi-point positioning algorithm.

[0027] Furthermore, after calculating the relative position relationship, the existing device uses the high-precision position information of the new mobile device as a reference to calculate its own positioning deviation, which is specifically expressed using the following formula:

[0028] Δx=x new -x current

[0029] Δy=y new -y current

[0030] Among them, y new and x new is the new position, y current and x current is the current position, that is, the corrected position. new is the new position of the existing device, x current is the current position of the existing equipment, Δx is the positioning deviation of the existing equipment;

[0031] y new ,y current y is the new position and current position of the mobile device, and Δy is the positioning deviation of the mobile device.

[0032] Furthermore, the existing device uses the calculated deviation values ​​Δx and Δy to correct its current position data:

[0033] x correct =Δx+x vurrent

[0034] y correct =Δy+y current

[0035] The corrected location data is used as the current accurate location of the current device and is updated to the internal location record of the current device.

[0036] Furthermore, the existing device after error correction broadcasts its updated location information to other devices in the decentralized network, wherein the updated location information includes the calibrated location information and the calibration deviation value.

[0037] Technical Effects

[0038] The present invention provides a decentralized indoor positioning system and positioning method, which have the following technical effects:

[0039] 1. Improve initial positioning accuracy: By giving higher weight to the location information of the device that has just entered the room, the system can use these high-precision reference points from the outdoors to effectively improve the overall positioning accuracy. This method ensures that the positioning is highly accurate immediately after entering the room, greatly reducing the possibility of initial error propagation.

[0040] 2. Enhance the stability and robustness of the system: When dynamic nodes join the P2P network, the system can quickly adapt to new environmental changes through the priority processing of high-precision information, and stabilize the positioning accuracy of the entire network through the calibration process. This mechanism is particularly suitable for indoor environments with frequent equipment entry and exit and complex layouts.

[0041] 3. Reduce the cumulative error of IMU sensors: Regularly calibrating the data of IMU sensors with high-precision reference points significantly reduces the accumulated errors during inertial navigation. Even in long-term operation, the system can still maintain a high positioning accuracy, which makes it more reliable in practical applications.

[0042] 4. Provide high-precision positioning services with wide coverage: Since each device entering the room can provide a new high-precision reference point for the system, the positioning accuracy of the system in different environments is guaranteed. This wide coverage and dynamically adjusted positioning system is suitable for various complex indoor scenes, such as shopping malls, airports and other large-scale application environments.

[0043] The concept, specific structure and technical effects of the present invention will be further described below to fully understand the purpose, characteristics and effects of the present invention. DETAILED DESCRIPTION

[0044] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] In the following description, specific details such as specific internal procedures and techniques are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0046] An embodiment of the present invention provides a decentralized indoor positioning system, including multiple mobile devices with IMU sensors and UWB communication modules. When the mobile devices enter the indoor space, a decentralized communication network is constructed through ultra-wideband technology, and each mobile device serves as a node in the decentralized communication network; whenever the mobile devices enter the indoor space after obtaining an initial precise position outdoors through real-time dynamic technology, their high-precision positioning data is used as a calibration reference for the entire system; the mobile devices use IMU sensors and / or UWB communication modules to perform positioning in an indoor environment without GPS signals.

[0047] Through the decentralized P2P network, the newly entered mobile device communicates with the existing mobile devices in the room via UWB, sharing location data to achieve mutual calibration between devices. In this implementation, a decentralized P2P network communication protocol is also included to coordinate the exchange of positioning data and calibration instructions between devices.

[0048] The decentralized indoor positioning system and positioning method provided by the present invention are innovative dynamic systems. The core of the system is to build a decentralized network environment, which is composed of many mobile devices entering the indoor space. Each device in the indoor space plays the role of a node in the network. They do not rely on fixed infrastructure, but use their own technology to achieve positioning functions. This architecture allows each node to independently determine its own position, and at the same time communicate with other nodes in the network, exchange position information with each other through UWB (ultra-wideband) technology, and assist each other in position calibration. The embodiment of the present invention establishes a decentralized point-to-point (P2P) network, allowing mobile devices to communicate directly without a central server. Each device is a node in the network and is responsible for maintaining the integrity of the network and the security of data transmission. The communication protocol between devices adopts an advanced handshake mechanism to ensure that the device can respond quickly when entering or leaving the network. When joining the network, the device authenticates its identity through a digital certificate and an encryption key to prevent unauthorized access and data tampering. The system places special emphasis on the security of data transmission. All location information transmitted through the network is encrypted to ensure the confidentiality and integrity of the data. In addition, the network is able to self-organize and self-repair. When device movement causes network topology changes, the system automatically adjusts data paths to maintain communication continuity and stability.

[0049] In this decentralized network, each mobile device not only locates itself, but also plays the role of providing calibration support for other devices. When new devices enter the network with the latest outdoor positioning data obtained through RTK technology, their high-precision positioning information is used as a calibration benchmark to correct those devices that are already indoors and may have experienced position drift.

[0050] When a mobile device enters a room, the IMU sensor and UWB communication module are first activated. Before a new mobile device enters a room, the mobile device obtains its precise outdoor location through GPS or other high-precision positioning systems. This precise location is used as the initial reference point and stored in the device. At the moment of entering the room, a decentralized indoor positioning system (hereinafter referred to as the system) in an embodiment of the present invention gives the location information of the device a higher weight, so that it has a greater influence on other nodes in the subsequent data calibration. The high-precision location will serve as a reference benchmark for the system to guide the calibration process of the entire indoor network.

[0051] When a mobile device enters a room, it automatically scans the surrounding environment through UWB technology, searches for other existing mobile devices and establishes communication with them, thus forming a decentralized peer-to-peer (P2P) network. When a new device joins the P2P network, the system first receives its high-precision location information obtained from the outdoors, and gives this information a higher priority in the network, where the priority is set to the length of time the device has been indoors. This information will be used as a reference to calibrate the positioning data of other devices in the network, ensuring that the newly added node can effectively improve the overall positioning accuracy of the system.

[0052] Newly entered devices broadcast their precise initial positions to other devices in the network via UWB, triggering a calibration process for the entire network. All devices will recalibrate their respective positioning data based on this high-precision reference point. Over time, devices in the network are continuously calibrated based on the initial positions of new nodes and dynamically updated data. High-weighted reference position information is gradually digested by the system and used to correct the accumulated errors of the IMU sensors to ensure high-precision positioning of the system as a whole. In indoor environments, devices rely on IMU sensors for positioning. The data of the IMU sensors is regularly calibrated using the aforementioned high-precision reference points to reduce the accumulated errors during inertial navigation. The system regularly integrates the data of all devices for comprehensive calibration, and uses the dynamically added high-precision node information to continuously correct the errors of the IMU sensors to maintain the high accuracy of the positioning data.

[0053] In order to solve the problem of IMU sensor error accumulation over time, the embodiment of the present invention also includes an innovative method for position data exchange and calibration. When a device enters an indoor environment after determining its precise outdoor position through RTK technology, it becomes a reference node for the calibration network. These reference nodes communicate with surrounding devices using UWB technology to transmit their high-precision location information. Indoor devices receive this information and compare it with their own IMU sensor data to calculate an error model. This error model is then used to adjust and calibrate the position of the device to compensate for the position offset caused by the accumulation of IMU sensor errors. As new reference nodes continue to enter, the system can continue to perform real-time calibration to maintain high positioning accuracy for all indoor devices.

[0054] An embodiment of the present invention provides a decentralized indoor positioning system and positioning method, comprising the following steps:

[0055] Step 100, when a new mobile device enters the room and connects to the decentralized network, it first scans the surrounding environment through UWB communication technology, identifies and connects to other devices already in the network;

[0056] Step 200, the new mobile device broadcasts its current location through high-precision outdoor RTK positioning data and joins the decentralized network;

[0057] Step 300, the existing devices in the decentralized network receive the high-precision location information of the new mobile device through the UWB communication module and calculate the relative position relationship; specifically, the existing devices receive the high-precision location information of the new device through the UWB communication module, and after each device receives the location information of the new mobile device, it confirms the relative position relationship with the new mobile device through the timestamp and UWB signal strength. Among them, the existing device calculates the current relative position through its own IMU sensor data calculator, and then converts the UWB signal strength into a distance to determine the relative position with the new mobile device; the relative position relationship between the existing device and the new mobile device is calculated through triangulation or multi-point positioning algorithm.

[0058] Step 400, after calculating the relative position relationship, the existing device uses the high-precision position information of the new mobile device as a reference, calculates its own positioning deviation, and then performs error correction; specifically, the following formula is used to express it:

[0059] Δx=x new -x current

[0060] Δy=y new -y current

[0061] Among them, y new and xnew is the new position, y current and x current is the current position, that is, the corrected position. new is the new position of the existing device, x current is the current position of the existing equipment, Δx is the positioning deviation of the existing equipment; y new ,y current x and y are the new and current positions of the mobile device, and Δy is the positioning deviation of the mobile device. x and y refer to two mutually perpendicular coordinate axes excluding the height.

[0062] The existing device uses the calculated deviation values ​​Δx and Δy to correct its current position data:

[0063] x correct =Δx+x current

[0064] y correct =Δy+y current

[0065] The corrected location data is used as the current accurate location of the current device and is updated to the internal location record of the current device.

[0066] Step 500, the existing device after error correction broadcasts its updated location information to other devices in the decentralized network; wherein the updated location information includes calibrated location information and calibration deviation value. Each device maintains a current accuracy value (using the length of time in the room as the accuracy, measured in time). The accuracy of the device that has just entered is the highest, and the longer it stays indoors, the lower the accuracy. When the current device receives information sent by other devices, it will check the accuracy value of the other party. If the accuracy value of the other party is relatively high, it will be corrected according to the location information of the other party and the relative information between the two, and its own accuracy value will be updated to the accuracy value of the other party. If the accuracy value of the other party is relatively low, its own information will be passed to the other party for correction.

[0067] Step 600, as more new mobile devices join the network, the above process is repeated iteratively, providing positioning information for each device while improving the positioning accuracy of the entire decentralized network. Specifically, as more new devices join the network, the system will continuously iterate the calibration process; each time a new device enters with the latest high-precision location information, it will trigger a recalibration of the entire network, further improving the positioning accuracy of the entire network; each device continuously updates and corrects its own location information through the combination of UWB communication and IMU sensor data, maintaining the accuracy consistency of devices in the entire network.

[0068] In this way, even devices that cannot directly obtain their precise location due to the lack of GPS signals in indoor environments can dynamically calibrate their location through UWB communication with newly entered devices.

[0069] Over time, devices that rely solely on IMU sensors will accumulate positioning errors, resulting in position drift. The system of the present invention continuously has new devices entering, and each new device brings the latest outdoor precise position information obtained through RTK technology. This information is propagated in a decentralized network, providing a reference standard for indoor devices to calibrate their position data. Therefore, the following steps are included:

[0070] Step 1: Initial data collection and network connection

[0071] Before the device enters the room, it uses RTK (Real-Time Kinematic) technology to obtain high-precision outdoor location data. This data is usually accurate to the centimeter level and is the best representation of the current location. After entering the room, the new device first scans the existing device network through UWB technology and tries to establish a connection. The new device enters the network and broadcasts its high-precision location information. The existing device receives this location information through UWB signals and stores it as calibration reference data.

[0072] Step 2: Drift error calculation and position deviation estimation

[0073] Calculate the drift error of an existing device:

[0074] Existing devices use data from IMU sensors to continuously track their movements. However, due to the inherent errors of IMU sensors (such as bias in accelerometers and gyroscopes), the device's position information drifts over time.

[0075] Each device compares its current IMU estimated position with the high-precision reference position provided by the new device and calculates the drift error:

[0076] Δx drift =x reference -x current

[0077] Δy drift =y reference -y current

[0078] Among them, x reference and reference is the precise location provided by the new device, x current and current is the current position of the device calculated based on the IMU data, Δy drift , Δxdrift Is the offset. Among them, x and y refer to two mutually perpendicular coordinate axes excluding height, which are plane coordinate axes, equivalent to y representing front and back, and y representing left and right.

[0079] Step 3: Calibration parameter calculation and position correction

[0080] Calibration parameter calculation: Existing equipment calculates calibration parameters based on drift errors to correct the errors of IMU sensors. These parameters can include position deviation correction factors and weighting factors for sensor data:

[0081] x corrected =x current +α×Δx drift

[0082] y corrected =y current +α×Δy drift

[0083] Here, α is a weighting factor, which is usually adjusted dynamically according to the accuracy of the new device data and the quality of the received data.

[0084] Calibrated location update: Each device corrects its current location information using the calibration parameters. The corrected location information is stored and used as the latest location reference for the device.

[0085] Step 4: Network synchronization and global optimization

[0086] Broadcasting and global optimization of position calibration data: The calibrated device broadcasts its updated position information to other devices in the network. This information includes not only the current position after calibration, but also error correction factors and weight information.

[0087] After receiving this updated information, other devices will incorporate it into the global optimization algorithm to further improve the overall positioning accuracy of the network.

[0088] Dynamic iteration and continuous calibration: As more new devices are added, the network continuously performs dynamic iteration calibration to further correct drift errors and ensure that the positioning accuracy of the entire system remains at a high level during long-term operation.

[0089] This process is a continuous cycle. Every time a new device brings in high-precision data, it triggers a recalibration of the entire network, allowing the positioning system to be continuously optimized and improved. This calibration mechanism ensures the positioning accuracy of all indoor devices, allowing the positioning service of the entire system to be continuously optimized and improved over time.

[0090] Through these innovative designs, the present invention not only improves the accuracy and reliability of indoor positioning, but also provides a novel decentralized positioning network mode, so that the indoor positioning system can self-maintain and improve positioning accuracy without external signals or infrastructure support.

[0091] The indoor positioning system of the present invention is designed to be dynamically adaptive and can be updated and maintained according to the real-time flow of devices in the indoor environment. The system can not only handle data changes caused by the entry and exit of devices, but also cope with the challenges brought by changes in the physical environment. Through the decentralized P2P network, the position calibration between devices becomes a continuous process. Whenever a device enters with new external RTK position data, all devices in the network can use this data to self-calibrate, and so on, forming a continuous calibration chain. In this way, the system can continuously provide accurate indoor positioning services, ensuring that the location information of each device can be maintained within an accuracy range of 2 cm even without external signal support. In addition, the system is designed with energy efficiency in mind to ensure that the battery consumption of the device is kept to a minimum when performing real-time position updates and calibrations.

[0092] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A decentralized indoor positioning system, characterized in that: The invention comprises a plurality of mobile devices with IMU sensors and UWB communication modules. When the mobile devices enter indoor spaces, a decentralized communication network is constructed by ultra-wideband technology, and each mobile device serves as a node in the decentralized communication network. Whenever the mobile devices enter indoor spaces after obtaining an initial precise position outdoors by real-time dynamic technology, their high-precision positioning data is used as a calibration reference for the entire system. The mobile devices use IMU sensors and / or UWB communication modules to perform positioning in indoor environments without GPS signals.

2. A decentralized indoor positioning system as claimed in claim 1, characterized in that: Through the decentralized P2P network, newly entered mobile devices communicate with existing mobile devices indoors via UWB, sharing location data to achieve mutual calibration between devices.

3. A decentralized indoor positioning system as claimed in claim 1, characterized in that: It also includes a decentralized P2P network communication protocol for coordinating the exchange of positioning data and calibration instructions between devices.

4. A positioning method using a decentralized indoor positioning system as described in any one of claims 1 to 3, characterized in that: The following steps are involved: When a new mobile device enters the room and connects to the decentralized network, it first scans the surrounding environment through UWB communication technology, identifies and connects to other devices already in the network; The new mobile device broadcasts its current location via high-precision outdoor RTK positioning data and joins the decentralized network; Existing devices in the decentralized network receive high-precision location information from new mobile devices through UWB communication modules and calculate relative position relationships; After calculating the relative position relationship, the existing device uses the high-precision position information of the new mobile device as a reference, calculates its own positioning deviation, and then performs error correction; The error-corrected existing device broadcasts its updated location information to other devices in the decentralized network; As more new mobile devices join the network, the above process is repeated iteratively, providing positioning information for each device while improving the positioning accuracy of the entire decentralized network.

5. A decentralized indoor positioning method as claimed in claim 4, characterized in that: The existing devices in the decentralized network receive the high-precision location information of the new mobile device through the UWB communication module and calculate the relative position relationship. Specifically, the existing devices receive the high-precision location information of the new device through the UWB communication module. After each device receives the location information of the new mobile device, it confirms the relative position relationship with the new mobile device through the timestamp and UWB signal strength.

6. A decentralized indoor positioning method as claimed in claim 5, characterized in that: The existing device calculates the current relative position through its own IMU sensor data, and then converts the UWB signal strength into a distance to determine the relative position between the existing device and the new mobile device; The relative position relationship between the existing device and the new mobile device is calculated through triangulation or multi-point positioning algorithm.

7. A decentralized indoor positioning method as claimed in claim 6, characterized in that: After calculating the relative position relationship, the existing device uses the high-precision position information of the new mobile device as a reference to calculate its own positioning deviation Δx, Δy, which is specifically expressed using the following formula: Δx=x new -x current Δy=y new -y current Among them, x new is the new position of the existing device, x current is the current position of the existing equipment, Δx is the positioning deviation of the existing equipment; y new ,y current y is the new position and current position of the mobile device, and Δy is the positioning deviation of the mobile device.

8. A decentralized indoor positioning method as claimed in claim 7, characterized in that: The existing device uses the calculated deviation values ​​Δx and Δy to correct its current position data: x correct =Δx+x current y correct =Δy+y current The corrected location data is used as the current accurate location of the current device and is updated to the internal location record of the current device.

9. A decentralized indoor positioning method as claimed in claim 4, characterized in that: The existing device after error correction broadcasts its updated location information to other devices in the decentralized network, wherein the updated location information includes the calibrated location information and the calibration deviation value.