An indoor precise positioning system based on wireless local area network

Abstract

The invention provides an indoor precise positioning system based on a wireless local area network, comprising: a positioning tag for transmitting a wireless signal; a plurality of wireless access points for receiving the wireless signal transmitted by the positioning tag; a signal processing module connected to the plurality of wireless access points for receiving and processing signal data from each wireless access point; a position calculation module connected to the signal processing module for calculating the specific position of the positioning tag according to the processed signal data; and a user terminal. Compared with the prior art, the invention has the following beneficial effects: through a series of data receiving, filtering, denoising, calibration, fusion and positioning calculation processes, the positioning accuracy and reliability of the system are ensured, and the system comprises a data receiving module, a filter module, a denoising module, a data calibration module and a data fusion module, and each submodule works in coordination to realize a high-precision and real-time indoor positioning function.

Description

Technical Field

[0001] The invention belongs to the technical field of indoor positioning, and in particular relates to an indoor precise positioning system based on a wireless local area network. Background Art

[0002] With the rapid development of the Internet of Things and smart home technologies, indoor positioning technology has broad application prospects in indoor navigation, asset management, security monitoring and other fields. Existing indoor positioning technologies, such as ultra-wideband (UWB), Bluetooth and infrared, have certain positioning accuracy, but they are costly and complex to implement, making them difficult to popularize on a large scale. Positioning technology based on wireless LAN has become a hot topic of research due to its advantages such as low cost and easy deployment. However, the existing indoor positioning system based on wireless LAN still has certain shortcomings in positioning accuracy, real-time performance and system stability. Most of these shortcomings come from the signal processing module, which is used to receive and process signal data from each wireless access point. If the signal processing module is not processed and sent directly to the position calculation module, too much and complicated information will double the calculation amount of the position calculation module, and the calculated accuracy is not high. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention aims to provide an indoor precise positioning system based on a wireless local area network to solve the problems raised in the above background technology.

[0004] The present invention is implemented by the following technical solution: an indoor precise positioning system based on a wireless local area network, comprising:

[0005] Positioning tags, used to transmit wireless signals;

[0006] Multiple wireless access points for receiving wireless signals transmitted by positioning tags;

[0007] A signal processing module, connected to multiple wireless access points, for receiving and processing signal data from each wireless access point;

[0008] A position calculation module, connected to the signal processing module, is used to calculate the specific position of the positioning tag according to the processed signal data;

[0009] The user terminal is connected to the location calculation module and is used by the user to view the positioning results in real time.

[0010] As a preferred implementation, the output end of the positioning tag is connected to the input end of the wireless access point, the output end of the wireless access point is connected to the input end of the signal processing module, the output end of the signal processing module is connected to the input end of the position calculation module, and the output end of the position calculation module is connected to the input end of the user terminal.

[0011] As a preferred embodiment, it also includes a database for storing positioning data and historical trajectory data.

[0012] As a preferred implementation, the output end of the position calculation module is also connected to the input end of the database.

[0013] As a preferred implementation, the signal processing module includes:

[0014] Data receiving module: used to receive RSSI data from multiple wireless access points;

[0015] Filter module: Filters the received RSSI data to remove noise and interference in the signal;

[0016] Denoising module: further processes the filtered data to remove outliers and instantaneous interference signals;

[0017] Data calibration module: calibrates RSSI data according to the preset calibration algorithm to correct signal deviation caused by environmental factors;

[0018] Data fusion module: The RSSI data from different access points are fused and processed, and the signal strength of each access point is comprehensively considered to obtain more accurate positioning information.

[0019] As a preferred implementation, the position calculation module includes the following submodules:

[0020] Data input module: responsible for receiving RSSI data after filtering, denoising, calibration and fusion from the signal processing module, and receiving the location information of each access point.

[0021] Specifically: RSSI data of each access point is received from the signal processing module.

[0022] Read the location information of the access point to ensure the accuracy of the location data during the calculation process.

[0023] Distance estimation module: estimates the distance between the positioning tag and each access point based on the received RSSI value.

[0024] Specifically: Use the signal propagation model to convert the RSSI value into distance. The signal propagation model formula is as follows:

[0025]

[0026] d represents the estimated distance between the tag and the access point;

[0027] p0 is the RSSI reference value when the distance is 1 meter;

[0028] n is the path loss exponent;

[0029] Positioning algorithm module: Based on the estimated distances of multiple access points, the location of the positioning tag is calculated through the positioning algorithm;

[0030] The following algorithm can be used:

[0031] Trilateration: When the coordinates of three access points and the distances to those access points are known, the location of the positioning tag can be calculated using geometric methods.

[0032] Least squares method: When there are multiple access points, the least squares method is used to optimize the estimation results to minimize the location error.

[0033] Specifically: Assume that the coordinates of access points AP1, AP2, and AP3 are (0,0), (5,0), and (3,4), respectively, and the estimated distances are 3.68 meters, 4.12 meters, and 2.78 meters, respectively. The trilateration method calculates the coordinates of the positioning tag by solving a set of three-variable equations.

[0034] Error correction module: corrects the distance estimation error caused by environmental factors and optimizes the final positioning result.

[0035] Specific: Weighted calibration: Perform weighted calibration on the calculated position according to the signal stability of the access point or the degree of environmental influence.

[0036] Multiple sampling average: By measuring the same positioning tag position multiple times, the average value is taken to reduce the error.

[0037] Example: If the signal of AP2 is greatly affected by obstacles, you can reduce the weight of AP2 so that the data of AP1 and AP3 account for a higher proportion in the positioning result to improve accuracy.

[0038] Data output module: outputs the positioning calculation results to the user terminal and database so that the user can view the location of the positioning tag in real time. Specifically:

[0039] Format the coordinate data of the positioning label as visual data.

[0040] The data is output to the user terminal and displayed on the map.

[0041] The data is transferred to the database for storage to facilitate subsequent analysis and backtracking.

[0042] After adopting the above technical solution, the beneficial effect of the present invention is: through a series of data receiving, filtering, denoising, calibration, fusion and positioning calculation processes, the positioning accuracy and reliability of the system are ensured. Its structure includes a data receiving module, a filter module, a denoising module, a data calibration module, and a data fusion module. Each submodule works together to achieve a high-precision, real-time indoor positioning function. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0044] Figure 1 The present invention is a principle block diagram of an indoor precise positioning system based on wireless local area network.

[0045] Figure 2 The present invention is a principle block diagram of an indoor precise positioning system based on wireless local area network including a database.

[0046] In the figure, there are positioning tag 1, wireless access point 2, signal processing module 3, position calculation module 4, user terminal 5, and database 6. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] See also Figure 1 The present invention provides a technical solution: an indoor precise positioning system based on a wireless local area network, comprising:

[0049] Positioning tag 1, used for transmitting wireless signals;

[0050] A plurality of wireless access points 2, used for receiving wireless signals transmitted by the positioning tags 1;

[0051] A signal processing module 3, connected to the plurality of wireless access points 2, for receiving and processing signal data from each wireless access point 2;

[0052] The position calculation module 4 is connected to the signal processing module 3 and is used to calculate the specific position of the positioning tag 1 according to the processed signal data;

[0053] The user terminal 5 is connected to the position calculation module 4 and is used for the user to view the positioning result in real time.

[0054] The output end of the positioning tag 1 is connected to the input end of the wireless access point 2, the output end of the wireless access point 2 is connected to the input end of the signal processing module 3, the output end of the signal processing module 3 is connected to the input end of the position calculation module 4, and the output end of the position calculation module 4 is connected to the input end of the user terminal 5.

[0055] See also Figure 2 , also includes a database 6 for storing positioning data and historical trajectory data.

[0056] The output terminal of the position calculation module 4 is also connected to the input terminal of the database 6 .

[0057] Embodiment 1, as an embodiment of the present invention: the signal processing module 3 includes:

[0058] Data receiving module: used for receiving RSSI data from multiple wireless access points 2;

[0059] Filter module: Filters the received RSSI data to remove noise and interference in the signal;

[0060] Denoising module: further processes the filtered data to remove outliers and instantaneous interference signals;

[0061] Data calibration module: calibrates RSSI data according to the preset calibration algorithm to correct signal deviation caused by environmental factors;

[0062] Data fusion module: The RSSI data from different access points are fused and processed, and the signal strength of each access point is comprehensively considered to obtain more accurate positioning information.

[0063] The signal processing module 3 plays a vital role in the indoor precise positioning system based on wireless LAN. It is mainly responsible for processing the signal strength indication (RSSI) data received by multiple wireless access points 2 (AP) to ensure the accuracy and reliability of the positioning data. Its working principle can be divided into the following steps:

[0064] Data reception: The signal processing module 3 receives RSSI data from multiple wireless access points 2 , and each wireless access point 2 periodically transmits the RSSI value of the received positioning tag 1 signal to the signal processing module 3 .

[0065] Data preprocessing: The received RSSI data may contain some noise and interference. The signal processing module 3 first filters and denoises the data to remove outliers and noise to ensure data stability.

[0066] Data calibration: Due to the influence of environmental factors (such as walls, furniture, etc.), RSSI data may be biased. The signal processing module 3 calibrates the RSSI data through a calibration algorithm to reduce the impact of environmental factors on signal strength.

[0067] Data fusion: In an indoor environment, the signal of the positioning tag 1 may be received by multiple access points. The signal processing module 3 fuses the RSSI data from different access points, comprehensively considers the signal strength of each access point, and obtains more accurate positioning information.

[0068] In signal processing module 3:

[0069] Data receiving module:

[0070] Function: Receive RSSI data from multiple wireless access points 2.

[0071] Implementation: Receive data via Wi-Fi or other wireless communication protocols, and perform preliminary verification and format conversion to ensure the validity of the data.

[0072] Filter module:

[0073] Function: Filter RSSI data to remove noise from the signal.

[0074] Implementation: Use filtering algorithms such as mean filtering and Kalman filtering to smooth the RSSI data and reduce high-frequency noise in the signal.

[0075] Denoising module:

[0076] Function: Further process the filtered data to remove outliers and transient interference signals.

[0077] Implementation: Use outlier detection algorithms (such as the 3σ rule and box plot method) to identify and remove outliers in RSSI data to ensure data stability.

[0078] Data calibration module:

[0079] Function: According to the preset calibration algorithm, calibrate the RSSI data to correct the signal deviation caused by environmental factors.

[0080] Implementation: Through the pre-established calibration model, use polynomial regression, neural network and other methods to correct RSSI data and reduce the impact of the environment on the signal.

[0081] Data fusion module:

[0082] Function: Fuse RSSI data from different access points, comprehensively consider the signal strength of each access point, and obtain more accurate positioning information.

[0083] Implementation: Use data fusion technologies such as weighted averaging and Bayesian fusion to comprehensively process RSSI data of multiple access points to improve positioning accuracy.

[0084] Based on Example 1, we propose Example 1.1:

[0085] 1. Filter module

[0086] Working principle:

[0087] The main task of the filter module is to filter the received RSSI data and remove the high-frequency noise in the signal. Common filtering methods include mean filtering and Kalman filtering.

[0088] Assume that the received RSSI data is as follows:

[0089] Access point 1 (AP1): -60, -58, -59, -61, -60

[0090] Access point 2 (AP2): -65, -64, -66, -65, -64

[0091] We use mean filtering to process the data:

[0092] AP1 filtered data: (-60+-58+-59+-61+-60) / 5=-59.6

[0093] AP2 filtered data: (-65+-64+-66+-65+-64) / 5=-64.8

[0094] The filtered data:

[0095] AP1: -59.6

[0096] AP2: -64.8

[0097] 2. Denoising Module

[0098] Working principle:

[0099] The denoising module further processes the filtered data to remove outliers and transient interference signals. Common denoising methods include the 3σ rule and the boxplot method.

[0100] Assume that the filtered RSSI data is as follows:

[0101] AP1:-59.6,-59.2,-59.8,-80,-59.4

[0102] AP2:-64.8,-64.5,-64.7,-90,-64.6

[0103] We use the 3σ rule to remove outliers:

[0104] Calculate the mean and standard deviation of the AP1 data:

[0105] Mean = (-59.6 + -59.2 + -59.8 + -80 + -59.4) / 5 = -63.6

[0106] Standard deviation ≈ 8.9

[0107] Abnormal value judgment range: -63.6±3×8.9=(-90.3,-36.9)

[0108] -80 is out of range and is identified as an outlier. After removal, AP1 data: -59.6, -59.2, -59.8, -59.4

[0109] Calculate the mean and standard deviation of the AP2 data:

[0110] Mean = (-64.8 + -64.5 + -64.7 + -90 + -64.6) / 5 = -69.72

[0111] Standard deviation ≈ 10.1

[0112] Abnormal value judgment range: -69.72±3×10.1=(-99.02,-40.42)

[0113] -90 is out of range and is identified as an outlier. After removal, AP2 data: -64.8, -64.5, -64.7, -64.6

[0114] The denoised data:

[0115] AP1: -59.6, -59.2, -59.8, -59.4

[0116] AP2: -64.8, -64.5, -64.7, -64.6

[0117] 3. Data Calibration Module

[0118] Working principle:

[0119] The data calibration module corrects the deviation in RSSI data through the calibration algorithm to reduce the impact of environmental factors on signal strength. Commonly used calibration methods include polynomial regression and neural network.

[0120] Assume that the RSSI data after denoising is as follows:

[0121] AP1: -59.6, -59.2, -59.8, -59.4

[0122] AP2: -64.8, -64.5, -64.7, -64.6

[0123] We use a simple linear regression calibration:

[0124] Calibration model: RSSI_calibrated = a*RSSI_raw + b

[0125] Model parameters obtained through historical data training: a = 0.98, b = -1.5

[0126] Calibrate the AP1 data:

[0127] -59.6->0.98*-59.6-1.5=-59.308

[0128] -59.2->0.98*-59.2-1.5=-58.916

[0129] -59.8->0.98*-59.8-1.5=-59.504

[0130] -59.4->0.98*-59.4-1.5=-59.112

[0131] Calibrate the AP2 data:

[0132] -64.8->0.98*-64.8-1.5=-64.704

[0133] -64.5->0.98*-64.5-1.5=-64.41

[0134] -64.7->0.98*-64.7-1.5=-64.608

[0135] -64.6->0.98*-64.6-1.5=-64.51

[0136] Calibrated data:

[0137] AP1:-59.308,-58.916,-59.504,-59.112

[0138] AP2:-64.704,-64.41,-64.608,-64.51

[0139] 4. Data Fusion Module

[0140] Working principle:

[0141] The data fusion module comprehensively processes RSSI data from different access points and uses data fusion technologies such as weighted average to improve positioning accuracy.

[0142] Assume that the RSSI data after calibration is as follows:

[0143] AP1:-59.308,-58.916,-59.504,-59.112

[0144] AP2:-64.704,-64.41,-64.608,-64.51

[0145] We use weighted averaging to fuse the data:

[0146] The weight is calculated based on the signal stability and distance of the access point. Assume that the weight of AP1 is 0.6 and the weight of AP2 is 0.4.

[0147] The fused data:

[0148] Position 1: (-59.308*0.6+-64.704*0.4)=-61.5368

[0149] Position 2: (-58.916*0.6+-64.41*0.4)=-61.1128

[0150] Position 3: (-59.504*0.6+-64.608*0.4)=-61.7456

[0151] Position 4: (-59.112*0.6+-64.51*0.4)=-61.6712

[0152] The fused data:

[0153] Position 1: -61.5368

[0154] Position 2: -61.1128

[0155] Position 3: -61.7456

[0156] Position 4: -61.6712

[0157] Through the processing of the above filter module, denoising module, data calibration module and data fusion module, RSSI data has been significantly optimized and corrected. These modules work together to ensure the data accuracy and reliability of the indoor positioning system and achieve high-precision indoor positioning.

[0158] Based on Example 1.1, we propose Example 1.2

[0159] The indoor precise positioning system based on wireless LAN realizes precise position monitoring of the positioning tag 1 through the cooperation of the positioning tag 1, the wireless access point 2 (AP), the signal processing module 3, the position calculation module 4, the user terminal 5 and the database 6. Its working principle includes the following main steps:

[0160] Positioning tag 1 transmits a signal: Positioning tag 1 periodically transmits a wireless signal containing its unique identification information to the surrounding environment.

[0161] Access point receiving signal: Multiple wireless access points 2 arranged indoors receive wireless signals from the positioning tag 1 and record received signal strength indication (RSSI) values.

[0162] Signal processing module 3 processes data:

[0163] Data reception: Receive RSSI data from multiple access points.

[0164] Data preprocessing: The filter module filters the RSSI data to remove noise.

[0165] Denoising: The denoising module further processes the filtered data to remove outliers and transient interference signals.

[0166] Data calibration: The data calibration module calibrates the RSSI data and corrects the signal deviation.

[0167] Data fusion: The data fusion module comprehensively processes the RSSI data of different access points.

[0168] The position calculation module 4 calculates the position: based on the processed RSSI data, a positioning algorithm is used to calculate the specific position of the positioning tag 1.

[0169] The user terminal 5 displays the results: the user terminal 5 is connected to the position calculation module 4 via the network to view the positioning results in real time.

[0170] Database 6 stores data: Database 6 stores the real-time location data and historical trajectory data of the positioning tag 1, which is convenient for users to query and analyze.

[0171] Specific:

[0172] Assume that there is a positioning tag 1 and three wireless access points 2 (AP1, AP2, AP3) in an indoor environment. The specific implementation steps are as follows:

[0173] Positioning tag 1 transmits signal:

[0174] The positioning tag 1 transmits a wireless signal containing its unique identification information once a second.

[0175] The access point receives the signal and records the RSSI data:

[0176] RSSI data received by AP1: -60, -58, -59, -61, -60

[0177] RSSI data received by AP2: -65, -64, -66, -65, -64

[0178] RSSI data received by AP3: -70, -68, -69, -71, -70

[0179] Signal processing module 3 processes data:

[0180] Filter module:

[0181] AP1 filtered data: (-60+-58+-59+-61+-60) / 5=-59.6

[0182] AP2 filtered data: (-65+-64+-66+-65+-64) / 5=-64.8

[0183] AP3 filtered data: (-70+-68+-69+-71+-70) / 5=-69.6

[0184] Denoising module (assuming there are no obvious outliers, no further denoising is required):

[0185] AP1 denoising data: -59.6

[0186] AP2 denoising data: -64.8

[0187] AP3 denoising data: -69.6

[0188] Data calibration module:

[0189] Calibration model: RSSI_calibrated = 0.98*RSSI_raw + b

[0190] Parameter: b = -1.5

[0191] AP1 calibrated data: 0.98*-59.6-1.5=-59.208

[0192] AP2 calibrated data: 0.98*-64.8-1.5=-64.304

[0193] AP3 calibrated data: 0.98*-69.6-1.5=-68.808

[0194] Data fusion module (assuming the weights are all 1 / 3):

[0195] The fused RSSI data: (-59.208+-64.304+-68.808) / 3=-64.1067

[0196] Position calculation module 4 calculates the position:

[0197] Assuming the trilateral measurement method is used, the position of positioning tag 1 is calculated based on the fused RSSI data and the known positions of the access points (AP1, AP2, and AP3 are at the vertices of the triangle).

[0198] User terminal 5 displays the result:

[0199] The user views the positioning result through the mobile application or the web interface, and the location of the positioning tag 1 is displayed on the indoor map.

[0200] Database 6 stores data:

[0201] The real-time location data and historical trajectory data are stored in the database 6, and the user can query and analyze them.

[0202] Through the above steps, the indoor precise positioning system based on wireless LAN successfully realizes the precise position monitoring of the positioning tag 1. The signal processing module 3 improves the accuracy and stability of the RSSI data through filtering, denoising, calibration and fusion processing, ensuring the accuracy of the positioning result. The system has the advantages of high precision, strong real-time performance, high system stability and low cost, and is suitable for various indoor positioning scenarios.

[0203] Based on Example 1.2, we propose Example 2

[0204] The position calculation module 4 includes the following submodules:

[0205] Data input module: responsible for receiving RSSI data after filtering, denoising, calibration and fusion from the signal processing module, and receiving the location information of each access point.

[0206] Specifically: RSSI data of each access point is received from the signal processing module.

[0207] Read the location information of the access point to ensure the accuracy of the location data during the calculation process.

[0208] Distance estimation module: estimates the distance between the positioning tag and each access point based on the received RSSI value.

[0209] Specifically: Use the signal propagation model to convert the RSSI value into distance. The signal propagation model formula is as follows:

[0210]

[0211] d represents the estimated distance between the tag and the access point;

[0212] p0 is the RSSI reference value when the distance is 1 meter;

[0213] n is the path loss exponent;

[0214] Positioning algorithm module: Based on the estimated distances of multiple access points, the location of the positioning tag is calculated through the positioning algorithm;

[0215] The following algorithm can be used:

[0216] Trilateration: When the coordinates of three access points and the distances to those access points are known, the location of the positioning tag can be calculated using geometric methods.

[0217] Least squares method: When there are multiple access points, the least squares method is used to optimize the estimation results to minimize the location error.

[0218] Specifically: Assume that the coordinates of access points AP1, AP2, and AP3 are (0,0), (5,0), and (3,4), respectively, and the estimated distances are 3.68 meters, 4.12 meters, and 2.78 meters, respectively. The trilateration method calculates the coordinates of the positioning tag by solving a set of three-variable equations.

[0219] Error correction module: corrects the distance estimation error caused by environmental factors and optimizes the final positioning result.

[0220] Specific: Weighted calibration: Perform weighted calibration on the calculated position according to the signal stability of the access point or the degree of environmental influence.

[0221] Multiple sampling average: By measuring the same positioning tag position multiple times, the average value is taken to reduce the error.

[0222] Example: If the signal of AP2 is greatly affected by obstacles, you can reduce the weight of AP2 so that the data of AP1 and AP3 account for a higher proportion in the positioning result to improve accuracy.

[0223] Data output module: outputs the positioning calculation results to the user terminal and database so that the user can view the location of the positioning tag in real time. Specifically:

[0224] Format the coordinate data of the positioning label as visual data.

[0225] The data is output to the user terminal and displayed on the map.

[0226] The data is transferred to the database for storage to facilitate subsequent analysis and backtracking.

[0227] Based on Example 2, we propose Example 2.1. In this example, we combine: positioning tag 1, multiple wireless access points 2, signal processing module 3, position calculation module 4, user terminal 5, and their specific structure to illustrate the specific working principle of the indoor precise positioning system based on wireless local area network.

[0228] Positioning tag: Installed on the equipment or person that needs to be located, it is responsible for periodically transmitting wireless signals into the environment. The signal is transmitted once per second and contains a unique identification ID.

[0229] Wireless access point (AP): Three wireless access points are deployed in the positioning area, located at AP1 (0,0), AP2 (5,0), and AP3 (3,4). Each AP is connected to the control center to receive and transmit signal strength data in real time.

[0230] Signal processing module: used to receive RSSI data transmitted from each AP and perform filtering, denoising, calibration and fusion processing on the data.

[0231] Position calculation module: Calculates the precise position of the positioning tag based on the processed RSSI data.

[0232] User terminal: Users view positioning results through mobile phones or computers to understand the current location of the tag.

[0233] System working process

[0234] Positioning tag transmits signal

[0235] The positioning tag transmits a wireless signal containing a unique identification ID once a second.

[0236] The unique ID of the target tag is "Tag001".

[0237] The wireless access point receives the signal and transmits RSSI data

[0238] The RSSI data received by AP1 is: -60, -59, -61, -58, -60

[0239] The RSSI data received by AP2 is: -64, -63, -65, -66, -64

[0240] The RSSI data received by AP3 is: -70, -69, -68, -71, -70

[0241] Each AP transmits the received signal strength data to the signal processing module in real time.

[0242] Data processing of signal processing module

[0243] Filter module: Filters the received RSSI data to remove random noise.

[0244] AP1 filtering result: (-60+-59+-61+-58+-60) / 5=-59.6

[0245] AP2 filtering result: (-64+-63+-65+-66+-64) / 5=-64.4

[0246] AP3 filtering result: (-70+-69+-68+-71+-70) / 5=-69.6

[0247] Denoising module: further removes outliers in the data to ensure the continuity and accuracy of the data.

[0248] Data calibration module: calibrates RSSI data to make it more accurate.

[0249] Calibration model: RSSI_calibrated = 0.98*RSSI_raw + b

[0250] Parameter: b = -1.5

[0251] AP1 calibrated data: 0.98*-59.6-1.5=-59.208

[0252] AP2 calibrated data: 0.98*-64.8-1.5=-64.304

[0253] AP3 calibrated data: 0.98*-69.6-1.5=-68.808

[0254] Data fusion module: Fusion of RSSI data of each access point to ensure data consistency of multiple access points.

[0255] Position calculation module positioning calculation

[0256] Distance estimation module: The distance between the positioning tag and each access point is estimated through the calibrated RSSI value. The formula is as follows:

[0257]

[0258] AP1 estimated distance: 3.56 meters

[0259] AP2 estimated distance: 5.13 meters

[0260] AP3 estimated distance: 7.24 meters

[0261] Positioning algorithm module: Use triangulation to calculate the location of the positioning tag, combined with the known AP location and estimated distance. Assume that the coordinates obtained by triangulation are (2.8, 1.5).

[0262] Error correction module: perform error correction on the coordinates to improve accuracy.

[0263] User terminal display

[0264] The user terminal connects to the location calculation module in real time through the network, displays the real-time location coordinates (2.8, 1.5) of the positioning tag, and maps it onto a flat map.

[0265] The user terminal displays the tag’s unique ID “Tag001” and current location information, making it easier for users to track and query.

[0266] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An indoor precise positioning system based on wireless local area network, characterized in that: include: Positioning tags, used to transmit wireless signals; Multiple wireless access points for receiving wireless signals transmitted by positioning tags; A signal processing module, connected to multiple wireless access points, for receiving and processing signal data from each wireless access point; A position calculation module, connected to the signal processing module, is used to calculate the specific position of the positioning tag according to the processed signal data; The user terminal is connected to the location calculation module and is used by the user to view the positioning results in real time.

2. The indoor precise positioning system based on wireless local area network as claimed in claim 1, characterized in that: The output end of the positioning tag is connected to the input end of the wireless access point, the output end of the wireless access point is connected to the input end of the signal processing module, the output end of the signal processing module is connected to the input end of the position calculation module, and the output end of the position calculation module is connected to the input end of the user terminal.

3. The indoor precise positioning system based on wireless local area network as claimed in claim 2, characterized in that: It also includes a database for storing positioning data and historical trajectory data.

4. The indoor precise positioning system based on wireless local area network as claimed in claim 3, characterized in that: The output end of the position calculation module is also connected to the input end of the database.

5. The indoor precise positioning system based on wireless local area network as claimed in claim 1 or 4, characterized in that: The signal processing module comprises: Data receiving module: used to receive RSSI data from multiple wireless access points; Filter module: Filters the received RSSI data to remove noise and interference in the signal; Denoising module: further processes the filtered data to remove outliers and instantaneous interference signals; Data calibration module: calibrates RSSI data according to the preset calibration algorithm to correct signal deviation caused by environmental factors; Data fusion module: The RSSI data from different access points are fused and processed, and the signal strength of each access point is comprehensively considered to obtain more accurate positioning information.

6. The indoor precise positioning system based on wireless local area network as claimed in claim 5, characterized in that: The position calculation module includes the following submodules: Data input module: responsible for receiving RSSI data after filtering, denoising, calibration and fusion from the signal processing module, and receiving the location information of each access point; Distance estimation module: estimates the distance between the positioning tag and each access point based on the received RSSI value; Specifically: Use the signal propagation model to convert the RSSI value into distance. The signal propagation model formula is as follows: d represents the estimated distance between the tag and the access point; p0 is the RSSI reference value when the distance is 1 meter; n is the path loss exponent; Positioning algorithm module: Based on the estimated distances of multiple access points, the location of the positioning tag is calculated through the positioning algorithm; Error correction module: corrects the distance estimation error caused by environmental factors and optimizes the final positioning result; Data output module: outputs the positioning calculation results to the user terminal and database so that the user can view the location of the positioning tag in real time. Specifically: Format the coordinate data of the positioning label into visual data; Output the data to the user terminal and display it on the map; The data is transferred to the database for storage to facilitate subsequent analysis and backtracking.

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