An indoor positioning method based on relative air pressure and UWB fusion

Abstract

The application discloses an indoor positioning method based on relative air pressure and UWB fusion, and comprises the following steps: in step one, an air pressure sensor is used to obtain an air pressure value; in step two, the relative air pressure value is fused with the original measured height value of UWB; in step three, a positioning framework of data uploaded to a server by an anchor base station; and in step four, a three-dimensional positioning system is constructed. The method has the beneficial effects that the accuracy of UWB positioning technology in the height direction is improved, the precision of indoor positioning is further improved, the real-time performance of relative air pressure acquisition is improved, the accuracy of relative air pressure is further improved, the height value fused with UWB is more accurate, and finally, high-precision and low-delay positioning values are obtained.

Description

Technical Field

[0001] This invention relates to an indoor positioning method, and more particularly to an indoor positioning method based on the fusion of relative air pressure and UWB. Background Technology

[0002] Currently, with the continuous development and improvement of new technologies such as the Internet of Things, people's understanding of location awareness is becoming increasingly profound, and the demand for location-based services is also growing daily. With the development of modern technology, location-based services have gradually permeated all aspects of human life as a lifestyle. Accurate location information is a prerequisite for providing location-based services. The need for accurate indoor positioning is particularly urgent, but existing technologies suffer from low accuracy, short range, and high cost for indoor positioning. In terms of accuracy, especially three-dimensional height accuracy, it is even lower and cannot meet the application requirements of some high-precision indoor positioning scenarios.

[0003] In some complex scenarios, existing technologies have some shortcomings. For example, infrared technology has a short transmission distance and cannot penetrate obstacles; ultrasonic positioning technology has a small detection angle and short detection distance, cannot accurately describe the position of obstacles, and has poor anti-interference ability.

[0004] Therefore, the starting point of this invention is to improve the accuracy and distance of indoor positioning at a lower cost, so that indoor positioning technology can be promoted and truly applied to various scenarios such as industry and life, and people can enjoy the convenience of positioning services indoors.

[0005] For the reasons mentioned above, this invention proposes an indoor positioning method based on the fusion of relative air pressure and UWB. By fusing the elevation difference obtained from relative air pressure with the positioning value obtained from UWB, the accuracy of UWB positioning technology in terms of elevation is improved, thereby further improving the accuracy and distance of indoor positioning, and thus promoting the application scale of indoor positioning technology in various fields.

[0006] Existing studies simply use relative air pressure as the elevation value and combine it with UWB's two-dimensional coordinates to obtain the positioning coordinates. This achieves a level of accuracy in distinguishing floors in terms of elevation. However, this invention filters the air pressure value to obtain an accurate air pressure value, thereby obtaining an accurate relative air pressure. By using an improved algorithm that adapts to elevation differences, it deeply integrates the relative air pressure with UWB, thereby significantly reducing the impact of DOP on UWB positioning accuracy and effectively improving indoor positioning accuracy.

[0007] This invention not only effectively improves positioning accuracy but also greatly enhances positioning distance. The positioning limit of UWB itself can reach 200 meters, while the relative air pressure value can be transmitted via WIFI. Its positioning distance depends on the network status, thus solving the shortcomings of the existing technology in terms of short positioning and transmission distance. Summary of the Invention

[0008] The main objective of this invention is to address the numerous problems commonly found in existing indoor positioning methods, such as low accuracy, short distance, and high cost, by providing an indoor positioning method based on the fusion of relative air pressure and UWB.

[0009] The indoor positioning method based on relative air pressure and UWB fusion provided by this invention includes the following steps:

[0010] Step 1: Obtain air pressure values ​​through air pressure sensors and perform Kalman filtering on them. Calculate the relative air pressure after performing Kalman filtering on two or more air pressure sensors.

[0011] Step 2: The relative pressure value processed in Step 1 and the original altitude value measured by UWB are fused together;

[0012] Step 3: A positioning framework that uses the slave device as the positioning node, the master device as the anchor base station, and the anchor base station to upload data to the server;

[0013] Step 4: Calculate the three-dimensional coordinates of the node to be measured using a collaborative algorithm consisting of a Kalman filter algorithm and an improved algorithm adapted to elevation difference, and construct a three-dimensional positioning system.

[0014] In step one, the obtained air pressure value is filtered. The specific process is as follows:

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] Let the point to be located be X(x,y,z), with unknown coordinates, and four anchor points A. i (x i ,y i ,z i(i = 1, 2, 3, 4), coordinates known; d i Anchor point A i The actual distance d between the point X to be located and the point X to be located i Anchor point A i The measured distance between the point X to be located is assumed to be... e u The measurement error follows the formula N ~ (0, σ). u 2 The random variable is distributed as follows:

[0022]

[0023] Subtracting the fourth equation from the first three equations in system (1), and then rearranging and simplifying, we obtain the following linear system of equations:

[0024] AX = B (2)

[0025] in

[0026]

[0027]

[0028]

[0029] Therefore, the estimated coordinates of the point to be located, determined by this measurement, are as follows:

[0030]

[0031] In step two, the raw coordinates measured by UWB are affected by DOP, resulting in a large error in altitude measurement. Therefore, the filtered relative pressure value and the raw measured altitude value are fused together, as follows:

[0032] Assuming the third-dimensional coordinates are measured by UWB e0 represents the UWB positioning error, and the third-dimensional coordinate z of the positioning point is obtained using a barometer. b =z+e b e b The measurement error follows the formula N ~ (0, σ). b 2 Given a random variable with a ) distribution, and drawing inspiration from the Kalman filter, let... z0 will achieve the effect of fusing the relative air pressure value and the original measured altitude value, and will be used as the coordinate value on the X altitude of the final undetermined point;

[0033] The determination of the value of k must ensure that the variable The covariance of z0 is the smallest, because and Since it is independent of k, we only need to ensure that the variance of z0 is minimized, as follows:

[0034]

[0035]

[0036]

[0037] To minimize D(z0), we need to set the first derivative of D(z0) with respect to k to zero.

[0038]

[0039]

[0040] because but have By χ 2 The properties of distribution are obtained

[0041]

[0042] Based on the properties of variance, we know:

[0043]

[0044] Let R = [0 01]

[0045]

[0046] Organized

[0047]

[0048] ,in

[0049] C1 = M1 2 +M2 2 +M3 2 +(M1+M2+M3) 2

[0050]

[0051] [M1 M2 M3]=R(A T A) -1 A T .

[0052] The 3D positioning system in step four includes a data acquisition system and a receiving system. The data acquisition system can transmit the acquired data to the receiving system in real time via data communication. The data acquisition system includes a barometric pressure sensor, a UWB module, a WIFI module, a serial port module, and an STM32 embedded module. The barometric pressure sensor, UWB module, WIFI module, and serial port module are all connected to the STM32 embedded module. The barometric pressure sensor and UWB module can transmit the acquired data to the STM32 embedded module in real time. The STM32 embedded module, through the WIFI module and serial port module, transmits the processed data to the receiving system in real time. The receiving system is a mobile terminal capable of receiving data, and it is equipped with corresponding software programs for data reception and processing. The specific process is as follows:

[0053] The barometric pressure sensor acquires and filters the barometric pressure values, which are then transmitted back through the gateway. These values ​​are fused with the positioning values ​​transmitted back by the UWB module to obtain accurate three-dimensional positioning values. Furthermore, the positioning coordinates are obtained through a pre-defined software program, thus providing the accurate three-dimensional position of the mobile terminal serving as the receiving system.

[0054] The barometric pressure sensor is model BMP390L; the UWB module is model DWM1000; the WIFI module is model ESP8266; and the STM32 embedded module is model STM32F4 system board.

[0055] The beneficial effects of this invention are:

[0056] This invention provides an indoor positioning method based on relative air pressure and UWB fusion. By adapting an improved algorithm to the elevation difference, it fuses the elevation difference obtained from relative air pressure with the positioning value obtained from UWB, improving the elevation accuracy of UWB positioning technology and thus further enhancing indoor positioning precision. This promotes the widespread application of indoor positioning technology in various fields. The air pressure filtering algorithm in this invention, through Kalman filtering, improves the real-time performance of relative air pressure acquisition while filtering out some noise, further enhancing the accuracy of relative air pressure. This makes the fused elevation value with UWB more accurate, resulting in high-precision, low-latency positioning values. The technical solution provided by this invention is less affected by distance, enabling long-distance indoor positioning and contributing to the promotion and application of indoor positioning technology. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the indoor positioning method described in this invention.

[0058] Figure 2 This is a schematic diagram of the system structure of the three-dimensional positioning system described in this invention.

[0059] Figure 3This is a schematic diagram of the implementation method described in this invention.

[0060] Figure 4 This is a schematic diagram of the three-dimensional ranging model described in this invention.

[0061] Figure 5 This is a schematic diagram of the simulation scenario described in this invention.

[0062] Figure 6 This is a schematic diagram of the simulation results at (5,5,5) in the simulation scenario described in this invention.

[0063] Figure 7 This is a schematic diagram of the simulation results at (4.56, 3.17, 1.88) in the simulation scenario described in this invention. Detailed Implementation

[0064] Please see Figures 1 to 7 As shown:

[0065] The indoor positioning method based on relative air pressure and UWB fusion provided by this invention includes the following steps:

[0066] Step 1: Obtain air pressure values ​​through air pressure sensors and perform Kalman filtering on them. Calculate the relative air pressure after performing Kalman filtering on two or more air pressure sensors.

[0067] Step 2: The relative pressure value processed in Step 1 and the original altitude value measured by UWB are fused together;

[0068] Step 3: A positioning framework that uses the slave device as the positioning node, the master device as the anchor base station, and the anchor base station to upload data to the server;

[0069] Step 4: Calculate the three-dimensional coordinates of the node to be measured using a collaborative algorithm consisting of a Kalman filter algorithm and an improved algorithm adapted to elevation difference, and construct a three-dimensional positioning system.

[0070] In step one, the obtained air pressure value is filtered. The specific process is as follows:

[0071]

[0072]

[0073]

[0074]

[0075]

[0076]

[0077] Let the point to be located be X(x,y,z), with unknown coordinates, and four anchor points A. i (x i ,y i ,z i (i = 1, 2, 3, 4), coordinates known; d i Anchor point A i The actual distance d between the point X to be located and the point X to be located i Anchor point A i The measured distance between the point X to be located is assumed to be... e u The measurement error follows the formula N ~ (0, σ). u 2 The random variable is distributed as follows:

[0078]

[0079] Subtracting the fourth equation from the first three equations in system (1), and then rearranging and simplifying, we obtain the following linear system of equations:

[0080] AX = B (2)

[0081] in

[0082]

[0083]

[0084]

[0085] Therefore, the estimated coordinates of the point to be located, determined by this measurement, are as follows:

[0086]

[0087] In step two, the raw coordinates measured by UWB are affected by DOP, resulting in a large error in altitude measurement. Therefore, the filtered relative pressure value and the raw measured altitude value are fused together, as follows:

[0088] Assuming the third-dimensional coordinates are measured by UWB e0 represents the UWB positioning error, and the third-dimensional coordinate z of the positioning point is obtained using a barometer. b =z+e b e b The measurement error follows the formula N ~ (0, σ). b 2 Given a random variable with a ) distribution, and drawing inspiration from the Kalman filter, let... z0 will achieve the effect of fusing the relative air pressure value and the original measured altitude value, and will be used as the coordinate value on the X altitude of the final undetermined point;

[0089] The determination of the value of k must ensure that the variable The covariance of z0 is the smallest, because and Since it is independent of k, we only need to ensure that the variance of z0 is minimized, as follows:

[0090]

[0091]

[0092]

[0093] To minimize D(z0), we need to set the first derivative of D(z0) with respect to k to zero.

[0094]

[0095]

[0096] because but have By χ 2 The properties of distribution are obtained

[0097]

[0098] Based on the properties of variance, we know:

[0099]

[0100] Let R = [0 01]

[0101]

[0102] Organized

[0103]

[0104] ,in

[0105] C1 = M1 2 +M2 2 +M3 2 +(M1+M2+M3) 2

[0106]

[0107] [M1 M2 M3]=R(A T A) -1A T .

[0108] The 3D positioning system in step four includes a data acquisition system and a receiving system. The data acquisition system can transmit the acquired data to the receiving system in real time via data communication. The data acquisition system includes a barometric pressure sensor, a UWB module, a WIFI module, a serial port module, and an STM32 embedded module. The barometric pressure sensor, UWB module, WIFI module, and serial port module are all connected to the STM32 embedded module. The barometric pressure sensor and UWB module can transmit the acquired data to the STM32 embedded module in real time. The STM32 embedded module, through the WIFI module and serial port module, transmits the processed data to the receiving system in real time. The receiving system is a mobile terminal capable of receiving data, and it is equipped with corresponding software programs for data reception and processing. The specific process is as follows:

[0109] The barometric pressure sensor acquires and filters the barometric pressure values, which are then transmitted back through the gateway. These values ​​are fused with the positioning values ​​transmitted back by the UWB module to obtain accurate three-dimensional positioning values. Furthermore, the positioning coordinates are obtained through a pre-defined software program, thus providing the accurate three-dimensional position of the mobile terminal serving as the receiving system.

[0110] The barometric pressure sensor is model BMP390L; the UWB module is model DWM1000; the WIFI module is model ESP8266; and the STM32 embedded module is model STM32F4 system board.

[0111] Based on the above positioning model, a simulation experiment was conducted. This experiment selected four UWB nodes as anchor points, and the anchor point coordinates are as follows: Figure 5 As shown, the coordinates of the four anchor points are (7.5, 7.5, 7.5), (7.5, 2.5, 7.5), (2.5, 7.5, 7.5), and (2.5, 2.5, 2.5).

[0112] For the simulation scenario, the coordinates of the test positioning nodes were taken as (5,5,5) and (4.56,3.17,1.88). Array experiments were conducted in the simulation, comparing the positioning error of the UWB-barometer fusion positioning method with the UWB positioning error and the barometer elevation positioning error. The distribution of positioning errors for each iteration is shown below. Figure 6 , Figure 7 As shown. By Figure 6 , Figure 7 The simulation results show that, under typical indoor conditions, the model built by this invention can reduce the positioning error to a certain extent.

[0113] The indoor positioning algorithm designed in this invention, which integrates relative air pressure and UWB, improves the accuracy of indoor three-dimensional positioning, enabling practical applications of indoor positioning.

[0114] In the above embodiments, core modules such as UWB and barometric pressure sensors are used to implement the data processing. These two modules can be driven by a microcontroller, and barometric pressure data can be transmitted to the receiving end using transmission methods such as WIFI. Corresponding code programs are written based on the Kalman filter algorithm and the improved algorithm adapted to elevation to process the data. Overall, the feasibility and operability are high.

Claims

1. An indoor positioning method based on the fusion of relative air pressure and UWB, characterized in that: The method includes the following steps: Step 1: Obtain air pressure values ​​through barometric pressure sensors and perform Kalman filtering on them. Calculate the relative air pressure after Kalman filtering using two or more barometric pressure sensors. The obtained air pressure values ​​are then filtered. The specific process is as follows: ； ； ； ； ； Set the point to be located Coordinates unknown, four anchor points ( =1,2,3,4), coordinates are known; anchor point With the point to be located The actual distance between them anchor point and the point to be located The measured distance between them, assuming , For measurement error, it is subject to The random variables with the following distribution are as follows: ； Subtracting the fourth equation from the first three equations in system (1), and then rearranging and simplifying, we obtain the following linear system of equations: ； in ； ； ； Therefore, the estimated coordinates of the point to be located, determined by this measurement, are as follows: ； Step 2: The relative pressure value processed in Step 1 and the original altitude value measured by UWB are fused together; The raw coordinates obtained by UWB are affected by DOP, resulting in a large error in altitude measurement. Therefore, a filtered relative pressure value is used to fuse the raw altitude measurement value, as follows: Assuming the third-dimensional coordinates are measured by UWB , To account for UWB positioning errors, the third-dimensional coordinates of the positioning point are obtained using a barometer. , For measurement error, it is subject to Given a distributed random variable, and drawing inspiration from the Kalman filter, let... , This will achieve the effect of fusing relative air pressure values ​​and original measured altitude values, and use them as the coordinate values ​​at the final X altitude of the point to be determined; Determining the value must ensure that the variable , , The covariance is minimized because and and It's irrelevant, so we only need to ensure The goal is to minimize the variance, as detailed below: ； ； because ,but ,have ,Depend on The properties of distribution are obtained ； Based on the properties of variance, we know: ； ； ； Summarized as follows: ； ； ； ； Step 3: A positioning framework that uses the slave device as the positioning node, the master device as the anchor base station, and the anchor base station to upload data to the server. Step 4: Calculate the 3D coordinates of the node to be measured using a collaborative algorithm based on Kalman filtering and an improved algorithm adapted to elevation differences, and construct a 3D positioning system. The 3D positioning system includes a data acquisition system and a receiving system. The data acquisition system transmits the acquired data to the receiving system in real time via data communication. The data acquisition system includes a barometric pressure sensor, a UWB module, a WIFI module, a serial port module, and an STM32 embedded module. The barometric pressure sensor, UWB module, WIFI module, and serial port module are all connected to the STM32 embedded module. The barometric pressure sensor and UWB module transmit the acquired data to the STM32 embedded module in real time. The STM32 embedded module then transmits the processed data to the receiving system in real time via the WIFI module and serial port module. The receiving system is a mobile terminal capable of receiving data, equipped with corresponding software programs for data reception and processing. The specific process is as follows: The barometric pressure sensor acquires and filters the barometric pressure values, which are then transmitted back through the gateway. These values ​​are fused with the positioning values ​​transmitted back by the UWB module to obtain accurate three-dimensional positioning values. Furthermore, the positioning coordinates are obtained through a pre-defined software program, thus providing the accurate three-dimensional position of the mobile terminal serving as the receiving system.

2. The indoor positioning method based on relative air pressure and UWB fusion according to claim 1, characterized in that: The barometric pressure sensor is model BMP390L; the UWB module is model DWM1000; the WIFI module is model ESP8266; and the STM32 embedded system is model STM32F4 system board.

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