A WIFI and INS fusion positioning system and method for mobile phones based on Kalman filtering

Abstract

The invention discloses a WIFI and INS fusion positioning system and method for a mobile phone based on Kalman filtering, comprising a data acquisition system, a data preprocessing system and a positioning solution system, wherein the data acquisition system collects and stores data into a data set, and at the same time, time matching of data is performed inside the data set; the data preprocessing system comprises an INS data preprocessing part and a WIFI data preprocessing part, and the data preprocessing part is a sliding time system based on the Wi-Fi data state, which is represented by the data preprocessing system processing a certain amount of WIFI data, and the data in the data set passes through the data preprocessing system to obtain the residual and Jacobian matrix of the corresponding part and determine the parameter vector to be determined, and the parameter vector to be determined is related to the number of routers that successfully communicate with the mobile phone at the current moment; finally, the positioning solution system constructs a Kalman filter equation according to the data, and at the same time obtains the positioning result and performs visualization processing.

Description

Technical Field

[0001] The present invention relates to the field of indoor positioning technology, and in particular to a WIFI and INS fusion positioning system and method for a mobile phone based on Kalman filtering. Background Art

[0002] The application of location-based services has attracted more and more attention to positioning technology, and at the same time, higher requirements have been put forward for the accuracy of positioning results. With the popularization and application of smart phones, mobile phones that provide a variety of location-based services will become the main carriers of high-precision positioning for the public in the future. Since navigation signals based on the Global Navigation Satellite System (GNSS) are difficult to receive indoors, they cannot be used for indoor positioning. In order to solve the problem of indoor positioning, a variety of technical solutions have been proposed, such as those based on WiFi, ultra-wideband, Bluetooth, inertial sensors, etc. Compared with other technologies, WiFi has received more attention due to its large audience and low price. Distance measurement methods such as ToA and TDoA have serious deviations in distance measurement results due to clock errors, so it is extremely unreasonable to use them for positioning. The RTT distance measurement method can provide more accurate distance measurement results by eliminating the influence of clock errors, but it cannot obtain good positioning results due to the existence of semi-system errors. Therefore, there is no reasonable solution to the problem of fusion positioning of WIFI and INS (Inertial Navigation System). Summary of the invention

[0003] The object of the present invention is to provide a WIFI and INS fusion positioning system and method for a mobile phone based on Kalman filtering to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: a WIFI and INS fusion positioning system based on Kalman filtering for mobile phones, including a data acquisition system, a data preprocessing system and a positioning solution system;

[0005] The data acquisition system is used to collect and store data into a data set, and at the same time, time matching of data is performed within the data set;

[0006] The data preprocessing part is a sliding time system based on the WiFi data status, including an INS data preprocessing part and a WIFI data preprocessing part;

[0007] The positioning and solving system is used to perform positioning and solving on the processing result of the sliding time system from the data preprocessing system, construct a Kalman filter equation according to the obtained data in the sliding time system, and simultaneously obtain the positioning result and perform visualization processing;

[0008] According to another aspect of the present invention, a WIFI and INS fusion positioning method based on Kalman filtering for a mobile phone is proposed, comprising the following steps:

[0009] Step 1: The data acquisition system collects and stores data into a data set, and the data acquisition system can retrieve database information and add it to the data set;

[0010] Step 2: The data acquisition system repackages the data set containing the database information and sends it to the data preprocessing system;

[0011] Step 3: The data preprocessing system performs noise reduction processing on the two types of data, and performs data matching within the data set in the time dimension, so that the WIFI data is point data, and the INS data is block data between the time difference between the two WIFI data, and generates a positioning solution signal according to the conditions;

[0012] Step 4, the WIFI data preprocessing part of the data preprocessing system obtains the first Jacobi matrix, the first residual vector and the first parameter vector to be determined after Taylor expansion by constructing a three-dimensional space distance formula including phase distortion noise through the location of the router, the WiFi-RTT ranging result, the RTT variance and the location at the previous moment; the first parameter vector to be determined includes the location of each point and the phase distortion noise result;

[0013] Step 5, the INS data preprocessing part of the data preprocessing system integrates and iterates the time difference, the noise matrix, the acceleration value, and the angular velocity value to obtain the overall velocity value, the angular velocity value, the position change, the second residual vector, the second Jacobi matrix corresponding to the residual, and the second parameter vector to be determined between two time intervals on the WiFi time scale, wherein the second parameter vector to be determined includes the velocity and angle of each point in the interval;

[0014] Step 6: Update the first Jacobi matrix, the first residual vector and the first parameter vector to be found obtained in step 4, and the second Jacobi matrix, the second residual vector and the second parameter vector to be found obtained in step 5; obtain a third Jacobi matrix, a third residual vector and a third parameter vector to be found containing both WIFI and INS data;

[0015] Step 7, saving the third Jacobi matrix, the third residual vector and the third parameter vector to be determined obtained in step 6 to a buffer, and returning to step 1, while waiting for the positioning solution signal, if the positioning solution signal is received, proceeding to step 8;

[0016] Step 8: passing the data in the buffer to the positioning solution system, the positioning solution system starts the Kalman filter and obtains the vector results of each parameter to be determined and the corresponding covariance matrix;

[0017] Step 9: Return the result of each parameter vector to be determined and the corresponding covariance matrix to step 7, and update the data in the buffer.

[0018] Further, the data set includes WIFI data, and the WIFI data specifically includes the BSSID address, RTT, RTT variance, RSSI, and timestamp of the router. The inertial navigation data includes the original inertial navigation data. Preferably, the original inertial navigation data includes the magnetic count value, the gravity count value, the acceleration count value, the gyroscope value, and the mobile phone hardware temperature value, and also includes the acceleration, angular velocity, angle, timestamp and other parameters preliminarily calculated by temperature calibration, turntable calibration, filtering, etc. of the data, and also includes retrieving database data related to the current data in the data of any intermediate link calculated previously;

[0019] Furthermore, in step 3, when the WIFI relative time series is inconsistent with the INS relative time series, the data matching needs to be based on the WIFI time series and the INS data needs to be divided according to time. Preferably, the division does not process the original data, speed and acceleration parameters, and the time dimensionless parameters such as position change and rotation angle are linearly divided by time to obtain INS data results consistent with the WIFI time series benchmark;

[0020] Furthermore, in step 3, the positioning solution signal is related to the sliding time system in claim 1, and when the data in the buffer exceeds the sliding time system threshold and a new WIFI RTT signal enters the buffer, the positioning solution signal is triggered;

[0021] Furthermore, the sliding time system is essentially a forgetful system. Furthermore, the forgetful system is described as processing a fixed amount of WiFi RTT data and the INS data of the corresponding time. After exceeding the fixed amount, new WiFi data enters the sliding time system, and the sliding time system discards the last WiFi RTT data in the sliding time system and the INS data corresponding to the last WiFi RTT data to ensure the balance and constancy of the data in the sliding time system.

[0022] Furthermore, the WIFI data preprocessing system includes operations such as monitoring the communication state change of WIFI-RTT data, white noise processing, phase distortion noise processing, multipath effect processing, kernelized ridge regression, reflection projection, etc. The communication state change monitoring signal is transmitted to the positioning solution system. If the information provided by the monitoring signal believes that the communication state has not been changed, but the phase distortion noise change calculated by the positioning solution system exceeds the rated threshold, it is believed that this part of the increment is caused by the multipath effect or non-line-of-sight phenomenon, and this phase distortion noise maintains the result of the previous moment. On the contrary, if the information provided by the monitoring signal believes that the communication state has changed, the difference between the two solution results is ignored, and the new value is taken as the phase distortion noise;

[0023] Furthermore, the data preprocessing system provides the positioning solution system with a noise matrix of both WiFi data and INS data, which is obtained by weighting the statistics of the data in the sliding time system at this moment with the proximity index;

[0024] Furthermore, the parameters to be estimated of the positioning solution system include the position, speed, and posture of all WIFI signals at the time of occurrence under the sliding time system, and also include the phase distortion noise of the mobile phone and all routers.

[0025] Furthermore, the result obtained by the positioning solution system is the data result within the sliding time system, that is, the present invention will obtain multiple estimates and variance results of a position, speed, posture, and phase distortion noise of the mobile phone and all routers, and calculate these estimates and variance results at the same time. The calculation results are used as a signal evaluation system to constrain the solution equation. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention, the drawings required for use in the embodiments 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 work.

[0027] Figure 1 Schematic diagram of a WIFI and INS fusion positioning system based on Kalman filtering for a mobile phone according to an embodiment of the present invention;

[0028] Figure 2 The present invention is a flowchart of a WIFI and INS fusion positioning method based on Kalman filtering for a mobile phone according to an embodiment of the present invention.

[0029] Reference numerals:

[0030] 1. Data acquisition system; 2. Data preprocessing system; 3. Positioning solution system; 4. Data set; 5. INS data preprocessing part; 6. WIFI data preprocessing part; 7. Sliding time system; 8. WIFI data; 9. Inertial navigation data. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all 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 protection scope of the present invention.

[0032] See also Figure 1 According to an embodiment of the present invention, a WIFI and INS fusion positioning system based on Kalman filtering for a mobile phone is provided. Figure 1 , the system includes a data acquisition system 1, a data preprocessing system 2 and a positioning solution system 3;

[0033] The data acquisition system 1 acquires and stores data into the data set 4, and at the same time, the data set 4 performs time matching inside the data set 4;

[0034] The data preprocessing system 2 includes an INS data preprocessing part 5 and a WIFI data preprocessing part 6. Further, the data preprocessing part is a sliding time system 7 based on the WiFi data state, which means that the data preprocessing system 2 processes a certain amount of WiFi data, which is irrelevant to the specific INS data.

[0035] The positioning and solving system 3 performs positioning and solving on the processing result of the sliding time system 7 from the data preprocessing system 2, constructs a Kalman filter equation according to the obtained data in the sliding time system 7, and obtains the positioning result and performs visualization processing at the same time;

[0036] According to another embodiment of the present invention, a WIFI and INS fusion positioning method based on Kalman filtering for a mobile phone is proposed, comprising the following steps:

[0037] Step 1: The data acquisition system collects and stores data into a data set, and the data acquisition system can retrieve database information and add it to the data set;

[0038] Step 2: The data acquisition system repackages the data set containing the database information and sends it to the data preprocessing system;

[0039] Step 3: The data preprocessing system performs noise reduction processing on the WIFI and INS data, and performs data matching within the data set in the time dimension, so that the WIFI data is point data, and the INS data is block data between the time difference between the WIFI data and the INS data, and generates a positioning solution signal according to the conditions;

[0040] Step 4, the WIFI data preprocessing part of the data preprocessing system obtains the first Jacobi matrix, the first residual vector and the first parameter vector to be determined after Taylor expansion by constructing a three-dimensional space distance formula containing phase distortion noise through the location of the router, the WiFi-RTT ranging result, the RTT variance, and the location at the previous moment; the first parameter vector to be determined includes the location (xyz) of each point and the phase distortion noise result;

[0041] Step 5, the INS data preprocessing part of the data preprocessing system integrates and iterates the time difference, the noise matrix, the acceleration value, and the angular velocity value to obtain the overall velocity value, the angular velocity value, the position change, the second residual vector, the second Jacobi matrix corresponding to the residual, and the second parameter vector to be determined between two time intervals on the WiFi time scale, wherein the second parameter vector to be determined includes the velocity (VxVyVz) and angle (quaternion) of each point in the interval;

[0042] Step 6: Update the first Jacobi matrix, the first residual vector and the first parameter vector to be found obtained in step 4, and the second Jacobi matrix, the second residual vector and the second parameter vector to be found obtained in step 5. Obtain a third Jacobi matrix, a third residual vector and a third parameter vector to be found that contain both WIFI and INS data;

[0043] Specifically, the process in steps 4-6 is as follows:

[0044] First, in step 4, obtain the distance measurement results of all routers and mobile phones, and construct the distance equation. Assume that the known router position is AP1 (x1, y1, z1), and the position of the mobile phone to be determined is P (x, y, z). According to the spatial distance formula, the equation can be listed as:

[0045]

[0046] Among them, d phase is the phase distortion noise; d RTT represents the RTT ranging result obtained by the mobile phone, and a represents the system error. Point P is the location of the mobile phone to be found, and it is expanded at the approximate position of point P0 (x0, y0, z0), where:

[0047]

[0048] Ranging residual ρ1 = d RTT -d1+a can be rewritten as:

[0049]

[0050] The equation Y = Ax is a least squares solution equation, A is a parameter, which is the first Jacobian matrix mentioned above, and Y is the first residual vector. The parameter vector x to be determined is the position of the point to be determined and the phase distortion noise of each router {dx dy dz d phase}.

[0051] Then, in step 5, INS data can be obtained, including accelerometer, gyroscope, gravimeter, etc. The data matched with the WIFI time is integrated to obtain the change of each point at the WIFI time, and the change equation is constructed. The details are as follows:

[0052] ins data is the integration of time, that is, the change in speed and angle is obtained, and integrated on the time scale. The change of the two positions can be calculated. Assume that the standard positions of the two points in the time window are P1 (x1, y1, z1) and P2 (x2, y2, z2). The reference position is P1 (x10, y10, z10) and P2 (x20, y20, z20). The parameter vector to be determined at point x1 is (dx1, dy1, dz1). Through integration, the result obtained is that the change distance from P1 to P2 is (px1, py1, pz1). Then we can list the equation, X10+dx1+px1=x20+dx2, and we can know that the relationship between the two positions to be determined is dx1-dx2=x20-x10-px1.

[0053] Finally, step 6 combines the above parameters and equation limitations together to construct an overall equation, and finally solves it uniformly; the above-mentioned combination refers to unifying all the relationships listed above by taking the parameter vector to be determined as the index unit;

[0054] Step 7, saving the third Jacobi matrix, the third residual vector and the third generation parameter vector obtained in step 6 to a buffer, and returning to step 1, while waiting for the positioning solution signal, if the positioning solution signal is received, proceeding to step 8;

[0055] Step 8: passing the data in the buffer to the positioning solution system, and the positioning solution system starts the Kalman filter and obtains the result of the parameter to be estimated and the corresponding covariance matrix;

[0056] Step 9, returning the estimated parameter result and the corresponding covariance matrix to step 7, and updating the data in the buffer;

[0057] According to another embodiment of the present invention, the data set 4 includes WIFI data 8, and the WIFI data specifically includes the BSSID address, RTT, RTT variance, RSSI, and timestamp of the router. The inertial navigation data 9 includes inertial navigation raw data. Preferably, the inertial navigation raw data includes magnetic count values, gravity count values, acceleration count values, gyroscope values, and mobile phone hardware temperature values, and also includes temperature calibration, turntable calibration, filtering, and other preliminary calculations of acceleration, angular velocity, angle, timestamp, and other parameters, and also includes retrieving database data related to the current data in all previously calculated data of any intermediate links;

[0058] According to an embodiment of the present invention, in step 3, when the WIFI relative time series is inconsistent with the INS relative time series, the data matching needs to be based on the WIFI time series, and the INS data needs to be divided according to time. Preferably, the division does not process the original data, speed and acceleration parameters, and the time dimensionless parameters such as position change and rotation angle are linearly divided by time to obtain INS data results consistent with the WIFI time series benchmark;

[0059] According to an embodiment of the present invention, in step 3, the positioning solution signal is related to the sliding time system. When the data in the buffer exceeds the sliding time system threshold and a new WIFI RTT signal enters the buffer, the positioning solution signal is triggered;

[0060] According to an embodiment of the present invention, the sliding time system is essentially a forgetful system. Further, the forgetful system is described as processing a fixed amount of WiFi RTT data and INS data of the corresponding time. After exceeding the fixed amount, new WiFi data enters the sliding time system, and the sliding time system discards the last WiFi RTT data in the sliding time system and the INS data corresponding to the last WiFi RTT data to ensure data balance and constancy in the sliding time system.

[0061] According to an embodiment of the present invention, the WIFI data preprocessing system includes operations such as monitoring the communication state change of WIFI-RTT data, white noise processing, phase distortion noise processing, multipath effect processing, kernelized ridge regression, reflection projection, etc. The communication state change monitoring signal is transmitted to the positioning solution system. If the information provided by the monitoring signal believes that the communication state has not been changed, but the phase distortion noise change calculated by the positioning solution system exceeds the rated threshold, it is believed that this part of the increment is caused by the multipath effect or the non-line-of-sight phenomenon, and the phase distortion noise maintains the result of the previous moment. On the contrary, if the information provided by the monitoring signal believes that the communication state has changed, the difference between the two solution results is ignored, and the new value is taken as the phase distortion noise;

[0062] According to an embodiment of the present invention, the data preprocessing system provides the positioning solution system with a noise matrix of both WiFi data and INS data, which is obtained by weighting the statistics of the data in the sliding time system at this moment with the proximity index;

[0063] According to an embodiment of the present invention, the parameters to be estimated in the positioning solution system include the position, speed, and posture of all WIFI signals at the time of occurrence in the sliding time system, and also include the phase distortion noise of the mobile phone and all routers.

[0064] According to an embodiment of the present invention, the result obtained by the positioning solution system is the data result within the sliding time system, that is, in this technology we will obtain multiple estimates and variance results of a position, speed, posture, and phase distortion noise of the mobile phone and all routers. This technology calculates these estimates and variance results at the same time, and the calculation results are used as a signal evaluation system to constrain the solution equation.

[0065] Although the above describes the illustrative specific embodiments of the present invention to facilitate those skilled in the art to understand the present invention, and it should be clear that the present invention is not limited to the scope of the specific embodiments, for those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations using the concept of the present invention are protected.

Claims

1. A WIFI and INS fusion positioning method based on Kalman filtering for mobile phones, using a WIFI and INS fusion positioning system based on Kalman filtering for mobile phones, the system includes a data acquisition system, a data preprocessing system and a positioning solution system; in, The data acquisition system is used to collect and store data into a data set, and at the same time, time matching of data is performed within the data set; The data preprocessing part is a sliding time system based on the WiFi data status, including an INS data preprocessing part and a WIFI data preprocessing part; The positioning solution system is used to perform positioning solution on the sliding time system processing result from the data preprocessing system, construct a Kalman filter equation based on the obtained sliding time system data, and simultaneously obtain the positioning result and perform visualization processing; it is characterized by comprising the following steps: Step 1: The data acquisition system collects and stores data into a data set, and the data acquisition system can retrieve database information and add it to the data set; Step 2: The data acquisition system repackages the data set containing the database information and sends it to the data preprocessing system; Step 3: The data preprocessing system performs noise reduction processing on the two types of data, and performs data matching within the data set in the time dimension, so that the WIFI data is point data, and the INS data is block data between the time difference between the two WIFI data, and generates a positioning solution signal according to the conditions; Step 4, the WIFI data preprocessing part of the data preprocessing system obtains the first Jacobi matrix, the first residual vector and the first parameter vector to be determined after Taylor expansion by constructing a three-dimensional space distance formula including phase distortion noise through the location of the router, the WiFi-RTT ranging result, the RTT variance and the location at the previous moment; the first parameter vector to be determined includes the location of each point and the phase distortion noise result; Step 5, the INS data preprocessing part of the data preprocessing system integrates and iterates the time difference, the noise matrix, the acceleration value, and the angular velocity value to obtain the overall velocity value, the angular velocity value, the position change, the second residual vector, the second Jacobi matrix corresponding to the residual, and the second parameter vector to be determined between two time intervals on the WiFi time scale, wherein the second parameter vector to be determined includes the velocity and angle of each point in the interval; Step 6: Update the first Jacobi matrix, the first residual vector and the first parameter vector to be found obtained in step 4, and the second Jacobi matrix, the second residual vector and the second parameter vector to be found obtained in step 5; obtain a third Jacobi matrix, a third residual vector and a third parameter vector to be found containing both WIFI and INS data; Step 7, saving the third Jacobi matrix, the third residual vector and the third parameter vector to be determined obtained in step 6 to a buffer, and returning to step 1, while waiting for the positioning solution signal, if the positioning solution signal is received, proceeding to step 8; Step 8: passing the data in the buffer to the positioning solution system, the positioning solution system starts the Kalman filter and obtains the vector results of each parameter to be determined and the corresponding covariance matrix; Step 9: Return the result of each parameter vector to be determined and the corresponding covariance matrix to step 7, and update the data in the buffer.

2. According to claim 1, a WIFI and INS fusion positioning method based on Kalman filtering for mobile phones is characterized in that: In the step 1, the data set includes WIFI data, and the WIFI data specifically includes the BSSID address, RTT, RTT variance, RSSI, and timestamp of the router. The inertial navigation data includes inertial navigation raw data; the inertial navigation raw data includes magnetic count values, gravity count values, acceleration count values, gyroscope values, and mobile phone hardware temperature values, and also includes acceleration, angular velocity, angle, and timestamp parameters obtained by preliminary calculation of temperature calibration, turntable calibration, and filtering of the data, and also includes retrieving database data related to the current data in the data of any intermediate link calculated previously.

3. The WIFI and INS fusion positioning method based on Kalman filtering for a mobile phone according to claim 1, characterized in that: In step 3, when the WIFI relative time series is inconsistent with the INS relative time series, the data matching needs to be based on the WIFI time series, and the INS data needs to be divided according to time. The division does not process the original data, speed and acceleration parameters, and the position change and rotation angle parameters are linearly divided by time to obtain INS data results consistent with the WIFI time series benchmark.

4. The WIFI and INS fusion positioning method for a mobile phone based on Kalman filtering according to claim 1, characterized in that: In step 3, the positioning solution signal is related to the sliding time system. When the data in the buffer exceeds the sliding time system threshold and a new WIFI RTT signal enters the buffer, the positioning solution signal is triggered.

5. The WIFI and INS fusion positioning method for a mobile phone based on Kalman filtering according to claim 1, characterized in that: The sliding time system is an amnesiac system, which is described as processing a fixed amount of WiFi RTT data and the INS data of the corresponding time. After exceeding the fixed amount, new WiFi data enters the sliding time system, and the sliding time system discards the last WiFi RTT data in the sliding time system and the INS data corresponding to the last WiFi RTT data to ensure the balance and constancy of the data in the sliding time system.

6. The WIFI and INS fusion positioning method for a mobile phone based on Kalman filtering according to claim 1, characterized in that: The WIFI data preprocessing system includes monitoring of communication state changes of WIFI-RTT data, white noise processing, phase distortion noise processing, multipath effect processing, kernelized ridge regression, and reflection projection operations. The communication state change monitoring signal is transmitted to the positioning solution system. If the information provided by the monitoring signal believes that the communication state has not been changed, but the phase distortion noise change calculated by the positioning solution system exceeds the rated threshold, it is believed that this part of the increment is caused by the multipath effect or non-line-of-sight phenomenon, and this phase distortion noise maintains the result of the previous moment. On the contrary, if the information provided by the monitoring signal believes that the communication state has changed, the difference between the two solution results is ignored, and the new value is taken as the phase distortion noise.

7. The WIFI and INS fusion positioning method for a mobile phone based on Kalman filtering according to claim 1, characterized in that: The data preprocessing system provides the positioning solution system with a noise matrix of both WiFi data and INS data, which is obtained by weighting the statistics of the data in the sliding time system at this moment with the proximity index.

8. The WIFI and INS fusion positioning method for a mobile phone based on Kalman filtering according to claim 1, characterized in that: The parameters to be estimated in the positioning solution system include the position, speed, and posture of all WIFI signals at the time of occurrence under the sliding time system, and also include the phase distortion noise of the mobile phone and all routers.

9. The WIFI and INS fusion positioning method based on Kalman filtering for a mobile phone according to claim 8, characterized in that: The result obtained by the positioning solution system is the data result in the sliding time system, that is, multiple estimates and variance results of a position, speed, posture, and phase distortion noise information of the mobile phone and all routers are obtained, and these estimates and variance results are calculated at the same time. The calculation results are used as a signal evaluation system to constrain the solution equation.

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