A posture-immune pedestrian dual-magnetic speed measurement method and device

Abstract

The present invention discloses a posture-immune pedestrian dual-magnetic speed measurement method and device, belonging to the field of indoor pedestrian positioning technology. The method adopts a static posture projection method to eliminate the problems of pedestrian posture changes, magnetometer zero bias and inconsistent magnetometer installation; adopts a dual-window weighted dynamic time warping algorithm to improve the ability to capture the trend of magnetic field changes, increase the ability to dynamically adjust the contribution of magnetic field intensity and change trend in the cost function, and reduce the impact of the temporary disappearance of magnetic field gradient on the matching result, thereby improving the matching speed and matching accuracy, thereby accurately estimating the pedestrian movement speed.

Description

Technical Field

[0001] The present invention belongs to the field of indoor pedestrian positioning technology, and specifically relates to a posture-immune pedestrian dual-magnetic speed measurement method and device, which can be used to achieve real-time, autonomous and accurate speed measurement of pedestrians in scenes with rich magnetic field characteristics such as cities and indoors. Background Art

[0002] After years of development, the Beidou satellite navigation system has become a stable and reliable positioning solution, but Beidou signals are difficult to penetrate buildings, and it is impossible to achieve continuous and effective indoor positioning and speed measurement by relying on Beidou. In satellite signal denial environments, pedestrian dead reckoning (PDR) technology is currently the mainstream positioning method in indoor scenarios. Its principle is to infer the position and attitude of pedestrians by integrating the angular velocity and acceleration measurements provided by inertial sensors, but it is limited by the drift error inherent in inertial sensors and cannot effectively guarantee the accuracy of the position and attitude obtained by integration. Therefore, high-precision and strong robust odometer speed information is crucial to the PDR system. It can not only improve the accuracy of pedestrian position and attitude estimation, but also ensure continuous and stable positioning capabilities in indoor environments where satellite signals cannot cover. This is extremely important for meeting the current and future needs of indoor pedestrian positioning.

[0003] In view of the current pedestrian speed measurement methods, dual magnetic odometer speed measurement is a potentially feasible speed measurement method. The key links of dual magnetic odometer speed measurement currently include: data preprocessing and waveform matching. The existing method installs magnetometers on the front and rear platforms of the robot car respectively, performs preprocessing operations such as low-pass filtering, framing and normalization on the magnetic field measurement values ​​collected by the two magnetometers, and then uses waveform matching to match the preprocessed measurement values ​​to obtain the time difference between the front and rear magnetometers passing the same position, and further obtain the carrier speed. In the data preprocessing stage, the current dual magnetic odometer speed measurement method does not consider the impact of changes in the carrier's posture on the matching speed measurement, and the posture of pedestrians will change in real time during walking, which makes the dual magnetic odometer speed measurement method not applicable to pedestrian speed.

[0004] In the matching speed measurement stage, the current waveform matching algorithms are mainly dynamic time warping (DTW) and its derivative algorithms. The DTW algorithm only uses the Euclidean distance of the magnetic field intensity value as the cost function. However, due to the influence of magnetometer bias or other interference, the measurement values ​​of the two magnetometers may not be equal, and the use of the DTW algorithm may cause mismatching. The derivative dynamic time warping (DDTW) algorithm only uses the Euclidean distance of the magnetic field intensity change trend as the cost function. The change trends of different sampling points may be the same, which may cause mismatching. The improved dynamic time warping algorithm considers the equal-weighted addition of the Euclidean distance of the magnetic field intensity value and the change trend as the cost function, but uses the magnetic field intensity values ​​of adjacent points to calculate the change trend. When the sampling rate is high (such as 100Hz), the magnetic field intensity values ​​of adjacent sampling points are very close or even basically the same, resulting in a small calculated change trend. This not only limits the ability to extract the magnetic field change trend from the magnetic field sequence, but also may degenerate into the DTW algorithm when calculating the cost function, resulting in mismatching. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a posture-immune pedestrian dual-magnetic speed measurement method and device, which uses static posture projection technology to solve the problems of installation differences between front and rear magnetometers and the continuous changes in pedestrian posture. At the same time, the influence of the zero bias of the front and rear magnetometers on speed estimation is eliminated by solving the gradient. A dual-window weighted dynamic time warping algorithm (dual-window weighted DTW) is proposed, which can not only enhance the ability to extract the trend of magnetic field changes, but also dynamically adjust the contribution of magnetic field intensity and change trend in the cost function. At the same time, it can reduce the influence of inconsistency of the sequence to be matched and the temporary disappearance of the magnetic field gradient on the matching results, thereby improving the matching speed and matching accuracy.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0007] A posture-immune pedestrian dual-magnetic speed measurement method, comprising:

[0008] Step 1: Collect IMU data, including three-axis magnetic field data of front and rear magnetometers, as well as three-axis accelerometer and three-axis gyroscope data;

[0009] Step 2: Based on the static attitude projection method, the three-axis magnetic field data of the front and rear magnetometers are projected into the navigation coordinate system determined by the initial static state to eliminate the attitude influence;

[0010] Step 3, based on the three-axis magnetic field data of the front and rear magnetometers after eliminating the influence of the posture, the magnetic field intensity gradient of each axis is obtained as the sequence to be matched;

[0011] Step 4, waveform matching is performed on the z-axis magnetic field intensity gradient sequence in the navigation coordinate system determined by projecting the front and rear magnetometers to the initial static state based on a double-window weighted dynamic time warping algorithm;

[0012] Step 5: Use the waveform matching result to calculate the time difference between the front and rear magnetometers passing the same position, and combine the fixed baseline length of the front and rear magnetometers to calculate the pedestrian's movement speed.

[0013] On the other hand, the present invention provides a posture-immune pedestrian dual-magnetic speed measuring device, comprising:

[0014] An IMU data acquisition unit is used to collect IMU data, including three-axis magnetic field data of front and rear magnetometers, as well as three-axis accelerometer and three-axis gyroscope data;

[0015] An attitude elimination unit, used for projecting the three-axis magnetic field data of the front and rear magnetometers into a navigation coordinate system determined by an initial static state based on a static attitude projection method to eliminate the influence of the attitude;

[0016] A sequence to be matched obtaining unit is used to obtain the magnetic field intensity gradient of each axis as the sequence to be matched based on the three-axis magnetic field data of the front and rear magnetometers after eliminating the influence of the posture;

[0017] A waveform matching unit, used for performing waveform matching on a z-axis magnetic field intensity gradient sequence in a navigation coordinate system determined by projecting the front and rear magnetometers to an initial stationary state based on a double-window weighted dynamic time warping algorithm;

[0018] The motion speed acquisition unit is used to calculate the time difference between the front and rear magnetometers passing the same position using the waveform matching results, and calculate the pedestrian's motion speed in combination with the fixed baseline length of the front and rear magnetometers.

[0019] In a third aspect, the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned posture-immune pedestrian dual-magnetic speed measurement method.

[0020] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned posture-immune pedestrian dual-magnetic speed measurement method.

[0021] The beneficial effects of the present invention are:

[0022] The present invention utilizes the static posture projection method to eliminate the problems of pedestrian posture changes, magnetometer zero bias and inconsistent magnetometer installation;

[0023] The present invention adopts a dual-window weighted dynamic time warping method, which can not only improve the ability to capture the trend of magnetic field changes; but also adjust the contribution of magnetic field intensity and change trend in the cost function by adjusting the weight value, thereby improving the matching speed measurement effect; at the same time, matching the waveform to be matched within the time window can not only improve the matching speed, but also resist the phenomenon of temporary disappearance of the gradient. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of a posture-immune dual-magnetic pedestrian speed measurement method of the present invention;

[0025] Figure 2 It is a matching graph of the double-window weighted dynamic time rule method. DETAILED DESCRIPTION

[0026] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0027] As mentioned above, the present invention provides a posture-immune pedestrian dual-magnetic speed measurement method, which mainly improves the present invention from the following two aspects:

[0028] Static attitude projection method: The present invention uses the attitude and heading reference system AHRS during the stationary stage of pedestrians to accurately calculate the attitude angle, and projects the front and rear magnetometer data from their respective carrier coordinate systems to the navigation coordinate system determined in the initial stationary stage, which helps to maintain the consistency and stability of the rotation matrix, and then obtains a set of magnetic field three-axis vector data in a unified coordinate system. By calculating the gradient of the magnetic field data after projection, the influence of the magnetometer zero bias is eliminated. This method can not only eliminate the influence of magnetometer installation and pedestrian attitude changes, but also eliminate the influence of magnetometer zero bias, thereby ensuring the accuracy and reliability of the entire matching process.

[0029] Dual-window weighted dynamic time warping algorithm: The present invention calculates the trend of magnetic field intensity changes within the time window to improve the ability to capture the trend of magnetic field changes, and can more accurately evaluate the change of magnetic field intensity; weighted magnetic field intensity and magnetic field intensity change trend are used to construct a cost function, and by adjusting the weight value, the contribution of magnetic field intensity and change trend in the cost function can be adjusted; matching speed measurement of the waveform to be matched within the time window is adopted, which can not only improve the matching speed, but also resist the phenomenon of temporary disappearance of the gradient. Only when the gradient in the window disappears completely, the matching will fail. This method can not only improve the matching effect, but also improve the matching speed.

[0030] like Figure 1 As shown, it is a flow chart of a posture-immune pedestrian dual-magnetic speed measurement method of the present invention. Specifically,

[0031] Step 1: Collecting front and rear IMU data, i.e. collecting IMU data, including front and rear magnetometer three-axis magnetic field data, as well as three-axis accelerometer and three-axis gyroscope data;

[0032] Step 2: Project the three-axis magnetic field data of the front and rear magnetometers into the navigation coordinate system determined by the initial static state based on the static attitude projection method;

[0033] When pedestrians are walking, factors such as body shaking may interfere with the measurement results of the accelerometer and gyroscope, thereby affecting the accuracy of the AHRS calculation of the attitude angle. This inaccuracy will further lead to inaccurate projection from the carrier coordinate system to the navigation coordinate system, which will affect the reliability of the matching results. To solve this problem, during the system initialization phase, ensure that the pedestrian is stationary. In this state, the readings of the accelerometer and gyroscope are more stable and accurate, and the AHRS system can calculate the attitude angle more accurately. Based on the assumption of rigid body motion, although pedestrians shake during walking, their front and back posture changes are essentially the same, and the front and back coordinate systems rotate consistently. In order to eliminate the influence of shaking on the rotation matrix during walking, the b-system data of the front and back magnetometers collected during walking are uniformly projected to the navigation coordinate system (n 1 This method helps to maintain the consistency and stability of the rotation matrix, solves the impact of inconsistent magnetometer installation and pedestrian posture changes on matching speed measurement, and thus ensures the accuracy and reliability of the entire matching process. Specifically:

[0034] Step 2.1, calculate the attitude angle in the static state, and then calculate the rotation matrix;

[0035] Assuming that in the initial static state, the attitude angle provided by AHRS is: roll angle , pitch angle , heading angle ; From the magnetometer carrier coordinate system b to the navigation coordinate system n obtained in the static state 1 The rotation process of the system is: first around Axis rotation Corner; rewind Axis rotation Corner; Last turn Axis rotation Angle, get the rotation matrix in the initial static state for:

[0036] ;

[0037] Step 2.2, attitude projection, based on the rotation matrix in the static state, project the three-axis magnetic field data of the front and rear magnetometers at each moment from the carrier coordinate system b of the magnetometer where it is located to the navigation coordinate system n in the static state 1 In the system, the three-axis magnetic field data of the front and rear magnetometers are obtained to eliminate the influence of attitude;

[0038] During the data collection process of the whole walk, the data at each sampling moment is projected, and the three-axis vectors of the magnetic field measured by the two magnetometers before and after each moment are projected from the b system where they are located to the n system determined by the initial static state. 1 In this way, a set of magnetic field three-axis vector data in a unified coordinate system can be obtained.

[0039] Assume that the three-axis magnetic field vectors collected by the two magnetometers before and after the pedestrian are:

[0040] ;

[0041] The subscripts 1 and 2 represent the front and rear magnetometers, respectively, and the superscript b 1 , b 2 Represents the carrier coordinate system of the front and rear magnetometers, , Respectively represent the front and rear magnetometer zero bias, , , respectively represent the true values ​​of the magnetic field of the front and rear magnetometers in the x, y, and z axes.

[0042] Then at time t, it is projected to the navigation coordinate system n determined by the stationary state 1 The three-axis vectors of the magnetic field under the system are:

[0043] ;

[0044] Among them, the superscript n 1 Represents the navigation coordinate system determined by the stationary state.

[0045] During the attitude projection phase, the initial bias (zero bias) of the magnetometer is also projected into the navigation coordinate system (n 1 Since only the Euler angles of the initial static state are used to calculate the rotation matrix, the rotation matrices at different times are the same, so the zero bias projection results of the same magnetometer at different times are also the same. Taking the front-end magnetometer as an example, let the magnetic field strength measured by the front-end magnetometer at the i, j, and k moments be:

[0046] ;

[0047] The subscripts i, j, and k represent the sampling time, respectively, and the superscript represents the carrier coordinate system of the front-end magnetometer at time i, j, k. The subscript 1 represents the front magnetometer, and the subscripts ix, iy, iz represent the x, y, z three-axis components at the sampling time i, j, k.

[0048] Project the three-axis magnetometer measurement values ​​of the front-end magnetometer carrier coordinate system at different times to the initial static state to determine n 1 Department:

[0049] ;

[0050] Since the rotation matrices are , so at different times b is projected to n 1 The zero bias of the system is the same, which is:

[0051] ;

[0052] Therefore, the influence of zero bias is eliminated by obtaining the gradient of the data of each axis after projection.

[0053] Step 3: Based on the three-axis magnetic field data of the front and rear magnetometers after eliminating the influence of the posture, the magnetic field intensity gradient of each axis is calculated as the sequence to be matched;

[0054] Calculating the gradient of each axis can eliminate the influence of the zero bias of the magnetometer, but in actual matching, since the AHRS calculates the horizontal attitude angle more accurately, the z-axis data is used as the sequence to be matched. In other words, due to the magnetic field interference in the room, the heading angle calculated by the AHRS may not be accurate, while the AHRS uses the accelerometer to sense gravity to calculate the horizontal attitude angle, and the calculated horizontal attitude angle is more accurate, so the gradient sequence of the z-axis after projection is selected as the sequence to be matched in the matching stage.

[0055] Step 4: Project the front and rear magnetometers to n based on the double window weighted dynamic time warping algorithm (double window weighted DTW algorithm). 1 The z-axis magnetic field intensity gradient sequence in the system is used for waveform matching;

[0056] The key to successful matching speed measurement is to ensure the accuracy of the data to be matched and the effectiveness of the matching process. The matching speed measurement workflow includes the following steps. First, the data is organized by smoothing and windowing framing technology after preprocessing. Then, the processed measurement values ​​are matched with waveforms using a dual-window weighted dynamic time warping algorithm to ensure the efficiency of the matching process and the reliability of the results.

[0057] Step 4.1, calculate the gradient of the sequence to be matched within the time window;

[0058] The data of the wth point before and after the ath point are used to jointly determine the trend of the magnetic field intensity change at the ath point:

[0059] ;

[0060] in, , , Respectively represent , , Sampling point, Indicates The changing trend of sampling points.

[0061] Step 4.2, constructing a cost function consisting of weighted magnetic field intensity and weighted magnetic field gradient;

[0062] The cost between two sampling points a and b is defined as follows:

[0063] ;

[0064] in, , Indicates , Sampling point, and Indicates Sampling point and The changing trend of sampling points, express , The Euclidean distance between points, express , The Euclidean distance of the changing trend of and is an empirical value, indicating the weight of Euclidean distance and change trend. , The value of can adjust the contribution of magnetic field strength and change trend in the cost function and improve the matching speed measurement effect.

[0065] Step 4.3, performing waveform matching within the time window;

[0066] Figure 2The implementation process of windowing, framing and waveform matching is shown. Window is the matching window of DTW, and w is the window length for calculating the gradient. According to the window length for calculating the gradient, the w-th sampling point before and after the sampling point mentioned above can be obtained. It can be seen that mismatches occur at the beginning and end of the waveform (black circle part). The back-end magnetometer has no corresponding data with the front-end magnetometer at the beginning of the sliding window, and the front-end magnetometer has no corresponding data with the back-end magnetometer at the end of the sliding window. In order to solve this problem, the mismatches at both ends are removed, and drop is the length of removal, while retaining the matching results of the middle area. In order to improve the accuracy of matching, adjacent windows are designed to overlap partially, and the final matching result is obtained by splicing and integrating the matching results of all time windows.

[0067] Step 5: Use the matching results to calculate the time difference between the front and rear magnetometers passing the same position, and combine the fixed baseline lengths of the front and rear magnetometers to calculate the pedestrian's movement speed.

[0068] Example

[0069] According to the above principle of the present invention, the specific real-time operation is performed in the following steps:

[0070] Step 1: Wear the dual magnetic helmet and stay still for a while, then walk or jog normally to collect the three-axis magnetic field data of the front and rear magnetometers. , as well as three-axis accelerometer and three-axis gyroscope data;

[0071] Step 2: Use the attitude and heading reference system (AHRS) to calculate the attitude angle of the front and rear magnetometers during the static phase: roll angle , pitch angle , heading angle ;

[0072] Step 3: Calculate the rotation matrix using the attitude angle in the static phase , project the forward and backward magnetometer data from their respective carrier coordinate systems (b system) to the navigation coordinate system (n 1 The front and rear magnetometer data with attitude effects eliminated are obtained. ;

[0073] Step 4: Calculate the magnetic field intensity gradient of each axis using the front and rear magnetometer data after eliminating the influence of the attitude, and obtain the z-axis magnetic field intensity gradient sequence , which is the sequence to be matched;

[0074] Step 5: Use a double-window weighted dynamic time warping algorithm for matching, and use a larger time window to calculate the gradient (changing trend) of the input magnetic sequence to be matched;

[0075] Step 6: Use the weighted magnetic field intensity and weighted gradient to construct a matching process cost function.

[0076] Step 7: Perform waveform matching within the time window, and use the matching results to calculate the time difference between the front and rear magnetometers passing the same position.

[0077] Step 8: Calculate the pedestrian's speed by combining the fixed baseline lengths of the front and rear magnetometers.

[0078] The above steps can be used to estimate the forward speed of pedestrians. The data collected by the front and rear magnetometers are projected from their respective carrier coordinate systems b to the navigation coordinate system n determined by the initial static stage through the static posture projection method. 1 In the system, the problem of inconsistent installation of front and rear magnetometers, zero bias of magnetometers, and inconsistent waveforms of front and rear magnetometers caused by changes in posture when pedestrians move (walking or trotting) can be solved; through the dual-window weighted dynamic time warping algorithm, the trend of magnetic field intensity change is calculated within the time window to improve the ability to capture changing features; the cost function is constructed by weighted magnetic field intensity and magnetic field intensity change trend, and the contribution of magnetic field intensity and change trend in the cost function can be adjusted by adjusting the weight value; the matching speed measurement of the waveform to be matched within the time window can not only improve the matching speed, but also resist the phenomenon of temporary disappearance of gradient. This method can not only improve the matching effect, but also improve the matching speed.

[0079] On the other hand, the present invention provides a posture-immune pedestrian dual-magnetic speed measuring device, which includes various units capable of executing various steps of the aforementioned method, specifically including:

[0080] An IMU data acquisition unit is used to collect IMU data, including three-axis magnetic field data of front and rear magnetometers, as well as three-axis accelerometer and three-axis gyroscope data;

[0081] An attitude elimination unit, used for projecting the three-axis magnetic field data of the front and rear magnetometers into a navigation coordinate system determined by an initial static state based on a static attitude projection method to eliminate the influence of the attitude;

[0082] A sequence to be matched obtaining unit is used to obtain the magnetic field intensity gradient of each axis as the sequence to be matched based on the three-axis magnetic field data of the front and rear magnetometers after eliminating the influence of the posture;

[0083] A waveform matching unit, used for performing waveform matching on a z-axis magnetic field intensity gradient sequence in a navigation coordinate system determined by projecting the front and rear magnetometers to an initial stationary state based on a double-window weighted dynamic time warping algorithm;

[0084] The motion speed acquisition unit is used to calculate the time difference between the front and rear magnetometers passing the same position using the waveform matching results, and calculate the pedestrian's motion speed in combination with the fixed baseline length of the front and rear magnetometers.

[0085] In a third aspect, the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned posture-immune pedestrian dual-magnetic speed measurement method.

[0086] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned posture-immune pedestrian dual-magnetic speed measurement method.

[0087] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific 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 principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A posture-immune pedestrian dual-magnetic speed measurement method, characterized in that: The method comprises: Step 1: Collect IMU data, including three-axis magnetic field data of front and rear magnetometers, as well as three-axis accelerometer and three-axis gyroscope data; Step 2: Based on the static attitude projection method, the three-axis magnetic field data of the front and rear magnetometers are projected into the navigation coordinate system determined by the initial static state to eliminate the attitude influence; Step 3, based on the three-axis magnetic field data of the front and rear magnetometers after eliminating the influence of the posture, the magnetic field intensity gradient of each axis is obtained as the sequence to be matched; Step 4, waveform matching is performed on the z-axis magnetic field intensity gradient sequence in the navigation coordinate system determined by projecting the front and rear magnetometers to the initial static state based on a double-window weighted dynamic time warping algorithm; Step 5: Use the waveform matching result to calculate the time difference between the front and rear magnetometers passing the same position, and combine the fixed baseline length of the front and rear magnetometers to calculate the pedestrian's movement speed.

2. The posture-immune pedestrian dual-magnetic speed measurement method according to claim 1, characterized in that: The step 2 comprises: Step 2.1, calculate the attitude angle in the static state, and calculate the rotation matrix based on the attitude angle; Step 2.2: Based on the rotation matrix in the static state, the three-axis magnetic field data of the front and rear magnetometers at each moment are projected from the carrier coordinate system b of the magnetometer in which they are located to the navigation coordinate system n1 in the static state to obtain the three-axis magnetic field data of the front and rear magnetometers with the attitude effect eliminated.

3. The posture-immune pedestrian dual-magnetic speed measurement method according to claim 2 is characterized in that: The step 2.1 comprises: assuming that in the initial static state, the roll angle is , the pitch angle is , the heading angle is , the rotation matrix in the initial stationary state for: 。 4. The posture-immune pedestrian dual-magnetic speed measurement method according to claim 2 is characterized in that: The step 2.2 includes: assuming that the three-axis magnetic field vectors collected by the two magnetometers before and after the pedestrian during walking are: ； Among them, the subscripts 1 and 2 represent the front and rear magnetometers respectively, and the superscripts b1 and b2 represent the carrier coordinate systems of the front and rear magnetometers respectively. , Respectively represent the front and rear magnetometer zero bias, , , respectively represent the true values ​​of the magnetic field of the front and rear magnetometers in the x, y, and z axes; Then the three-axis vector of the magnetic field projected to the navigation coordinate system n1 determined by the static state at time t is: ； Wherein, the superscript n1 represents the navigation coordinate system determined by the stationary state; Project the three-axis magnetometer measurement values ​​of the front and rear magnetometer carrier coordinate systems at different times to the n1 system determined by the initial static state; The influence of zero bias is eliminated by obtaining the gradient of the data of each axis after projection.

5. The posture-immune pedestrian dual-magnetic speed measurement method according to claim 1, characterized in that: The step 4 comprises: Step 4.1, calculate the gradient of the sequence to be matched within the time window; Step 4.2, constructing a cost function consisting of weighted magnetic field intensity and weighted magnetic field gradient; Step 4.3: Perform waveform matching within the time window.

6. The posture-immune pedestrian dual-magnetic speed measurement method according to claim 5, characterized in that: The step 4.1 includes: using the data of the wth sampling points before and after the ath sampling point to jointly determine the change trend of the magnetic field intensity at the ath sampling point: ； in, , , Respectively represent , , Sampling point, Indicates The changing trend of sampling points.

7. The posture-immune pedestrian dual-magnetic speed measurement method according to claim 5, characterized in that: The step 4.2 includes defining the cost between two sampling points a and b as follows: ； in, , Indicates , Sampling point, and Indicates Sampling point and The changing trend of sampling points, Indicates , Sampling point , The Euclidean distance between Indicates , Sampling point , The Euclidean distance of the changing trend, and is an empirical value, indicating the weight of Euclidean distance and change trend. , The value of adjusts the contribution of magnetic field strength and change trend in the cost function.

8. A posture-immune pedestrian dual-magnetic speed measuring device, characterized in that: include: An IMU data acquisition unit is used to collect IMU data, including three-axis magnetic field data of front and rear magnetometers, as well as three-axis accelerometer and three-axis gyroscope data; An attitude elimination unit, used for projecting the three-axis magnetic field data of the front and rear magnetometers into a navigation coordinate system determined by an initial static state based on a static attitude projection method to eliminate the influence of the attitude; A sequence to be matched obtaining unit is used to obtain the magnetic field intensity gradient of each axis as the sequence to be matched based on the three-axis magnetic field data of the front and rear magnetometers after eliminating the influence of the posture; A waveform matching unit, used for performing waveform matching on a z-axis magnetic field intensity gradient sequence in a navigation coordinate system determined by projecting the front and rear magnetometers to an initial stationary state based on a double-window weighted dynamic time warping algorithm; The motion speed acquisition unit is used to calculate the time difference between the front and rear magnetometers passing the same position using the waveform matching results, and calculate the pedestrian's motion speed in combination with the fixed baseline length of the front and rear magnetometers.

9. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; Wherein, when one or more programs are executed by the one or more processors, the one or more processors implement the posture-immune pedestrian dual-magnetic speed measurement method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: Executable instructions are stored thereon, and when the instructions are executed by the processor, the processor can implement the posture-immune pedestrian dual-magnetic speed measurement method described in any one of claims 1-7.

Images