A magnetic-inertial fusion carrier velocity estimation method, device and system

Abstract

The application discloses a magnetic-inertial fusion carrier velocity estimation method, comprising the following steps: extracting sensor data of front and rear sensors in a preset time window; the sensor data comprises a magnetic induction intensity sequence and acceleration information; performing a traversal search on the carrier velocity in a preset velocity range, and combining the acceleration information to calculate a plurality of displacement sequences of the front and rear sensors respectively; the preset velocity range comprises a plurality of candidate carrier velocities; combining each displacement sequence and the corresponding magnetic induction intensity sequence to obtain a plurality of original displacement-magnetic field sequence pairs of the front and rear sensors respectively; performing equal-distance resampling on each original displacement-magnetic field sequence pair to obtain a plurality of new displacement-magnetic field sequence pairs of the front and rear sensors respectively; performing similarity calculation on two new displacement-magnetic field sequence pairs corresponding to the same candidate carrier velocity, and determining the candidate carrier velocity corresponding to the displacement-magnetic field sequence pair with the maximum similarity as the carrier velocity.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of speed measurement, in particular to a magnetic-inertial fusion carrier speed estimation method, device and system. BACKGROUND

[0002] Global Navigation Satellite System (GNSS) is the main means to obtain speed, position and other information. However, in the scene of GNSS signal loss such as tunnel, underground parking lot, urban canyon, the carrier is difficult to realize continuous and reliable speed measurement through satellite signal. At this time, inertial sensors are usually used as an alternative, but low-cost inertial sensors have large cumulative error and significant drift, and it is difficult to independently provide long-time stable speed estimation. Especially in complex dynamic environment, the limitations of single sensor are further highlighted, and a robust speed measurement method based on low-cost sensors is needed to make up for the data gap when GNSS fails.

[0003] In the existing odometer technology, the wheel odometer information cannot be accessed in the mobile phone or other mobile terminal, and the public navigation positioning equipment cannot be used. Although the visual odometer and the laser radar odometer can calculate the speed by matching the environmental features, the requirement for computing resources is high, and the performance of the visual odometer drops sharply in insufficient light, sparse features or bad weather conditions. These methods are difficult to adapt to low-cost and low-power sensors.

[0004] In artificial buildings, a large number of steel structures will cause the distortion of the geomagnetic field, so that a unique magnetic field feature is formed at each position. Based on this characteristic, there are currently two methods of using magnetic sensors to measure the speed of the carrier. The first method is a speed measurement method based on a multi-magnetic sensor array, which estimates the speed according to Maxwell's equations through the indoor magnetic field characteristics without the need to establish magnetic map information in advance. However, this method needs to rely on a precisely installed and calibrated sensor array, which is complex to operate and difficult to implement for mass application. The second method is a double-magnetic sensor measurement method, which places two sensors at the front and rear ends of the carrier respectively, and estimates the speed by matching the time sequence of the magnetic field. Although this method avoids the sensor calibration problem, the matching accuracy of the magnetic field time sequence is extremely high, and the existing method is only suitable for low-speed moving carriers such as pedestrians or robots. For high-speed moving carriers such as vehicles, the existing magnetic field time sequence matching accuracy is significantly reduced, and high-precision carrier speed estimation cannot be obtained. SUMMARY

[0005] In order to overcome the shortcomings of the above-mentioned prior art, the present application proposes a magnetic-inertial fusion carrier speed estimation method, device and system based on an inertial measurement unit and a magnetometer, which is low-cost, does not need to be calibrated in advance and is suitable for high-speed motion scenes.

[0006] According to an aspect of the present application, the present application provides a magnetic-inertial fusion carrier speed estimation method, comprising: extracting sensor data of a front sensor and a rear sensor within a preset time window; wherein the front sensor and the rear sensor are respectively installed at the front and rear of a carrier and are both nine-axis sensors, and the sensor data comprises a magnetic induction intensity sequence and acceleration information; performing an exhaustive search on the carrier speed within a preset speed range, and calculating a plurality of displacement sequences of the front sensor and the rear sensor respectively in combination with the acceleration information; wherein the preset speed range comprises a plurality of candidate carrier speeds; combining each displacement sequence and a corresponding magnetic induction intensity sequence to obtain a plurality of original displacement-magnetic field sequence pairs of the front sensor and the rear sensor respectively; performing equal-distance resampling on each original displacement-magnetic field sequence pair to obtain a plurality of new displacement-magnetic field sequence pairs of the front sensor and the rear sensor respectively; performing similarity calculation on the new displacement-magnetic field sequence pair of the front sensor and the new displacement-magnetic field sequence pair of the rear sensor corresponding to the same candidate carrier speed, and determining the candidate carrier speed corresponding to the displacement-magnetic field sequence pair with the maximum similarity as the carrier speed.

[0007] Further, the magnetic induction intensity sequence and the acceleration information are further projected into a carrier coordinate system.

[0008] Further, the exhaustive search on the carrier speed within the preset speed range and the calculation of a plurality of displacement sequences of the front sensor and the rear sensor respectively in combination with the acceleration information comprise: calculating a speed change sequence of the carrier within the preset time window based on the acceleration information collected by the front sensor; calculating an absolute speed sequence corresponding to each candidate carrier speed in combination with the speed change sequence; wherein a plurality of candidate carrier speeds correspond to a plurality of absolute speed sequences; calculating a plurality of displacement sequences of the front sensor based on the plurality of absolute speed sequences; and calculating a plurality of displacement sequences of the rear sensor based on the plurality of displacement sequences of the front sensor and the distance between the two sensors.

[0009] Further, the calculation of the speed change sequence of the carrier within the preset time window based on the acceleration information collected by the front sensor comprises: calculating the acceleration of the carrier at each time within the preset time window based on the acceleration information collected by the front sensor; calculating the speed change amount between each time and the next time of the carrier based on the acceleration; integrating the speed change amount to obtain the speed change amount from each time to the last time of the carrier; and combining the speed change amounts from each time to the last time within the preset time window to obtain the speed change sequence.

[0010] Further, the absolute speed sequence corresponding to each candidate carrier speed is calculated in combination with the speed change sequence; wherein a plurality of candidate carrier speeds correspond to a plurality of absolute speed sequences, including: combining each candidate carrier speed with the speed change sequence to calculate the absolute speed corresponding to each time in a preset time window; and combining the absolute speeds at each time in the preset time window to form the absolute speed sequence; wherein the plurality of candidate carrier speeds correspond to the plurality of absolute speed sequences.

[0011] Further, a plurality of displacement sequences of the front sensor are calculated based on the plurality of absolute speed sequences, including:

[0012] The displacement amount of the carrier between each time and the next time is calculated based on each absolute speed sequence; the displacement amount of the carrier between each time and the last time is obtained by integrating the displacement amount; and the displacement sequences are formed by combining the displacement amounts between each time and the last time in the preset time window; wherein the plurality of absolute speed sequences correspond to the plurality of displacement sequences.

[0013] Further, each original displacement-magnetic field sequence pair is equally spaced resampled to obtain a plurality of new displacement-magnetic field sequence pairs of the front sensor and the rear sensor, including: reconstructing a set of target sampling points equally spaced; aligning each original displacement-magnetic field sequence pair at the target sampling points; and linearly interpolating the aligned original displacement-magnetic field sequence pair at the target sampling points to obtain a plurality of new displacement-magnetic field matching sequence pairs of the front sensor and the rear sensor.

[0014] Further, the similarity of the new displacement-magnetic field sequence pair of the front sensor and the new displacement-magnetic field sequence pair of the rear sensor corresponding to the same candidate carrier speed is calculated, including: comparing the similarity of the new displacement-magnetic field sequence pair of the front sensor and the new displacement-magnetic field sequence pair of the rear sensor corresponding to each candidate carrier speed by using Pearson correlation coefficient, Euclidean distance, Manhattan distance, Chebyshev distance, Mahalanobis distance, cosine similarity or dynamic time warping.

[0015] According to an aspect of the present application, the present application provides a magnetic-inertial fusion carrier speed estimation device, the estimation device comprising: a data extraction module for extracting sensor data of a front sensor and a rear sensor within a preset time window; wherein the front sensor and the rear sensor are respectively installed at the front and rear of the carrier and are both nine-axis sensors, and the sensor data comprises a magnetic induction intensity sequence and acceleration information; a displacement calculation module for performing a traversal search on the carrier speed within a preset speed range, and calculating a plurality of displacement sequences of the front sensor and the rear sensor respectively in combination with the acceleration information; wherein the preset speed range comprises a plurality of candidate carrier speeds; a first magnetic distance matching module for combining each displacement sequence and a corresponding magnetic induction intensity sequence to obtain a plurality of original displacement-magnetic field sequence pairs of the front sensor and the rear sensor respectively; a second magnetic distance matching module for performing equidistance resampling on each original displacement-magnetic field sequence pair to obtain a plurality of new displacement-magnetic field sequence pairs of the front sensor and the rear sensor respectively; and a carrier speed determination module for performing similarity calculation on the new displacement-magnetic field sequence pair of the front sensor and the new displacement-magnetic field sequence pair of the rear sensor corresponding to the same candidate carrier speed, and determining the candidate carrier speed corresponding to the displacement-magnetic field sequence pair with the largest similarity as the carrier speed.

[0016] According to an aspect of the present application, the present application provides a magnetic-inertial fusion carrier speed estimation system, characterized in that the estimation system comprises a front sensor, a rear sensor and a processing module, the front sensor and the rear sensor are respectively installed at the front and rear of the carrier and are both nine-axis sensors; and the processing module is used for executing the magnetic-inertial fusion carrier speed estimation method according to sensor data collected by the front and rear sensors.

[0017] The above technical solution installs nine-axis sensors at the front and rear of the carrier respectively, utilizes the characteristic that the magnetic sequences collected by the front and rear nine-axis sensors at the same position are consistent, combines lever arm compensation and sequence matching optimization algorithm, searches for an optimal carrier speed solution, and thus accurate carrier speed measurement results are obtained.

[0018] Compared with the prior art, the present application has the following beneficial effects:

[0019] (1) The present application only calculates the speed by using the internal nine-axis sensor, and does not rely on external signals (such as GNSS signals), so that the present application can still obtain accurate carrier speed measurement results in GNSS signal denial environments such as tunnels, urban canyons and underground parking lots, thereby effectively overcoming the problem of speed measurement accuracy decline caused by signal loss of traditional satellite navigation.

[0020] (2) The acceleration information measured by the inertial measurement unit is combined with the magnetic field information measured by the magnetometer, so that the change of the carrier speed can be timely reflected even in high-speed motion, and therefore, the carrier speed measurement in a high-speed motion scene can be realized.

[0021] (3) Compared with the scheme relying on external infrastructure, the scheme does not rely on a precisely installed and calibrated sensor array, and only two low-cost nine-axis sensors are needed to realize autonomous speed measurement, has the advantages of convenient deployment, low cost and strong robustness, and significantly improves the navigation reliability in a complex environment. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0023] Figure 1 The flow chart of the carrier speed estimation method provided by the embodiment of the present application

[0024] Figure 2 The first sub-flow chart of the carrier speed estimation method provided by the embodiment of the present application

[0025] Figure 3 The second sub-flow chart of the carrier speed estimation method provided by the embodiment of the present application

[0026] Figure 4 The sensor installation schematic diagram in vehicle application provided by the embodiment of the present application

[0027] Figure 5 The sensor installation schematic diagram in unmanned aerial vehicle application provided by the embodiment of the present application

[0028] Figure 6 The sensor installation schematic diagram in pedestrian application provided by the embodiment of the present application

[0029] Figure 7 The structure schematic diagram of the estimation device provided by the embodiment of the present application

[0030] Figure 8 The structure schematic diagram of the estimation system provided by the embodiment of the present application

[0031] In the figure, 1, front sensor; 2, rear sensor; 3, carrier; 4, estimation device; 41, data extraction module; 42, displacement calculation module; 43, first magnetic distance matching module; 44, second magnetic distance matching module; 45, carrier speed determination module; 100, processing module; 1000, estimation system. DETAILED DESCRIPTION

[0032] The terms "comprising" and "having" and any variations thereof in the specification and in the claims and the accompanying drawings, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products, or apparatuses.

[0033] The block diagrams shown in the accompanying drawings are only functional entities, which do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices. The flowcharts shown in the accompanying drawings are only exemplary descriptions, which do not necessarily include all contents and operations / steps, and are not necessarily executed in the order described. For example, some operations / steps can be further divided, and some operations / steps can be combined or partially combined, so the actual execution order can be changed according to the actual situation.

[0034] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. In addition, the technical features in each embodiment or single embodiment provided by the present application can be combined with each other to form new technical solutions, which are not restricted by the order of steps and / or structure composition mode, but must be based on the implementation by those skilled in the art, when the combination of technical solutions appears contradictory or unimplementable, it should be considered that the combination of technical solutions does not exist, and is not within the scope of protection required by the present application.

[0035] Please refer to the accompanying drawings Figure 1 The present application provides a magnetic-inertial fusion carrier speed estimation method, which includes steps S101-S109.

[0036] Step S101: Extract sensor data from the front sensor and the rear sensor within a preset time window; wherein, the front sensor and the rear sensor are respectively installed at the front and rear of the carrier and are both nine-axis sensors, and the sensor data includes magnetic induction intensity sequence and acceleration information.

[0037] In step S101, both the front sensor 1 and the rear sensor 2 are nine-axis sensors, each including a magnetometer and an inertial measurement unit (IMU). The magnetometer is used to collect magnetic field strength sequences, and the IMU includes an accelerometer used to collect acceleration information. The nine-axis sensor can be in the form of a smart sensor, a smart terminal, or a customized device. In other words, the nine-axis sensor is not limited to a single hardware form; it can be mounted on various types of hardware devices, such as common consumer electronics products like smartphones and smartwatches, or applied to professional fields such as customized devices according to actual needs. The types of magnetometers include, but are not limited to, vector magnetometers, scalar magnetometers, and gradient magnetometers. The carrier type 3 includes, but is not limited to, vehicles, wheeled robots, humanoid robots, drones, underwater vehicles, and pedestrians (as shown in the attached image). Figures 4-6 (As shown). Furthermore, the distance between the front sensor 1 and the rear sensor 2 is fixed. In this embodiment, the distance between the centers of the two nine-axis sensors can be measured using the geometric center of one of the nine-axis sensors as the origin and projected onto the forward direction of the carrier 3, thereby obtaining the distance between the two nine-axis sensors in the carrier coordinate system, which facilitates the subsequent displacement calculation of the rear sensor 2. The distance between the centers of the two nine-axis sensors can be measured using tools such as a meter stick, tape measure, or total station, and is not limited here.

[0038] Furthermore, the front sensor 1 is installed at the front of the carrier 3, and the rear sensor 2 is installed at the rear of the carrier 3. Understandably, since this invention requires collecting magnetic field data at different positions based on the difference in front and rear positions, and combining this with the similarity calculation of displacement and magnetic field sequences to achieve carrier velocity measurement, it is necessary to ensure that the two nine-axis sensors are installed at the front and rear of the carrier 3, respectively. The specific installation positions of the nine-axis sensors are not specified. In this embodiment, when installing the two nine-axis sensors, it is necessary to ensure that the nine-axis sensors are tightly and securely connected to their installation positions to avoid insecure installation. Installation methods include, but are not limited to, straps and tape.

[0039] Step S102, projecting the sensor data into the carrier coordinate system. Specifically, a direction cosine matrix is constructed according to the mounting angle of the nine-axis sensor, and the sensor data is converted from the sensor coordinate system to the carrier coordinate system through coordinate transformation. Understandably, the two nine-axis sensors collect sensor data in their respective sensor coordinate systems. Therefore, after extracting the sensor data of the two nine-axis sensors within the preset time window, the sensor data collected by the two nine-axis sensors needs to be projected into the carrier coordinate system for subsequent comprehensive processing of the sensor data of the two nine-axis sensors. Please refer to the accompanying drawings for a better understanding of the application. Figure 2 Step S102 will be further described below (including steps S1021-S1023).

[0040] Step S1021, projecting the magnetic induction intensity sequence into the carrier coordinate system. In this embodiment, the magnetic induction intensity sequences collected by the front and rear magnetometers in the preset time window in step S101 are and respectively, where f represents the front sensor 1 and r represents the rear sensor 2. The magnetic induction intensity sequence is projected into the carrier coordinate system, and the formula is: , where represents the sensor coordinate system, represents the carrier coordinate system; and represent the magnetic induction intensity sequence in the carrier coordinate system and the sensor coordinate system, respectively, represents the direction cosine matrix from the sensor coordinate system to the carrier coordinate system.

[0041] Step S1023, projecting the acceleration information into the carrier coordinate system. In this embodiment, the specific forces (i.e. acceleration information) are synchronously collected by the front and rear accelerometers in the preset time window in step S101. The specific forces in the sensor coordinate system are projected into the carrier coordinate system, and the formula is: . Wherein, is the specific force in the carrier coordinate system at time , and is the accelerometer zero offset.

[0042] Step S103, traversing the carrier velocity within a preset velocity range and calculating a plurality of displacement sequences of the front sensor and the rear sensor respectively in combination with the acceleration information. The preset velocity range includes a plurality of candidate carrier velocities.

[0043] In step S103, based on the distance between the two nine-axis sensors in the carrier coordinate system obtained in step S101 and the acceleration information in the carrier coordinate system obtained in step S102, several displacement sequences for the front and rear sensors are calculated respectively. Please refer to the appendix for further details. Figure 3 The following will further describe step S103 (including steps S1031-S1037).

[0044] Step S1031: Calculate the velocity change sequence of the carrier within a preset time window based on the acceleration information collected by the front sensor. Specifically, calculate the acceleration of the carrier at each moment within the preset time window based on the acceleration information collected by the front sensor. Calculate the velocity change between each moment and the next moment based on the acceleration. Integrate the velocity change to obtain the velocity change from each moment to the last moment. Combine the velocity changes from each moment to the last moment within the preset time window to form the velocity change sequence.

[0045] In this embodiment, the acceleration of the carrier 3 at each moment within a preset time window is calculated based on the acceleration information collected by the front sensor, using the following formula: In the formula, Indicates the navigation coordinate system. express The acceleration vector in the carrier coordinate system at any given moment. This is the gravity vector. express The direction cosine matrix from the navigation coordinate system to the sensor coordinate system at any given time is obtained through attitude calculation of the inertial navigation system and serves as known information in this invention. Further, the velocity change of the carrier between each moment and the next moment is calculated based on acceleration, using the following formula: In the formula, for Time and The change in velocity between moments, for The forward acceleration of the carrier at each time step. Furthermore, the velocity change is integrated to obtain the velocity change of carrier 3 from each time step to the last time step, as shown in the formula: That is to say The moment reaches the last moment of the time window The total change in velocity between them. Understandably, a preset time window... From each moment within the time window to the last moment of the time window The total velocity changes between them together constitute the velocity change sequence of carrier 3.

[0046] Step S1033, combine the speed change sequence to calculate the absolute speed sequence corresponding to each candidate carrier speed; wherein, several candidate carrier speeds correspond to several absolute speed sequences. Specifically, combine each candidate carrier speed with the speed change sequence to calculate the absolute speed corresponding to each time in the preset time window. The absolute speeds of each time in the preset time window are collectively composed of the absolute speed sequence. Wherein, several candidate carrier speeds correspond to several absolute speed sequences.

[0047] In this embodiment, the carrier speed refers to the speed of the carrier 3 at the last time of the preset time window , and a group of candidate carrier speeds are initialized. For each candidate carrier speed , the absolute speed sequence of the carrier 3 in the preset time window is calculated from the current time, and the formula is: . In the formula, and are the speeds at the time and the last time of the time window, is the speed change amount between the time and the time . It can be understood that knowing the total speed change amount between the time and the last time of the time window , and knowing the speed at the last time , the speed at any time can be calculated, and the speeds at each time in the preset time window collectively constitute an absolute speed sequence of the carrier 3. Since the speed at the last time of the present application is a group of candidate carrier speeds , step S1033 calculates several absolute speed sequences of the front sensor.

[0048] Step S1035, based on the several absolute speed sequences, calculate several displacement sequences of the front sensor. Specifically, based on each absolute speed sequence, calculate the displacement amount of the carrier between each time and the next time. Integrate the displacement amount to obtain the displacement amount of the carrier between each time and the last time. The displacement amounts between each time and the last time in the preset time window are collectively composed of the displacement sequence. Wherein, several absolute speed sequences correspond to several displacement sequences.

[0049] In this embodiment, based on each absolute speed sequence, calculate the displacement amount of the carrier between each time and the next time, and integrate the displacement amount to obtain the displacement amount of the carrier between each time and the last time, and the formula is: . In the formula, It is a front sensor The displacement relative to the last moment of the time window. Understandably, by... The moment until the very last moment By accumulating the displacements at each moment in between, we can obtain... Time compared to the last moment The displacement, and the preset time window Each moment in time compared to the last moment The displacements together constitute a displacement sequence of carrier 3. Since step S1033 calculates several absolute velocity sequences, step S1035 calculates several displacement sequences. It should be noted that the methods for integrating the forward acceleration and absolute velocity sequences include, but are not limited to, left rectangular integration, right rectangular integration, middle rectangular integration, trapezoidal integration, and Simpson integration, and are not limited here.

[0050] Step S1037: Based on several displacement sequences of the front sensor and the distance between the two sensors, several displacement sequences of the rear sensor are calculated.

[0051] In this embodiment, based on several displacement sequences of the front sensor and the distance between the two sensors, several displacement sequences of the rear sensor are calculated, using the following formula: In the formula, It is the distance between the two nine-axis sensors. It is a rear sensor The displacement of a given moment relative to the last moment of the time window.

[0052] Step S105: Combine each displacement sequence with the corresponding magnetic induction intensity sequence to obtain several original displacement-magnetic field sequence pairs for the front sensor and the rear sensor respectively.

[0053] In step S105, the two nine-axis sensors are within the time window. Sensor data is acquired synchronously within the time window. At each sampling moment, the displacement and magnetic induction intensity at that moment are combined into a raw displacement-magnetic field data pair. That is, the displacement sequence and magnetic induction intensity sequence corresponding to all moments within the time window are combined into a raw displacement-magnetic field sequence pair. Several displacement sequences and corresponding magnetic induction intensity sequences are combined to form several raw displacement-magnetic field sequence pairs.

[0054] Step S107: Resample each original displacement-magnetic field sequence pair at equal intervals to obtain several new displacement-magnetic field sequence pairs for the front sensor and the rear sensor respectively.

[0055] In step S107, a set of equidistantly spaced target sampling points is reconstructed. Each of the original displacement-magnetic field sequence is aligned at the target sampling points. The aligned original displacement-magnetic field sequence pairs are linearly interpolated at the target sampling points, thereby obtaining a new displacement-magnetic field matching sequence pair for each of the front sensor and the back sensor. The new displacement-magnetic field matching sequence pair can be directly used to compare the magnetic field similarity of the two nine-axis sensors at the same position. It is understood that the original displacement-magnetic field sequence obtained in step S105 is data collected at equi-time intervals, and the sampling points are uniformly distributed in the time dimension. In order to calculate the similarity of the displacement-magnetic field sequence pairs of the two nine-axis sensors corresponding to the same candidate carrier velocity by using the characteristic that the magnetic sequences collected by the front and back magnetometers at the same position are consistent. Therefore, it is necessary to equidistantly resample the original displacement-magnetic field sequence to convert the original displacement-magnetic field sequence from "uniform sampling in the time dimension" to "uniform sampling in the distance dimension".

[0056] In step S109, the new displacement-magnetic field sequence pair of the front sensor and the new displacement-magnetic field sequence pair of the back sensor corresponding to the same candidate carrier velocity are calculated for similarity, and the candidate carrier velocity corresponding to the displacement-magnetic field sequence pair with the maximum similarity is determined as the carrier velocity.

[0057] In step S109, the embodiment uses the Pearson correlation coefficient to compare the similarity of the displacement-magnetic field sequence corresponding to each candidate carrier velocity. and The formula for calculating the Pearson correlation coefficient is as follows: , wherein is the Pearson correlation coefficient, is the number of samples of the sequence (i.e., the number of target sampling points), and are the sample values of and , respectively, and are the mean values of and , respectively. It is understood that because the magnetic field at the same position is unique, the displacement-magnetic field sequence pair with the maximum similarity indicates that the magnetic field data of the front and back magnetometers at the corresponding position are the most consistent, and the candidate carrier velocity corresponding thereto is the most consistent with the actual motion of the carrier, and therefore it is determined as the carrier velocity. In some other feasible embodiments, the similarity of the displacement-magnetic field sequence pair can also be calculated using the Euclidean distance, Manhattan distance, Chebyshev distance, Mahalanobis distance, cosine similarity, dynamic time warping (DTW), etc. Similarity measurement methods are not limited herein.

[0058] Please refer to the accompanying drawings Figure 7The application further provides a carrier velocity estimation device 4 of magnetic-inertial fusion, which comprises a data extraction module 41, a displacement calculation module 42, a first magnetic distance matching module 43, a second magnetic distance matching module 44 and a carrier velocity determination module 45. The data extraction module 41 is used for extracting sensor data of the front sensor 1 and the rear sensor 2 in a preset time window; wherein the front sensor 1 and the rear sensor 2 are respectively installed at the front and the rear of the carrier 3 and are both nine-axis sensors, and the sensor data comprises a magnetic induction intensity sequence and acceleration information. The displacement calculation module 42 is used for performing traversal search on the carrier velocity in a preset velocity range, and a plurality of displacement sequences of the front sensor and the rear sensor are calculated in combination with the acceleration information; wherein the preset velocity range comprises a plurality of candidate carrier velocities. The first magnetic distance matching module 43 is used for combining each displacement sequence and the corresponding magnetic induction intensity sequence to obtain a plurality of original displacement-magnetic field sequence pairs of the front sensor 1 and the rear sensor 2. The second magnetic distance matching module 44 is used for performing equal-distance resampling on each original displacement-magnetic field sequence pair to obtain a plurality of new displacement-magnetic field sequence pairs of the front sensor 1 and the rear sensor 2. The carrier velocity determination module 45 is used for performing similarity calculation on the new displacement-magnetic field sequence pair of the front sensor 1 and the new displacement-magnetic field sequence pair of the rear sensor 2 corresponding to the same candidate carrier velocity, and determining the candidate carrier velocity corresponding to the displacement-magnetic field sequence pair with the maximum similarity as the carrier velocity.

[0059] Please refer to the accompanying drawings Figure 8 The application further provides a carrier velocity estimation system 1000 of magnetic-inertial fusion, which comprises a front sensor 1, a rear sensor 2 and a processing module 100. The front sensor 1 and the rear sensor 2 are respectively installed at the front and the rear of the carrier 3 and are both nine-axis sensors. The processing module 100 is used for executing the magnetic-inertial fusion carrier velocity estimation method according to sensor data collected by the front and rear sensors.

[0060] In summary, the application installs nine-axis sensors on the front and rear parts of the carrier respectively, uses the characteristics of the same magnetic sequence collected by the front and rear nine-axis sensors, combines the bar arm compensation and sequence matching optimization algorithm, searches for the optimal carrier speed solution, and thus obtains accurate carrier speed measurement results. That is, the application only uses internal nine-axis sensors to measure speed, and does not rely on external signals (such as GNSS signals), so the application can still obtain accurate carrier speed measurement results in GNSS signal denial environments such as tunnels, urban canyons, underground parking lots, etc., thereby effectively overcoming the problem of reduced speed measurement accuracy caused by signal loss in traditional satellite navigation. Secondly, the application combines the acceleration information measured by the inertial measurement unit with the magnetic field information measured by the magnetometer, so that the change in carrier speed can be reflected in time even in high-speed motion, and thus the application can be used for carrier speed measurement in high-speed motion scenarios. In addition, compared with the scheme relying on external infrastructure, the application does not rely on a precisely installed and calibrated sensor array, and only two low-cost nine-axis sensors are needed to achieve autonomous speed measurement, which has the advantages of convenient deployment, low cost, strong robustness, etc., and significantly improves the navigation reliability in complex environments.

[0061] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the application, and not to limit them; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the application.

Claims

1. A magnetic-inertial fusion carrier velocity estimation method, characterized by, The method comprises the following steps: extracting sensor data of front sensor and rear sensor within a preset time window; wherein, the front sensor and the rear sensor are respectively installed at the front and rear of the carrier and are both nine-axis sensors, and the sensor data comprises a magnetic induction intensity sequence and acceleration information; performing traversal search on the carrier speed within a preset speed range, and calculating a plurality of displacement sequences of the front sensor and the rear sensor respectively in combination with the acceleration information; wherein, the preset speed range comprises a plurality of candidate carrier speeds; combining each displacement sequence and the corresponding magnetic induction intensity sequence to obtain a plurality of original displacement-magnetic field sequence pairs of the front sensor and the rear sensor respectively; performing equal-distance resampling on each original displacement-magnetic field sequence pair to obtain a plurality of new displacement-magnetic field sequence pairs of the front sensor and the rear sensor respectively; performing similarity calculation on the new displacement-magnetic field sequence pair of the front sensor and the new displacement-magnetic field sequence pair of the rear sensor corresponding to the same candidate carrier speed, and determining the candidate carrier speed corresponding to the displacement-magnetic field sequence pair with the maximum similarity as the carrier speed.

2. A magnetic- inertial fused carrier velocity estimation method as claimed in claim 1, characterized in that, Further comprising: projecting the magnetic induction intensity sequence and the acceleration information into the carrier coordinate system.

3. A magnetic and inertial fused vehicle velocity estimation method as claimed in claim 1, wherein, performing traversal search on the carrier speed within a preset speed range, and calculating a plurality of displacement sequences of the front sensor and the rear sensor respectively in combination with the acceleration information, comprising: calculating a speed change sequence of the carrier within the preset time window based on the acceleration information collected by the front sensor; calculating an absolute speed sequence corresponding to each candidate carrier speed in combination with the speed change sequence; wherein, a plurality of candidate carrier speeds correspond to a plurality of absolute speed sequences; calculating a plurality of displacement sequences of the front sensor based on the plurality of absolute speed sequences; calculating a plurality of displacement sequences of the rear sensor based on the plurality of displacement sequences of the front sensor and the distance between the two sensors.

4. A magnetic and inertial fused vehicle velocity estimation method as claimed in claim 3, wherein, calculating a speed change sequence of the carrier within the preset time window based on the acceleration information collected by the front sensor, comprising: calculating the acceleration of the carrier at each time within the preset time window based on the acceleration information collected by the front sensor; calculating the speed change amount between each time and the next time of the carrier based on the acceleration; integrating the speed change amount to obtain the speed change amount from each time to the last time of the carrier; combining the speed change amount from each time to the last time within the preset time window to form the speed change sequence.

5. A magnetic and inertial fused vehicle velocity estimation method as claimed in claim 3, wherein, calculating an absolute speed sequence corresponding to each candidate carrier speed in combination with the speed change sequence; wherein, a plurality of candidate carrier speeds correspond to a plurality of absolute speed sequences, comprising: combining each candidate carrier speed with the speed change sequence to calculate the absolute speed corresponding to each time within the preset time window; combining the absolute speed of each time within the preset time window to form the absolute speed sequence; wherein, the plurality of candidate carrier speeds correspond to a plurality of absolute speed sequences.

6. A magnetic and inertial fused vehicle velocity estimation method as claimed in claim 3, wherein, calculating a plurality of displacement sequences of the front sensor based on the plurality of absolute speed sequences, comprising; calculating a displacement of the carrier between each time and the next time based on each absolute speed sequence; integrating the displacement to obtain a displacement between each time and a last time; combining the displacements between each time and the last time in a preset time window to obtain the displacement sequence; wherein a plurality of absolute speed sequences correspond to a plurality of displacement sequences.

7. A magnetic and inertial fused vehicle velocity estimation method as claimed in claim 1, wherein, re-sampling each original displacement-magnetic field sequence pair at equal intervals to obtain a plurality of new displacement-magnetic field sequence pairs of the front sensor and the rear sensor, including: reconstructing a set of target sampling points at equal intervals; aligning each original displacement-magnetic field sequence pair at the target sampling points; linearly interpolating the aligned original displacement-magnetic field sequence pair at the target sampling points to obtain a plurality of new displacement-magnetic field matching sequence pairs of the front sensor and the rear sensor.

8. A magnetic and inertial fused vehicle velocity estimation method as claimed in claim 1, wherein, calculating the similarity of the new displacement-magnetic field sequence pair of the front sensor and the new displacement-magnetic field sequence pair of the rear sensor corresponding to the same candidate carrier speed, including: comparing the similarity of the new displacement-magnetic field sequence pair of the front sensor and the new displacement-magnetic field sequence pair of the rear sensor corresponding to each candidate carrier speed by using Pearson correlation coefficient, Euclidean distance, Manhattan distance, Chebyshev distance, Mahalanobis distance, cosine similarity or dynamic time warping.

9. A magnetic-inertial fusion carrier velocity estimation device, characterized in that, The estimation device includes: a data extraction module configured to extract sensor data of a front sensor and a rear sensor in a preset time window; wherein the front sensor and the rear sensor are respectively installed at the front and rear of a carrier and are both nine-axis sensors, and the sensor data includes a magnetic induction intensity sequence and acceleration information; a displacement calculation module configured to perform a traversal search on a carrier speed in a preset speed range and calculate a plurality of displacement sequences of the front sensor and the rear sensor in combination with the acceleration information; wherein the preset speed range includes a plurality of candidate carrier speeds; a first magnetic distance matching module configured to combine each displacement sequence and a corresponding magnetic induction intensity sequence to obtain a plurality of original displacement-magnetic field sequence pairs of the front sensor and the rear sensor; a second magnetic distance matching module configured to re-sample each original displacement-magnetic field sequence pair at equal intervals to obtain a plurality of new displacement-magnetic field sequence pairs of the front sensor and the rear sensor; a carrier speed determination module configured to calculate the similarity of the new displacement-magnetic field sequence pair of the front sensor and the new displacement-magnetic field sequence pair of the rear sensor corresponding to the same candidate carrier speed, and determine the candidate carrier speed corresponding to the displacement-magnetic field sequence pair with the greatest similarity as the carrier speed.

10. A magnetic-inertial fusion hybrid carrier velocity estimation system, characterized by, The estimation system includes: a front sensor and a rear sensor, which are respectively installed at the front and rear of a carrier and are both nine-axis sensors; and a processing module configured to perform a magnetic-inertial fusion carrier speed estimation method according to sensor data collected by the front and rear sensors, the method being any one of claims 1-8.

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