DTW-based pedestrian navigation method and device and storage medium

Abstract

The invention discloses a DTW-based pedestrian navigation method and device and a storage medium, and relates to the technical field of pedestrian navigation, and the technical scheme is characterized in that the method comprises the following steps: S1, obtaining triaxial accelerometer data of an inertial sensor, carrying out secondary processing on the triaxial accelerometer data, and regenerating a new modulus value of a triaxial accelerometer by using an exponential function; s2, performing matching item construction on the regenerated new module value of the triaxial accelerometer by using a DTW algorithm, and performing zero-speed state detection in a gait cycle; and S3, performing position recursion of pedestrian navigation by using a Kalman filter according to a zero-speed state detection result in the gait cycle. According to the invention, the adaptability and flexibility of navigation are improved, the cost is reduced, and more accurate pedestrian navigation is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of pedestrian navigation, and more specifically, it relates to a pedestrian navigation method, device, and storage medium based on DTW. Background Art

[0002] In an indoor scenario, although technologies such as WiFi and Bluetooth can provide meter-level positioning services, in scenarios such as emergency rescue, since base stations or beacons cannot be deployed in advance, these technologies will lose their positioning capabilities. The pedestrian positioning technology based on inertial sensors is an autonomous pedestrian dead reckoning system that does not rely on prior information (such as signal base stations or signal fingerprint databases) and is not affected by the external environment. It has great application prospects in fields such as fire fighting and emergency rescue. The pedestrian navigation device implemented using inertial sensors has many advantages such as small size, easy installation, maintenance-free, and low full-life cycle cost.

[0003] However, there are still some deficiencies in existing pedestrian navigation solutions. For example, some solutions need to store static data for a certain period of time after startup and perform adaptation for multiple gaits through a pre-assumed variety of thresholds, which results in a large number of empirical threshold settings and poor adaptability. Other solutions perform zero-speed judgment based on a neural network model, which depends on the training sample set of the neural network model, has limited application scope, high device cost, and high platform computing power requirements.

[0004] Therefore, a new solution needs to be proposed to solve this problem. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a pedestrian navigation method, device, and storage medium based on DTW, which improves the adaptability and flexibility of navigation, reduces costs, and realizes more accurate pedestrian navigation.

[0006] The above technical purpose of the present invention is achieved through the following technical solutions: A pedestrian navigation method based on DTW, where an inertial sensor is worn on the foot of the pedestrian to be navigated, and the navigation method includes the following steps:

[0007] S1. Obtain the triaxial accelerometer data of the inertial sensor, perform secondary processing on the triaxial accelerometer data, and use an exponential function to regenerate a new modulus value of the triaxial accelerometer;

[0008] S2. Use the DTW algorithm to construct matching items for the newly generated modulus value of the triaxial accelerometer and perform zero-speed state detection within the gait cycle;

[0009] S3. According to the zero-speed state detection result within the gait cycle, use a Kalman filter to perform position recursion for pedestrian navigation.

[0010] In one embodiment, in step S1, after obtaining the triaxial accelerometer data of the inertial sensor, the initial modulus of the triaxial accelerometer data is first calculated, and then the new modulus is calculated based on the initial modulus. The calculation formula is as follows:

[0011]

[0012] where Acc noem is the initial modulus of the triaxial accelerometer data, is the new modulus of the triaxial accelerometer data, e is the Euler number, and acc x 、acc y and acc z are the acceleration measurement data of the triaxial accelerometer in the three axial directions of the x-axis, y-axis, and z-axis.

[0013] In one embodiment, the specific method of step S2 is as follows:

[0014] S21. In step S1, when the newly obtained modulus is less than the preset threshold for the first time, it is marked as the starting point of the gait cycle, and the newly obtained modulus starting from this point is started to be stored. Then, continue to store until it is detected again that the newly obtained modulus is less than the preset threshold, and it is marked as the end point of the gait cycle and the storage is stopped. At this time, a complete gait cycle data segment is obtained;

[0015] S22. Use the DTW algorithm to match the gait cycle data segment obtained in step S2 with the preset reference gait cycle and calculate the matching distance;

[0016] S23. If the matching distance is less than the preset threshold, it is considered that the gait cycle obtained in step S2 is similar to the reference gait cycle, indicating a successful match and in a zero-speed state;

[0017] S24. If the match fails, repeat step S21.

[0018] In one embodiment, the specific method of step S3 is as follows:

[0019] S31. When it is detected that the cumulative duration of the pedestrian's zero-speed state exceeds 1 second, start the initialization process of the Kalman filter and initialize the heading angle of the inertial sensor to 0 degrees;

[0020] S32. After completion of the initialization, the Kalman filter updates the state vector according to the following formula:

[0021]

[0022] where x k is the state estimate value at time k, x k / k-1 is the state prediction value at time k, and P kThe state estimation error covariance matrix at time k, P k / k-1 The state prediction error covariance matrix at time k, B k The control input matrix at time k, H is the observation matrix, R is the observation noise covariance matrix, Q is the process noise covariance matrix, I is the identity matrix, Z is the observation vector, and U is the control input vector;

[0023] The expression of the state vector is as follows:

[0024]

[0025] where, δr n is the position error, δv n is the velocity error, is the attitude error, b g is the zero-velocity state deviation of the triaxial accelerometer data, b a is the zero-velocity state deviation of the gyroscope;

[0026] S34. When it is detected that the pedestrian is in the zero-velocity state, zero-velocity constraint is performed, the velocity observation value is set to 0, and the state vector of the Kalman filter is updated according to the observation value. The expression of the observation equation under zero-velocity constraint is:

[0027] O = H k x k + η k ,

[0028] where, O represents the velocity measurement information in the zero-velocity state. At this time, all three-dimensional ideal velocities are 0. H k represents the transition matrix corresponding to the state vector. Except for the velocity term, all elements of the H matrix are 0, and the H matrix corresponding to the velocity term is the identity matrix. η k represents the observation information noise;

[0029] S34. Based on steps S31 to S33, continuously update the position error, velocity error, attitude error, zero-velocity state deviation of the triaxial accelerometer data, and zero-velocity state deviation of the gyroscope in the state vector, and the positioning result of pedestrian navigation can be deduced.

[0030] A pedestrian navigation device based on DTW, comprising:

[0031] A data acquisition module configured to obtain the triaxial accelerometer data of the inertial sensor, perform secondary processing on the triaxial accelerometer data, and regenerate a new modulus value of the triaxial accelerometer using an exponential function;

[0032] A zero-velocity state detection module configured to use the DTW algorithm to construct a matching item for the regenerated new modulus value of the triaxial accelerometer and perform zero-velocity state detection within the gait cycle;

[0033] A position recursion module, according to the zero-speed state detection result within the gait cycle, uses a Kalman filter to perform position recursion for pedestrian navigation

[0034] A machine-readable storage medium, which includes a program for the DTW-based pedestrian navigation method. When the program for the DTW-based pedestrian navigation method is executed by a processor, any of the DTW-based pedestrian navigation methods is implemented.

[0035] In summary, the present invention has the following beneficial effects: By acquiring and processing the triaxial accelerometer data of the inertial sensor, generating a new modulus value using an exponential function, and using the DTW algorithm to construct matching terms and detect the zero-speed state within the gait cycle, the present invention does not rely on empirical thresholds or neural network models, thereby improving the adaptability and scope of use, while reducing the device cost and platform computing power requirements. Using a Kalman filter for position recursion realizes more accurate and flexible pedestrian navigation, which is particularly suitable for emergency rescue and other scenarios where it is impossible to deploy base stations or beacons in advance. Description of the Drawings

[0036] Figure 1 It is a flowchart of the DTW-based pedestrian navigation method according to an embodiment of the present application. Detailed Embodiments

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] As Figure 1 shown, an embodiment of the present application provides a DTW-based pedestrian navigation method. The pedestrian to be navigated wears an inertial sensor on the foot. The navigation method includes the following steps:

[0039] S1. Acquire the triaxial accelerometer data of the inertial sensor. The triaxial acceleration data includes the acceleration data in the X, Y, and Z axes. The data format includes the timestamp and the corresponding acceleration values of the X, Y, and Z axes. Perform secondary processing on the triaxial accelerometer data, and use an exponential function to regenerate the new modulus value of the triaxial accelerometer. Of course, the gyroscope data of the inertial sensor can also be acquired to measure the angular velocity and the change in angular velocity, so as to sense the motion posture of the pedestrian, including actions such as tilting and rotating, thereby helping the navigation system to more accurately judge the motion state of the pedestrian;

[0040] S2. Use the DTW algorithm to construct matching items for the newly generated modulus values of the triaxial accelerometer, and detect the zero-velocity state within the gait cycle;

[0041] S3. According to the zero-velocity state detection results within the gait cycle, use the Kalman filter for position recursion of pedestrian navigation.

[0042] In the above method, by acquiring and processing the triaxial accelerometer data of the inertial sensor, generating new modulus values using the exponential function, and using the DTW algorithm for constructing matching items and detecting the zero-velocity state within the gait cycle, it does not rely on empirical thresholds or neural network models, thereby improving the adaptability and scope of use, while reducing the device cost and platform computing power requirements. Using the Kalman filter for position recursion realizes more accurate and flexible pedestrian navigation, which is especially suitable for scenarios such as emergency rescue where base stations or beacons cannot be deployed in advance.

[0043] In this embodiment, in step S1, after acquiring the triaxial accelerometer data of the inertial sensor (IMU), first calculate the initial modulus value of the triaxial accelerometer data, and then calculate the new modulus value based on the initial modulus value. The calculation formula is as follows:

[0044]

[0045] where Acc noem is the initial modulus value of the triaxial accelerometer data, is the new modulus value of the triaxial accelerometer data, e is the Euler number, acc x 、acc y and acc z are the acceleration measurement data of the triaxial accelerometer in the three axes of x, y, and z.

[0046] In the above method, in a complex or dynamic environment, the motion state of the pedestrian may change. By calculating the new modulus value, these changes can be better captured, and the navigation and positioning algorithms can be adjusted accordingly, thereby improving the robustness and adaptability of the system.

[0047] In this embodiment, the specific method of step S2 is as follows:

[0048] S21. In step S1, when the newly obtained modulus value is less than the preset threshold for the first time, mark it as the starting point of the gait cycle, and start storing the new modulus values starting from this point. Then continue to store until it is detected again that the new modulus value is less than the preset threshold, mark it as the end point of the gait cycle, and stop storing. At this time, a complete gait cycle data segment is obtained;

[0049] S22. Use the DTW algorithm to match the gait cycle data segment obtained in step S2 with a preset reference gait cycle (such as the instant when the pedestrian's foot touches the ground), and calculate the matching distance;

[0050] S23. If the matching distance is less than the preset threshold, it is considered that the gait cycle obtained in step S2 is similar to the reference gait cycle, indicating a successful match and a zero-speed state. If it is determined to be in a zero-speed state, corresponding markings are made for subsequent processing;

[0051] S24. If the match fails, repeat step S21, start detecting and storing new modulus data from a new starting point until the end point of the gait cycle is detected again.

[0052] In the above method, accurate detection of the gait cycle can be achieved, and the zero-speed state can be determined based on the DTW algorithm and the preset reference gait cycle. This method has broad application prospects in pedestrian navigation systems, especially in occasions where it is necessary to accurately judge the pedestrian's motion state to achieve accurate navigation and positioning.

[0053] In this embodiment, the specific method of step S3 is as follows:

[0054] S31. When the cumulative duration of the detected zero-speed state of the pedestrian exceeds 1 second, it is considered that the pedestrian is in a relatively static state and is suitable for initializing the Kalman filter. Therefore, start the initialization process of the Kalman filter and initialize the heading angle of the inertial sensor to 0 degrees. The reason for this setting is that in the zero-speed state, the change in the pedestrian's direction can be ignored, so resetting the heading angle to 0 degrees is a reasonable assumption;

[0055] S32. After completion of the initialization, the Kalman filter updates the state vector according to the following formula:

[0056]

[0057] where, x k is the state estimate value at time k, x k / k-1 is the state prediction value at time k, P k is the state estimation error covariance matrix at time k, P k / k-1 is the state prediction error covariance matrix at time k, B k is the control input matrix at time k, H is the observation matrix, R is the observation noise covariance matrix, Q is the process noise covariance matrix, I is the identity matrix, Z is the observation vector, and U is the control input vector;

[0058] Specifically, it includes the following steps:

[0059] S321. At time k, calculate the state prediction value at the current time according to the state estimation value at the previous time and the system model;

[0060] S322. Meanwhile, update the state estimation error covariance matrix and the state prediction error covariance matrix to reflect the uncertainty of the state estimation;

[0061] S333. Use the observation matrix H, the observation noise covariance matrix R, the process noise covariance matrix Q, and the identity matrix I to calculate the Kalman gain for fusing the observation information and the prediction information;

[0062] S334. According to the observation vector Z and the control input vector U (if any), and the calculated Kalman gain, update the state vector. The state vector includes the position error, the velocity error, the attitude error, the zero-velocity state deviation of the three-axis accelerometer data, and the zero-velocity state deviation of the gyroscope.

[0063] The expression of the state vector is as follows:

[0064]

[0065] where, δr n is the position error, δv n is the velocity error, is the attitude error, b g is the zero-velocity state deviation of the three-axis accelerometer data, b a is the zero-velocity state deviation of the gyroscope;

[0066] S34. When it is detected that the pedestrian is in the zero-velocity state, perform zero-velocity constraint, set the velocity observation value to 0, and update the state vector of the Kalman filter according to the observation value. The expression of the observation equation under the zero-velocity constraint is:

[0067] O = H k x k + η k ,

[0068] where, O represents the velocity measurement information in the zero-velocity state. At this time, the three-dimensional ideal velocity is all 0. H k represents the transition matrix corresponding to the state vector. Except for the velocity term, all elements of the H matrix are 0, and the H matrix corresponding to the velocity term is the identity matrix. η k represents the observation information noise;

[0069] S34. Based on steps S31 to S33, continuously update the position error, velocity error, attitude error, zero-velocity state deviation of the three-axis accelerometer data, and zero-velocity state deviation of the gyroscope in the state vector, and then the positioning result of the pedestrian navigation can be deduced.

[0070] The present invention also discloses a pedestrian navigation device based on DTW, including:

[0071] A data acquisition module configured to obtain the triaxial accelerometer data of an inertial sensor, perform secondary processing on the triaxial accelerometer data, and regenerate a new modulus value of the triaxial accelerometer by using an exponential function;

[0072] A zero-speed state detection module configured to use the DTW algorithm to construct matching items for the newly generated modulus value of the triaxial accelerometer and perform zero-speed state detection within a gait cycle;

[0073] A position recursion module that performs position recursion for pedestrian navigation by using a Kalman filter according to the zero-speed state detection result within a gait cycle

[0074] The present invention also discloses a machine-readable storage medium, which includes a program for a pedestrian navigation method based on DTW. When the program for the pedestrian navigation method based on DTW is executed by a processor, any of the pedestrian navigation methods based on DTW is implemented.

[0075] The above are only the preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements should also be regarded as within the protection scope of the present invention.

Claims

1. A pedestrian navigation method based on DTW, where an inertial sensor is worn on the foot of the pedestrian to be navigated, characterized in that: The navigation method includes the following steps: S1. Obtain the triaxial accelerometer data of the inertial sensor, perform secondary processing on the triaxial accelerometer data, and use an exponential function to regenerate the new modulus value of the triaxial accelerometer; S2. Use the DTW algorithm to construct matching items for the newly generated modulus value of the triaxial accelerometer, and perform zero-speed state detection within the gait cycle; S3. According to the zero-speed state detection result within the gait cycle, use the Kalman filter to perform position recursion for pedestrian navigation.

2. The pedestrian navigation method based on DTW according to claim 1, characterized in that: In step S1, after obtaining the triaxial accelerometer data of the inertial sensor, first calculate the initial modulus value of the triaxial accelerometer data, and then calculate the new modulus value based on the initial modulus value. The calculation formula is as follows: Among them, Acc noem is the initial modulus value of the three-axis accelerometer data, is the new modulus value of the three-axis accelerometer data, e is the Euler number, acc x , acc y and acc z are the acceleration measurement data of the three-axis accelerometer in the three axial directions of the x-axis, y-axis and z-axis.

3. The pedestrian navigation method based on DTW according to claim 2, wherein: The specific method of step S2 is as follows: S21. In step S1, when the newly obtained modulus value is less than the preset threshold for the first time, mark it as the starting point of the gait cycle, and start storing the newly generated modulus value from this point. Then continue to store until it is detected again that the newly generated modulus value is less than the preset threshold, mark it as the end point of the gait cycle, and stop storing. At this time, a complete gait cycle data segment is obtained; S22. Use the DTW algorithm to match the gait cycle data segment obtained in step S2 with the preset reference gait cycle, and calculate the matching distance; S23. If the matching distance is less than the preset threshold, it is considered that the gait cycle obtained in step S2 is similar to the reference gait cycle, indicating successful matching and in a zero-speed state; S24. If the matching fails, repeat step S21.

4. The pedestrian navigation method based on DTW according to claim 1, wherein: The specific method of step S3 is as follows: S31. When it is detected that the cumulative duration of the pedestrian's zero-speed state exceeds 1 second, start the initialization process of the Kalman filter, and initialize the heading angle of the inertial sensor to 0 degrees; S32. After completion of the initialization, the Kalman filter updates the state vector according to the following formula: where, x k is the state estimation value at time k, x k / k-1 is the state prediction value at time k, P k is the state estimation error covariance matrix at time k, P k / k-1 is the state prediction error covariance matrix at time k, B k is the control input matrix at time k, H is the observation matrix, R is the observation noise covariance matrix, Q is the process noise covariance matrix, I is the identity matrix, Z is the observation vector, and U is the control input vector; The expression of the state vector is as follows: b g Among them, δr n is the position error, δv n is the velocity error, is the attitude error, b g is the zero-velocity state deviation of the three-axis accelerometer data, b a is the zero-velocity state deviation of the gyroscope; S34. When it is detected that the pedestrian is in a zero-speed state, perform zero-speed constraint, set the speed observation value to 0, and update the state vector of the Kalman filter according to the observation value. The expression of the observation equation under zero-speed constraint is: O=H k x k +η k , Among them, O represents the velocity measurement information in the zero-velocity state, at this time all three-dimensional ideal velocities are 0, H k represents the transition matrix corresponding to the state vector. Except for the velocity terms, all elements of the H matrix are 0, and the H matrix corresponding to the velocity terms is the identity matrix, η k represents the observation information noise; S34. Based on steps S31 to S33, continuously update the position error, speed error, attitude error, zero-speed state deviation of the triaxial accelerometer data, and zero-speed state deviation of the gyroscope in the state vector, and then the positioning result of pedestrian navigation can be deduced.

5. A pedestrian navigation device based on DTW, characterized in that: It includes: A data acquisition module configured to obtain the triaxial accelerometer data of the inertial sensor, perform secondary processing on the triaxial accelerometer data, and use an exponential function to regenerate the new modulus value of the triaxial accelerometer; A zero-speed state detection module configured to use the DTW algorithm to construct matching items for the newly generated modulus value of the triaxial accelerometer, and perform zero-speed state detection within the gait cycle; A position recursion module that, according to the zero-speed state detection result within the gait cycle, uses the Kalman filter to perform position recursion for pedestrian navigation.

6. A machine-readable storage medium, characterized in that: The machine-readable storage medium includes a program for the DTW-based pedestrian navigation method. When the program for the DTW-based pedestrian navigation method is executed by a processor, it implements the DTW-based pedestrian navigation method described in any one of claims 1-4.

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