Personalized PDR positioning method and system based on ubiquitous positioning signal enhancement

Abstract

The present invention discloses a personalized PDR positioning method and system based on ubiquitous positioning signal enhancement. The method first uses the WRLS algorithm to estimate the parameters of a preset PDR step length estimation model based on the back-end real-time position parameters output by the SLAM odometer to obtain estimated parameters. A PDR step length estimation model is then obtained based on the estimated parameters, and the step length is estimated using the PDR step length estimation model. Finally, an AHRS algorithm with back-end real-time attitude parameter enhancement is used to fuse the back-end real-time attitude parameters output by the SLAM odometer with sensor data to obtain the pedestrian's heading. The estimated step length and pedestrian heading are used as the positioning results of the PDR positioning method. This method can significantly improve the accuracy of PDR positioning.

Description

Technical Field

[0001] The present invention relates to the field of positioning technology, and in particular to a personalized PDR positioning method and system based on ubiquitous positioning signal enhancement. Background Art

[0002] Location-based services (LBS) have numerous applications, including automated parking, mobile car tracking, and personnel management. They are widely used in indoor and outdoor positioning and are attracting increasing attention. In indoor and outdoor positioning, the complexity of the positioning environment and individual differences between pedestrians can easily lead to inaccurate step length estimation. Furthermore, walking introduces acceleration, which reduces the accuracy of horizontal attitude angle calculations, further reducing the leveling accuracy of magnetometer observations and ultimately affecting the accuracy of heading angle calculations. These two factors contribute to excessive cumulative errors in PDR (pedestrian dead reckoning) positioning algorithms. Therefore, research on PDR positioning algorithms for indoor and outdoor positioning has become a pressing scientific issue, and related positioning algorithms and technological achievements are constantly emerging.

[0003] The PDR algorithm primarily consists of two components: step length estimation and heading estimation. Currently, most common PDR positioning algorithms employ a step length estimation model, achieving real-time step length estimation based on offline trained model parameters. Heading estimation is primarily based on MARS (magnetometer, gyroscope, and accelerometer) sensors, integrated into an AHRS (attitude and heading reference system) algorithm to achieve real-time heading estimation. These two components together form the PDR positioning algorithm, ultimately resulting in pedestrian positioning results.

[0004] During the process of implementing the present invention, the inventors of this application discovered that the methods of the prior art have at least the following technical problems:

[0005] Existing technologies often suffer from inaccurate step length estimation due to the complexity of indoor and outdoor positioning environments and individual differences between pedestrians. Furthermore, the acceleration associated with walking can easily lead to inaccurate horizontal attitude angles, making it impossible to accurately level the magnetometer observations, leading to inaccurate heading estimation. These two factors together lead to excessive cumulative errors in the PDR positioning algorithm.

[0006] It can be seen from this that the methods in the existing technology have the problem of inaccurate step length estimation due to the complexity of indoor and outdoor positioning environments and individual differences between pedestrians, and inaccurate heading estimation due to the existence of pedestrian motion acceleration, which ultimately leads to the problem of excessive cumulative error of the PDR positioning algorithm. Summary of the Invention

[0007] The present invention proposes a personalized PDR positioning method and system based on ubiquitous positioning signal enhancement, which is used to solve or at least partially solve the technical problem of large positioning error existing in the prior art.

[0008] In order to solve the above technical problems, the technical solution of the present invention is:

[0009] The first aspect provides a personalized PDR positioning method based on ubiquitous positioning signal enhancement, including:

[0010] The WRLS algorithm is used to estimate the parameters of the preset PDR step length estimation model based on the back-end real-time position parameters output by the SLAM odometer to obtain the estimated parameters. The WRLS algorithm is a recursive weighted least squares algorithm.

[0011] A PDR step length estimation model is obtained according to the obtained estimation parameters, and the step length is estimated using the PDR step length estimation model;

[0012] The AHRS algorithm with enhanced back-end real-time attitude parameters is used to fuse the back-end real-time attitude parameters output by the SLAM odometer and the sensor data to obtain the pedestrian's heading. The estimated step length and the pedestrian's heading are used as the positioning results of the PDR positioning method. The AHRS algorithm with enhanced back-end real-time attitude parameters is a heading reference system algorithm with enhanced back-end real-time attitude parameters.

[0013] In one embodiment, the WRLS algorithm is used to estimate the parameters of the preset PDR step length estimation model based on the back-end real-time position parameters output by the SLAM odometer by the following formula:

[0014]

[0015]

[0016] P k =(IK k H k )P k-1 (3)

[0017] Among them, k represents the number of steps, K k represents the gain matrix, P k-1 Denotes the estimated variance of the model parameters at the k-1th step, H k represents the coefficient matrix of the k-th step-size estimation model, R k represents the variance of the step length observed in real time by the SLAM backend at step k, represents the parameters of the estimated model at the k-th step length, as the estimated parameters, Denotes the parameters of the estimated model at the k-1th step, Z krepresents the step length of the k-th step SLAM backend real-time observation, that is, the backend real-time position parameter output by the SLAM algorithm, P k represents the estimated variance of the model parameters estimated at the kth step.

[0018] In one embodiment, the sensor data includes magnetometer data, gyroscope data, and accelerometer data.

[0019] In one embodiment, an AHRS algorithm enhanced with back-end real-time attitude parameters is used to fuse the back-end real-time attitude parameters output by the SLAM odometer with sensor data, including:

[0020] Define the system state vector, system state equation and measurement equation, where the formula of the system state vector is:

[0021]

[0022] Among them: δx represents the system state vector, φ 1×3 represents the misalignment angle error, Indicates gyro zero bias;

[0023] The system state equation is:

[0024] δx t,t-1 =Φ t-1 δx t-1,t-1 +w t

[0025] Where: δx t-1,t-1 represents the system state vector at time t-1, δx t,t-1 represents the predicted system state vector at time t, w t represents the system process noise, Φ t-1 represents the state transition matrix;

[0026] Φ t-1 Expressed as:

[0027]

[0028] Among them: I 3×3 represents the identity matrix, 0 3×3 represents the 0 matrix, Indicates that at time t, system b is relative to n c The posture transformation matrix of the system, b (t) represents the b system at time t, i.e. the load system, n c represents the world coordinate system of the calculation, Δt represents the time interval from time t-1 to time t;

[0029] The measurement equation is:

[0030] δzt =H t δx t,t-1 +v t

[0031] Where: δz t Represents the measured closure error vector, H t represents the measurement matrix, v t Represents the measurement noise. In the measurement equation, the measurement equation for the real-time attitude parameter update of the backend of the SLAM odometer is:

[0032]

[0033]

[0034]

[0035] Among them, δz represents the measurement closure error vector of the real-time attitude parameters of the SLAM odometer backend, φ represents the misalignment angle error, Represents the measured value of the backend real-time attitude parameter output by the SLAM odometry, and its form is as follows:

[0036]

[0037] It represents the 2:4 element, v represents the measurement noise of the back-end real-time posture parameters, Obtained by SINS attitude mechanical arrangement, Express Find the conjugate, represents quaternion multiplication, |φ| represents the modulus value of φ;

[0038] Based on the defined system state vector, system state equation and measurement equation, the system state vector is solved based on Kalman filtering:

[0039] δx t,t-1 =Φ t-1 δx t-1,t-1 (1)

[0040]

[0041]

[0042] δx t,t =δx t,t-1 +K t (Z t -H t δx t,t-1 ) (4)

[0043] P t,t=(IK t H t )P t,t-1 (5)

[0044] Among them, δx t,t-1 represents the predicted system state vector at time t, Φ t-1 represents the state transition matrix, δx t-1,t-1 represents the system state vector at time t-1, P t,t-1 represents the variance of the predicted system state vector at time t, P t-1,t-1 represents the variance of the system state vector at time t-1, Q t-1 represents the system process noise matrix, K t represents the gain matrix, Ht represents the measurement matrix, Rt represents the variance of the posture observed in real time by the SLAM backend at time t, δx t,t represents the estimated system state vector at time t, Z t represents the real-time posture observed by the SLAM backend at time t, that is, the real-time posture parameter output by the SLAM algorithm, P t,t Represents the variance of the estimated system state vector at time t.

[0045] Based on the same inventive concept, the second aspect of the present invention provides a personalized PDR positioning system based on ubiquitous positioning signal enhancement, comprising:

[0046] The parameter estimation module is used to estimate the parameters of the preset PDR step length estimation model based on the back-end real-time position parameters output by the SLAM odometer using the WRLS algorithm to obtain the estimated parameters. The WRLS algorithm is a recursive weighted least squares algorithm.

[0047] A step length estimation module is used to obtain a PDR step length estimation model based on the obtained estimation parameters, and estimate the step length using the PDR step length estimation model;

[0048] The heading estimation module is used to fuse the back-end real-time attitude parameters output by the SLAM odometer and the sensor data using the AHRS algorithm enhanced with back-end real-time attitude parameters to obtain the pedestrian's heading. The estimated step length and the pedestrian's heading are used as the positioning results of the PDR positioning method. The AHRS algorithm enhanced with back-end real-time attitude parameters is a heading reference system algorithm enhanced with back-end real-time attitude parameters.

[0049] Based on the same inventive concept, the third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the method described in the first aspect when the program is executed.

[0050] Based on the same inventive concept, the fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the program.

[0051] Compared with the prior art, the technical solution provided by the present invention has at least the following technical effects:

[0052] In actual indoor and outdoor pedestrian positioning environments, when SLAM (Simultaneous Localization and Mapping) works normally, the back-end real-time position parameters in the SLAM odometer output results have very high position accuracy, and the back-end real-time attitude parameters have very high attitude accuracy. The present invention uses the back-end real-time position parameters and, based on the WRLS algorithm, performs personalized estimation of the step length estimation model parameters, so that the step length estimation model can adapt to the pedestrian's own gait characteristics and the real-time positioning environment, thereby improving the step length estimation accuracy; in the AHRS algorithm, the back-end real-time attitude parameters are integrated to enhance the accuracy of the horizontal attitude angle solution, so as to achieve more accurate leveling of the magnetometer observation, thereby improving the estimation accuracy of the heading angle. Ultimately, the accuracy of the PDR positioning method is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 This is the overall framework diagram of the personalized PDR positioning method based on ubiquitous positioning signal enhancement provided by the present invention. DETAILED DESCRIPTION

[0055] The present invention provides a personalized PDR positioning method with ubiquitous positioning signal enhancement, which is used to improve the problems existing in the existing technology methods, such as inaccurate step length estimation caused by the complexity of indoor and outdoor positioning environments and individual differences between pedestrians, and inaccurate heading estimation caused by the existence of pedestrian motion acceleration, which ultimately leads to excessive cumulative errors in the PDR positioning algorithm.

[0056] In order to solve the above technical problems, the main concept of the present invention is: in the actual indoor and outdoor pedestrian positioning environment, when SLAM (Simultaneous Localization and Mapping) works normally, in the SLAM odometer output result, its back-end real-time position parameters have very high position accuracy, and its back-end real-time attitude parameters have very high attitude accuracy. Therefore, using its back-end real-time position parameters, based on the WRLS algorithm, the step length estimation model parameters are personalized estimated, so that the step length estimation model can adapt to the pedestrian's own gait characteristics and the real-time positioning environment, thereby improving the step length estimation accuracy; in the AHRS algorithm, its back-end real-time attitude parameters are integrated to enhance the accuracy of the horizontal attitude angle solution, so as to achieve more accurate leveling of the magnetometer observation, thereby improving the estimation accuracy of the heading angle. Ultimately, the accuracy of the PDR positioning method is improved.

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0058] Example 1

[0059] The present invention provides a personalized PDR positioning method based on ubiquitous positioning signal enhancement, comprising:

[0060] The WRLS algorithm is used to estimate the parameters of the preset PDR step length estimation model based on the back-end real-time position parameters output by the SLAM odometer to obtain the estimated parameters. The WRLS algorithm is a recursive weighted least squares algorithm.

[0061] A PDR step length estimation model is obtained according to the obtained estimation parameters, and the step length is estimated using the PDR step length estimation model;

[0062] The AHRS algorithm with enhanced back-end real-time attitude parameters is used to fuse the back-end real-time attitude parameters output by the SLAM odometer and the sensor data to obtain the pedestrian's heading. The estimated step length and the pedestrian's heading are used as the positioning results of the PDR positioning method. The AHRS algorithm with enhanced back-end real-time attitude parameters is a heading reference system algorithm with enhanced back-end real-time attitude parameters.

[0063] See Figure 1 , which is an overall framework diagram of the personalized PDR positioning method based on ubiquitous positioning signal enhancement provided by the present invention.

[0064] In the figure, MARG is the abbreviation of three sensors, including magnetometer (Magnetic), gyroscope (Angular Rate) and accelerometer (Gravity). The personalized PDR step length estimation algorithm is the algorithm proposed by the present invention, that is, based on the WRLS algorithm, when SLAM is available, the parameters of the step length estimation model are estimated in real time based on the back-end real-time position parameters of the SLAM odometer with very high position accuracy. In other words: the personalized PDR step length estimation algorithm is used to estimate the parameters of the step length estimation model in real time; and the step length estimation model is a model for estimating step length established by researchers based on the analysis of step length and its influencing factors. Among them, the more representative models are the following three:

[0065] (1) L = (a + b * f) * h

[0066] L represents step length, f represents step frequency, h represents pedestrian height, and a and b represent model parameters;

[0067] (2) L=a+b*f+c*V

[0068] L represents step length, f represents step frequency, V represents acceleration variance, and a, b, and c represent model parameters;

[0069]

[0070] L represents step length, f represents step frequency, h represents pedestrian height, and k represents model parameters.

[0071] Three step-size estimation models are listed above. In the specific implementation process, the model parameters of the model are estimated based on which step-size estimation model is used.

[0072] The main innovations of the present invention include the following two points:

[0073] 1. It is proposed that during real-time positioning, based on the WRLS algorithm, when SLAM is available, the parameters of the step length estimation model are estimated based on the back-end real-time position parameters of the SLAM odometry with high position accuracy. The parameters are made suitable for the current pedestrian's own gait characteristics and the current real-time positioning environment, thereby improving the accuracy of step length estimation.

[0074] 2. It is proposed that in real-time positioning, when SLAM is available, the back-end real-time attitude parameters of the SLAM odometer with high attitude accuracy are integrated into the AHRS algorithm to achieve accurate estimation of the horizontal attitude angle, so as to achieve accurate leveling of the magnetometer observation and ultimately improve the heading estimation accuracy.

[0075] The present invention combines the above two points, that is, step length estimation plus heading estimation, to obtain the proposed personalized PDR positioning method.

[0076] In one embodiment, the WRLS algorithm is used to estimate the parameters of the preset PDR step length estimation model based on the back-end real-time position parameters output by the SLAM odometer by the following formula:

[0077]

[0078]

[0079] P k =(IK k H k )P k-1 (3)

[0080] Among them, k represents the number of steps, K k represents the gain matrix, P k-1 Denotes the estimated variance of the model parameters at the k-1th step, H k represents the coefficient matrix of the k-th step-size estimation model, R k represents the variance of the step length observed in real time by the SLAM backend at step k, represents the parameters of the estimated model at the k-th step length, as the estimated parameters, Denotes the parameters of the estimated model at the k-1th step, Z k represents the step length of the k-th step SLAM backend real-time observation, that is, the backend real-time position parameter output by the SLAM algorithm, P k represents the estimated variance of the model parameters estimated at the kth step.

[0081] Specifically, the objective function of the WRLS algorithm is:

[0082]

[0083] Where k represents the number of steps, Represents the parameters of the estimated model at the k-th step, P k represents the estimated variance of the estimated model parameters at the k-th step, Represents the estimated parameters of the k-1th step estimation model, P k-1 represents the estimated variance of the estimated model parameters at the k-1th step, H k Represents the coefficient matrix of the k-th step-size estimation model, Z k represents the step length of the k-th step of SLAM backend real-time observation, R k Represents the variance of the step length of the k-th step SLAM backend real-time observation.

[0084] It should be noted that Z kThe actual physical meaning of refers to the position difference between the back-end real-time position parameters output by SLAM at the start and end times of the kth step. Based on this, the above formulas (1), (2) and (3) can be derived. Among them, the parameters that need to be solved are: and P k , the known parameters are: P k-1 、H k , Z k and R k The gain matrix is ​​obtained by formula (1), the parameters of the k-th step-length estimation model are estimated by formula (2), and the estimated variance of the estimated k-th step-length estimation model parameters is calculated by formula (3).

[0085] In one embodiment, the sensor data includes magnetometer data, gyroscope data, and accelerometer data.

[0086] It should be noted that the sensor data mentioned above refers to the sensor data fused into the AHRS algorithm for real-time attitude parameter enhancement. The SLAM odometry uses a camera or lidar, while the WRLS algorithm uses accelerometers and gyroscopes.

[0087] In one embodiment, an AHRS algorithm enhanced with back-end real-time attitude parameters is used to fuse the back-end real-time attitude parameters output by the SLAM odometer with sensor data, including:

[0088] Define the system state vector, system state equation and measurement equation, where the formula of the system state vector is:

[0089]

[0090] Among them: δx represents the system state vector, φ 1×3 represents the misalignment angle error, Indicates gyro zero bias;

[0091] The system state equation is:

[0092] δx t,t-1 =Φ t-1 δx t-1,t-1 +w t

[0093] Where: δx t-1,t-1 represents the system state vector at time t-1, δx t,t-1 represents the predicted system state vector at time t, w t represents the system process noise, Φ t-1 represents the state transition matrix;

[0094] Φ t-1 Expressed as:

[0095]

[0096] Among them: I 3×3 represents the identity matrix, 0 3×3 represents the 0 matrix, Indicates that at time t, system b is relative to n c The posture transformation matrix of the system, b (t) represents the b system at time t, i.e. the load system, n c represents the world coordinate system of the calculation, Δt represents the time interval from time t-1 to time t;

[0097] The measurement equation is:

[0098] δz t =H t δx t,t-1 +v t

[0099] Where: δz t Represents the measured closure error vector, H t represents the measurement matrix, v t Represents the measurement noise. In the measurement equation, the measurement equation for the real-time attitude parameter update of the backend of the SLAM odometer is:

[0100]

[0101]

[0102]

[0103] Among them, δ z represents the measurement closure error vector of the real-time attitude parameters of the SLAM odometer backend, φ represents the misalignment angle error, Represents the measured value of the backend real-time attitude parameter output by the SLAM odometry, and its form is as follows:

[0104]

[0105] It represents the 2:4 element, v represents the measurement noise of the back-end real-time posture parameters, Obtained by SINS attitude mechanical arrangement, Express Find the conjugate, represents quaternion multiplication, |φ| represents the modulus value of φ;

[0106] Based on the defined system state vector, system state equation and measurement equation, the system state vector is solved based on Kalman filtering:

[0107] δx t,t-1 =Φ t-1 δx t-1,t-1 (4)

[0108]

[0109]

[0110] δx t,t =δx t,t-1 +K t (Z t -H t δx t,t-1 ) (7)

[0111] P t,t =(IK t H t )P t,t-1 (8)

[0112] Among them, δx t,t-1 represents the predicted system state vector at time t, Φ t-1 represents the state transition matrix, δx t-1,t-1 represents the system state vector at time t-1, P t,t-1 represents the variance of the predicted system state vector at time t, P t-1,t-1 represents the variance of the system state vector at time t-1, Q t-1 represents the system process noise matrix, K t represents the gain matrix, H t represents the measurement matrix, R t Represents the variance of the posture observed by the SLAM backend in real time at time t, δx t,t represents the estimated system state vector at time t, Z t represents the real-time posture observed by the SLAM backend at time t, that is, the real-time posture parameter output by the SLAM algorithm, P t,t represents the variance of the estimated system state vector at time t. The subscript t represents the current time, and t-1 represents the previous time.

[0113] Specifically, the AHRS algorithm for back-end real-time attitude parameter enhancement includes:

[0114] System state vector:

[0115]

[0116] Among them: δx represents the system state vector, φ 1×3 represents the misalignment angle error, Represents the gyro bias.

[0117] System state equation:

[0118] δx t,t-1 =Φ t-1 δx t-1,t-1 +w t

[0119] Where: δx t-1,t-1 represents the error state vector at time t-1, δx t,t-1 Represents the predicted error state vector, w t represents the system process noise, Φ t-1 Represents the state transition matrix.

[0120] Φ t-1 It can be expressed as:

[0121]

[0122] Among them: I 3×3 represents the identity matrix, 0 3×3 represents the 0 matrix, Represents the time t of system b relative to n c The posture transformation matrix of the system, b (t) represents the b system at time t, i.e. the load system, n c Represents the world coordinate system of the calculation, and Δt represents the time interval from time t-1 to time t.

[0123] The measurement equation is:

[0124] δz t =H t δx t,t-1 +v t

[0125] Where: δz t Represents the measured closure error vector, H t represents the measurement matrix, v t Represents the measurement noise.

[0126] In the measurement equation, the measurement equation for measuring and updating the back-end real-time attitude parameters of the SLAM odometer is:

[0127]

[0128]

[0129]

[0130] in: Represents the backend real-time attitude parameter measurement value (i.e., the backend real-time attitude parameter integrated into the AHRS algorithm), and its format is as follows:

[0131]

[0132] represents the 2:4 element, v represents the measurement noise of the backend real-time posture parameters,

[0133] Obtained by SINS attitude mechanical arrangement,

[0134] Representatives Find the conjugate,

[0135] represents quaternion multiplication,

[0136] δz represents the measurement closure error vector of the real-time attitude parameters of the SLAM odometry backend.

[0137] φ represents the misalignment angle error, which is part of the system state vector.

[0138] v represents the error of the measurement closure error vector of the real-time attitude parameters of the SLAM odometry backend,

[0139] |φ| represents the modulo value of φ.

[0140] After defining the system state vector, system state equation, and measurement equation, the AHRS algorithm can solve the system state vector based on Kalman filtering. The steps of Kalman filtering are shown in formulas (4) to (8).

[0141] 1. First, predict the system state vector at the next moment using formula (4);

[0142] 2. Predict the variance of the system state vector at the next moment using formula (5);

[0143] 3. Gain matrix through formula (6);

[0144] 4. Estimate the system state vector at the current moment (the quantity that needs to be solved in the end) through formula (7);

[0145] 5. Estimate the variance of the system state vector at the current moment using formula (8).

[0146] Example 2

[0147] Based on the same inventive concept, this embodiment discloses a personalized PDR positioning system based on ubiquitous positioning signal enhancement, including:

[0148] The parameter estimation module is used to estimate the parameters of the preset PDR step length estimation model based on the back-end real-time position parameters output by the SLAM odometer using the WRLS algorithm to obtain the estimated parameters. The WRLS algorithm is a recursive weighted least squares algorithm.

[0149] A step length estimation module is used to obtain a PDR step length estimation model based on the obtained estimation parameters, and estimate the step length using the PDR step length estimation model;

[0150] The heading estimation module is used to fuse the back-end real-time attitude parameters output by the SLAM odometer and the sensor data using the AHRS algorithm enhanced with back-end real-time attitude parameters to obtain the pedestrian's heading. The estimated step length and the pedestrian's heading are used as the positioning results of the PDR positioning method. The AHRS algorithm enhanced with back-end real-time attitude parameters is a heading reference system algorithm enhanced with back-end real-time attitude parameters.

[0151] Since the system described in Example 2 of the present invention is the system used to implement the personalized PDR positioning method based on ubiquitous positioning signal enhancement in Example 1 of the present invention, those skilled in the art will be able to understand the specific structure and variations of this system based on the method described in Example 1 of the present invention, and therefore will not be described in detail here. All systems used in the method of Example 1 of the present invention are within the scope of protection of the present invention.

[0152] Example 3

[0153] Based on the same inventive concept, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed, the method described in the first embodiment is implemented.

[0154] Since the computer-readable storage medium described in Example 3 of the present invention is used to implement the personalized PDR positioning method based on ubiquitous positioning signal enhancement in Example 1 of the present invention, the specific structure and variations of the computer-readable storage medium are readily understood by those skilled in the art based on the method described in Example 1 of the present invention, and thus will not be further described here. All computer-readable storage media used in the method of Example 1 of the present invention fall within the scope of protection of the present invention.

[0155] Example 4

[0156] Based on the same inventive concept, the present application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method in the first embodiment is implemented.

[0157] Since the computer device described in Example 4 of the present invention is used to implement the personalized PDR positioning method based on ubiquitous positioning signal enhancement in Example 1 of the present invention, the specific structure and variations of the computer device are readily understood by those skilled in the art based on the method described in Example 1 of the present invention, and therefore will not be further described here. All computer devices used in the method of Example 1 of the present invention fall within the scope of protection of the present invention.

[0158] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0159] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0160] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the embodiments of the present invention without departing from the spirit and scope of the embodiments of the present invention. Thus, the present invention is intended to include such changes and modifications to the embodiments of the present invention if they fall within the scope of the claims and their equivalents.

Claims

1. A personalized pedestrian dead reckoning (PDR) positioning method based on ubiquitous positioning signal enhancement, characterized by: include: The WRLS algorithm is used to estimate the parameters of the preset PDR step length estimation model based on the back-end real-time position parameters output by the SLAM odometer to obtain the estimated parameters. The WRLS algorithm is a recursive weighted least squares algorithm. A pedestrian dead reckoning (PDR) step length estimation model is obtained based on the obtained estimated parameters, and the step length is estimated using the PDR step length estimation model; An AHRS algorithm enhanced with back-end real-time attitude parameters is used to fuse the back-end real-time attitude parameters output by the SLAM odometer with sensor data to obtain the pedestrian's heading. The estimated step length and pedestrian's heading are used as the positioning results of the PDR positioning method. The AHRS algorithm enhanced with back-end real-time attitude parameters is a heading reference system algorithm enhanced with back-end real-time attitude parameters, where the sensor data includes magnetometer data, gyroscope data, and accelerometer data.

2. The personalized pedestrian dead reckoning (PDR) positioning method based on ubiquitous positioning signal enhancement according to claim 1, characterized in that: The WRLS algorithm is used to estimate the parameters of the preset PDR step length estimation model based on the back-end real-time position parameters output by the SLAM odometer through the following formula: in, Indicates the number of steps, represents the gain matrix, Represents the estimated The estimated variance of the model parameters is estimated by step size, Indicates the The coefficient matrix of the model estimated by step size, Indicates the The variance of the step length observed in real time by the SLAM backend, Represents the estimated The parameters of the model are estimated by step length, as the estimated parameters, Represents the estimated The step size is used to estimate the parameters of the model, Indicates the The step length of the SLAM backend real-time observation, that is, the backend real-time position parameter output by the SLAM algorithm, Represents the estimated The estimated variance of the model parameters is estimated using the step size.

3. The personalized pedestrian dead reckoning (PDR) positioning method based on ubiquitous positioning signal enhancement according to claim 1, characterized in that: The AHRS algorithm enhanced with backend real-time attitude parameters is used to fuse the backend real-time attitude parameters output by the SLAM odometer with sensor data, including: Define the system state vector, system state equation and measurement equation, where the formula of the system state vector is: in: represents the system state vector, represents the misalignment angle error, Indicates gyro bias; The system state equation is: in: represents the system state vector at time t-1, represents the predicted system state vector at time t, represents the system process noise, represents the state transition matrix; Expressed as: in: represents the identity matrix, represents the 0 matrix, Indicates that at time t, the b system is relative to The posture transformation matrix of the system, represents the b system at time t, i.e. the load system, Represents the world coordinate system of the calculation, Represents the time interval from time t-1 to time t; The measurement equation is: in: represents the measurement closure error vector, represents the measurement matrix, Represents the measurement noise. In the measurement equation, the measurement equation for the real-time attitude parameter update of the backend of the SLAM odometer is: in, Represents the measurement closure difference vector of the real-time attitude parameters of the SLAM odometer backend, represents the misalignment angle error, Represents the measured value of the backend real-time attitude parameter output by the SLAM odometry, and its form is as follows: ， It means taking the elements of 2:

4. Represents the measurement noise of the backend real-time posture parameters, Obtained by SINS attitude mechanical arrangement, Express Find the conjugate, represents quaternion multiplication, Express Find the modulus value; Based on the defined system state vector, system state equation and measurement equation, the system state vector is solved based on Kalman filtering: in, represents the predicted system state vector at time t, represents the state transition matrix, represents the system state vector at time t-1, represents the variance of the predicted system state vector at time t, represents the variance of the system state vector at time t-1, represents the system process noise matrix, represents the gain matrix, represents the measurement matrix, Represents the variance of the posture observed in real time by the SLAM backend at time t, represents the estimated system state vector at time t, It represents the real-time posture observed by the SLAM backend at time t, that is, the real-time posture parameters of the backend output by the SLAM algorithm. Represents the variance of the estimated system state vector at time t.

4. Personalized pedestrian dead reckoning (PDR) positioning system based on ubiquitous positioning signal enhancement, characterized by: include: The parameter estimation module is used to estimate the parameters of the preset PDR step length estimation model based on the back-end real-time position parameters output by the SLAM odometer using the WRLS algorithm to obtain the estimated parameters. The WRLS algorithm is a recursive weighted least squares algorithm. A step length estimation module is used to obtain a PDR step length estimation model based on the obtained estimation parameters, and estimate the step length using the PDR step length estimation model; The heading estimation module is used to fuse the back-end real-time attitude parameters output by the SLAM odometer and the sensor data using the AHRS algorithm enhanced with back-end real-time attitude parameters to obtain the pedestrian's heading, and use the estimated step length and the pedestrian's heading as the positioning result of the PDR positioning method. The AHRS algorithm enhanced with back-end real-time attitude parameters is a heading reference system algorithm enhanced with back-end real-time attitude parameters, wherein the sensor data includes magnetometer data, gyroscope data and accelerometer data.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed, the method according to any one of claims 1 to 3 is implemented.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 3 is implemented.

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