Moving speed determination method and device, electronic equipment and storage medium

Abstract

The invention relates to a moving speed determination method and device, electronic equipment and a storage medium. The moving speed determination method comprises the steps of obtaining an acceleration signal of the electronic equipment, determining a target stride frequency and a first step length of the acceleration signal in the main axis direction, determining an initial error parameter of the first step length according to a preset stride frequency interval corresponding to the target stride frequency, and determining the moving speed of the electronic equipment according to the target stride frequency, the first step length and the initial error parameter. And determining the initial moving speed of the electronic equipment, and correcting the initial moving speed to obtain the target moving speed. According to the method, the initial error parameter of the first step length adapts to different motion states, real-time correction is carried out on the basis of the initial error parameter, the influence of the motion state change on the speed estimation value can be avoided, and the real-time performance and accuracy of the speed estimation result are effectively improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, and particularly relates to a moving speed determination method and device, electronic equipment and storage medium. BACKGROUND

[0002] GNSS (Global Navigation Satellite System) / PDR (Pedestrian Dead Reckoning) combined navigation positioning technology is a main method for estimating the heading, position and speed of a pedestrian, wherein estimating the moving speed of the pedestrian based on gait analysis is an important application in electronic equipment. SUMMARY

[0003] To overcome the problems in the related art, the present disclosure provides a moving speed determination method, device, electronic equipment and storage medium.

[0004] According to a first aspect of an embodiment of the present disclosure, a moving speed determination method is provided, and the method comprises:

[0005] obtaining an acceleration signal of an electronic device, determining a target step frequency and a first step length of the acceleration signal in a main axis direction;

[0006] determining an initial error parameter of the first step length according to a preset step frequency interval corresponding to the target step frequency;

[0007] determining an initial moving speed of the electronic device according to the target step frequency, the first step length and the initial error parameter;

[0008] correcting the initial moving speed to obtain a target moving speed.

[0009] In an exemplary embodiment, the determining the initial error parameter of the first step length according to the preset step frequency interval corresponding to the target step frequency comprises:

[0010] if the target step frequency is located in a first preset step frequency interval range, determining the initial error parameter of the first step length according to a preset error parameter of the first preset step frequency interval;

[0011] if the target step frequency is located outside all preset step frequency interval ranges and between adjacent second preset step frequency interval and third preset step frequency interval, determining the initial error parameter of the first step length according to a preset error parameter of the second preset step frequency interval and a preset error parameter of the third preset step frequency interval.

[0012] In an exemplary embodiment, the correcting the initial moving speed to obtain the target moving speed comprises:

[0013] correct the initial error parameter according to the target GNSS observation speed and the initial moving speed to obtain a target error parameter;

[0014] determine a target moving speed of the electronic device according to the target step frequency, the first step length and the target error parameter.

[0015] In an example embodiment, the correcting the initial error parameter according to the target GNSS observation speed and the initial moving speed to obtain the target error parameter comprises:

[0016] determining a target step length error according to the target GNSS observation speed and the initial moving speed;

[0017] correcting the initial error parameter according to the target step length error to obtain the target error parameter.

[0018] In an example embodiment, the determining the target step length error according to the target GNSS observation speed and the initial moving speed comprises:

[0019] determining a Kalman gain according to an initial error variance of the target step frequency and the first step length;

[0020] determining the target step length error according to the Kalman gain, the target GNSS observation speed and the initial moving speed.

[0021] In an example embodiment, the correcting the initial error parameter according to the target step length error to obtain the target error parameter comprises:

[0022] if the target step frequency is located in the first preset step frequency interval range, determining the target error parameter according to a preset error parameter of the first preset step frequency interval and the target step length error;

[0023] if the target step frequency is located outside all preset step frequency interval ranges and between the second preset step frequency interval and the third preset step frequency interval, determining the target error parameter according to the target step length error, a preset error parameter of the second preset step frequency interval and a preset error parameter of the third preset step frequency interval.

[0024] In an example embodiment, the determining the target moving speed of the electronic device according to the target step frequency, the first step length and the target error parameter comprises:

[0025] determining a target step length according to the first step length and the target error parameter;

[0026] determining the target moving speed according to the target step frequency and the target step length.

[0027] In an example embodiment, the method further comprises:

[0028] determining an observation variance of a reference GNSS observation speed according to a horizontal precision of a GNSS observation position and an autocorrelation coefficient;

[0029] correcting the observation variance according to a Doppler observation speed and a visible satellite signal-to-noise ratio to obtain a corrected observation variance;

[0030] determining the target GNSS observation speed according to the corrected observation variance.

[0031] In an example embodiment, the correcting the observation variance according to the Doppler observation speed and the visible satellite signal-to-noise ratio to obtain the corrected observation variance comprises:

[0032] determining a first correction value according to the reference GNSS observation speed and the Doppler observation speed;

[0033] determining a second correction value according to the visible satellite signal-to-noise ratio;

[0034] correcting the observation variance according to the first correction value and the second correction value to obtain the corrected observation variance.

[0035] In an example embodiment, the method further comprises:

[0036] obtaining a plurality of gait features of the acceleration signal within a current sliding window, the gait features comprising a peak-valley difference, a valley-valley difference, a gait period and a signal variance;

[0037] updating a principal axis direction of the acceleration signal if a similarity between the plurality of gait features and a plurality of historical gait features is less than a first threshold value or a difference between the signal variance and a historical signal variance is greater than a second threshold value.

[0038] In an example embodiment, the determining the target step frequency of the acceleration signal in the principal axis direction comprises:

[0039] taking two adjacent gait periods of the acceleration signal in the principal axis direction as a target gait period;

[0040] determining the target step frequency according to a sampling rate of the acceleration signal and a number of sampling points between adjacent peaks and valleys within the target gait period.

[0041] In an example embodiment, the determining the first step length of the acceleration signal in the principal axis direction comprises:

[0042] According to a preset step length model, the first step length is determined, the preset step length model being used to determine the first step length according to the target step frequency and signal variance of the acceleration signal in a target gait cycle.

[0043] According to a second aspect of the embodiments of the present disclosure, a mobile speed determination apparatus is provided, the apparatus comprising:

[0044] A first determination module is configured to acquire an acceleration signal of an electronic device, and determine a target step frequency and a first step length of the acceleration signal in a main axis direction;

[0045] A second determination module is configured to determine an initial error parameter of the first step length according to a preset step frequency interval corresponding to the target step frequency;

[0046] A third determination module is configured to determine an initial mobile speed of the electronic device according to the target step frequency, the first step length and the initial error parameter;

[0047] A correction module is configured to correct the initial mobile speed to obtain a target mobile speed.

[0048] According to a third aspect of the embodiments of the present disclosure, an electronic device is provided, comprising:

[0049] A processor;

[0050] A memory for storing processor-executable instructions;

[0051] The processor is configured to execute the method as described in the first aspect of the embodiments of the present disclosure.

[0052] According to a fourth aspect of the embodiments of the present disclosure, a non-transitory computer readable storage medium is provided, when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method as described in the first aspect of the embodiments of the present disclosure.

[0053] The above method of the present disclosure has the following beneficial effects: the initial error parameter of the first step length is determined according to the preset step frequency interval corresponding to the target step frequency of the acceleration signal in the main axis direction, which can adapt the initial error parameter to the change of the motion state, the initial mobile speed of the electronic device is determined according to the target step frequency, the first step length and the initial error parameter, the initial mobile speed is corrected to obtain the target mobile speed, and the real-time correction is performed on the basis of the initial error parameter, which can avoid the influence of the motion state change on the speed estimation value, and effectively improve the real-time performance and accuracy of the speed estimation result.

[0054] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. Attached Figure Description

[0055] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0056] Figure 1 This is a flowchart illustrating a method for determining movement speed according to an exemplary embodiment;

[0057] Figure 2 This is a schematic diagram illustrating an acceleration signal according to an exemplary embodiment;

[0058] Figure 3 This is a flowchart illustrating a method for determining movement speed according to an exemplary embodiment;

[0059] Figure 4 This is a block diagram illustrating a moving speed determining device according to an exemplary embodiment;

[0060] Figure 5 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0061] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0062] In related technologies, when estimating pedestrian movement speed based on gait analysis, a uniform error value is usually used to correct the error of the fitted gait model. However, in practical applications, since the user's movement state will affect the error of the relevant gait model, the accuracy of the estimated pedestrian movement speed cannot be guaranteed when the user's movement state changes.

[0063] In the exemplary embodiments of the present disclosure, in order to ensure the accuracy of the estimated value of the moving speed of the pedestrian, a moving speed determination method is provided, comprising: obtaining an acceleration signal of an electronic device, determining a target step frequency and a first step length of the acceleration signal in a main axis direction, determining an initial error parameter of the first step length according to a preset step frequency interval corresponding to the target step frequency, determining an initial moving speed of the electronic device according to the target step frequency, the first step length and the initial error parameter, correcting the initial moving speed to obtain a target moving speed. In the method, the initial error parameter of the first step length is adapted to different motion states, and the initial error parameter is corrected in real time, which can avoid the influence of the change of the motion state on the estimated value of the speed and effectively improve the real-time performance and accuracy of the estimated result of the speed.

[0064] In the exemplary embodiments of the present disclosure, a moving speed determination method is provided, Figure 1 is a flowchart of a moving speed determination method according to an exemplary embodiment, as shown in Figure 1 comprising the following steps:

[0065] In step S101, an acceleration signal of an electronic device is obtained, and a target step frequency and a first step length of the acceleration signal in a main axis direction are determined.

[0066] In step S102, an initial error parameter of the first step length is determined according to a preset step frequency interval corresponding to the target step frequency.

[0067] In step S103, an initial moving speed of the electronic device is determined according to the target step frequency, the first step length and the initial error parameter.

[0068] In step S104, the initial moving speed is corrected to obtain a target moving speed.

[0069] The moving speed determination method in the embodiments of the present disclosure is applied to electronic devices, including smart phones, tablets, personal computers, smart bracelets, smart watches, other smart wearable devices, smart Internet of Things devices, smart car machines and the like. The application scenario of the embodiments of the present disclosure is to detect the moving speed of the user when the user wears the electronic device or holds the electronic device.

[0070] In step S101, the acceleration signal is obtained by an accelerometer, a gyroscope, an Inertial Measurement Unit (IMU) or the like capable of measuring acceleration in the electronic device. In order to ensure the accuracy of the acceleration signal, a three-axis accelerometer or a three-axis gyroscope is used to measure a three-axis acceleration signal, and the axis with the strongest and most stable signal strength is selected as the main axis direction, and the target step frequency and the first step length of the acceleration signal in the main axis direction are calculated.

[0071] In some embodiments, after determining the principal axis, further comprising:

[0072] obtaining a plurality of gait features of the acceleration signal within a current sliding window, the gait features comprising a peak-valley difference, a valley-valley difference, a gait period, and a signal variance;

[0073] if the plurality of gait features are less similar to a plurality of historical gait features than a first threshold, or a difference between the signal variance and a historical signal variance is greater than a second threshold, updating the principal axis of the acceleration signal.

[0074] The sliding window represents a time window, and a length of the sliding window is a preset value, for example, 3 seconds or 5 seconds. The acceleration signal is collected within the sliding window. The gait features of the acceleration signal within the current sliding window include a peak-valley difference, a valley-valley difference, a gait period, and a signal variance. The peak-valley difference represents a signal difference between adjacent peaks and valleys, the valley-valley difference represents a signal difference between two adjacent valleys, the gait period represents a time interval between two adjacent valleys, and the signal variance represents a fluctuation range of the acceleration signal. In an example, Figure 2 is a schematic diagram of an acceleration signal according to an example embodiment. As shown in Figure 2 , the horizontal axis represents time, and the vertical axis represents acceleration signal intensity. The time t1, t3, and t5 are peaks of the acceleration signal, and the time t2, t4, and t6 are valleys of the acceleration signal. The time t2-t4 and t4-t6 are each a gait period.

[0075] The plurality of gait features are combined into a gait feature vector. For example, the peak value is denoted as peak i , the time of occurrence of the peak is denoted as Pos(peak i ), the valley value is denoted as valley i , the time of occurrence of the last valley is denoted as Pos(valley i-1 ), the peak-valley difference is denoted as peak i -valley i , the valley-valley difference is denoted as valley i-1 -valley i , and the gait period is denoted as Pos(valley i )-Pos(valley i-1), and a signal variance var, to constitute a gait feature vector. A historical gait feature is a vector composed of a plurality of gait features in a historical sliding window, and a plurality of historical gait feature vectors constitute a gait feature queue. An average vector of the gait feature queue is determined, and a similarity between the current gait feature vector and the average vector of the gait feature queue is calculated. If the similarity is greater than or equal to a first threshold value, it indicates that the acceleration signal is stable, and the current gait feature vector is added to the gait feature queue. If the similarity is less than the first threshold value, it indicates that the acceleration signal is unstable, and the principal axis direction of the acceleration signal is updated. Alternatively, if a difference between the signal variance and a historical signal variance is greater than a second threshold value, the principal axis direction of the acceleration signal is updated. The signal variance is used to represent the fluctuation of the acceleration signal, and a large change in the variance indicates that the fluctuation of the acceleration signal is unstable. The first threshold value and the second threshold value are empirical values. The principal axis direction is updated when there is a large fluctuation in the similarity or the signal variance, which can ensure the accuracy of the related parameters on the principal axis direction, and further ensure the accuracy of the subsequent calculation of the moving speed.

[0076] In some embodiments, in order to eliminate environmental noise and reduce the influence of irregular motion on the final calculation result, after reading the original acceleration signal, the original acceleration signal is subjected to low-pass filtering, and the filtered acceleration signal is taken as a target acceleration signal to calculate a target step frequency and a first step length.

[0077] The step frequency and the step length are gait parameters determined according to the gait characteristics of the user. For example, when the electronic device is a smart bracelet, the user wears the smart bracelet, the target step frequency represents the frequency of the user's foot landing in the current measurement period, and the first step length represents the length of the user's foot in the current measurement period. The target step frequency is calculated according to the signal fluctuation rule of the acceleration signal, and the first step length is calculated according to a step length prediction model. The step length prediction model can adopt any model capable of predicting the step length according to the step frequency and the acceleration signal.

[0078] In some embodiments, the target step frequency of the acceleration signal on the principal axis direction is determined by the following steps:

[0079] The adjacent two gait periods of the acceleration signal on the principal axis direction are taken as target gait periods.

[0080] The target step frequency is determined according to the sampling rate of the acceleration signal and the number of sampling points between adjacent peaks and troughs in the target gait period.

[0081] The adjacent two gait periods are taken as target gait periods, and the target step frequency is determined according to the sampling rate of the acceleration signal and the number of sampling points between adjacent peaks and troughs in the target gait period. Figure 2For example, the time t2 to the time t4 and the time t4 to the time t6 are two adjacent gait cycles, and thus the time t2 to the time t6 represents a target gait cycle. Taking the target gait cycle as a measurement cycle can further ensure the accuracy of the gait parameters. The sampling rate of the acceleration signal is determined by the acquisition parameters of the acquisition device of the acceleration signal, for example, the sampling rate of the acceleration signal is 25 Hz. The sampling points represent the time points at which the acceleration signal is acquired, and the number of sampling points between adjacent wave crests and wave troughs in the target gait cycle is counted, so as to obtain the signal variance of the acceleration signal in the target gait cycle. Figure 2 For example, the target gait cycle is the time t2 to the time t6, the number of sampling points between the adjacent wave trough at the time t2 and the wave crest at the time t3, or the number of sampling points between the adjacent wave crest at the time t3 and the wave trough at the time t4, or the number of sampling points between the adjacent wave trough at the time t4 and the wave crest at the time t5, or the number of sampling points between the adjacent wave crest at the time t5 and the wave trough at the time t6 is counted, or the above four values are counted respectively, and the average of the above four values is taken as the final number of sampling points.

[0082] The ratio of the sampling rate of the acceleration signal and the number of sampling points between adjacent wave crests and wave troughs in the target gait cycle is taken as the target step frequency. For example, the sampling rate of the acceleration signal is denoted as fs, the number of sampling points between adjacent wave crests and wave troughs in the target gait cycle is denoted as period, and the target step frequency is denoted as cad. i Then, the target step frequency is calculated by the following formula: cad i = fs / period.

[0083] In some embodiments, according to a preset step length model, the first step length is determined, and the preset step length model is used to determine the first step length according to the target step frequency and the signal variance of the acceleration signal in the target gait cycle.

[0084] In an example, the step length prediction model is f(cad, var, height, gender), where f represents the model, cad, var, height, and gender are input parameters of the model, cad represents the step frequency, var represents the signal variance, height represents the user height, and gender represents the user gender, and the output parameter of the model is the step length. The target step frequency cad i , the signal variance of the acceleration signal in the target gait cycle The user height height and the user gender gender are input into the above step length prediction model to obtain the first step length Wherein, the user height and the user gender are set by the user himself.

[0085] In step S102, a plurality of step frequency intervals are pre-set in the electronic device, each step frequency interval corresponding to a motion state, for example, slow walking (0, 90], walking (110, 130], jogging (150, 160], running (170, 190], and high step frequency running (210, 250] five step frequency intervals are set. At the same time, pre-set error parameters corresponding to each pre-set step frequency interval are pre-set in the electronic device, the pre-set error parameters representing the error generated when estimating the step length using the step length prediction model, and the pre-set error parameters corresponding to different pre-set step frequency intervals can be the same or different, which is set according to the actual situation. The pre-set error parameters corresponding to each pre-set step frequency interval are calculated through an experimental data set, in an example, the step length prediction model is f(cad, var, height, gender), and the experimental data set includes a plurality of corresponding relationships between step frequency and step length. Any one of the pre-set step frequency intervals is denoted as the rth pre-set step frequency interval, and the calculation method of the pre-set error parameter is described taking the rth pre-set step frequency interval as an example. A plurality of step frequency values in the rth pre-set step frequency interval in the experimental data set are counted, and a plurality of step length values corresponding to the plurality of step frequency values are denoted as s1, s2, …, s n The plurality of step frequency values are input into the step length prediction model, and a plurality of step length estimation values corresponding to the plurality of step frequency values are obtained, which are denoted as f1, f2, …, f n Therefore, the error of the ith step length estimation value can be expressed as: i -f i The average value of the errors of all step length estimation values is taken as the pre-set error parameter of the rth pre-set step frequency interval, and the pre-set error parameter of the rth pre-set step frequency interval is denoted as:

[0086]

[0087] After determining the target step frequency, the pre-set step frequency interval corresponding to the target step frequency is determined, and the initial error parameter of the first step length is determined according to the pre-set error parameter of the pre-set step frequency interval corresponding to the target step frequency.

[0088] In some embodiments, if the plurality of pre-set step frequency intervals cannot cover all step frequencies, when the initial error parameter of the first step length is determined according to the pre-set step frequency interval corresponding to the target step frequency, the following two cases are included:

[0089] First, if the target step frequency is within the range of the first pre-set step frequency interval, the initial error parameter of the first step length is determined according to the pre-set error parameter of the first pre-set step frequency interval.

[0090] The first pre-set step frequency interval is any pre-set step frequency interval, and if the target step frequency is within the range of the first pre-set step frequency interval, the pre-set error parameter of the first pre-set step frequency interval is taken as the initial error parameter of the first step length. For example, the first pre-set step frequency interval is denoted as The preset error parameter of the first preset cadence interval is denoted as The target cadence is denoted as cad i The initial error parameter of the first step length is denoted as If the target cadence satisfies the relationship: The initial error parameter of the first step length is:

[0091] Secondly, if the target cadence is located outside all preset cadence intervals and between adjacent second and third preset cadence intervals, the initial error parameter of the first step length is determined according to the preset error parameter of the second preset cadence interval and the preset error parameter of the third preset cadence interval.

[0092] The second and third preset cadence intervals are any two adjacent preset cadence intervals, and the adjacent preset cadence intervals refer to the two preset cadence intervals with the minimum difference between the minimum cadence value and the maximum cadence value. For example, when the preset cadence intervals include slow walking (0, 90], slow running (150, 160], and walking (110, 130], the slow walking (0, 90] and walking (110, 130] are adjacent cadence intervals, the slow running (150, 160] and walking (110, 130] are adjacent cadence intervals, and the slow walking (0, 90] and slow running (150, 160] are not adjacent cadence intervals.

[0093] If the target cadence is located outside all preset cadence intervals and between adjacent second and third preset cadence intervals, the weighted sum of the preset error parameter of the second preset cadence interval and the preset error parameter of the third preset cadence interval is taken as the initial error parameter of the first step length. For example, the target cadence is denoted as cad i The second preset cadence interval is denoted as The third preset cadence interval is denoted as If the target cadence satisfies the relationship: and The initial error parameter of the first step length is calculated by the following formula:

[0094]

[0095]

[0096]

[0097] wherein, denotes the initial error parameter of the first step length, denotes the preset error parameter of the second preset cadence interval, denotes the preset error parameter of the third preset cadence interval, and w is a weight value.

[0098] In step S103, the initial error parameter represents the error generated when the first step length is estimated, and the sum of the first step length and the initial error parameter is taken as the second step length, which represents the initial step length value. After the second step length is obtained, the product of the second step length and the target step frequency is the initial moving speed of the electronic device. For example, let the initial error parameter be denoted as the first step length be denoted as and the second step length be denoted as

[0099]

[0100] wherein sl i represents the second step length, cad i represents the target step frequency, and represents the signal variance, and height and gender represent the user height and user gender, respectively.

[0101] The initial moving speed is represented as

[0102]

[0103] wherein represents the initial moving speed.

[0104] In step S104, the observation speed is obtained through GNSS observation, visual observation or other observation methods, the second step length is corrected through the observation speed, and the effect of correcting the initial moving speed is achieved, and the target moving speed is obtained. In some embodiments, the error state Kalman filtering method is used to correct the initial moving speed through the GNSS observation speed, and the target moving speed is obtained. Let the state quantity be denoted as [δsl], and the system state equation be

[0105] δsl i = δsl i-1 + w i-1

[0106] wherein δsl i represents the current initial step length error, δsl i-1 represents the last initial step length error, and w i-1 represents the system noise, which is a preset value.

[0107] The observation equation is

[0108]

[0109] wherein δv i represents the speed error, represents the GNSS observation speed, represents the initial moving speed, and cadi denotes target step frequency, n i denotes GNSS observation noise, which is a preset value.

[0110] Therefore, the target moving speed is the sum of the initial moving speed and the speed error, and the target moving speed is:

[0111] In the exemplary embodiments of the present disclosure, the acceleration signal of the electronic device is obtained, the target step frequency and the first step length of the acceleration signal in the main axis direction are determined, the initial error parameter of the first step length is determined according to the preset step frequency interval corresponding to the target step frequency, the initial error parameter can adapt to the change of the motion state, the initial moving speed of the electronic device is determined according to the target step frequency, the first step length and the initial error parameter, the initial moving speed is corrected to obtain the target moving speed, and the target moving speed is corrected in real time on the basis of the initial error parameter, which can avoid the influence of the motion state change on the speed estimation value and effectively improve the real-time performance and accuracy of the speed estimation result.

[0112] In the exemplary embodiments of the present disclosure, a moving speed determination method is provided, Figure 3 is a flowchart of a moving speed determination method according to an exemplary embodiment, as shown in Figure 3 includes the following steps:

[0113] In step S301, the acceleration signal of the electronic device is obtained, and the target step frequency and the first step length of the acceleration signal in the main axis direction are determined.

[0114] In step S302, the initial error parameter of the first step length is determined according to the preset step frequency interval corresponding to the target step frequency.

[0115] In step S303, the initial moving speed of the electronic device is determined according to the target step frequency, the first step length and the initial error parameter.

[0116] In step S304, the observation variance of the reference GNSS observation speed is determined according to the horizontal accuracy of the GNSS observation position and the autocorrelation coefficient.

[0117] In step S305, the observation variance is corrected according to the Doppler observation speed and the visual satellite signal-to-noise ratio to obtain the corrected observation variance.

[0118] In step S306, the target GNSS observation speed is determined according to the corrected observation variance.

[0119] In step S307, the initial error parameter is corrected according to the target GNSS observation speed and the initial moving speed to obtain the target error parameter.

[0120] Step S308, determining the target moving speed of the electronic device according to the target step frequency, the first step length and the target error parameter.

[0121] The specific implementation of steps S301-S303 can refer to steps S101-S103, which will not be repeated here.

[0122] In step S304, the speed of the electronic device is observed at different positions by multiple visible satellites in the satellite system, and the observed speed at each position is the reference GNSS observed speed. The reference GNSS observed speed can be calculated according to the GNSS position observation result by using a direct difference method or a position filtering method.

[0123] In an example, the reference GNSS observed speed is calculated by using the direct difference method:

[0124]

[0125] wherein, represents the reference GNSS observed speed, dis(pos i ,pos i-1 ) represents the distance between the adjacent two observation positions pos i and pos i-1 , and dt represents the observation time difference between the adjacent two observation positions.

[0126] In another example, the position filtering method is used to calculate the reference GNSS observed speed by smoothing the position by using the Kalman filtering method. The state quantity of the Kalman filtering is s=[x,y,dx,dy] T , which is used to represent the position, and the system state equation is:

[0127] s i =As i-1 +v i-1

[0128] wherein, s i represents the current position, s i-1 represents the last position, v i-1 represents the system noise, and A is:

[0129]

[0130] The observation equation is z i =Hs i +u i-1 , z i =[x i ,y i ] T wherein, u i-1 represents the observation noise, and H is:

[0131]

[0132] The reference GNSS observation velocity is calculated using the following formula:

[0133]

[0134] in, dx and dy represent the reference GNSS observation velocity, x and y represent the x-coordinate and y-coordinate of the position, respectively, dx and dy represent the derivatives of the x-coordinate and y-coordinate of the position, respectively, and dt represents the observation time difference between two consecutive observations.

[0135] The observation variance for each reference GNSS observation velocity is determined using the following formula:

[0136]

[0137] in, Indicates the observed variance. Indicates the observation position pos i Horizontal accuracy, Indicates the observation position pos i-1 The horizontal accuracy is given by ρ, which represents the autocorrelation coefficient of the location observation and is a preset value, for example, 0.9.

[0138] In step S305, due to the influence of obstruction, multipath effect, electromagnetic interference, etc., the GNSS observation results may show a large observation error at the current position but a small observation variance. Therefore, based on the characteristics of GNSS observation data, two methods, Doppler velocity observation verification and visible satellite signal-to-noise ratio verification, are used to correct the observation variance and obtain the corrected observation variance, which can ensure the accuracy of the observation variance.

[0139] In some implementations, the observation variance is corrected based on the Doppler observation velocity and the signal-to-noise ratio of the visible satellite to obtain the corrected observation variance, including the following steps:

[0140] The first correction value is determined based on the reference GNSS observation velocity and Doppler observation velocity;

[0141] The second correction value is determined based on the signal-to-noise ratio of the visible satellite.

[0142] The observation variance is corrected based on the first and second correction values ​​to obtain the corrected observation variance.

[0143] In one example, the first correction value is determined using the following formula:

[0144]

[0145] Where, ΔRdoppler represents the first correction value, v GNSS represents the reference GNSS observation velocity, v doppler represents the Doppler observation velocity, thr1 and thr2 are respectively the first and second set values, and are empirical values.

[0146] The second correction value is determined by the following formula:

[0147]

[0148] wherein, ΔR satellite represents the second correction value, Average Top-6 SNR represents the average value of the signal-to-noise ratios of the top 6 visible satellites in descending order of the signal-to-noise ratio. The visible satellites represent the satellites whose signal coverage ranges include the electronic device.

[0149] After obtaining the first correction value and the second correction value, the sum of the first correction value, the second correction value and the observation variance is taken as the corrected observation variance. In an example, the corrected observation variance is:

[0150] R = R GNSS + ΔR doppler + ΔR satellite

[0151] wherein, R represents the corrected observation variance, R GNSS represents the observation variance, ΔR doppler represents the first correction value, ΔR satellite represents the second correction value.

[0152] In some embodiments, if the number of visible satellites is less than the number threshold, it is determined that the GNSS observation is invalid. If the number of visible satellites is less than the number threshold, it indicates that the current is a satellite denial environment, the target GNSS observation velocity is invalid, other observation methods can be used to correct the initial moving velocity, or the initial moving velocity can be taken as the target moving velocity.

[0153] In step S306, all reference GNSS observation velocities are evaluated according to the observation error and the corrected observation variance, and a target GNSS observation velocity is selected therefrom, for example, the reference GNSS observation velocity with the minimum observation error and the minimum observation variance is taken as the target GNSS observation velocity.

[0154] By correcting the GNSS observation variance, the influence of GNSS abnormal observation under multipath and shielding environment can be reduced, the accuracy of the target observation velocity is ensured, and the stability and accuracy of the speed estimation result are further improved.

[0155] In step S307, the initial error parameter is corrected according to the target GNSS observation speed and the initial moving speed, and the target error parameter is obtained. In some embodiments, the target error parameter is obtained by the following two steps:

[0156] S1, determining a target step error according to the target GNSS observation speed and the initial moving speed.

[0157] The target step error is calculated by the error state Kalman filtering method, and in some embodiments, the target step error is determined by the following steps:

[0158] Determining a Kalman gain according to the target step frequency and the initial error variance of the first step.

[0159] Determining the target step error according to the Kalman gain, the target GNSS observation speed and the initial moving speed.

[0160] The initial error variance of the first step is related to the initial error parameter of the first step, and the initial error variance represents the fluctuation of the error generated when the first step is estimated using the step prediction model, that is, the fluctuation of the initial error parameter. While the preset error parameters corresponding to each preset step frequency interval are pre-stored in the electronic device, the preset error variances are also pre-stored, and the preset error variances are the fluctuations of the preset error parameters. While the preset error parameters corresponding to each preset step frequency interval are calculated by the experimental data set, the preset error variances corresponding to each preset step frequency interval are calculated. In an example, as described in step S102, taking the rth preset step frequency interval as an example, the preset error parameter of the rth preset step frequency interval is represented as:

[0161]

[0162] Then, the preset error variance of the rth preset step frequency interval is represented as:

[0163]

[0164] wherein, represents the preset error variance of the rth preset step frequency interval.

[0165] According to the preset step frequency interval corresponding to the target step frequency, the initial error variance of the first step is determined, including the following two cases:

[0166] First, if the target step frequency is within the range of the first preset step frequency interval, the initial error variance of the first step is determined according to the preset error variance of the first preset step frequency interval.

[0167] The first preset step frequency interval is any preset step frequency interval, and if the target step frequency is within the range of the first preset step frequency interval, a preset error variance of the first preset step frequency interval is taken as an initial error variance of the first step length. For example, the first preset step frequency interval is denoted as The preset error variance of the first preset step frequency interval is denoted as The target step frequency is denoted as cad i The initial error variance of the first step length is denoted as If the target step frequency satisfies the relationship: The initial error variance of the first step length is:

[0168] Secondly, if the target step frequency is outside the range of all preset step frequency intervals and between adjacent second and third preset step frequency intervals, an initial error variance of the first step length is determined according to a preset error variance of the second preset step frequency interval and a preset error variance of the third preset step frequency interval.

[0169] The second and third preset step frequency intervals are any two adjacent preset step frequency intervals, and if the target step frequency is outside the range of all preset step frequency intervals and between adjacent second and third preset step frequency intervals, a weighted sum of a preset error variance of the second preset step frequency interval and a preset error variance of the third preset step frequency interval is taken as an initial error variance of the first step length. For example, the target step frequency is denoted as cad i The second preset step frequency interval is denoted as The third preset step frequency interval is denoted as If the target step frequency satisfies the relationship: and The initial error variance of the first step length is calculated by the following formula:

[0170]

[0171]

[0172]

[0173] wherein, represents the initial error variance of the first step length, represents the preset error variance of the second preset step frequency interval, represents the preset error variance of the third preset step frequency interval, and w is a weight value.

[0174] After the initial error variance of the first step length is obtained, a target step length error is calculated by a Kalman filtering equation to correct the initial error parameter. The Kalman gain is calculated by the following formula:

[0175]

[0176] wherein K i represents the Kalman gain, cad i represents the target step frequency, R represents the covariance matrix of GNSS observation noise, represents the covariance matrix of the last state estimation.

[0177] obtained by covariance state transmission:

[0178]

[0179] wherein, represents the initial error variance of the first step, Q represents the covariance matrix of system noise.

[0180] On this basis, the system state equation is updated as:

[0181]

[0182] wherein, represents the target step error, K i represents the Kalman gain, represents the target GNSS observation speed, represents the initial moving speed.

[0183] At the same time of determining the target step error, the target error variance of the first step needs to be determined at the same time, so as to be used in the next iteration calculation of the target step error, on the basis of the above, the system state equation is updated at the same time:

[0184]

[0185] wherein P i represents the covariance matrix of the current state estimation. If the target step frequency is located in the first preset step frequency interval range, the target error variance of the first step is: If the target step frequency is located outside all preset step frequency interval ranges and between the adjacent second preset step frequency interval and the third preset step frequency interval, the target error variance of the first step is:

[0186]

[0187]

[0188] wherein, represents the initial error variance of the first step, represents the preset error variance of the second preset step frequency interval, represents the preset error variance of the third preset step frequency interval, a third preset cadence interval is denoted as a third preset cadence interval is denoted as

[0189] S2, the initial error parameter is corrected according to the target step length error, and the target error parameter is obtained.

[0190] Since the initial error parameter is determined according to the preset cadence interval corresponding to the target cadence, the initial error parameter is also corrected according to the preset cadence interval corresponding to the target cadence, which specifically includes the following two cases:

[0191] The first kind, if the target cadence is located in the range of the first preset cadence interval, the preset error parameter of the first preset cadence interval and the target step length error are determined as the target error parameter.

[0192] The first preset cadence interval is any preset cadence interval, if the target cadence is located in the range of the first preset cadence interval, the sum of the preset error parameter of the first preset cadence interval and the target step length error is taken as the target error parameter of the first step length. For example, the first preset cadence interval is denoted as The preset error parameter of the first preset cadence interval is denoted as If the target cadence satisfies the relationship: The target error parameter of the first step length is is denoted as: Wherein, The target step length error is denoted as cad i The target cadence is denoted as cad.

[0193] The second kind, if the target cadence is located outside the range of all preset cadence intervals, and is located between the adjacent second preset cadence interval and the third preset cadence interval, the target error parameter is determined according to the target step length error, the preset error parameter of the second preset cadence interval and the preset error parameter of the third preset cadence interval.

[0194] The second preset cadence interval and the third preset cadence interval are any two adjacent preset cadence intervals, if the target cadence is located outside the range of all preset cadence intervals, and is located between the adjacent second preset cadence interval and the third preset cadence interval, the target cadence is denoted as cad i The second preset cadence interval is denoted as The third preset cadence interval is denoted as If the target cadence satisfies the relationship: And The target error parameter is is denoted as:

[0195]

[0196] Wherein, denotes a target step length error, w denotes a weight, and the weight is calculated by the following formula:

[0197]

[0198]

[0199] wherein, denotes a preset error parameter of a second preset step frequency interval, denotes a preset error parameter of a third preset step frequency interval.

[0200] In this step, the error variance can also adapt to the change of the motion state, and the influence of the change of the motion state on the error variance can be avoided, so that the accuracy of the error variance is effectively improved. Meanwhile, the initial error parameter is corrected in real time in combination with the error variance, so that the accuracy of the target error parameter is improved, and the accuracy of the speed estimation result is further improved.

[0201] In step S308, a target moving speed of the electronic device is determined according to the target step frequency, the first step length and the target error parameter.

[0202] The target error parameter is a target error generated when the first step length is estimated, and therefore, the sum of the first step length and the target error parameter is taken as the target step length. After the target step length is obtained, the product of the target step length and the target step frequency is the target moving speed of the electronic device. For example, the target step frequency is denoted as cad i the first step length is denoted as the target error parameter is denoted as and the target step length is denoted as:

[0203]

[0204] wherein, denotes a target step length, denotes a signal variance, and height and gender respectively denote a user height and a user gender.

[0205] and the target moving speed is denoted as:

[0206]

[0207] wherein, denotes a target moving speed.

[0208] In some embodiments, steps S301-S303 and steps S307-S308 in the above embodiments can be implemented as separate embodiments, and steps S304-S306 can be omitted or replaced.

[0209] In an exemplary embodiment of the present disclosure, a moving speed determination apparatus is provided,Figure 4 is a block diagram of a moving speed determination apparatus according to an exemplary embodiment, as shown in Figure 4 The moving speed determination apparatus comprises:

[0210] A first determination module 401 configured to acquire an acceleration signal of the electronic device, and determine a target step frequency and a first step length of the acceleration signal in a main axis direction;

[0211] A second determination module 402 configured to determine an initial error parameter of the first step length according to a preset step frequency interval corresponding to the target step frequency;

[0212] A third determination module 403 configured to determine an initial moving speed of the electronic device according to the target step frequency, the first step length and the initial error parameter;

[0213] A correction module 404 configured to correct the initial moving speed to obtain a target moving speed.

[0214] In an exemplary embodiment, the second determination module 402 is further configured to:

[0215] If the target step frequency is within a first preset step frequency interval, determine the initial error parameter of the first step length according to a preset error parameter of the first preset step frequency interval;

[0216] If the target step frequency is outside all preset step frequency intervals and between a second preset step frequency interval and a third preset step frequency interval, determine the initial error parameter of the first step length according to a preset error parameter of the second preset step frequency interval and a preset error parameter of the third preset step frequency interval.

[0217] In an exemplary embodiment, the correction module 404 is further configured to:

[0218] Correct the initial error parameter according to the target GNSS observation speed and the initial moving speed to obtain a target error parameter;

[0219] Determine the target moving speed of the electronic device according to the target step frequency, the first step length and the target error parameter.

[0220] In an exemplary embodiment, the correction module 404 is further configured to:

[0221] Determine a target step length error according to the target GNSS observation speed and the initial moving speed;

[0222] Correct the initial error parameter according to the target step length error to obtain a target error parameter.

[0223] In an exemplary embodiment, the correction module 404 is further configured to:

[0224] determine a Kalman gain according to an initial error variance of the target step frequency and the first step length;

[0225] determine a target step length error according to the Kalman gain, a target GNSS observation velocity and an initial moving velocity.

[0226] In an example embodiment, the correction module 404 is further configured to:

[0227] if the target step frequency is within the range of the first preset step frequency interval, determine a target error parameter according to a preset error parameter of the first preset step frequency interval and the target step length error;

[0228] if the target step frequency is outside the range of all preset step frequency intervals and between the second preset step frequency interval and the third preset step frequency interval, determine the target error parameter according to the target step length error, a preset error parameter of the second preset step frequency interval and a preset error parameter of the third preset step frequency interval.

[0229] In an example embodiment, the correction module 404 is further configured to:

[0230] determine a target step length according to the first step length and the target error parameter;

[0231] determine a target moving velocity according to the target step frequency and the target step length.

[0232] In an example embodiment, the correction module 404 is further configured to:

[0233] determine an observation variance of a reference GNSS observation velocity according to a horizontal precision of a GNSS observation position and an autocorrelation coefficient;

[0234] correct the observation variance according to a Doppler observation velocity and a visible satellite signal-to-noise ratio to obtain a corrected observation variance;

[0235] determine the target GNSS observation velocity according to the corrected observation variance.

[0236] In an example embodiment, the correction module 404 is further configured to:

[0237] determine a first correction value according to the reference GNSS observation velocity and the Doppler observation velocity;

[0238] determine a second correction value according to the visible satellite signal-to-noise ratio;

[0239] correct the observation variance according to the first correction value and the second correction value to obtain the corrected observation variance.

[0240] In an example embodiment, the first determination module 401 is further configured to:

[0241] obtain a plurality of gait features of the acceleration signal in the current sliding window, the gait features comprising peak-trough difference, trough-trough difference, gait period and signal variance;

[0242] if the plurality of gait features are less similar to the plurality of historical gait features than a first threshold, or the difference between the signal variance and the historical signal variance is greater than a second threshold, update the principal axis direction of the acceleration signal.

[0243] In an example embodiment, the first determining module 401 is further configured to:

[0244] take two adjacent gait periods of the principal axis direction acceleration signal as target gait periods;

[0245] determine a target step frequency according to a sampling rate of the acceleration signal and a number of sampling points between adjacent peaks and troughs in the target gait periods.

[0246] In an example embodiment, the first determining module 401 is further configured to:

[0247] determine a first step length according to a preset step length model, the preset step length model being used to determine the first step length according to the target step frequency and the signal variance of the acceleration signal in the target gait periods.

[0248] As to the apparatus in the above embodiments, the specific manners in which various modules perform operations have been described in detail in the embodiments of the method, and thus will not be described in detail here.

[0249] Figure 5 is a block diagram of an electronic device 500 according to an example embodiment.

[0250] Referring to Figure 5 , the electronic device 500 can include one or more of the following components: a processing component 502, a memory 504, a power supply component 506, a multimedia component 508, an audio component 510, an input / output (I / O) interface 512, a sensor component 514, and a communication component 516.

[0251] The processing component 502 generally controls the overall operations of the electronic device 500, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 502 can include one or more processors 520 to execute instructions to complete all or part of the steps of the methods described above. In addition, the processing component 502 can include one or more modules to facilitate the interaction between the processing component 502 and other components. For example, the processing component 502 can include a multimedia module to facilitate the interaction between the multimedia component 508 and the processing component 502.

[0252] The memory 504 is configured to store various types of data to support the operation of the electronic device 500. Examples of such data include instructions for any application or method operating on the electronic device 500, contact data, phonebook data, messages, pictures, videos, etc. The memory 504 can be implemented by any type of volatile or nonvolatile memory, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disc, or optical disc.

[0253] The power supply component 506 supplies power for various components of the electronic device 500. The power supply component 506 can include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 500.

[0254] The multimedia component 508 includes a screen providing an output interface between the electronic device 500 and a user. In some embodiments, the screen can include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense a touch, a slide, and a gesture on the touch panel. The touch sensor can not only sense a boundary of a touching or a sliding action, but also detect duration and pressure related to the touching or sliding action. In some embodiments, the multimedia component 508 includes a front camera and / or a back camera. The front camera and / or the back camera can receive external multimedia data when the electronic device 500 is in an operation mode, such as a photographing mode or a video mode. Each of the front camera and the back camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0255] The audio component 510 is configured to output and / or input an audio signal. For example, the audio component 510 includes a microphone (MIC) configured to receive an external audio signal when the electronic device 500 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 504 or transmitted via the communication component 516. In some embodiments, the audio component 510 also includes a speaker for outputting an audio signal.

[0256] The I / O interface 512 provides an interface between the processing component 502 and peripheral interface modules, which can be a keypad, a click wheel, buttons, etc. The buttons can include, but are not limited to, a home button, a volume button, a start button, and a lock button.

[0257] The sensor component 514 includes one or more sensors for providing status assessments for various aspects of the electronic device 500. For example, the sensor component 514 can detect an open / closed position of the electronic device 500, relative positioning of components of the electronic device 500, such as a display and a keypad of the electronic device 500, a change in position of the electronic device 500 or a component of the electronic device 500, presence or absence of user contact with the electronic device 500, orientation or acceleration / deceleration / g-force and temperature changes of the electronic device 500. The sensor component 514 can include an optical sensor for detecting ambient light, a proximity sensor configured to detect proximity of an object without any physical contact, or a CMOS or CCD image sensor for use in imaging applications. In some embodiments, the sensor component 514 can further include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0258] The communication component 516 is configured to facilitate wired or wireless communication between the electronic device 500 and other devices. The electronic device 500 can access a wireless network based on a corresponding communication standard, such as WiFi, 2G, or 3G, or a combination thereof. In an example embodiment, the communication component 516 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an example embodiment, the communication component 516 further includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technology.

[0259] In an example embodiment, the electronic device 500 can be implemented using one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic elements, to perform the above-described methods.

[0260] In an example embodiment, a non-transitory computer-readable storage medium including instructions, such as the memory 504 including instructions, is also provided, which can be executed by the processor 520 of the electronic device 500 to implement the above-described methods. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disc, and an optical data storage device, etc.

[0261] A non-transitory computer readable storage medium, when instructions in the storage medium are executed by a processor of an electronic device, enable the electronic device to perform a moving speed determination method, the moving speed determination method comprising any one of the above methods.

[0262] Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the features disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the disclosure being indicated by the following claims.

[0263] It is to be understood that the disclosure is not limited to the precise structures herein described and illustrated in the drawings, and that various modifications and changes can be made without departing from its scope. The scope of the disclosure is limited only by the claims that follow.

Claims

1. A method of determining a moving speed, characterized by, The method comprises: obtaining an acceleration signal of an electronic device, determining a target step frequency and a first step length of the acceleration signal in a main axis direction; determining an initial error parameter of the first step length according to a preset step frequency interval corresponding to the target step frequency; determining an initial moving speed of the electronic device according to the target step frequency, the first step length and the initial error parameter; correcting the initial moving speed to obtain a target moving speed.

2. The moving speed determination method according to claim 1, characterized by, The method comprises: if the target step frequency is within a first preset step frequency interval, determining the initial error parameter of the first step length according to a preset error parameter of the first preset step frequency interval; if the target step frequency is outside all preset step frequency intervals and between adjacent second and third preset step frequency intervals, determining the initial error parameter of the first step length according to a preset error parameter of the second preset step frequency interval and a preset error parameter of the third preset step frequency interval.

3. The moving speed determination method according to claim 2, characterized by, The method comprises: correcting the initial error parameter according to a target GNSS observation speed and the initial moving speed to obtain a target error parameter; determining a target moving speed of the electronic device according to the target step frequency, the first step length and the target error parameter.

4. The moving speed determination method according to claim 3, characterized by, The method comprises: determining a target step length error according to the target GNSS observation speed and the initial moving speed; correcting the initial error parameter according to the target step length error to obtain the target error parameter.

5. The moving speed determination method according to claim 4, characterized by, The method comprises: determining a Kalman gain according to an initial error variance of the target step frequency and the first step length; determining the target step length error according to the Kalman gain, the target GNSS observation speed and the initial moving speed.

6. The moving speed determination method according to claim 4, characterized by, The method comprises: if the target step frequency is within the first preset step frequency interval, determining the target error parameter according to a preset error parameter of the first preset step frequency interval and the target step length error; if the target step frequency is outside all preset step frequency intervals and between adjacent the second and third preset step frequency intervals, determining the target error parameter according to the target step length error, a preset error parameter of the second preset step frequency interval and a preset error parameter of the third preset step frequency interval.

7. The moving speed determination method according to claim 3, characterized by, The method comprises: determining a target step length according to the first step length and the target error parameter; determining the target moving speed according to the target step frequency and the target step length.

8. The moving speed determination method according to claim 3, characterized by, The method further comprises: determining an observation variance of a reference GNSS observation velocity according to a horizontal precision of a GNSS observation position and an autocorrelation coefficient; correcting the observation variance according to a Doppler observation velocity and a visible satellite signal-to-noise ratio to obtain a corrected observation variance; determining the target GNSS observation velocity according to the corrected observation variance.

9. The moving speed determination method according to claim 8, characterized by, The correcting the observation variance according to a Doppler observation velocity and a visible satellite signal-to-noise ratio to obtain a corrected observation variance comprises: determining a first correction value according to the reference GNSS observation velocity and the Doppler observation velocity; determining a second correction value according to the visible satellite signal-to-noise ratio; correcting the observation variance according to the first correction value and the second correction value to obtain the corrected observation variance.

10. The moving speed determination method according to claim 1, characterized by, The method further comprises: obtaining a plurality of gait features of the acceleration signal in a current sliding window, the gait features comprising a peak-valley difference, a valley-valley difference, a gait period and a signal variance; updating a main axis direction of the acceleration signal if a similarity between the plurality of gait features and a plurality of historical gait features is less than a first threshold value or a difference between the signal variance and a historical signal variance is greater than a second threshold value.

11. The moving speed determination method according to claim 1, characterized by, determining a target step frequency of the acceleration signal in the main axis direction comprises: taking two adjacent gait periods of the acceleration signal in the main axis direction as a target gait period; determining the target step frequency according to a sampling rate of the acceleration signal and a number of sampling points between adjacent peaks and valleys in the target gait period.

12. The moving speed determination method according to claim 1, characterized by, determining a first step length of the acceleration signal in the main axis direction comprises: determining the first step length according to a preset step length model, the preset step length model being used to determine the first step length according to the target step frequency and a signal variance of the acceleration signal in the target gait period.

13. A moving speed determination apparatus characterized by comprising: The apparatus comprises: a first determining module configured to obtain an acceleration signal of an electronic device, and determine a target step frequency and a first step length of the acceleration signal in a main axis direction; a second determining module configured to determine an initial error parameter of the first step length according to a preset step frequency interval corresponding to the target step frequency; a third determining module configured to determine an initial moving speed of the electronic device according to the target step frequency, the first step length and the initial error parameter; a correction module configured to correct the initial moving speed to obtain a target moving speed.

14. An electronic device, comprising: comprise: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the method of any one of claims 1-12.

15. A non-transitory computer-readable storage medium, comprising: When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is enabled to perform the method of any one of claims 1-12.

Images